CN107292755A - A kind of Analysis on Selecting method and device in corn planting environment Typical Representative area - Google Patents

Abstract

The invention discloses a method and device for selecting and analyzing a typical representative area of a corn planting environment. The method includes: calculating the cumulative value and sum of each division index in each minimum division unit during the annual growth period according to the division of the minimum division unit and the regional data. The annual average value; according to the annual average value of each regional index, the spatial attribute integration clustering of the smallest regional unit is carried out to obtain the comprehensive environmental regionalization of the crop planting environment; according to the weight and annual average value of each regional index, the Characteristic description; cluster all the smallest divisional units in all years, and calculate the fluctuation of each clustered division; analyze the clustered division according to the characteristic description and fluctuation, and select a typical representative area of the corn planting environment. The invention clusters the division units through the annual average value of the division index, and the division boundary is clear and fine; and through the characteristic description and fluctuation of different comprehensive environmental divisions, the typical representative area of the corn planting environment is selected.

Description

A method and device for selecting and analyzing typical representative areas of corn planting environment

technical field

The invention relates to the technical field of agricultural informatization, in particular to a method and device for selecting and analyzing a typical representative area of a corn planting environment.

Background technique

Agricultural planting regionalization is to divide the research area according to the similarity principle according to the agricultural production conditions and characteristics, so that the climate conditions, farming systems, soil conditions and other characteristics in the same division have similar characteristics, while different divisions have larger differences. difference. There are significant differences in heat, moisture, light, soil and other conditions across China, which also determine the uneven distribution of crop varieties in my country and the differences in variety selection and planting systems. In order to optimize the planting layout of crop varieties and achieve large-scale planting According to different ecological conditions, the selection of varieties and production methods according to time and place, and the needs of selecting and setting test sites for variety selection and promotion according to local environmental conditions make it important to carry out crop variety planting divisions from different perspectives.

There are many studies on zoning methods for crop planting environments. The main zoning methods can be roughly divided into two categories. The first category is based on expert experience and the suitability of the corn planting environment. The suitability of multiple attributes is comprehensively superimposed, and the suitability of the obtained zoning is evaluated. This method is difficult to determine the interval value of the attribute suitability, the obtained zoning results are highly subjective, and the zoning boundaries are difficult to determine. The second category is to select the attributes of the planting division, and use methods such as spatial attribute clustering and classification models to divide the region. This method determines the regional classification in a quantitative manner. Compared with the first method, the results are more objective, but the interpretation is not good. powerful. Therefore, due to the limitations of data and methods, the existing crop planting divisions often have unclear boundaries and large scales, making it difficult to express the differences between different subregions within major ecological regions and between small ecological environments. Therefore, the existing crop planting environment zoning methods cannot identify the attribute characteristics of different crop zoning at the same time, clarify the fine zoning boundaries, and perform fluctuation analysis.

Contents of the invention

Since the existing crop planting environment zoning method cannot simultaneously identify the attribute characteristics of different zoning areas of crops, clarify fine partition boundaries to perform fluctuation analysis, the present invention proposes a method and device for selecting and analyzing typical representative areas of corn planting environment.

In the first aspect, the present invention proposes a method for selecting and analyzing a typical representative area of a corn planting environment, including:

According to the division of the smallest divisional unit and regional data, calculate the cumulative value of each divisional indicator in each smallest divisional unit during the annual growth period, and calculate the annual average value of each divisional indicator based on the cumulative value;

According to the annual average value of each regional index, the spatial attribute integration clustering of the smallest regional unit is carried out, and the comprehensive environmental regionalization of the crop planting environment is obtained;

According to the weight of each regional index and the annual average value of each regional index in each smallest regional unit, determine the characteristic description of each comprehensive environmental regionalization;

Carry out integrated clustering of spatial attributes for all the smallest regionalization units in all years, and calculate the fluctuation of regionalization after each cluster;

According to the feature description and fluctuations, the clustered regionalization was analyzed, and the typical representative area of the corn planting environment was selected.

Preferably, the typical representative area includes: a typical mean representative unit area, a typical average specific unit area, a typical stability representative unit area, and a typical stability specific unit area.

Preferably, according to the division of the smallest divisional unit and the regional data, calculating the cumulative value of each divisional indicator in each smallest divisional unit during the annual growth period, before calculating the annual average value of each divisional indicator according to the cumulative value, it also includes:

Get the area data for the area to be analyzed.

Preferably, after performing integrated clustering of spatial attributes on all the smallest divisional units in all years, and calculating the fluctuation of divisions after each cluster, it also includes:

Calculate the category fluctuation frequency and belonging probability of each comprehensive environmental division according to the attribute characteristics of each comprehensive environmental division, and obtain the clustered division fluctuation according to the category fluctuation frequency and the belonging probability.

Preferably, the regional data include: basic geographic data, multi-year growth period data of crops in the region to be analyzed, and multi-year environmental data of the region to be analyzed.

In the second aspect, the present invention also proposes a selection and analysis device for a typical representative area of a corn planting environment, including:

The annual average value calculation module is used to calculate the cumulative value of each regional index in each smallest regional unit during the annual growth period according to the smallest regional unit division and regional data, and calculate the annual average value of each regional index according to the accumulated value;

The comprehensive environmental zoning acquisition module is used to perform integrated spatial attribute clustering on the smallest zoning unit according to the annual average value of each zoning index, and obtain the comprehensive environmental zoning of the crop planting environment;

A feature description determining module, used to determine the feature description of each comprehensive environmental zoning according to the weight of each zoning index and the annual average value of each zoning index in each minimum zoning unit;

The fluctuation calculation module is used to perform integrated clustering of spatial attributes on all the smallest division units in all years, and calculate the fluctuation of division after each cluster;

The typical representative area selection module is used to analyze the clustered regionalization according to the characteristic description and fluctuation situation, and select the typical representative area of the corn planting environment.

Preferably, the typical representative area includes: a typical mean representative unit area, a typical average specific unit area, a typical stability representative unit area, and a typical stability specific unit area.

Preferably, it also includes:

The area data acquisition module is used to acquire the area data of the area to be analyzed.

Preferably, it also includes:

The fluctuation calculation module is used to calculate the category fluctuation frequency and attribution probability of each comprehensive environmental division according to the attribute characteristics of each comprehensive environmental division, and obtain the clustered division fluctuation according to the category fluctuation frequency and the attribution probability Condition.

Preferably, the regional data include: basic geographic data, multi-year growth period data of crops in the region to be analyzed, and multi-year environmental data of the region to be analyzed.

It can be seen from the above technical scheme that the present invention clusters the division units through the annual average value of the division index, and the division boundary is clear and fine; and through the characteristic description and fluctuation of different comprehensive environmental divisions, the typical representative area of the corn planting environment is selected.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a schematic flow chart of a method for selecting and analyzing a typical representative area of a corn planting environment provided by an embodiment of the present invention;

Fig. 2 is a result of comprehensive environmental zoning of corn planting environment in the three eastern provinces and the distribution map of specific areas provided by an embodiment of the present invention;

Fig. 3 is a kind of interannual fluctuation zoning and pixel attribution probability analysis diagram of corn planting environment in the three eastern provinces provided by an embodiment of the present invention;

Fig. 4 is a 20-year interannual fluctuation frequency of corn planting environment in the three eastern provinces provided by an embodiment of the present invention;

Fig. 5 is a kind of corn planting environment comprehensive environmental zoning boundary and interannual fluctuation zoning superposition result provided by an embodiment of the present invention;

Fig. 6 is a schematic structural diagram of a selection and analysis device for a typical representative area of a corn planting environment provided by an embodiment of the present invention.

detailed description

The specific embodiments of the invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

Fig. 1 shows the selection and analysis method of a typical representative area of a kind of corn planting environment provided by an embodiment of the present invention, comprising:

S1. According to the division of the smallest divisional unit and regional data, calculate the cumulative value of each divisional index in each smallest divisional unit during the annual growth period, and calculate the annual average value of each divisional index based on the cumulative value;

S2. According to the annual average value of each zoning index, the smallest zoning unit is clustered with integrated spatial attributes to obtain the comprehensive environmental zoning of the crop planting environment;

S3. According to the weight of each regional index and the annual average value of each regional index in each smallest regional unit, determine the characteristic description of each comprehensive environmental regionalization;

S4. Carry out integrated clustering of spatial attributes for all the smallest regionalization units in all years, and calculate the fluctuation of regionalization after each cluster;

S5. According to the characteristic description and the fluctuation situation, analyze the regionalization after clustering, and select the typical representative area of the corn planting environment.

In this example, the regionalization units are clustered by the annual average value of the regionalization indicators, and the partition boundaries are clear and fine; and the typical representative areas of the corn planting environment are selected through the characteristic description and fluctuation of different comprehensive environmental regionalizations.

Optionally, the typical representative region includes: a typical mean representative unit region, a typical mean specific unit region, a typical stability representative unit region, and a typical stability specific unit region.

By selecting different typical representative areas, it can provide decision support for crop variety promotion and test station selection.

Further, before S1, it also includes:

S0. Obtain the area data of the area to be analyzed.

Furthermore, in order to obtain the fluctuation distribution and environmental characteristics of the crop planting environment for further analysis, after S4, it also includes:

S45. Calculate the category fluctuation frequency and belonging probability of each comprehensive environmental division according to the attribute characteristics of each comprehensive environmental division, and obtain the clustered division fluctuation according to the category fluctuation frequency and the belonging probability.

Specifically, the regional data includes: basic geographic data, multi-year growth period data of crops in the area to be analyzed, and multi-year environmental data of the area to be analyzed.

By collecting basic geographic data, multi-year growth period data and multi-year environmental data, the characteristics of the crop planting environment can be more fully reflected.

In order to illustrate in more detail the selection and analysis method of the typical representative area of the corn planting environment provided in this example, the specific steps are described below:

Step 1: Obtain the regional data of the region to be analyzed, the regional data includes: basic geographic data, multi-year growth period data of crops in the region to be analyzed, and multi-year environmental data of the region to be analyzed;

Step 2: Determine the smallest divisional unit and divisional attributes, and calculate the average value of the cumulative value of the planting environment divisional attributes from the sowing period to the maturity period and the cumulative value of the attribute accumulation values from the sowing period to the growth period for each smallest unit in each division.

Step 3: Take the average value of the attributes for many years as the clustering attribute, and carry out the integrated clustering of the spatial attributes of all the regionalization units in the study area to obtain the comprehensive environmental regionalization of the crop planting environment; identify the specific unit; in each partition, determine the difference in the results of the partition Attributes with a large contribution rate, and the interval values of each attribute, finally obtain the agronomic description information of the zoning results;

In the cluster analysis of the smallest unit of division, attribute clustering is first performed on the multi-attributes of the smallest unit of division, where the clustering attribute is the mean value of the attribute of the smallest division unit, and the values of different attributes are set according to the influence of each attribute on crop planting division. Different weights are normalized to eliminate dimensions. Secondly, to ensure the spatial continuity of the zoning results, the X and Y coordinates of the smallest zoning unit can be used as attributes of attribute clustering and weights can be set. At the same time, fine-grained units should be adjusted according to the spatial continuity adjustment criterion to obtain the integrated clustering results of spatial attributes.

Calculate the distance between the attribute value of each divisional unit in all divisions and the attribute mean of all divisional units in the division to which it belongs, because these distance values conform to the normal distribution characteristics, after the set significance level a, the divisional unit of the domain will be rejected into a specific area.

For each partition, the weighted normalized variance value of each attribute of all units is calculated (where the weight setting is consistent with the clustering), and the attribute with the largest and smallest contribution rate to the difference of each partition is obtained.

Statistics such as extreme value and variance size before the weighted normalization of each attribute in each partition are calculated to obtain the agronomic description information of the partition result.

Step 4: Use the annual attribute values of all the smallest division units as clustering attributes, perform integrated spatial attribute clustering of all the smallest division units in all years, calculate the frequency of category fluctuations and attribution probabilities of each division unit over the years, and obtain crop planting The distribution of environmental fluctuations, and the identification of strong and weak change areas of environmental characteristics over the years;

According to the same method of attribute selection and weight setting in step 3, the smallest units of all divisions in all years are mixed for cluster analysis. In the clustering results obtained, the same unit may belong to different categories in different years in geographical space.

Calculate the probability that each smallest regional division unit belongs to a certain category in many years in geographical space, analyze the interannual environmental fluctuations of the unit according to the probability value, and use the category with the highest probability as the final category of the division unit to generate the study area. The multi-year class attribution probability map of the unit, the unit whose interannual maximum probability value is less than the threshold has strong interannual fluctuations, and is defined as a typical unit.

Calculate the distance value between any two classes, set the initial value of unit fluctuation frequency to 0, and arrange the value of each year’s belonging category of each smallest division unit in geographical space in chronological order. When the unit belongs to the category in the year When inter-annual changes occur, the fluctuation frequency value is added to the distance value between the change categories, until each unit completes the cumulative value of all inter-annual fluctuation frequencies, and a unit inter-annual category fluctuation frequency map of the research area is generated as the unit’s A measure of the inter-annual fluctuation of a composite attribute.

Step 5: Comprehensively evaluate the environmental representativeness of the smallest zoning unit of the planting environment by integrating the results of environmental mean and fluctuation status zoning, and select typical environmental units, including typical mean representative unit areas, typical mean specific unit areas, typical stability representative The characteristic unit area and the typical stability-specific unit area provide decision support for crop variety promotion and test station selection.

The class center of each subregion in the results of comprehensive environmental zoning is calculated as the grid area with the highest representativeness of the average planting environment in the subregion, and it is used as the typical mean value to represent the grid;

Select the specific area of comprehensive environmental zoning obtained in step 3 as the typical mean specific grid of each subregion.

Superimpose the zoning boundary of the comprehensive environmental zoning of the environment, the unit multi-year category attribution probability map, and the unit interannual category fluctuation frequency map, find all the zoning types of environmental fluctuations that exist within the boundaries of each mean value zoning, and select the grid with small class variation as a typical The stability represents the grid, and the grid with large class variation is selected as the typical stability-specific grid.

The method provided in this example conducts planting environment zoning, regional microenvironment identification, and stress risk assessment from the two aspects of crop planting environment average performance and interannual fluctuation performance, and uses attribute clustering to clarify the boundaries of the zoning and realize the agronomic description of the zoning results , is of great significance for guiding the testing and promotion of varieties, and at the same time helps to improve the cognition of the spatial distribution of crop planting environments, determine the main environmental characteristics of the region and identify special small environmental areas and characteristics, promote the accurate positioning of varieties in advantageous promotion areas, and improve the quality of varieties Test efficiency.

For example, the three provinces of Liaoning, Jilin and Heilongjiang are the main planting areas of the spring sowing corn area in the north of East China. The present invention takes the three provinces in the east as the area to be analyzed, and uses corn as the regional crop, including the following steps:

Step 1: Obtain the regional data of the region to be analyzed, the regional data includes: basic geographic data, multi-year growth period data of crops in the region to be analyzed, and multi-year environmental data of the region to be analyzed;

The basic geographical data of the area obtained from the area to be analyzed includes provincial and county administrative division data, and DEM elevation data. Obtain the data of agricultural meteorological stations within 100km of the buffer zone of the three northeastern provinces for 20 years. The data of agricultural meteorological stations mainly include the date of sowing and maturity of corn every year. Obtain weather station data within 100km of the buffer zone of the three northeastern provinces for 20 years, the weather station data includes daily average temperature, daily maximum temperature, daily minimum temperature, daily rainfall, daily sunshine hours, etc., wherein the weather station and agricultural The weather stations are evenly distributed in the area, and the weather stations whose altitude is too different from the regional average altitude are eliminated.

Calculate the days from January 1st to corn sowing and corn maturity at the agricultural meteorological stations respectively, and use the radial basis interpolation method to obtain the days from January 1st to corn sowing and maturity in the whole study area for 20 years, and Convert it to a specific year, month, day and date, assign the two dates to the weather station, and obtain the annual sowing and maturity dates of each weather station.

Step 2: Determine the smallest divisional unit and divisional attributes, and calculate the average value of the cumulative value of the planting environment divisional attributes from the sowing period to the maturity period and the cumulative value of the attribute accumulation values from the sowing period to the growth period for each smallest unit in each division.

In this example, the research area is divided into 10km x 10km grid, with the grid as the smallest division unit, and the accumulated accumulated temperature, accumulated rainfall, accumulated sunshine hours, and DEM elevation of the corn in the research area from the sowing stage to the mature stage are the regionalization attributes.

Calculate the cumulative daily average temperature, cumulative rainfall, and cumulative sunshine hours from the sowing period to the maturity period of the weather station, and use the Kriging interpolation method to obtain the annual cumulative temperature, cumulative rainfall, and cumulative sunshine values of each grid. The sampling method obtains DEM elevation values for each grid.

In addition, calculate the average value of each attribute for each grid over a period of 20 years.

Step 3: Carry out multi-attribute spatial attribute integration clustering for the mean attributes of all division units in the study area to obtain the mean division of crop planting environment; identify specific units; in each division, determine the larger attributes of similarity and difference, and the interval values of each attribute, and finally obtain the agronomic description information of the regionalization results;

In this embodiment, the 20-year average value of each attribute of all grids in the research area is normalized to 0-100 to eliminate dimensions and assign different weights to perform attribute clustering. In this research example, the accumulated accumulated temperature, accumulated rainfall, The cumulative sunshine weight is set to 0.25, the elevation weight is set to 0.15, and the weights of the X and Y coordinates of the grid center are respectively assigned to 0.05. The k-means method is used for clustering, and the sample affinity is calculated using the Euclidean distance method. The size of the partial R-square statistic determines the final number of clusters (the optimal number of clusters is 8 in this embodiment), where the addition of X and Y coordinates ensures better spatial continuity of the zoning results.

The method of selecting the special area is as follows: Calculate the Euclidean distance between the attribute value of each grid in all partitions and the average value of each attribute of all the grids in the partition. For different partitions, the selection of the special area should be based on the same standard, because all different The distance value between the grid of the partition and the center of the class where it is located conforms to the normal distribution characteristics. After the set significance level a, the partition of the rejection domain is unitized into a specific area, and the specific area is the outlier of the cluster partition. Special attention should be paid to the practical application. The results of regionalization and specific area selection are shown in Figure 2.

Calculate the weighted normalized variance value of each attribute of all grids in each partition (the weight setting is consistent with the clustering), and obtain the attribute with the largest and smallest contribution rate to the difference of each partition, as shown in Table 1 below.

Table 1 The largest contribution attribute to the difference in the results of comprehensive environmental zoning of corn planting environment in the three eastern provinces

VAR (accumulated temperature) VAR(Rainfall) VAR (elevation) VAR(Rizhao) 1 18.00398 16.54014 6.591056 15.70887 2 8.154028 34.62356 12.80553 8.623617 3 17.2428 12.71221 2.072623 12.53993 4 6.777262 9.356085 9.795064 5.142063 5 18.91218 18.15093 4.197132 15.55342 6 24.29578 7.252329 3.867112 17.12033 7 10.52315 4.678987 12.83869 4.939667 8 10.56928 18.94914 13.40226 11.96777

Calculate the real standard deviation and extreme value of each attribute of all grids in each partition, obtain the attribute characteristics of the partition, and obtain the agronomic description of the partition.

Step 4: Use the annual attribute values of all the smallest division units as clustering attributes, perform integrated spatial attribute clustering of all the smallest division units in all years, calculate the frequency of category fluctuations and attribution probabilities of each division unit over the years, and obtain crop planting The distribution of environmental fluctuations, and the identification of strong and weak change areas of environmental characteristics over the years;

The grids that are divided geographically in the study area are called "geographic grids". There are 7,832 geographic grids in this research example. grid, called "value grid", there are 20 years of data in this research example, so each geographic grid has 20 value grids, and all geographic grids have a total of 7832*20 value grids. All these value grids are clustered together, where the attribute and attribute weight setting, the method of determining the number of clusters, and the clustering method are exactly the same as those in step 3 (the optimal number of clusters in this example is 7). From the above, it is known that the multiple value grids corresponding to each geographical grid must have the same dem elevation value and X, Y scalar value. The value grids of different years of the same geographical grid in the obtained clustering results may belong to different categories.

Calculate the probability that each geographic grid belongs to a certain class. The specific calculation method is to calculate the frequency of all value grids belonging to a certain class in the geographic grid, and divide the frequency by the number of years to obtain the probability that the geographic grid belongs to this class. , if there is more than one category of the value grid, there are many possibilities for the category to which the geographic grid belongs. In this research example, the category with the highest belonging rate is taken as the final category of the geographic grid to generate the grid of the research area Multi-year category belonging probability map. In the sense of agronomy, if a certain geographic grid belongs to a certain category with a high probability, it means that the environmental variation of the geographic grid is relatively small. If a certain geographic grid has the largest degree of belonging If the probability value of the class is less than the threshold (here set to 30%), it is set as a typical fluctuating grid. In the legend shown in Figure 3 below, the black grid is a typical fluctuating grid, and the meaning of the rest of the grid colors is: the hundreds digit is the category number to which the geographic grid finally belongs, and the tens and ones digits belong to this category For example, 295 means that the probability of the geographic grid belonging to the second category is 95%, and 700 means that the probability of the geographic grid belonging to the seventh category is 100%.

Calculate the fluctuation frequency of each geographic grid environment category. The specific method is: obtain the cluster centers of each of the above categories, and calculate the Euclidean distance of all cluster centers as the distance measure between the categories. Set the "value grid fluctuation frequency" attribute for each geographic grid, the initial value is set to 0, arrange all the value grids of each geographic grid in chronological order, and compare the category of the value grids of adjacent years, When the two category values are different, the value grid fluctuation frequency of each geographic grid is added to the distance value between the change categories until each geographic grid completes the category comparison and value grid of all inter-annual value grids Grid fluctuation frequency superposition. Generate the interannual category fluctuation frequency map of the grid in the study area, as shown in Figure 4, and use the value grid fluctuation frequency value as the measurement attribute of the interannual fluctuation of the geographic grid.

Step 5: Comprehensively evaluate the environmental representativeness of the smallest zoning unit of the planting environment by integrating the results of environmental mean and fluctuation status zoning, and select typical environmental units, including typical mean representative unit areas, typical mean specific unit areas, typical stability representative The characteristic unit area and the typical stability-specific unit area provide decision support for crop variety promotion and test station selection.

The class center of each subregion in the results of comprehensive environmental zoning is used as the grid area with the highest representativeness of the average planting environment in the subregion, and it is used as the typical mean value to represent the grid, and the specific area of comprehensive environmental zoning obtained in step 3 is selected as the typical mean specific area. grid. Superimpose the zoning boundaries and environmental fluctuation zoning results of the comprehensive environmental zoning (as shown in Figure 5), and find all the zoning types of environmental fluctuations that exist within the boundaries of each mean value zoning, and select the grid with small variation as the representative character of typical stability At the same time, the grid with large class variation is selected as the typical stability-specific grid.

Fig. 6 shows the selection and analysis device of a typical representative area of a kind of corn planting environment provided by the present embodiment, including:

The annual average value calculation module 11 is used to calculate the cumulative value of each regional index in each smallest regional unit during the annual growth period according to the division of the smallest regional unit and the regional data, and calculate the annual average value of each regional index according to the accumulated value;

The integrated environmental zoning acquisition module 12 is used to perform integrated spatial attribute clustering on the smallest zoning unit according to the annual average value of each zoning index, to obtain the comprehensive environmental zoning of the crop planting environment;

Feature description determination module 13, used to determine the feature description of each comprehensive environmental zone according to the weight of each zone index and the annual average value of each zone index in each minimum zone unit;

Fluctuation calculation module 14, used to carry out integrated clustering of spatial attributes to all the smallest division units in all years, and calculate the fluctuation of division after each cluster;

The typical representative area selection module 15 is used to analyze the clustered area according to the characteristic description and fluctuation situation, and select the typical representative area of the corn planting environment.

Optionally, the typical representative region includes: a typical mean representative unit region, a typical mean specific unit region, a typical stability representative unit region, and a typical stability specific unit region.

Further, it also includes:

The area data acquisition module is used to acquire the area data of the area to be analyzed.

Further, it also includes:

The fluctuation calculation module is used to calculate the category fluctuation frequency and attribution probability of each comprehensive environmental division according to the attribute characteristics of each comprehensive environmental division, and obtain the clustered division fluctuation according to the category fluctuation frequency and the attribution probability Condition.

Furthermore, the regional data includes: basic geographic data, multi-year growth period data of crops in the area to be analyzed, and multi-year environmental data of the area to be analyzed.

The device for selecting and analyzing a typical representative area of corn planting environment described in this embodiment can be used to implement the above method embodiment, its principle and technical effect are similar, and will not be repeated here.

In the description of the invention, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Claims (10)

1. a kind of Analysis on Selecting method in corn planting environment Typical Representative area, it is characterised in that Including：

According to minimum zoning dividing elements and area data, calculate every in each minimum zoning unit Aggregate-value in individual division index annual breeding time, each division index is calculated according to aggregate-value Year average；

Space attribute one is carried out to minimum zoning unit according to the year average of each division index Change cluster, obtain the integrated environment zoning of crop-planting environment；

According to each division index in the weight of each division index and each minimum zoning unit Year average, it is determined that the feature description of each integrated environment zoning；

Space attribute integration cluster was carried out to all minimum zoning units of all years, calculates every The fluctuation situation of zoning after individual cluster；

Zoning after cluster is analyzed according to feature description and fluctuation situation, corn planting is chosen The Typical Representative area of environment.

2. according to the method described in claim 1, it is characterised in that the Typical Representative area Including：Typical average Representative Volume Element area, the special cellular zone of typical average, Typical stability generation The special cellular zone of table cellular zone, Typical stability.

3. method according to claim 2, it is characterised in that described according to smallest region Dividing elements and area data are drawn, each division index in each minimum zoning unit is calculated annual Aggregate-value in breeding time, calculates year of each division index before average, also according to aggregate-value Including：

Obtain the area data in region to be analyzed.

4. method according to claim 3, it is characterised in that described pair all years All minimum zoning units carry out space attribute integration cluster, calculate zoning after each cluster After fluctuation situation, in addition to：

According to the attributive character of each integrated environment zoning, the class of each integrated environment zoning is calculated Not Bo Dong the frequency and ownership probability, and the frequency and the ownership probability are fluctuated according to the classification, The fluctuation situation of zoning after being clustered.

5. method according to claim 4, it is characterised in that the area data bag Include：The issue of fertility for many years of geo-spatial data, crop in region to be analyzed is according to this and to be analyzed The environmental data for many years in region.

6. a kind of Analysis on Selecting device in corn planting environment Typical Representative area, it is characterised in that Including：

Year average computing module, for according to minimum zoning dividing elements and area data, calculating Aggregate-value in each minimum zoning unit in each division index annual breeding time, according to accumulative Value calculates the year average of each division index；

Integrated environment zoning acquisition module, for according to each division index year average to minimum Zoning unit carries out space attribute integration cluster, obtains the integrated environment area of crop-planting environment Draw；

Feature describes determining module, for the weight according to each division index and each smallest region The year average of each division index in unit is drawn, it is determined that the feature of each integrated environment zoning is retouched State；

Fluctuation situation computing module, space is carried out for all minimum zoning units to all years Attribute integration cluster, calculates the fluctuation situation of zoning after each cluster；

Typical Representative area chooses module, for situation to be described and fluctuated according to feature to cluster back zone Draw and analyzed, choose the Typical Representative area of corn planting environment.

7. device according to claim 6, it is characterised in that the Typical Representative area Including：Typical average Representative Volume Element area, the special cellular zone of typical average, Typical stability generation The special cellular zone of table cellular zone, Typical stability.

8. device according to claim 7, it is characterised in that also include：

Region query module, the area data for obtaining region to be analyzed.

9. device according to claim 8, it is characterised in that also include：

Computing module is fluctuated, for the attributive character according to each integrated environment zoning, calculates every The classification fluctuation frequency and ownership probability of individual integrated environment zoning, and frequency is fluctuated according to the classification Secondary and described ownership probability, the fluctuation situation of zoning after being clustered.

10. device according to claim 9, it is characterised in that the area data bag Include：The issue of fertility for many years of geo-spatial data, crop in region to be analyzed is according to this and to be analyzed The environmental data for many years in region.