CN106709814A - Big data-based plant type selection method - Google Patents
Big data-based plant type selection method Download PDFInfo
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- CN106709814A CN106709814A CN201611251310.6A CN201611251310A CN106709814A CN 106709814 A CN106709814 A CN 106709814A CN 201611251310 A CN201611251310 A CN 201611251310A CN 106709814 A CN106709814 A CN 106709814A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The invention discloses a big data-based plant type selection method. The method comprises the following steps of: collecting meteorological data and soil data in a target region; drawing various thematic maps; and establishing ecological big data so as to determine regions fit for the growth of plants, judge whether interspecific competition exists between target plants and native plants or not and then select other plants suitable for intergrowth. According to the method disclosed by the invention, a large number of ecology-related data is divided according to types and then integrated together; regions suitable for the growth of plants are rapidly divided by means of a big data platform according to the weather and soil information, so as to grasp the native plants of the regions; and the ecological characteristics of the plants are combined with the history big data of the local soil and weather to predict and judge whether a competition relationship exists or not, so as to finally correctly, efficiently and scientifically collocate the plants, effectively lighten the competition between plant interspecies and soil mineral nutrient elements, and enable various nutrients in the soil to be sufficiently utilized.
Description
Technical field
The present invention relates to a kind of method of the Plants types choice based on big data.
Background technology
, it is necessary to business personnel advances to local understanding and gathers during tradition selection floristics carries out plant collocation
The ecological data of plant, such as carries out soil organism composition, trace element investigation, exploration, also needs to sometimes from each department
The historical data and latest data of some meteorology, soil, vegetation etc. are obtained by buying.The dispersion of this data and do not concentrate
Storage condition causes the time of enterprise and cost greatly to waste, and the problems such as under technical staff's inefficiency.The opposing party
Face, in target area, it is that time and soil are underused individually to plant a kind of deficiency of plant, and is easily grown miscellaneous
Grass, moisture, nutrient in solar energy and soil also have certain waste, and various plants would generally shadow mutually when planting simultaneously
Growth is rung, fighting for space and resource between plant sometimes causes mutually to suppress other side, finally often a kind of plant obtains excellent
Gesture and it is another suppressed or even be destroyed.
The content of the invention
It is an object of the invention to provide a kind of method of the Plants types choice based on big data, above-mentioned background skill is solved
Deficiency in art.
To achieve the above object, the present invention is adopted the following technical scheme that：A kind of Plants types choice based on big data
Method, comprises the following steps：
Step one, sets up target area items thematic maps：Collect in target area, when temperature, Windy Days, sunshine
Number, frost-free period, humidity, rainfall, soil organic matter content, microelement contents of soil and soil types data, and by data
Uniform format, sets up unified coordinate system, is depicted as temperature thematic maps, Windy Days thematic maps, sunshine time special topic
Map, frost-free period thematic maps, humidity thematic maps, rainfall thematic maps, soil organism thematic maps, the micro unit of soil
Plain thematic maps and soil types thematic maps；
Step 2, sets up ecological big data：Collect protophyte information and thematic maps group described in step 1 in target area
Into ecological thematic data, by ecological thematic data and the basis of digital elevation map and administrative division the map composition of target area
Geographical spatial data is combined as ecological big data；
Step 3, determines plant suitable growth region：Using the space of GIS-Geographic Information System (GIS) in ecological big data
Overlay analysis, attribute selection and shearing function, with the temperature of target area, Windy Days, sunshine time, frost-free period, humidity, drop
Rainfall, soil types, soil organic matter content, microelement contents of soil and digital elevation map are screening conditions, corresponding
Thematic maps in filter out the data that meet codomain and be cut out, the data that will be cut out are superimposed conjunction with administrative division
And, the comprehensive thematic maps in administrative division is obtained, its intersection area is target plant suitable growth region；
Step 4, determines that target plant whether there is interspecies competition with protophyte：Obtained in ecological big data described
Protophyte information in the region of appropriate target plant growth, and by the temperature needed for protophyte and target plant growth, big
Wind number of days, sunshine time, frost-free period, humidity, rainfall, soil organic matter content, microelement contents of soil codomain scope and
Soil types is contrasted, and completes the judgement that target plant whether there is interspecies competition with protophyte；
Step 5, selects the plant of other suitable symbiosis：Mesh is excluded according to step 4 codomain scope and soil types contrast
Mark is not suitable for the target plant planted jointly with protophyte in region, select other suitably with the plant of protophyte symbiosis.
Further, protophyte described in step 4 and the codomain scope and soil types phase needed for target plant growth
Closely, determine that the protophyte of target area is not suitable for being planted jointly with target plant, if protophyte grows with target plant
Required codomain scope and soil types be not close, it is determined that the protophyte of target area is suitably planted jointly with target plant
Plant.
Further, temperature described in step one is including average annual temperature, more than 0 degree of accumulated temperature, more than 10 degree of accumulated temperature, lowest temperatures
Degree, maximum temperature.
Further, soil trace element described in step one is iron, manganese, copper, zinc, nickel.
Further, digital elevation map described in step 2 is Inner Mongolia Autonomous Region area height above sea level.
Further, administrative division map described in step 2 is Inner Mongolia Autonomous Region administrative division map.
The beneficial effects of the present invention are：The ecological related data of magnanimity are pressed class by the present invention using ecological big data
It is integrated together with dividing, and relies on big data platform, according to weather and soil information, the quick area for dividing plant suitable growth
Domain, can grasp the protophyte of this area, while according to the ecosystem characterization of plant, being counted greatly over the years with reference to local soil and weather
According to prediction judges whether competitive relation, final accurate, efficient, plant of scientifically arranging in pairs or groups, and effectively mitigates plant inter-species to soil
The competition of earth mineral nutrient element, while making various nutrients in soil be fully utilized, moreover it is possible to recover soil texture, improve
Soil fertility, maintains the productivity of lasting great number.
Brief description of the drawings
Fig. 1 is the flow chart of the method for Plants types choice of the present invention based on big data；
Fig. 2 is that the present invention is based on ecological big data schematic diagram in the method for the Plants types choice of big data；
Fig. 3 is that Radix Glycyrrhizae is the analysis chart of embodiment during the present invention is based on the method for the Plants types choice of big data.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
As shown in the figures 1 and 2, a kind of method of the Plants types choice based on big data, comprises the following steps：
Step one, sets up target area items thematic maps：Collect in target area, when temperature, Windy Days, sunshine
Number, frost-free period, humidity, rainfall, soil organic matter content, microelement contents of soil and soil types data, and by data
Uniform format, sets up unified coordinate system, is depicted as temperature thematic maps, Windy Days thematic maps, sunshine time special topic
Map, frost-free period thematic maps, humidity thematic maps, rainfall thematic maps, soil organism thematic maps, the micro unit of soil
Plain thematic maps and soil types thematic maps.
Specifically, influence plant growth over the years in target area is collected for information about.The information content covers
Including temperature, Windy Days, sunshine time, frost-free period, humidity, the meteorological data of rainfall and including soil organic matter content,
The soil data of microelement contents of soil and soil types, is depicted as corresponding thematic maps.
Step 2, sets up ecological big data：Collect protophyte information and thematic maps group described in step 1 in target area
Into ecological thematic data, by ecological thematic data and the basis of digital elevation map and administrative division the map composition of target area
Geographical spatial data is combined as ecological big data, that is, the title of protophyte in target area is collected, in administrative division map
On the title of corresponding protophyte is divided in regional, represent all kinds of ecological index situations of each administrative region.
Step 3, determines plant suitable growth region：Using the space of GIS-Geographic Information System (GIS) in ecological big data
Overlay analysis, attribute selection and shearing function, with the temperature of target area, Windy Days, sunshine time, frost-free period, humidity, drop
Rainfall, soil types, soil organic matter content, microelement contents of soil and digital elevation map are screening conditions, corresponding
Thematic maps in filter out the data that meet codomain and be cut out, the data that will be cut out are superimposed conjunction with administrative division
And, the comprehensive thematic maps in administrative division is obtained, its intersection area is target plant suitable growth region.
Specifically, temperature, Windy Days, sunshine time, frost-free period, humidity, rainfall are included by influence plant growth
The meteorological factor of amount and the edaphic factor including soil organic matter content, microelement contents of soil and soil types judge
Whether target plant suitably grows in objective area, by taking Radix Glycyrrhizae as an example, the growing environment of Radix Glycyrrhizae mainly consider average annual rainfall and
Content of Soil Component (such as nitrogen, phosphorus, potassium), design parameter see the table below：
By Inner Mongolia Autonomous Region ecology big data, by GIS-Geographic Information System (GIS) means, space overlapping point is taken
The function such as analysis, attribute selection and shearing, with the rainfall (300-500mm) of Radix Glycyrrhizae, the codomain of height above sea level (150-1400m) and required
Soil trace element (iron, manganese, copper, zinc, nickel) is screening conditions, micro- in rainfall thematic maps, digital elevation map and soil
The data for meeting codomain are filtered out in secondary element thematic maps, and these data are cut out coming from respective thematic maps, most
Superposition afterwards merges the above-mentioned data for being cut out and coming, and its intersection area is exactly to meet three kinds of data of condition simultaneously, as suitable sweet
The area distribution of grass growth.As shown in figure 3, the result after GIS calculates analysis is identified on map with green, so green
Mark distribution is the region of Radix Glycyrrhizae suitable growth.
Step 4, determines that target plant whether there is interspecies competition with protophyte：Obtained in ecological big data described
Protophyte information in the region of appropriate target plant growth, and by the temperature needed for protophyte and target plant growth, big
Wind number of days, sunshine time, frost-free period, humidity, rainfall, soil organic matter content, microelement contents of soil codomain scope and
Soil types is contrasted, and completes the judgement that target plant whether there is interspecies competition with protophyte.
Specifically, interspecies competition is the one kind produced for contention living space, resource, food etc. between different population
Directly or indirectly suppress the phenomenon of other side.In interspecies competition it is often that a side gets the mastery and the opposing party is suppressed or even disappeared
Go out.Therefore, there is interspecies competition is considered whether during various plants kind parasymbiosis, in case to protophyte or suitable growth plant
Have a negative impact.The existence resource of plant competition is typically illumination, nutrient and moisture etc., in ecological big data, by symbiosis
Other plants the growing environment factor and protophyte or the codomain scope pair of envirment factor needed for suitable growth plant
Than thus judging that the two whether there is competitive relation.I.e. protophyte grows the value of required items ecological index with target plant
Domain scope and soil types are close, determine that the protophyte of target area is not suitable for being planted jointly with target plant, if primary
Plant is not close with the codomain scope and soil types of items ecological index needed for target plant growth, it is determined that target area
Protophyte is suitably planted jointly with target plant.For example, iron, manganese, copper, zinc, the plant of the mineral matter element of nickel are needed also exist for,
It is not suitable for being planted jointly with Radix Glycyrrhizae, because also including iron, manganese, copper, zinc, nickel in soil trace element needed for Radix Glycyrrhizae.
Step 5, selects the plant of other suitable symbiosis：Mesh is excluded according to step 4 codomain scope and soil types contrast
Mark is not suitable for the target plant planted jointly with protophyte in region, select other suitably with the plant of protophyte symbiosis.
Specifically, target area meteorological factor, edaphic factor or the digital elevation map for being provided by ecological big data
Data, envirment factor needed for the protophyte growth in query target region
According to step 5 select other suitably with the plant of protophyte symbiosis, by select target plant growth needed for
Based on soil trace element, the plant that selection is beneficial to increase soil trace element is collocated with each other plantation, example with target plant
Such as, needed for target plant growth of Glycyrrhiza uralensis trace element be iron, manganese, copper, zinc, nickel, and needed for Growth of Caragana trace element for nitrogen,
Phosphorus, potassium, the trace element needed for Radix Glycyrrhizae is not absorbed can survive, and plant under the rainfall and altitude conditions needed for Radix Glycyrrhizae again
The content that caragana microphylla is remarkably improved Iron in Soil, manganese, zinc is planted, therefore caragana microphylla Radix Glycyrrhizae composite plant is beneficial to the growth of Radix Glycyrrhizae, can
Selection Radix Glycyrrhizae is planted in target area jointly with caragana microphylla.
Temperature described in step one is including average annual temperature, more than 0 degree of accumulated temperature, more than 10 degree of accumulated temperature, minimum temperature, highest temperatures
Degree, the soil trace element is iron, manganese, copper, zinc, nickel.
Digital elevation data described in step 2 are Inner Mongolia Autonomous Region area height above sea level, and the administrative division data are interior
Mongolian autonomous region's administrative division map.
Above-described embodiment is exemplary, it is impossible to be interpreted as limitation of the present invention, and one of ordinary skill in the art exists
Above-described embodiment is changed within the scope of the invention in the case of not departing from principle of the invention and objective, is changed, replaced
Change among the protection domain with modification still in the application type.
Claims (6)
1. a kind of method of the Plants types choice based on big data, it is characterised in that comprise the following steps：
Step one, sets up target area items thematic maps：Collect in target area, temperature, Windy Days, sunshine time, nothing
Frost season, humidity, rainfall, soil organic matter content, microelement contents of soil and soil types data, and data form is united
One, unified coordinate system is set up, it is depicted as temperature thematic maps, Windy Days thematic maps, sunshine time thematic maps, nothing
Frost season thematic maps, humidity thematic maps, rainfall thematic maps, soil organism thematic maps, soil trace element special topic
Map and soil types thematic maps；
Step 2, sets up ecological big data：Collect protophyte information and thematic maps composition life described in step 1 in target area
State thematic data, by ecological thematic data and the fundamental geological of digital elevation map and administrative division the map composition of target area
Spatial data is combined as ecological big data；
Step 3, determines plant suitable growth region：Using the space overlapping of GIS-Geographic Information System (GIS) in ecological big data
Analysis, attribute selection and shearing function, with the temperature of target area, Windy Days, sunshine time, frost-free period, humidity, rainfall
Amount, soil types, soil organic matter content, microelement contents of soil and digital elevation map are screening conditions, corresponding
The data that meet codomain are filtered out in thematic maps and are cut out, the data that will be cut out are superimposed merging with administrative division,
The comprehensive thematic maps in administrative division is obtained, its intersection area is target plant suitable growth region；
Step 4, determines that target plant whether there is interspecies competition with protophyte：Obtain described suitable in ecological big data
Protophyte information in the region of target plant growth, and protophyte and target plant are grown into required temperature, day with wind of gale force
Number, sunshine time, frost-free period, humidity, rainfall, soil organic matter content, microelement contents of soil codomain scope and soil
Type is contrasted, and completes the judgement that target plant whether there is interspecies competition with protophyte；
Step 5, selects the plant of other suitable symbiosis：Target area is excluded according to step 4 codomain scope and soil types contrast
Be not suitable for the target plant planted jointly with protophyte in domain, select other suitably with the plant of protophyte symbiosis.
2. the method for the Plants types choice based on big data according to claim 1, it is characterised in that described in step 4
Protophyte is close with the codomain scope and soil types needed for target plant growth, determines that the protophyte of target area is uncomfortable
Preferably planted jointly with target plant, if codomain scope and soil types needed for the growth of protophyte and target plant not phase
Closely, it is determined that the protophyte of target area is suitably planted jointly with target plant.
3. the method for the Plants types choice based on big data according to claim 1, it is characterised in that described in step one
Temperature is including average annual temperature, more than 0 degree of accumulated temperature, more than 10 degree of accumulated temperature, minimum temperature, maximum temperatures.
4. the method for the Plants types choice based on big data according to claim 1, it is characterised in that described in step one
Soil trace element is iron, manganese, copper, zinc, nickel.
5. the method for the Plants types choice based on big data according to claim 1, it is characterised in that described in step 2
Digital elevation map is Inner Mongolia Autonomous Region area height above sea level.
6. the method for the Plants types choice based on big data according to claim 1, it is characterised in that described in step 2
Administrative division map is Inner Mongolia Autonomous Region administrative division map.
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Cited By (11)
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| CN108956197A (en) * | 2018-07-14 | 2018-12-07 | 广州海川信息科技有限公司 | A kind of soil sample acquisition management method and system Internet-based |
| CN109258263A (en) * | 2018-08-24 | 2019-01-25 | 武汉迪因生物科技有限公司 | Utilize the method for energy crop south reed restoration of the ecosystem loess plateau marginal land |
| CN109741205A (en) * | 2019-01-11 | 2019-05-10 | 成都工业学院 | Planting site searches modeling method, device, planting site lookup method and device |
| CN110134733A (en) * | 2019-05-22 | 2019-08-16 | 上海理工大学 | Land Administration System and method based on generalized information system and big data analysis |
| CN111060674A (en) * | 2019-12-31 | 2020-04-24 | 内蒙古蒙草生命共同体大数据有限公司 | Comprehensive evaluation method and equipment for soil nutrients |
| CN111489092A (en) * | 2020-04-15 | 2020-08-04 | 云南户外图科技有限公司 | Method and system for evaluating suitable growing area of plant cultivation and planting environment |
| CN111861836A (en) * | 2020-07-20 | 2020-10-30 | 云南财经大学 | Three-dimensional mountain land planning method and device, storage medium and computer equipment |
| CN112116514A (en) * | 2020-09-10 | 2020-12-22 | 深圳文科园林股份有限公司 | Plant planting recommendation method, device, equipment and computer-readable storage medium |
| CN115456476A (en) * | 2022-10-17 | 2022-12-09 | 东平鑫隆建筑安装有限公司 | Territorial space planning data acquisition and analysis system based on machine vision |
| CN115860434A (en) * | 2023-02-16 | 2023-03-28 | 四川省林业科学研究院 | Vegetation restoration planning method and device based on soil moisture resource bearing capacity |
| CN112116514B (en) * | 2020-09-10 | 2024-04-19 | 广东文科绿色科技股份有限公司 | Plant planting recommendation method, device, equipment and computer readable storage medium |
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108956197A (en) * | 2018-07-14 | 2018-12-07 | 广州海川信息科技有限公司 | A kind of soil sample acquisition management method and system Internet-based |
| CN109258263A (en) * | 2018-08-24 | 2019-01-25 | 武汉迪因生物科技有限公司 | Utilize the method for energy crop south reed restoration of the ecosystem loess plateau marginal land |
| CN109741205B (en) * | 2019-01-11 | 2020-12-22 | 成都工业学院 | Planting area searching and modeling method and device and planting area searching method and device |
| CN109741205A (en) * | 2019-01-11 | 2019-05-10 | 成都工业学院 | Planting site searches modeling method, device, planting site lookup method and device |
| CN110134733A (en) * | 2019-05-22 | 2019-08-16 | 上海理工大学 | Land Administration System and method based on generalized information system and big data analysis |
| CN110134733B (en) * | 2019-05-22 | 2023-01-17 | 上海理工大学 | Land management system and method based on GIS system and big data analysis |
| CN111060674A (en) * | 2019-12-31 | 2020-04-24 | 内蒙古蒙草生命共同体大数据有限公司 | Comprehensive evaluation method and equipment for soil nutrients |
| CN111489092A (en) * | 2020-04-15 | 2020-08-04 | 云南户外图科技有限公司 | Method and system for evaluating suitable growing area of plant cultivation and planting environment |
| CN111489092B (en) * | 2020-04-15 | 2023-01-31 | 云南户外图科技有限公司 | Method and system for evaluating suitable growing area of plant cultivation and planting environment |
| CN111861836A (en) * | 2020-07-20 | 2020-10-30 | 云南财经大学 | Three-dimensional mountain land planning method and device, storage medium and computer equipment |
| CN111861836B (en) * | 2020-07-20 | 2022-10-18 | 云南财经大学 | Three-dimensional mountain land planning method and device, storage medium and computer equipment |
| CN112116514A (en) * | 2020-09-10 | 2020-12-22 | 深圳文科园林股份有限公司 | Plant planting recommendation method, device, equipment and computer-readable storage medium |
| CN112116514B (en) * | 2020-09-10 | 2024-04-19 | 广东文科绿色科技股份有限公司 | Plant planting recommendation method, device, equipment and computer readable storage medium |
| CN115456476A (en) * | 2022-10-17 | 2022-12-09 | 东平鑫隆建筑安装有限公司 | Territorial space planning data acquisition and analysis system based on machine vision |
| CN115456476B (en) * | 2022-10-17 | 2023-08-22 | 山东彭集建设工程有限公司 | Homeland space planning data acquisition and analysis system based on machine vision |
| CN115860434A (en) * | 2023-02-16 | 2023-03-28 | 四川省林业科学研究院 | Vegetation restoration planning method and device based on soil moisture resource bearing capacity |
| CN115860434B (en) * | 2023-02-16 | 2023-05-16 | 四川省林业科学研究院 | Vegetation restoration planning method and device based on soil moisture resource bearing capacity |
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Application publication date: 20170524 |