Big data analysis platform based on smart agriculture

Abstract

The invention discloses a big data analysis platform based on smart agriculture, and relates to the technical field of data analysis, and the platform comprises the steps: building a regional multi-source heterogeneous database; according to the comprehensive factors influencing the planting crops of the regional land in the regional multi-source heterogeneous database, analyzing the matching indexes of the seasonal planting crops of the regional land; performing positive correlation planting crop screening based on the dynamic suitable correlation coefficient of the plantable crops of the regional land, and establishing a plantable crop data matrix A of the regional land; based on the plantable crop data matrix and the multi-source heterogeneous data of the regional land, carrying out yield estimation on the plantable crops, analyzing and estimating the yield quality of the plantable crops, and determining the yield income of the plantable crops of the regional land; and according to the output income maximum value of the planted crops of the regional land, an optimal crop planting scheme of the regional land is generated, and the method has the advantages that the total grain yield is improved, and the income of farmers is increased.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a big data analysis platform based on smart agriculture. Background Art

[0002] Big data analysis in smart agriculture refers to the use of modern information technology and big data technology to collect, store, process, analyze and utilize large amounts of data generated in the agricultural production process to optimize agricultural production, improve agricultural production efficiency, ensure the quality of agricultural products and promote sustainable development.

[0003] Since smart agriculture involves a large span of data, existing systems mainly rely on network agricultural big data to conduct regional similarity training models to assist in agricultural task decision-making, but they cannot be applied to different regions, and the actual agricultural assistance implementation effect is poor. Summary of the invention

[0004] In order to solve the above technical problems, a big data analysis platform based on smart agriculture is provided. This technical solution solves the above-mentioned problem that due to the large data span involved in smart agriculture, the existing systems mainly rely on network agricultural big data to conduct regional similarity training models to achieve auxiliary agricultural task decision-making, but cannot be applied to different regions, and the actual agricultural assistance implementation effect is poor.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A big data analysis platform based on smart agriculture, including:

[0007] Based on the relevant data of various departments in the region, a regional multi-source heterogeneous database is established; the relevant data of various departments in the region include: natural resources data, agricultural and rural data, and water conservancy data;

[0008] According to the comprehensive factors affecting the crops planted on regional land in the regional multi-source heterogeneous database, the matching index of seasonal crops that can be planted on regional land is analyzed;

[0009] Based on the dynamic suitability correlation coefficient of the crops that can be planted on the regional land, positively correlated crops are screened and a data matrix A of crops that can be planted on the regional land is established;

[0010]

[0011] Among them, G ik is the matching index of the kth arable crop in the i-th season

[0012] Based on the regional land's cultivable crop data matrix and multi-source heterogeneous data, the yield of cultivable crops is estimated, the output quality of cultivable crops is analyzed and estimated, and the yield income of cultivable crops on regional land is determined; external impact risks and internal response capabilities

[0013] According to the maximum yield of crops that can be planted in the regional land, the optimal planting crop plan for the regional land is generated.

[0014] Preferably, according to the comprehensive factors affecting the crops planted on the regional land in the regional multi-source heterogeneous database, the matching indexes for analyzing the seasonal crops planted on the regional land specifically include:

[0015] Acquire multi-source heterogeneous parameters in each season in a regional multi-source heterogeneous database to construct seasonal multi-source heterogeneous time series data; the multi-source heterogeneous time series data includes: soil fertility parameters, environmental climate parameters, and water resource parameters;

[0016] Based on the crops that can be grown in each season, determine the growth demand data of the crops that can be grown in the season;

[0017] Normalize seasonal multi-source heterogeneous time series data and seasonal crop demand data;

[0018] Based on seasonal multi-source heterogeneous time series data, the unit time is used as the time window, and the multi-source heterogeneous data of each season is used as the index attribute of the time window. By sliding the time window, the multi-source heterogeneous time series feature vectors of each season are obtained;

[0019] One-Hot coding is used to discretize the demand data of crops that can be planted within a season into the seasonal demand vector of crops that can be planted;

[0020] Data alignment is performed according to seasonal attributes, and the matching degree between the multi-source heterogeneous time series feature vectors of each season and the seasonal plantable crop growth demand vectors is calculated to obtain the matching index of seasonal plantable crops on regional land.

[0021] The specific matching indexes of seasonal crops that can be planted in the land of the region are:

[0022]

[0023] In the formula, G ik is the matching index of the kth plantable crop in the i-th season, x ij is the j-th multi-source heterogeneous time series feature vector in the i-th season, y iK is the kth arable crop growth demand vector in the i-th season, is the mean of the j-th multi-source heterogeneous time series feature vector, is the mean value of the growth demand vector of the kth arable crop, is the standard deviation of the j-th multi-source heterogeneous time series feature vector, is the standard deviation of the growth demand vector of the kth cultivable crop, and n is the total number of seasons.

[0024] Preferably, the yield of the cultivable crops is estimated based on the cultivable crop data matrix of the regional land and the multi-source heterogeneous data, the output quality of the cultivable crops is analyzed and estimated, and the output income of the cultivable crops of the regional land is determined, which specifically includes:

[0025] Based on SVR support vector regression, a prediction model for the yield of arable crops in regional land is constructed;

[0026] Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the multi-source heterogeneous data with associated matching is labeled according to the demand of seasonal arable crops as the influencing feature data of seasonal arable crops. The yield of arable crops is used as label data and packaged into the original sample data set of seasonal arable crops.

[0027] Substitute the original sample data set of seasonal cultivable crops into the cultivable crop yield prediction model of regional land, take the influencing characteristic data of seasonal cultivable crops as input, take the predicted cultivable crop yield label data as output, and take minimizing the error function of the model as the end goal;

[0028] Determine several hidden dangers in the growth process of crops that can be planted in the season, which are recorded as external growth hidden dangers; the external growth hidden dangers include: drought, floods, diseases and insect pests;

[0029] Based on the growth risks of crops that can be planted in the season, several coping strategies for the growth risks in the marked areas are recorded as internal coping capabilities; the internal coping capabilities include: irrigation control, pest control;

[0030] Based on external growth risks and internal response capabilities, assess the growth risk factor of crops that can be planted in the season;

[0031] Using the growth risk coefficient of seasonal cultivable crops, the predicted yield of cultivable crops is corrected to obtain the output quality index of cultivable crops;

[0032] The crop yield prediction model for the land in the region specifically includes:

[0033]

[0034] Where P is the predicted yield of crops that can be grown, d z is the zth influencing characteristic data of crops that can be planted in the season, α z is the Lagrange multiplier of the zth influencing characteristic data, d is the influencing characteristic data of the input seasonal crops, V(dz ,d) is the kernel function of the model, b is the bias term;

[0035] Among them, the output quality indicators of cultivable crops are as follows:

[0036] S=P×R

[0037] Where S is the output quality index of cultivable crops, and R is the growth risk coefficient of cultivable crops in that season.

[0038] Preferably, based on external growth risks and internal coping capabilities, the growth risk coefficients of crops that can be planted in the season are evaluated to specifically include:

[0039] Based on various growth hazards within the growth cycle of seasonal crops that can be planted, calculate the output quality reduction value of crops that can be planted due to external growth hazards;

[0040] Based on the occurrence of various growth hazards during the growth cycle of seasonal crops, the added value of internal response capacity for the output quality of crops that can be planted is calculated;

[0041] Based on the decrease in the output quality of arable crops due to external growth hazards and the increase in the output quality of arable crops due to internal coping capabilities, the growth risk coefficient of seasonal arable crops is calculated;

[0042] The calculation of the external growth hazard for the output quality reduction value of the plantable crops is specifically:

[0043]

[0044] In the formula, ΔA i' is the output quality reduction value of the 'i'th external growth hazard for the cultivatable crops, U0 is the initial value of the output quality of the cultivatable crops, U(t) is the actual value of the output quality of the cultivatable crops, is the sensitivity coefficient of arable crops to external growth hazards, E i' (t) is the impact intensity of the ith external growth hazard on the t growth cycles of the plantable crops, n' is the total number of external growth hazards, T is the length of the growth cycle of the plantable crops, and dt is the differential sign;

[0045] The calculation of the added value of the output quality of the cultivable crops by the internal coping capacity is specifically as follows:

[0046]

[0047] In the formula, ΔB j' The j'th is the added value of the internal coping capacity for the output quality of arable crops, β is the weight coefficient of arable crops to external growth hazards, C j'(t) is the improvement intensity of the j'th internal coping capacity for t growth cycles of the plantable crops, m' is the total number of internal coping capacities, T is the length of the growth cycle of the plantable crops, and dt is the differential sign;

[0048] The growth risk coefficient of crops that can be planted in the calculation season is specifically:

[0049]

[0050] Where R is the growth risk coefficient of crops that can be planted in the season.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention proposes a big data analysis platform based on smart agriculture. By integrating natural resources, agriculture, rural areas, water conservancy and other data in regional multi-source heterogeneous databases, the platform analyzes the comprehensive factors affecting the crops planted on the land in the region, dynamically evaluates the correlation coefficient of the land's suitability for crop planting, and uses external risks and internal response capabilities to predict the crop output quality and income for the crops planted, and finally generates the optimal crop planting plan for the region. The beneficial effects are: increasing total grain output and increasing farmers' income. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a big data analysis platform method based on smart agriculture;

[0054] Figure 2 Flow chart of matching indicator method for analyzing seasonal crops that can be planted on regional land;

[0055] Figure 3 Flow chart of the method for assessing the yield of crops that can be grown on regional land;

[0056] Figure 4 Flowchart of the method for assessing the growth risk factor of seasonal arable crops. DETAILED DESCRIPTION

[0057] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0058] Reference Figure 1 As shown, a big data analysis platform based on smart agriculture includes:

[0059] Based on the relevant data of various departments in the region, a regional multi-source heterogeneous database is established; the relevant data of various departments in the region include: natural resources data, agricultural and rural data, and water conservancy data;

[0060] According to the comprehensive factors affecting the crops planted on regional land in the regional multi-source heterogeneous database, the matching index of seasonal crops that can be planted on regional land is analyzed;

[0061] Based on the dynamic suitability correlation coefficient of the crops that can be planted on the regional land, positively correlated crops are screened and a data matrix A of crops that can be planted on the regional land is established;

[0062]

[0063] Among them, G ik is the matching index of the kth arable crop in the i-th season

[0064] Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the yield of arable crops is estimated, the output quality of arable crops is analyzed and estimated, and the output income of arable crops on regional land is determined;

[0065] According to the maximum yield of crops that can be planted in the regional land, the optimal planting crop plan for the regional land is generated.

[0066] This plan integrates natural resources, agriculture, rural areas, water conservancy and other data from regional multi-source heterogeneous databases, analyzes the comprehensive factors that affect the crops planted on the land in the region, dynamically evaluates the correlation coefficient of the land's suitability for crop planting, and uses external risks and internal response capabilities to predict the quality and income of crop output for the crops planted, and ultimately generates the optimal crop planting plan for the region. The beneficial effects are: increasing total grain output and increasing farmers' income.

[0067] Reference Figure 2 As shown in the figure, according to the comprehensive factors affecting the crops planted on regional land in the regional multi-source heterogeneous database, the matching indicators for seasonal crops that can be planted on regional land are analyzed, including:

[0068] Acquire multi-source heterogeneous parameters in each season in a regional multi-source heterogeneous database to construct seasonal multi-source heterogeneous time series data; the multi-source heterogeneous time series data includes: soil fertility parameters, environmental climate parameters, and water resource parameters;

[0069] Based on the crops that can be grown in each season, determine the growth demand data of the crops that can be grown in the season;

[0070] Normalize seasonal multi-source heterogeneous time series data and seasonal crop demand data;

[0071] Based on seasonal multi-source heterogeneous time series data, the unit time is used as the time window, and the multi-source heterogeneous data of each season is used as the index attribute of the time window. By sliding the time window, the multi-source heterogeneous time series feature vectors of each season are obtained;

[0072] One-Hot coding is used to discretize the demand data of crops that can be planted within a season into the seasonal demand vector of crops that can be planted;

[0073] Data alignment is performed according to seasonal attributes, and the matching degree between the multi-source heterogeneous time series feature vectors of each season and the seasonal plantable crop growth demand vectors is calculated to obtain the matching index of seasonal plantable crops on regional land.

[0074] The specific matching indexes of seasonal crops that can be planted in the land of the region are:

[0075]

[0076] In the formula, G ik is the matching index of the kth plantable crop in the i-th season, x ij is the j-th multi-source heterogeneous time series feature vector in the i-th season, y iK is the kth arable crop growth demand vector in the i-th season, is the mean of the j-th multi-source heterogeneous time series feature vector, is the mean value of the growth demand vector of the kth arable crop, is the standard deviation of the j-th multi-source heterogeneous time series feature vector, is the standard deviation of the growth demand vector of the kth cultivable crop, and n is the total number of seasons.

[0077] It should be noted that the matching index of seasonal crops in the above-mentioned regional land is in the range of [-1 to 1], where -1 means the vector directions are opposite, 0 means there is no matching in the vector directions, and 1 means the vector directions are the same. Therefore, by calculating the matching degree between the multi-source heterogeneous time series feature vectors of each season and the seasonal crop growth demand vectors, the seasonal crops in the current regional land can be quickly screened out.

[0078] Reference Figure 3 As shown, based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the yield of arable crops is estimated, the output quality of arable crops is analyzed and estimated, and the output benefits of arable crops on regional land are determined. Specifically, they include:

[0079] Based on SVR support vector regression, a prediction model for the yield of arable crops in regional land is constructed;

[0080] Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the multi-source heterogeneous data with associated matching is labeled according to the demand of seasonal arable crops as the influencing feature data of seasonal arable crops. The yield of arable crops is used as label data and packaged into the original sample data set of seasonal arable crops.

[0081] Substitute the original sample data set of seasonal cultivable crops into the cultivable crop yield prediction model of regional land, take the influencing characteristic data of seasonal cultivable crops as input, take the predicted cultivable crop yield label data as output, and take minimizing the error function of the model as the end goal;

[0082] Determine several hidden dangers in the growth process of crops that can be planted in the season, which are recorded as external growth hidden dangers; the external growth hidden dangers include: drought, floods, diseases and insect pests;

[0083] Based on the growth risks of crops that can be planted in the season, several coping strategies for the growth risks in the marked areas are recorded as internal coping capabilities; the internal coping capabilities include: irrigation control, pest control;

[0084] Based on external growth risks and internal response capabilities, assess the growth risk factor of crops that can be planted in the season;

[0085] Using the growth risk coefficient of seasonal cultivable crops, the predicted yield of cultivable crops is corrected to obtain the output quality index of cultivable crops;

[0086] The crop yield prediction model for the land in the region specifically includes:

[0087]

[0088] Where P is the predicted yield of crops that can be grown, d z is the zth influencing characteristic data of crops that can be planted in the season, α z is the Lagrange multiplier of the zth influencing characteristic data, d is the influencing characteristic data of the input seasonal crops, V(d z ,d) is the kernel function of the model, b is the bias term;

[0089] Among them, the output quality indicators of cultivable crops are as follows:

[0090] S=P×R

[0091] Where S is the output quality index of cultivable crops, and R is the growth risk coefficient of cultivable crops in that season.

[0092] This solution is based on SVR support vector regression technology, combined with the regional land plantable crop data matrix and multi-source heterogeneous data, to build a yield prediction model, and by analyzing the influencing characteristics of seasonal crops, growth risks and response strategies, evaluate the growth risk coefficient, and then correct the predicted yield to obtain crop output quality indicators. The beneficial effect is that it can comprehensively consider multiple factors, accurately estimate crop yield and quality, provide a basis for regional land planting planning and benefit evaluation, and improve the efficiency and benefits of agricultural production.

[0093] Reference Figure 4 As shown in the figure, based on external growth risks and internal response capabilities, the growth risk factors of crops that can be planted in the season are evaluated, including:

[0094] Based on various growth hazards within the growth cycle of seasonal crops that can be planted, calculate the output quality reduction value of crops that can be planted due to external growth hazards;

[0095] Based on the occurrence of various growth hazards during the growth cycle of seasonal crops, the added value of internal response capacity for the output quality of crops that can be planted is calculated;

[0096] Based on the decrease in the output quality of arable crops due to external growth hazards and the increase in the output quality of arable crops due to internal coping capabilities, the growth risk coefficient of seasonal arable crops is calculated;

[0097] The calculation of the external growth hazard for the output quality reduction value of the plantable crops is specifically:

[0098]

[0099] In the formula, ΔA i' is the output quality reduction value of the 'i'th external growth hazard for the cultivatable crops, U0 is the initial value of the output quality of the cultivatable crops, U(t) is the actual value of the output quality of the cultivatable crops, is the sensitivity coefficient of arable crops to external growth hazards, E i' (t) is the impact intensity of the ith external growth hazard on the t growth cycles of the plantable crops, n' is the total number of external growth hazards, T is the length of the growth cycle of the plantable crops, and dt is the differential sign;

[0100] The calculation of the added value of the output quality of the cultivable crops by the internal coping capacity is specifically as follows:

[0101]

[0102] In the formula, ΔB j' The j'th is the added value of the internal coping capacity for the output quality of arable crops, β is the weight coefficient of arable crops to external growth hazards, C j' (t) is the improvement intensity of the j'th internal coping capacity for t growth cycles of the plantable crops, m' is the total number of internal coping capacities, T is the length of the growth cycle of the plantable crops, and dt is the differential sign;

[0103] The growth risk coefficient of crops that can be planted in the calculation season is specifically:

[0104]

[0105] Where R is the growth risk coefficient of crops that can be planted in the season.

[0106] It can be understood that by quantitatively analyzing the output quality decline caused by various external growth risks during the growth cycle of seasonal crops and the output quality increase brought about by internal coping capabilities, the growth risk coefficient of seasonal crops can be comprehensively calculated to evaluate the potential risks in the crop growth process and its impact on output quality, so as to promote the rational use of agricultural resources and optimize planting decisions.

[0107] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A big data analysis platform based on smart agriculture, characterized in that: include: Based on the relevant data of various departments in the region, a regional multi-source heterogeneous database is established; The relevant data of various departments in the region include: natural resources data, agricultural and rural data, and water conservancy data; According to the comprehensive factors affecting the crops planted on regional land in the regional multi-source heterogeneous database, the matching index of seasonal crops that can be planted on regional land is analyzed; Based on the dynamic suitability correlation coefficient of the crops that can be planted on the regional land, positively correlated crops are screened and a data matrix A of crops that can be planted on the regional land is established; Among them, G ik is the matching index of the kth arable crop in the i-th season Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the yield of arable crops is estimated, the output quality of arable crops is analyzed and estimated, and the output income of arable crops on regional land is determined; According to the maximum yield of crops that can be planted in the regional land, the optimal planting crop plan for the regional land is generated.

2. A big data analysis platform based on smart agriculture according to claim 1, characterized in that: According to the comprehensive factors affecting the crops planted on regional land in the regional multi-source heterogeneous database, the matching indicators of seasonal crops that can be planted on regional land are analyzed, including: Acquire multi-source heterogeneous parameters in each season in a regional multi-source heterogeneous database to construct seasonal multi-source heterogeneous time series data; the multi-source heterogeneous time series data includes: soil fertility parameters, environmental climate parameters, and water resource parameters; Based on the crops that can be grown in each season, determine the growth demand data of the crops that can be grown in the season; Normalize seasonal multi-source heterogeneous time series data and seasonal crop demand data; Based on seasonal multi-source heterogeneous time series data, the unit time is used as the time window, and the multi-source heterogeneous data of each season is used as the index attribute of the time window. By sliding the time window, the multi-source heterogeneous time series feature vectors of each season are obtained; One-Hot coding is used to discretize the demand data of crops that can be planted within a season into the seasonal demand vector of crops that can be planted; Data alignment is performed according to seasonal attributes, and the matching degree between the multi-source heterogeneous time series feature vectors of each season and the seasonal plantable crop growth demand vector is calculated to obtain the matching index of seasonal plantable crops on regional land.

3. A big data analysis platform based on smart agriculture according to claim 2, characterized in that: The matching indexes of seasonal crops that can be planted in the land of the said area are as follows: In the formula, G ik is the matching index of the kth plantable crop in the i-th season, x ij is the j-th multi-source heterogeneous time series feature vector in the i-th season, y iK is the kth arable crop growth demand vector in the i-th season, is the mean of the j-th multi-source heterogeneous time series feature vector, is the mean value of the growth demand vector of the kth arable crop, is the standard deviation of the j-th multi-source heterogeneous time series feature vector, is the standard deviation of the growth demand vector of the kth cultivable crop, and n is the total number of seasons.

4. A big data analysis platform based on smart agriculture according to claim 3, characterized in that: Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the yield of arable crops is estimated, the output quality of arable crops is analyzed and estimated, and the output benefits of arable crops on regional land are determined. Specifically, the following are included: Based on SVR support vector regression, a prediction model for the yield of arable crops in regional land is constructed; Based on the data matrix of arable crops on regional land and multi-source heterogeneous data, the multi-source heterogeneous data with associated matching is labeled according to the demand of seasonal arable crops as the influencing feature data of seasonal arable crops. The yield of arable crops is used as label data and packaged into the original sample data set of seasonal arable crops. Substitute the original sample data set of seasonal cultivable crops into the cultivable crop yield prediction model of regional land, take the influencing characteristic data of seasonal cultivable crops as input, take the predicted cultivable crop yield label data as output, and take minimizing the error function of the model as the end goal; Determine several hidden dangers in the growth process of crops that can be planted in the season, which are recorded as external growth hidden dangers; the external growth hidden dangers include: drought, floods, diseases and insect pests; Based on the growth risks of crops that can be planted in the season, several coping strategies for the growth risks in the marked areas are recorded as internal coping capabilities; the internal coping capabilities include: irrigation control, pest control; Based on external growth risks and internal response capabilities, assess the growth risk factor of crops that can be planted in the season; The growth risk coefficient of seasonal plantable crops is used to correct the predicted yield of plantable crops and obtain the output quality index of plantable crops.

5. A big data analysis platform based on smart agriculture according to claim 4, characterized in that: The crop yield prediction model for the land in the region specifically includes: Where P is the predicted yield of crops that can be grown, d z is the zth influencing characteristic data of crops that can be planted in the season, α z is the Lagrange multiplier of the zth influencing characteristic data, d is the influencing characteristic data of the input seasonal crops, V(d z ,d) is the kernel function of the model, b is the bias term; Among them, the output quality indicators of cultivable crops are as follows: S=P×R Where S is the output quality index of cultivable crops, and R is the growth risk coefficient of cultivable crops in that season.

6. A big data analysis platform based on smart agriculture according to claim 5, characterized in that: Based on external growth risks and internal response capabilities, the growth risk factors of crops that can be planted in the season are evaluated, including: Based on various growth hazards within the growth cycle of seasonal crops that can be planted, calculate the output quality reduction value of crops that can be planted due to external growth hazards; Based on the occurrence of various growth hazards during the growth cycle of seasonal crops, the added value of internal response capacity for the output quality of crops that can be planted is calculated; The growth risk coefficient of seasonal cultivable crops is calculated based on the decrease in output quality of cultivable crops due to external growth risks and the increase in output quality of cultivable crops due to internal coping capabilities.

7. A big data analysis platform based on smart agriculture according to claim 6, characterized in that: The calculation of the external growth hazard for the output quality reduction value of the cultivable crops is specifically as follows: In the formula, ΔA i' is the output quality reduction value of the 'i'th external growth hazard for the cultivatable crops, U0 is the initial value of the output quality of the cultivatable crops, U(t) is the actual value of the output quality of the cultivatable crops, is the sensitivity coefficient of arable crops to external growth hazards, E i' (t) is the impact intensity of the ith external growth hazard on the t growth cycles of the plantable crops, n' is the total number of external growth hazards, T is the length of the growth cycle of the plantable crops, and dt is the differential sign; The calculation of the added value of the output quality of the cultivable crops by the internal coping capacity is specifically as follows: In the formula, ΔB j' The j'th is the added value of the internal coping capacity for the output quality of arable crops, β is the weight coefficient of arable crops to external growth hazards, C j' (t) is the improvement intensity of the j'th internal coping capacity for t growth cycles of the plantable crops, m' is the total number of internal coping capacities, T is the length of the growth cycle of the plantable crops, and dt is the differential sign; The growth risk coefficient of crops that can be planted in the calculation season is specifically: Where R is the growth risk coefficient of crops that can be planted in the season.

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