A method and system for creating a crop area sampling statistical model cluster

Abstract

The present application belongs to the field of agricultural remote sensing technology, and specifically relates to a method and system for creating a crop area sampling statistical model cluster, including the following steps: obtaining the cultivated land area of ​​multiple sub-populations contained in the monitoring target population; using the cultivated land area of ​​each sub-population as the stratification basic data; setting a sub-population name directory table; constructing the stratification basic data file name of each sub-population; performing stratification to obtain the sampling unit layer information table of each sub-population; cyclically reading the sampling unit layer information table to obtain the layer information statistics table; judging whether the main data in the layer information statistics table meets the requirements, and if not, returning to modify the grouping step size and the number of levels, and then running the program until it meets the requirements. The method and system for creating a crop area sampling statistical model cluster of the present application can establish a crop area sampling monitoring model cluster for multiple survey populations at one time, and can modify one or more model parameters at any time, quickly complete the parameter table update, and meet the business operation needs.

Description

Technical Field

[0001] The present application belongs to the field of agricultural remote sensing technology, and specifically relates to a method and system for creating a crop area sampling statistical model cluster. Background Art

[0002] Crop planting area survey is an important part of agricultural monitoring. Traditional crop planting area statistics are completed through reporting and summarizing by relevant government departments at each level, which consumes a lot of manpower and time, and the results are somewhat subjective.

[0003] In recent years, with the continuous development of remote sensing and geographic information system (GIS) technologies, they have also been widely used in the agricultural field, providing a new and broad working platform for agricultural monitoring. Using remote sensing and geographic information system (GIS) technologies to conduct crop area surveys has significant advantages: objective, economical, rapid, and comprehensive.

[0004] Currently, there are two methods for remote sensing surveys of crop area: a full-coverage census using remote sensing imagery, and a sample survey. Both full-coverage censuses and sample surveys have their advantages and disadvantages. Full-coverage censuses are free of sampling error, yield relatively accurate and reliable results, and can answer many questions about the survey population and its subpopulations. However, they are costly and time-consuming, and due to limitations in satellite remote sensing image acquisition rates, they are difficult to achieve within a single planting season or year. Sample surveys are relatively inexpensive, rapid, and easy to implement, providing relatively accurate, reliable, and reliable results. Therefore, sample surveys are commonly used in remote sensing surveys of crop area.

[0005] However, the traditional sampling survey method cannot establish a crop area remote sensing sampling monitoring model cluster for multiple survey populations at one time. Moreover, when the sampling basic data changes, the traditional sampling survey method is not only costly but also inefficient in modifying the model parameters. Summary of the Invention

[0006] In order to solve at least one technical problem existing in the prior art, the present application provides a method and system for creating a crop area sampling statistical model cluster.

[0007] In a first aspect, the present application discloses a method for creating a crop area sampling statistical model cluster, comprising the following steps:

[0008] Step 101: Obtain the cultivated land areas of multiple sub-populations included in the monitoring target population;

[0009] Step 102: The cultivated land area data of each sub-population is used as the stratified basic data of each sub-population and stored in a file directory according to a predetermined format;

[0010] Step 103: Set up a subpopulation name directory table, where each subpopulation corresponds to a record, and the field value of each record includes at least the full name, abbreviation, and code of the subpopulation;

[0011] Step 104: cyclically read the corresponding field values ​​of each sub-population in the sub-population name directory table, dynamically construct the hierarchical basic data file name of each sub-population, thereby reading the corresponding hierarchical basic data of each sub-population, and sorting the data in ascending order;

[0012] Step 105: stratify the basic data of each sub-population after ascending sorting in step 104, and display the stratification results in a table to obtain a stratum information table of each sub-population sampling unit, wherein the stratification is based on the size of the cultivated land area, the stratification method is a frequency accumulation method, and the grouping step size and the number of levels are preset. After the stratification is completed, all sampling unit individuals of each sub-population are assigned corresponding level codes;

[0013] Step 106: cyclically read the stratum information table of each sub-population sampling unit, calculate the stratum statistics of each sub-population, and display the calculation results in a table form, thereby obtaining the stratum information statistics table of each sub-population;

[0014] Step 107: Determine whether the stratification results meet the requirements through the predetermined parameters in the statistical table of each sub-population layer information. If not, return to step 105 to modify the preset grouping step and number of levels, and then execute steps 105 and 106 until the requirements are met, wherein the predetermined parameters include at least the total number of stratum populations, the minimum sample size, and the sampling ratio.

[0015] In an optional implementation, step 101 includes:

[0016] Step 1011: Obtain a total cultivated land spatial distribution vector diagram of the monitoring target population and sub-population boundary vector diagrams of multiple sub-populations;

[0017] Step 1012: Set a sampling unit and obtain a sampling unit layer vector map of the sampling unit, wherein the sampling unit has multiple individuals, each of the sampling units has a unique identification code, and in addition, in geographical space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target;

[0018] Step 1013: Obtain the spatial distribution vector diagram of the sub-cultivated land of each sub-population and the sub-sampling unit layer vector diagram;

[0019] Step 1014: superimpose the spatial distribution vector diagram of each sub-cultivated land with the corresponding sub-sampling unit layer vector diagram to extract the cultivated land area of ​​each sub-population based on the sampling unit individual.

[0020] In an optional embodiment, in step 1013, the total cultivated land spatial distribution vector diagram and the sampling unit layer vector diagram are respectively clipped by the sub-population boundary vector diagrams of the multiple sub-populations, thereby forming the sub-cultivated land spatial distribution vector diagram and the sub-sampling unit layer vector diagram of each sub-population.

[0021] In an optional implementation, step 1013 is processed in a GIS platform.

[0022] In an optional embodiment, in step 1014, the cultivated land area of ​​each sub-population extracted based on the sampling unit individuals is a two-dimensional data table set with each sub-population as a storage set.

[0023] In an optional embodiment, in step 105, the field values ​​of the header of each sub-population sampling unit layer information table include at least the sub-population code, sampling unit code, cultivated land area, frequency, frequency accumulation, frequency accumulation square root and layer.

[0024] In an optional embodiment, in step 107, the minimum sample size n is calculated using the following formula:

[0025]

[0026] Pro rata:

[0027]

[0028]

[0029] Among them, L is the number of layers of stratified sampling; N h is the total number of sampling layers; h=1,2,…,L; S h 2 is the variance of each sampling layer; N is the total number; W h =N h / N, that is, the sampling weight of the hth layer; V is the variance of the estimator. If V is not given, but the error limit d is given, then V=(d / t) 2 , when the sample size is quite large, we can assume that t = u 0.025 =1.96; d = (1-δ)Y, δ is the sampling precision, Y is the total value of the population; n0 has no practical meaning and is only used to facilitate the writing of the formula;

[0030] The sampling ratio r is calculated by the following formula:

[0031] r = n / N;

[0032] Finally, the minimum sample size n of each layer h Calculated by the following formula:

[0033] n h =N h ×r.

[0034] In a second aspect, the present application further discloses a system for creating a crop area sampling statistical model cluster, comprising:

[0035] An acquisition module is used to obtain the cultivated land areas of multiple sub-populations contained in the monitoring target population;

[0036] a basic data processing module, configured to use the cultivated land area data of each sub-population as the stratified basic data of each sub-population and store the data in a file directory according to a predetermined format; and

[0037] Used to set up a subpopulation name directory table, where each subpopulation corresponds to a record, and the field values ​​of each record include at least the full name, abbreviation, and code of the subpopulation; and

[0038] It is used to cyclically read the corresponding field values ​​of each sub-population in the sub-population name directory table, dynamically construct the hierarchical basic data file name of each sub-population, thereby reading the corresponding hierarchical basic data of each sub-population, and sorting the data in ascending order;

[0039] a stratification processing module, configured to stratify the stratified basic data of each sub-population after being sorted in ascending order, and to display the stratification results in a table to obtain a stratum information table of each sub-population sampling unit, wherein the stratification is based on the size of the cultivated land area, the stratification method is a frequency accumulation method, and the grouping step size and the number of levels are preset. After the stratification is completed, all sampling unit individuals of each sub-population are assigned corresponding stratum codes; and

[0040] It is used to cyclically read the stratum information table of each sub-population sampling unit, calculate the stratum statistics of each sub-population, and display the calculation results in a table form, thereby obtaining the stratum information statistics table of each sub-population; and

[0041] It is used to determine whether the stratification results meet the requirements through the predetermined parameters in the statistical table of each sub-population layer information. If not, it returns to modify the preset grouping step and number of levels, and then executes subsequent steps until it meets the requirements, wherein the predetermined parameters include at least the total number of stratum populations, the minimum sample size, and the sampling ratio.

[0042] In an optional embodiment, the acquisition module acquires the cultivated land areas of multiple sub-populations including the following steps:

[0043] Obtain the total cultivated land spatial distribution vector of the monitoring target population and the sub-population boundary vectors of multiple sub-populations;

[0044] Setting a sampling unit and obtaining a sampling unit layer vector map of the sampling unit, wherein the sampling unit has a plurality of individuals, each of the sampling units has a unique identification code, and in addition, in geographical space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target;

[0045] Obtain the spatial distribution vector diagram of sub-cultivated land of each sub-population and the vector diagram of sub-sampling unit layer;

[0046] The spatial distribution vector diagram of each sub-cultivated land was superimposed with the corresponding sub-sampling unit layer vector diagram to extract the cultivated land area of ​​each sub-population based on the sampling unit individual.

[0047] In an optional embodiment, the acquisition module is to clip the total cultivated land spatial distribution vector diagram and the sampling unit layer vector diagram respectively through the sub-population boundary vector diagrams of the multiple sub-populations in the GIS platform, thereby obtaining the sub-cultivated land spatial distribution vector diagram and the sub-sampling unit layer vector diagram of each sub-population.

[0048] This application has at least the following beneficial technical effects:

[0049] 1) The method and system for creating a crop area sampling statistical model cluster of the present application stores the stratified basic data of each sub-population in a unified format in a file directory, sets a corresponding sub-population name directory table, and then implements ascending sorting of the stratified basic data by cyclically reading the sub-population name directory table. Finally, these data are processed in layers accordingly. Therefore, a crop area remote sensing sampling monitoring model cluster for multiple survey populations can be quickly and conveniently established at one time. Moreover, since the model parameter modification is programmed, one or more model parameters can be modified at any time according to the changed sampling basic data, thereby quickly completing the parameter table update and effectively meeting the business operation requirements.

[0050] 2) The method for creating a crop area sampling statistical model cluster and the sampling method used in the system of this application are based on scientific statistical theory. The model operation results have been tested in production practice and are considered to be reliable and accurate. Compared with traditional crop area statistical methods and other crop area remote sensing methods, it has the characteristics of low cost, high efficiency and good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flowchart of the method for creating a crop area sampling statistical model cluster in this application;

[0052] Figure 2 This is a schematic diagram of the sampling unit design process in the method for creating a crop area sampling statistical model cluster in this application;

[0053] Figure 3This is a flowchart of modeling data stratification and basic model parameter generation in the method of creating a crop area sampling statistical model cluster in this application;

[0054] Figure 4 This is a flowchart for calculating the minimum sample size and sampling ratio in the method for creating a crop area sampling statistical model cluster in this application;

[0055] Figure 5 In Example 1 of the method for creating a crop area sampling statistical model cluster of the present application, a general distribution map of the stratified sampling layers of single-season rice in Heilongjiang Province is modeled;

[0056] Figure 6 This is a diagram of the system for creating a crop area sampling statistical model cluster in this application. DETAILED DESCRIPTION

[0057] To make the purpose, technical solutions, and advantages of the present application more clear, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings. The described embodiments are part of the embodiments of the present application, not all of them. The embodiments described below with reference to the accompanying drawings are illustrative and intended to be used to explain the present application, and should not be understood as limiting the present application.

[0058] Example 1

[0059] Take the sampling survey on the planting area of ​​single-season rice and double-season late rice in 25 provinces of my country as an example. The survey target is rice, and the 25 provinces surveyed are designed as sub-populations with a total number of 25.

[0060] First, as Figures 1 - 5 As shown, the present invention's method for creating a crop area sampling statistical model clustering method based on cultivated land (cultivated land can be dry land or paddy field, in this case paddy field) area specifically includes the following steps:

[0061] Step 101: Obtain the paddy field area of ​​each of the 25 provinces.

[0062] Furthermore, this step 101 is further divided into the following steps:

[0063] Step 1011: Obtain the total paddy field spatial distribution vector diagram of the 25 major rice-growing provinces in my country through data exchange, purchase, or self-production (i.e., the total cultivated land spatial distribution vector diagram, which in this embodiment is specifically derived from the second national land survey data and obtained through data exchange).

[0064] Furthermore, the number and geographical location of 25 provinces (sub-populations) were determined within the total paddy field spatial distribution vector map, and the sub-population boundary vector maps (or administrative boundary vector maps) of the 25 provinces (sub-populations) were obtained through data exchange, purchase or self-production.

[0065] It should be noted that, in this step 1011, the following settings are also included:

[0066] 1) Set the spatial geographic coordinate system to CGCS2000 (China Geodetic Coordinate System2000) and perform Albers projection in this spatial geographic coordinate system, that is, the projection is "orthogonal equal-area double standard parallel secant conic projection"; among which, the projection parameters are: the first standard parallel is 25, the second standard parallel is 47, and the central meridian is 105.

[0067] 2) Let the original domain U, U1, U2... be a subset of U, x∈U; then:

[0068] U={U1,U2,...,Un};U={x1,x2,...,xn};

[0069] Among them, U is the survey population (i.e., my country), Un is the sub-population (i.e., province), and x is the sampling unit.

[0070] Step 1012: Determine the sampling unit as a 1:50000 topographic map frame (see Figure 2 As shown), there are multiple sampling units, and each sampling unit has a unique identification code (i.e., map sheet number);

[0071] Furthermore, a sampling unit layer vector map is obtained; wherein, the sampling unit layer vector map is a vector map having the geographic coordinates described in step 1011. Depending on the sampling unit type, the sampling unit layer vector map can be purchased or exchanged from relevant units, or produced by oneself in GIS. In this embodiment, it is produced by oneself on the GIS platform; in addition, in geographic space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target.

[0072] Step 1013: On the GIS platform, use the sub-population boundary vector diagrams of the 25 provinces (sub-populations) to cut the total paddy field spatial distribution vector diagram to form the paddy field spatial distribution vector diagram (i.e., the sub-arable land spatial distribution vector diagram) of each province (sub-population); similarly, use the sub-population boundary vector diagrams of the 25 provinces (sub-populations) to cut the sampling unit layer vector diagram on the GIS platform to form the sub-sampling unit layer vector diagram of each province (sub-population).

[0073] Among them, the total paddy field spatial distribution map and the sub-population boundary vector map of 25 provinces (sub-populations) are vector data files, and their coordinate information conforms to the geographic coordinates described in step 1011.

[0074] Step 1014: On the GIS platform, superimpose the spatial distribution vector of paddy fields in each province (sub-population) with the corresponding sub-sampling unit layer vector (seeFigure 2 As shown in the figure, the paddy field area of ​​each province (sub-population) is extracted based on the sampling unit individual.

[0075] In addition, the paddy field area of ​​each province (sub-population) extracted in this step is a two-dimensional data table set with each province as the storage set (see the first three columns of Table 2 below), one table for each province (sub-population), and a total of 25 tables for 25 provinces (sub-populations).

[0076] Step 102: The paddy field area data of each province (sub-population) according to the sampling unit is used as the stratified basic data of each province (sub-population). The data files are named according to the convention and stored in a file directory.

[0077] The specific naming method can be set appropriately as needed to facilitate subsequent programs to automatically read the file. For example, the data file of Heilongjiang Province can be named: sthlj23callayer.

[0078] Step 103. Set up a directory table of province names (see Table 1 below), with one record for each province (sub-population). The record fields include the province name (such as Heilongjiang Province, Hunan Province, etc.), abbreviation (such as HLJ, HN, etc.) and code (such as 23, 43, etc.). The abbreviation is agreed upon in the program, and the code complies with the requirements of the GB2260 standard.

[0079] Table 1: Province Name Directory

[0080]

[0081]

[0082] Step 104, design program code, open the province (sub-population) name directory table, loop to read the field values ​​of each province (sub-population), dynamically construct the hierarchical basic data file name (character string, such as Heilongjiang Province: sthlj23callayer) of each province (sub-population) according to the convention, read the character string, and thus open the hierarchical basic data files of each province named according to the convention and stored in the agreed directory, and sort the data in the file in ascending order.

[0083] Step 105: stratify the basic data of each province (sub-population) after ascending sorting in step 104, and display the stratification results in a table form (using the preset table shown in Table 6 below), thereby obtaining the sampling unit layer information table of each province (sub-population).

[0084] Furthermore, the stratification method designed in this application is a frequency accumulation method. The mathematical principle and application process of frequency accumulation will not be discussed in detail here. The stratification process is briefly described as follows:

[0085] As shown in Table 2 below, for the purpose of automatic execution of the program, the header field names of the sampling unit layer information table for each province (sub-population) are preset as sub-population code (DM), sampling unit code (BM), paddy field area (AREA_HA), group identifier (GROUP_FLAG), frequency (FREQUENCY), frequency accumulation (SUM_FRE), frequency accumulation square root (SQRTSUM_F) and layer (LAYER).

[0086] Specifically, the initial grouping step size is 4 hectares, and the number of levels is 6. Design a program to read the "paddy field area" in Table 2 below one by one, group them by a step size of 4 hectares, and calculate the frequency, frequency accumulation, and frequency accumulation square root of each group. The number of design levels is 6, and all sampling units are divided into 6 layers according to the "frequency accumulation square root". Each sampling unit is assigned a layer code, so as to dynamically generate the sampling unit layer information table of each province (sub-population) (for example: the layer information table of the single-season rice sampling unit of the Heilongjiang Province sub-population is shown in Table 2 below). The names of the layer information tables of each province (sub-population) are executed according to the agreement. Each province (sub-population) has a layer information table, and there are 25 layer information tables for a total of 25 provinces (sub-populations).

[0087] Table 2: Single-season rice sampling unit information table in Heilongjiang Province (excerpt)

[0088]

[0089] Step 106: Design program code to use the preset table shown in Table 7 below to loop through the provincial (sub-population) layer information table files, assigned with layer codes, generated in Step 105, and calculate the layer information statistics for each province (sub-population): including the total number of each layer, total_cell; the minimum sample size, min_s_cell, calculated according to the aforementioned formula with a sampling accuracy of 95%; the layer mean, ave_a_ha; and the sampling ratio. Each of these values ​​is calculated once for each province (sub-population), dynamically generating a layer information statistics table for each province (sub-population) (for example, see Table 3 below for the layer information statistics table for single-season rice in Heilongjiang Province). The table names are automatically executed according to the agreed-upon procedure. Each province (sub-population) generates a layer information statistics table, for a total of 25 layer information statistics tables for the 25 provinces (sub-populations).

[0090] Table 3: Statistics of rice layer information in Heilongjiang Province

[0091]

[0092] Step 107: Based on the program's automatic run results, check the stratum information statistics table to see if the stratum population, minimum sample size, sampling ratio, etc. meet the requirements. If not, reset the grouping step size and number of levels, and execute steps 105 and 106 until the results meet the requirements. In this application example, the grouping step size is ultimately determined to be 4.5 hectares and the number of levels is 6. Finally, the program automatically generates the stratum information statistics table for the Heilongjiang Province sub-population, as shown in Table 3 above.

[0093] Each province (subpopulation) has one stratum information table and one stratum information statistics table, resulting in a total of 25 stratum information tables and 25 stratum information statistics tables for the 25 provinces (subpopulations). The cluster model for the sampling survey of single-season and double-season late rice planting areas in 25 provinces in my country has been created.

[0094] Finally, it should be noted that in the method of creating a crop area sampling statistical model cluster in this application, in order to establish the model and calculate the model parameters, it is necessary to construct a series of preset tables. The preset table structure is shown in Tables 4-8 below:

[0095] Table 4: Subpopulation name directory table structure (Provinces_list.dbf)

[0096] field_name field_type field_len field_dec DM N 2 0 PNAME C 10 0 NAME_JC C 3 0

[0097] Table 5: Subpopulation sampling basic data table structure (XXcallayer.dbf)

[0098] field_name s field_type field_len field_dec N 13 0 BM N 14 2

[0099] Table 6: Layer Information Process File Table and Layer Information Table Structure (Frequency_cal_layer.dbf)

[0100] AREA_HA field_name field_type field_len field_dec N 2 0 DM N 16 0 BM N 10 2 AREA_HA N 10 0 GROUP_FLAG N 10 0 [[ID=3I]]FREQUENCY N 10 0 SUM_FRE N 15 4 SQRTSUM_F N 1 0

[0101] Table 7: Layer information statistics table structure (Parameters_layer.dbf)

[0102] LAYER field_name field_type field_len field_dec N 1 0 LAYER N 11 0 TOTAL_CELL N 11 0 MIN_S_CELL N 18 1 AVE_A_HA N 10 4

[0103] Table 8: Layer information statistics data exchange process file table structure (Data_shifting.dbf)

[0104] RATE_SAMPL field_name field_type [[ID=4S]]field_len field_dec N 1 0 LAYER N 8 0 TOTAL_CELL N 10 1 DATAVAR N 14 1

[0105] Among them, Table 4 corresponds to Table 1 in step 103; Table 5 corresponds to the two-dimensional data table set obtained in step 1014, that is, the first three columns of Table 2; Table 6 corresponds to Table 2 in step 105; Table 7 corresponds to Table 3 in step 106; Table 8 is used when the program exchanges data in step 105, and the table is cleared when the program ends.

[0106] Second, as NVAR Figure 6 As shown, the present application also discloses a system for creating a crop area sampling statistical model cluster, which may include an acquisition module 201 , a basic data processing module 202 and a hierarchical processing module 203 .

[0107] Specifically, the acquisition module 201 is used to obtain the paddy field area of ​​each of the 25 provinces in my country.

[0108] The basic data processing module 202 is used to:

[0109] The paddy field area data of each province is used as the basic data of each province stratification, and is stored in a file directory according to a predetermined format; and

[0110] Set up a directory table of province names, where each province corresponds to a record, and the field values ​​of each record include at least the province's full name, abbreviation, and code; and

[0111] Loop through the corresponding field values ​​of each province in the province name directory table, dynamically construct the hierarchical basic data file name for each province, and then read the corresponding hierarchical basic data of each province and sort the data in ascending order.

[0112] The layered processing module 203 is used to:

[0113] Stratify the ascending sorted provincial stratification basic data and display the stratification results in a table to obtain the sampling unit layer information table of each province. The stratification is based on the size of the cultivated land (paddy field) area, the stratification method is the frequency accumulation method, and the grouping step size and the number of levels are preset. After the stratification is completed, all sampling unit individuals in each province are assigned corresponding level codes; and

[0114] Circularly read the sampling unit layer information table of each province, calculate the layer statistics of each province, and display the calculation results in table form, thereby obtaining the layer information statistics table of each province; and

[0115] The predetermined parameters in the statistical table of provincial layer information are used to determine whether the stratification results meet the requirements. If not, the system returns to modify the preset grouping step and number of levels, and then executes subsequent steps until the requirements are met. The predetermined parameters include at least the total number of layers, the minimum sample size, and the sampling ratio.

[0116] Furthermore, the acquisition module 201 acquires the paddy field area of ​​each of the 25 provinces, including the following steps:

[0117] Obtain the total paddy field spatial distribution vectors of the 25 provinces that mainly grow rice in my country and the sub-population boundary vectors (or administrative boundary vectors) of the 25 provinces (sub-populations);

[0118] Set the sampling unit and obtain the sampling unit layer vector map of the sampling unit, wherein the number of individuals in the sampling unit is multiple, each sampling unit has a unique identification code, and in addition, in terms of geographical space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target;

[0119] In the GIS platform, the paddy field spatial distribution vector diagram and the sampling unit layer vector diagram were clipped by the sub-population boundary vector diagrams of the 25 provinces, thereby obtaining the paddy field spatial distribution vector diagram (i.e., the sub-cultivated land spatial distribution vector diagram) and the sub-sampling unit layer vector diagram of each province (sub-population);

[0120] The spatial distribution vector of paddy fields in each province (sub-population) was superimposed with the corresponding sub-sampling unit layer vector to extract the paddy field area of ​​each province based on the individual sampling unit.

[0121] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for creating a cluster of crop area sampling statistical models, characterized in that: The steps include: Step 101: Obtain the cultivated land areas of multiple sub-populations included in the monitoring target population; Step 102: The cultivated land area data of each sub-population is used as the stratified basic data of each sub-population and stored in a file directory according to a predetermined format; Step 103: Set up a subpopulation name directory table, where each subpopulation corresponds to a record, and the field value of each record includes at least the full name, abbreviation, and code of the subpopulation; Step 104: cyclically read the corresponding field values ​​of each sub-population in the sub-population name directory table, dynamically construct the hierarchical basic data file name of each sub-population, thereby reading the corresponding hierarchical basic data of each sub-population, and sorting the data in ascending order; Step 105: stratify the basic data of each sub-population after ascending sorting in step 104, and display the stratification results in a table to obtain a stratum information table of each sub-population sampling unit, wherein the stratification is based on the size of the cultivated land area, the stratification method is a frequency accumulation method, and the grouping step size and the number of levels are preset. After the stratification is completed, all sampling unit individuals of each sub-population are assigned corresponding level codes; Step 106: cyclically read the stratum information table of each sub-population sampling unit, calculate the stratum statistics of each sub-population, and display the calculation results in a table form, thereby obtaining the stratum information statistics table of each sub-population; Step 107: Determine whether the stratification results meet the requirements through the predetermined parameters in the statistical table of each sub-population layer information. If not, return to step 105 to modify the preset grouping step and number of levels, and then execute steps 105 and 106 until the requirements are met, wherein the predetermined parameters include at least the total number of stratum populations, the minimum sample size, and the sampling ratio.

2. The method for creating a crop area sampling statistical model cluster according to claim 1, characterized in that: The step 101 includes: Step 1011: Obtain a total cultivated land spatial distribution vector diagram of the monitoring target population and sub-population boundary vector diagrams of multiple sub-populations; Step 1012: Set a sampling unit and obtain a sampling unit layer vector map of the sampling unit, wherein the sampling unit has multiple individuals, each of the sampling units has a unique identification code, and in addition, in geographical space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target; Step 1013: Obtain the spatial distribution vector diagram of the sub-cultivated land of each sub-population and the sub-sampling unit layer vector diagram; Step 1014: superimpose the spatial distribution vector diagram of each sub-cultivated land with the corresponding sub-sampling unit layer vector diagram to extract the cultivated land area of ​​each sub-population based on the sampling unit individual.

3. The method for creating a crop area sampling statistical model cluster according to claim 2, characterized in that: In step 1013, the total cultivated land spatial distribution vector diagram and the sampling unit layer vector diagram are respectively clipped by the sub-population boundary vector diagrams of the multiple sub-populations, thereby forming the sub-cultivated land spatial distribution vector diagram and the sub-sampling unit layer vector diagram of each sub-population.

4. The method for creating a crop area sampling statistical model cluster according to claim 3, characterized in that: The step 1013 is processed in the GIS platform.

5. The method for creating a crop area sampling statistical model cluster according to claim 2, characterized in that: In step 1014, the cultivated land area of ​​each sub-population extracted based on the sampling unit individual is a two-dimensional data table set with each sub-population as a storage set.

6. The method for creating a crop area sampling statistical model cluster according to claim 1, characterized in that: In step 105, the field values ​​of the header of each sub-population sampling unit layer information table include at least sub-population code, sampling unit code, cultivated land area, frequency, frequency accumulation, frequency accumulation square root and layer.

7. The method for creating a crop area sampling statistical model cluster according to claim 1, characterized in that: In step 107, the minimum sample size n is calculated using the following formula: Among them, L is the number of layers of stratified sampling; N h is the total number of sampling layers; h=1,2,…,L; is the variance of each sampling layer; N is the total number; W h =N h / N, that is, the sampling weight of the hth layer; V is the variance of the estimator. If V is not given, but the error limit d is given, then V=(d / t) 2 , when the sample size is quite large, we can assume that t = u 0.025 =1.96; d = (1-δ)Y, δ is the sampling precision, Y is the total value of the population; The sampling ratio r is calculated by the following formula: r = n / N; Finally, the minimum sample size n of each layer h Calculated by the following formula: n h =N h ×r。 8. A system for creating a cluster of crop area sampling statistical models, characterized in that: include: An acquisition module (201) is used to acquire the cultivated land areas of multiple sub-populations included in the monitoring target population; A basic data processing module (202) is used to use the cultivated land area data of each sub-population as the stratified basic data of each sub-population and store it in a file directory according to a predetermined format; as well as Used to set up a subpopulation name directory table, where each subpopulation corresponds to a record, and the field values ​​of each record include at least the full name, abbreviation, and code of the subpopulation; and It is used to cyclically read the corresponding field values ​​of each sub-population in the sub-population name directory table, dynamically construct the hierarchical basic data file name of each sub-population, thereby reading the corresponding hierarchical basic data of each sub-population, and sorting the data in ascending order; A stratification processing module (203) is used to stratify the stratification basic data of each sub-population after ascending sorting, and display the stratification results in a table form to obtain a stratum information table of each sub-population sampling unit, wherein the stratification is based on the size of the cultivated land area, the stratification method is a frequency accumulation method, and the grouping step size and the number of levels are preset. After the stratification is completed, all sampling unit individuals of each sub-population are assigned corresponding level codes; and It is used to cyclically read the stratum information table of each sub-population sampling unit, calculate the stratum statistics of each sub-population, and display the calculation results in a table form, thereby obtaining the stratum information statistics table of each sub-population; and It is used to determine whether the stratification results meet the requirements through the predetermined parameters in the statistical table of each sub-population layer information. If not, it returns to modify the preset grouping step and number of levels, and then executes subsequent steps until it meets the requirements, wherein the predetermined parameters include at least the total number of stratum populations, the minimum sample size, and the sampling ratio.

9. The system for creating a crop area sampling statistical model cluster according to claim 8, characterized in that: The acquisition module (201) acquires the cultivated land areas of multiple sub-populations, including the following steps: Obtain the total cultivated land spatial distribution vector of the monitoring target population and the sub-population boundary vectors of multiple sub-populations; Setting a sampling unit and obtaining a sampling unit layer vector map of the sampling unit, wherein the sampling unit has a plurality of individuals, each of the sampling units has a unique identification code, and in addition, in geographical space, the sampling unit layer vector map is consistent with the overall scope of the monitoring target; Obtain the spatial distribution vector diagram of sub-cultivated land of each sub-population and the vector diagram of sub-sampling unit layer; The spatial distribution vector diagram of each sub-cultivated land was superimposed with the corresponding sub-sampling unit layer vector diagram to extract the cultivated land area of ​​each sub-population based on the sampling unit individual.

10. The system for creating a crop area sampling statistical model cluster according to claim 9, characterized in that: The acquisition module (201) is used in a GIS platform to clip the total cultivated land spatial distribution vector diagram and the sampling unit layer vector diagram respectively using the sub-population boundary vector diagrams of the multiple sub-populations, thereby obtaining the sub-cultivated land spatial distribution vector diagram and the sub-sampling unit layer vector diagram of each sub-population.

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