Agricultural drought risk assessment method and system

Abstract

The present invention discloses an agricultural drought risk assessment method, which comprises the following steps: dividing a target area into a plurality of assessment units according to its geospatial raster data; collecting agricultural disaster loss data for each assessment unit, using the K-Means classification method and correcting it by the moisture-yield coefficient to obtain a quantitative agricultural loss rate; extracting the total output value of the target area and the agricultural output value and regional output value of each assessment unit, constructing an agricultural exposure model, and obtaining the agricultural exposure of the target area; constructing an agricultural drought risk assessment model using drought hazard, the agricultural loss rate, and the agricultural exposure, and using the neighborhood spatial analysis-primary filtering method and spatial difference method to obtain agricultural drought risk pattern raster data for each assessment unit; and distributing the agricultural drought risk pattern raster data for each assessment unit over the target area according to its geospatial raster data to obtain the agricultural drought risk pattern of the target area. By accurately quantifying agricultural drought risk and identifying high-vulnerability and high-risk areas, the present invention provides a scientific basis for optimizing agricultural resource allocation, formulating disaster prevention and mitigation strategies, and managing drought emergencies.

Description

Technical Field

[0001] The present invention belongs to the technical field of natural disaster risk assessment, and in particular relates to an agricultural drought risk assessment method and system. Background Art

[0002] Agricultural drought is a major disaster affecting agricultural production under climate change, characterized by high frequency, wide impact, long duration, and severe damage. Traditional agricultural drought risk assessment methods are unable to meet the needs of refined management due to insufficient spatial resolution, a lack of dynamics and comprehensiveness, limited model applicability, and a lack of risk stratification and response guidance. Therefore, a new approach based on multi-source data fusion, dynamic analysis, and high-resolution spatial assessment is urgently needed to accurately identify drought risk distribution patterns, support regional disaster prevention and mitigation, optimize agricultural resource allocation, and provide technical support for scientific decision-making. Summary of the Invention

[0003] In response to the above problems, the present invention proposes an agricultural drought risk assessment method, which adopts the following technical solutions:

[0004] In a first aspect, the present invention provides a method for agricultural drought risk assessment, comprising the following steps:

[0005] S1. Dividing the target area into a number of evaluation units according to its geospatial raster data;

[0006] S2. collecting agricultural disaster loss data of each assessment unit, using a K-Means classification method and correcting it by a moisture-yield coefficient to obtain a quantitative agricultural loss rate;

[0007] S3. Extract the total output value of the target area and the agricultural output value and regional output value of each assessment unit, build an agricultural exposure model, and obtain the agricultural exposure of the target area;

[0008] S4. Construct an agricultural drought risk assessment model using drought hazard, the agricultural loss rate, and the agricultural exposure, and adopt neighborhood spatial analysis-primary filtering method and spatial difference method to obtain the agricultural drought risk pattern raster data of each assessment unit; distribute the agricultural drought risk pattern raster data of each assessment unit in the target area according to its geographic space raster data to obtain the agricultural drought risk pattern of the target area.

[0009] Furthermore, the method further includes the step of constructing a meteorological drought disaster risk assessment model to assess the drought risk, specifically including:

[0010] Collecting precipitation and historical temperature data within a historical time period of the assessment unit, calculating a meteorological drought comprehensive index (MCI) based on the historical precipitation and historical temperature data, identifying drought processes according to national standards (GB / T20481-20062017), and evaluating the drought level of each drought process;

[0011] Calculate the occurrence frequency p of drought processes of different drought levels in the historical period i ;

[0012] Drought risk is calculated as follows:

[0013] H=∑w i p i (1)

[0014] In formula (1), H is the drought risk, w i is the weight of different drought levels, p i is the frequency of occurrence of drought processes at different drought levels.

[0015] Furthermore, the meteorological drought comprehensive index (MCI) is calculated according to the following formula:

[0016] MCI=K a ×(a×SPIW 60 +b×MI 30 +c×SPI 90 +d×SPI 150 ) (2)

[0017] In formula (2), SPIW 60 The standardized weighted precipitation index for the past 60 days, MI 30 SPI is the humidity index for the past 30 days. 90 、SPI 150 are the 90-day and 150-day standardized precipitation indices respectively; a, b, c, and d are weight coefficients adjusted according to the region. In the north and west, a, b, c, and d are 0.3, 0.5, 0.3, and 0.2 respectively; in the south, a, b, c, and d are 0.5, 0.6, 0.2, and 0.1 respectively. K a is the seasonal adjustment coefficient.

[0018] Furthermore, when a certain evaluation unit experiences mild or above drought for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate or above on at least one day, it is identified as a drought process, and the highest drought level in the drought process is set as the drought level of the drought process.

[0019] Furthermore, the method of S2 specifically includes the following steps:

[0020] S2.1. Collect the crop drought-affected, drought-stricken, or crop-completed areas within the assessment unit, set their loss weights using an expert scoring method, and calculate the agricultural loss rate L for each assessment unit as shown in the following formula:

[0021] L=I3×90%+(I2-I3)×55%+(I1-I2)×20% (3)

[0022] In formula (3), L is the comprehensive yield reduction rate (%), I1 is the proportion of crop area with a yield reduction of more than 10% to the sown area; I2 is the proportion of crop area with a yield reduction of more than 30% to the sown area; I3 is the proportion of crop area with a yield reduction of more than 80% to the sown area;

[0023] S2.2. The sample space composed of all assessment units in the entire target area is composed of agricultural loss rate of each assessment unit as the agricultural loss rate sample value; all agricultural loss rate sample values ​​are classified according to the drought level using the K-Means method, and the center value of each class is used as the agricultural comprehensive average loss rate L when different drought levels occur in the target area. m ;

[0024] S2.3. Further revise the agricultural comprehensive average loss rate using the crop moisture-yield coefficient;

[0025] L c =K y ×L m (4)

[0026] In formula (4): L c is the revised average agricultural comprehensive loss rate; L m is the average comprehensive agricultural loss rate; K y is the crop water-yield coefficient.

[0027] Furthermore, in S3, the method for constructing the agricultural exposure model is as follows:

[0028]

[0029] In formula (5), E is the exposure to agricultural drought; V is the proportion of the total agricultural output value of the assessment unit to the total output value of the assessment unit. GDP is the total output value of the target area.

[0030] Furthermore, the specific steps of S4 include:

[0031] S4.1. Construct an agricultural drought risk assessment model as follows:

[0032] Risk=H×L c ×E×η (6)

[0033] In formula (6), Risk is the agricultural drought risk, H is the drought hazard, Lc is the corrected agricultural loss rate, E is the exposure to agricultural drought, and η is the unit conversion coefficient;

[0034] S4.2. Smoothing local noise in the agricultural drought risk pattern raster data of the target area using a neighborhood spatial analysis-primary filtering method based on the geospatial correlations between each assessment unit in the target area;

[0035] S4.3. Use a spatial difference method based on spatial standard deviation to process the spatial distribution data of agricultural drought risk into the spatial distribution of agricultural drought risk levels in the target area as the agricultural drought risk pattern.

[0036] Furthermore, the spatial standard deviation described in S4.3 is calculated as follows:

[0037]

[0038] Where δ is the spatial standard deviation of agricultural drought disaster risk, x j is the agricultural drought disaster risk of the jth grid, is the average agricultural drought disaster risk, and n is the total number of grids.

[0039] Furthermore, the neighborhood space analysis-main filtering method includes the following steps:

[0040] Determine raster data for quantitative assessment of agricultural drought risk in target areas;

[0041] Taking the target element grid as the center, its four orthogonal adjacent element grids are used as the range of the main filter;

[0042] Statistical analysis was performed on the quantitative assessment data of agricultural drought risk within the filter, and the mode was calculated. If the grid data within the filter met any of the following conditions, the value of the target meta-grid was adjusted: the mode of the agricultural drought risk data within the filter existed and was unique; at least half of the adjacent meta-grids had the same quantitative assessment data of agricultural drought risk;

[0043] When the above conditions are met, the agricultural drought risk raster data of the target element grid is modified to the mode value within the filter.

[0044] In another aspect, the present invention provides an agricultural drought risk assessment system, which includes at least one processor; and a memory storing instructions, which, when executed by the at least one processor, implements the steps of the above method.

[0045] The beneficial technical effects of this invention are reflected in the following: Based on multi-source data fusion and neighborhood spatial analysis-primary filtering methods, it accurately quantifies agricultural drought risk and identifies high-risk areas, providing data support for the scientific formulation of regional disaster prevention and mitigation strategies. With the assistance of information technology, it effectively carries out pre-disaster prevention, post-disaster emergency management, and adaptive capacity building, improving the efficiency of drought emergency management, helping to optimize agricultural resource allocation, and promoting sustainable agricultural development and regional disaster resilience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 :A flow chart of the agricultural drought risk assessment program implemented in the present invention;

[0047] Figure 2 : is a schematic diagram of the principle of the main filtering method in an embodiment of the present invention;

[0048] Figure 3 : A product model diagram of a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following examples further illustrate the present invention, but should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, modifications or substitutions made to the methods, steps or conditions of the present invention are within the scope of the present invention.

[0050] It should be noted that the raw data used in the following implementations are based on annual county-level drought disaster data collected by the National Disaster Reduction Center of the Ministry of Emergency Management. These data primarily include drought-affected crop area, disaster-stricken area, crop failure area, and direct economic losses. Annual agricultural statistics for provinces (autonomous regions, municipalities directly under the central government) and cities over the same period were also collected, primarily including total crop planting area, gross domestic product (GDP), and the consumer price index (CPI, with the previous year = 100). The statistical data is sourced from the China Statistical Yearbook (http: / / www.stats.gov.cn / tjsj / ndsj / ) of the National Bureau of Statistics of the People's Republic of China and the National Data Query System (http: / / data.stats.gov.cn).

[0051] Some of the following implementation schemes use station data such as precipitation and temperature provided by the China Meteorological Data Network's "China Surface Climate Data Daily Dataset (V3.0)" and calculate the daily meteorological drought composite index (MCI) according to the General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and the Standardization Administration of China (2017).

[0052] The k-means clustering algorithm (k-means) adopted in some of the following embodiments is an iterative clustering analysis algorithm.

[0053] It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] Some options include Figure 1 In the embodiment shown, the steps are as follows:

[0055] S1. Dividing the target area into a number of evaluation units according to its geospatial raster data;

[0056] S2. collecting agricultural disaster loss data of each assessment unit, using a K-Means classification method and correcting it by a moisture-yield coefficient to obtain a quantitative agricultural loss rate;

[0057] S3. Extract the total output value of the target area and the agricultural output value and regional output value of each assessment unit, build an agricultural exposure model, and obtain the agricultural exposure of the target area;

[0058] S4. Construct an agricultural drought risk assessment model using drought hazard, the agricultural loss rate, and the agricultural exposure, and adopt neighborhood spatial analysis-primary filtering method and spatial difference method to obtain the agricultural drought risk pattern raster data of each assessment unit; distribute the agricultural drought risk pattern raster data of each assessment unit in the target area according to its geographic space raster data to obtain the agricultural drought risk pattern of the target area.

[0059] Some plans in Figure 1 Based on the steps shown, the method further includes the step of constructing a meteorological drought disaster risk assessment model to assess the drought risk, specifically including:

[0060] Collecting precipitation and historical temperature data within a historical time period of the assessment unit, calculating a meteorological drought comprehensive index (MCI) based on the historical precipitation and historical temperature data, identifying drought processes, and evaluating the drought level of each drought process;

[0061] Calculate the occurrence frequency pi of drought processes of different drought levels in the historical time period;

[0062] Drought risk is calculated as follows:

[0063] H=∑w i p i (1)

[0064] In formula (1), is the drought risk, is the weight of different drought levels, is the occurrence frequency of drought processes of different drought levels, and i is the identifier of the drought level, i = 1, 2, 3, 4….

[0065] More specifically, the meteorological drought comprehensive index (MCI) is calculated as follows:

[0066] MCI=K a ×(a×SPIW 60 +b×MI 30 +c×SPI 90 +d×SPI 150 ) (2)

[0067] In formula (2), SPIW 60 The standardized weighted precipitation index for the past 60 days, MI 30 SPI is the humidity index for the past 30 days. 90 、SPI 150 They are the 90-day and 150-day standardized precipitation indices, respectively;

[0068] a, b, c, d are weight coefficients adjusted according to the region. In the north and west, a, b, c, d are 0.3, 0.5, 0.3, 0.2 respectively; in the south, a, b, c, d are 0.5, 0.6, 0.2, 0.1 respectively. K a is the seasonal adjustment coefficient.

[0069] By looking up the table below based on the calculated Meteorological Drought Comprehensive Index (MCI), you can identify drought processes and evaluate the drought level of each drought process.

[0070] Table 1. Classification criteria for meteorological drought comprehensive index

[0071]

[0072] When a certain assessment unit experiences mild or above drought for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate or above on at least one day, it is identified as a drought process, and the highest drought level in the drought process is set as the drought level of the drought process.

[0073] Based on the above scheme, some schemes collect agricultural disaster loss data of each assessment unit, use K-Means classification method and correct it by moisture-yield coefficient to obtain quantitative agricultural loss rate, which specifically includes the following steps:

[0074] S2.1. Collect the crop drought-affected, drought-stricken, or crop-completed areas within the assessment unit, set their loss weights using an expert scoring method, and calculate the agricultural loss rate L for each assessment unit as shown in the following formula:

[0075] L=I3×90%+(I2-I3)×55%+(I1-I2)×20% (3)

[0076] In formula (3), L is the comprehensive yield reduction rate (%), I1 is the proportion of crop area with a yield reduction of more than 10% to the sown area; I2 is the proportion of crop area with a yield reduction of more than 30% to the sown area; I3 is the proportion of crop area with a yield reduction of more than 80% to the sown area;

[0077] S2.2. The sample space of all assessment units in the entire target area is composed of agricultural loss rate L in each assessment unit as the agricultural loss rate sample value; all agricultural loss rate sample values ​​are classified according to drought level using K-Means method, and the center value of each class is used as the comprehensive average crop loss rate L when different drought levels occur in the target area. m ;

[0078] S2.3. Further correct the comprehensive average loss rate of the crops using the crop moisture-yield coefficient;

[0079] L c =K y ×L m (4)

[0080] In formula (4): L c is the corrected crop loss rate; L m is the comprehensive average loss rate of crops; K y is the crop water-yield coefficient.

[0081] Crop moisture-yield coefficient K y The calculation method is an existing technology disclosed in the literature, for example, the paper: Mikhail Smilovic, Tom Gleeson, Jan Adamowski, Crop kites: Determining crop-water production functions using crop coefficients and sensitivity indices. Advances in Water Resources, 2016, V97: 193-204. (Crop kites: Determining crop-water production functions using crop coefficients and sensitivity indices https: / / doi.org / 10.1016 / j.advwatres.2016.09.010).

[0082] Based on the above schemes, some schemes extract the total output value of the target area and the agricultural output value and regional output value of each assessment unit, construct an agricultural exposure model, and obtain the agricultural exposure of the target area. The method of constructing the agricultural exposure model is as follows:

[0083]

[0084] In formula (5), is the exposure degree of agricultural drought; is the proportion of the total agricultural output value of the assessment unit to the total output value of the assessment unit; is the total output value of the target area.

[0085] Some schemes based on the above schemes use drought hazard, the agricultural loss rate, and the agricultural exposure to construct an agricultural drought risk assessment model, and adopt neighborhood spatial analysis-primary filter method and spatial difference method to obtain the agricultural drought risk pattern raster data of each assessment unit. The specific steps include:

[0086] S4.1. Construct an agricultural drought risk assessment model as follows:

[0087] Risk=H×L c ×E×η (6)

[0088] In formula (6), Risk is the agricultural drought risk, H is the drought hazard, L c is the corrected agricultural loss rate, E is the exposure to agricultural drought, and η is the unit conversion coefficient;

[0089] S4.2. Smoothing local noise in the agricultural drought risk pattern raster data of the target area using a neighborhood spatial analysis-primary filtering method based on the geospatial correlations between each assessment unit in the target area;

[0090] S4.3. Using a spatial difference method based on spatial standard deviation, process the spatial distribution data of agricultural drought risk into the spatial distribution of agricultural drought risk levels in the target area as the agricultural drought risk pattern. The spatial standard deviation is calculated as follows:

[0091]

[0092] Where δ is the standard deviation of agricultural drought disaster risk, x j is the agricultural drought disaster risk of the jth grid, is the average agricultural drought disaster risk, and n is the total number of grids.

[0093] In the above schemes, the neighborhood space analysis-main filtering method of some schemes is as follows Figure 2 , including the following steps:

[0094] Determine raster data for quantitative assessment of agricultural drought risk in target areas;

[0095] Taking the target element grid as the center, its four orthogonal adjacent element grids are used as the range of the main filter;

[0096] Statistical analysis was performed on the quantitative assessment data of agricultural drought risk within the filter, and the mode was calculated. If the grid data within the filter met any of the following conditions, the value of the target meta-grid was adjusted: the mode of the agricultural drought risk data within the filter existed and was unique; at least half of the adjacent meta-grids had the same quantitative assessment data of agricultural drought risk;

[0097] When the above conditions are met, the agricultural drought risk raster data of the target element grid is modified to the mode value within the filter.

[0098] Example 1

[0099] Using the aforementioned methodology, agricultural drought risk was assessed in Southwest China. From 1991 to 2020, the Sichuan Basin, Guizhou, and southeastern Yunnan experienced low drought risk, while most of Guangxi experienced moderate drought risk. Sichuan and western Yunnan experienced high drought risk. The spatial distribution of agricultural drought vulnerability showed high vulnerability in parts of northeastern, southeastern, and central China, while vulnerability was low in northwestern China. High vulnerability (Levels 4 and 5) was primarily concentrated in the Sichuan Basin, southeastern Guangxi, northwestern Guizhou, and eastern Yunnan. High agricultural drought risk areas were primarily located in the central Sichuan Basin, central Yunnan, southern Guangxi, and northeastern Guangxi.

[0100] An agricultural drought risk assessment system, such as Figure 3 As shown, it includes at least one processor; and a memory storing instructions, which implement the steps of the above solution when the instructions are executed by the at least one processor.

[0101] The embodiments and functional operations of the subject matter described in this specification may be implemented in digital electronic circuitry, tangibly implemented computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of more than one of the foregoing. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible, non-transitory program carriers, for execution by, or to control the operation of, a data processing apparatus.

[0102] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, such as a data server, or an intermediate component, such as an application server. Alternatively or additionally, program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to an appropriate receiver device for execution by a data processing device. A computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the foregoing.

[0103] A computer program (which may also be referred to or described as a program, software, software application, module, software module, script, or code) may be written in any form of programming language, including compiled or interpreted languages ​​or declarative or procedural languages, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system.

[0104] Certain embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the activities recited in the claims can be performed in a different order and still achieve the desired results. As an example, the processes depicted in the accompanying figures do not necessarily require the particular order or sequential sequence shown to achieve the desired results. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A method for agricultural drought risk assessment, characterized in that: The following steps are involved: S1. Divide the target area into several evaluation units according to its geospatial raster data; S2. collecting agricultural disaster loss data of each assessment unit, using a K-Means classification method and correcting it by a moisture-yield coefficient to obtain a quantitative agricultural loss rate; S3. Extract the total output value of the target area and the agricultural output value and regional output value of each assessment unit, build an agricultural exposure model, and obtain the agricultural exposure of the target area; S4. Constructing an agricultural drought risk assessment model using the drought hazard, the agricultural loss rate, and the agricultural exposure, and using a neighborhood spatial analysis-primary filter method and a spatial difference method to obtain agricultural drought risk pattern raster data for each assessment unit; distributing the agricultural drought risk pattern raster data for each assessment unit in a target area, and obtaining the agricultural drought risk pattern of the target area based on its geographic spatial raster data; The method further includes the steps of constructing a meteorological drought disaster risk assessment model to assess the drought risk, specifically including: Collecting historical precipitation and historical temperature data within a historical time period of the assessment unit, calculating a meteorological drought comprehensive index (MCI) based on the historical precipitation and historical temperature data, identifying drought processes, and evaluating the drought level of each drought process; Calculate the occurrence frequency p of drought processes of different drought levels in the historical period i ; Drought risk is calculated as follows: H=∑w i p i (1) In formula (1), H is the drought risk, w i is the weight of different drought levels, p i is the occurrence frequency of drought processes of different drought levels, i is the drought level identifier, i = 1, 2, 3, 4... The method of S2 specifically includes the following steps: S2.

1. Collect the crop drought-affected, drought-stricken, or crop-completed areas within the assessment unit, set their loss weights using an expert scoring method, and calculate the agricultural loss rate L for each assessment unit as shown in the following formula: L=I3×90%+(I2-I3)×55%+(I1-I2)×20% (3) In formula (3), L is the comprehensive yield reduction rate (%), I1 is the proportion of crop area with a yield reduction of more than 10% to the sown area; I2 is the proportion of crop area with a yield reduction of more than 30% to the sown area; I3 is the proportion of crop area with a yield reduction of more than 80% to the sown area; S2.

2. The sample space of all assessment units in the entire target area is composed of agricultural loss rate L of each assessment unit as the agricultural loss rate sample value; all agricultural loss rate sample values ​​are classified according to drought level using K-Means method, and the center value of each class is used as the agricultural comprehensive average loss rate L when different drought levels occur in the target area. m ; S2.

3. Further correct the agricultural comprehensive average loss rate using the crop moisture-yield coefficient; L c =K y ×L m (4) In formula (4): L c is the corrected agricultural loss rate; L m is the average comprehensive agricultural loss rate; K y is the crop water-yield coefficient.

2. The method according to claim 1, characterized in that The meteorological drought comprehensive index (MCI) is calculated as follows: MCI=K a ×(a×SPIW 60 +b×MI 30 +c×SPI 90 +d×SPI 150 ) (2) In formula (2), SPIW 60 The standardized weighted precipitation index for the past 60 days, MI 30 SPI is the humidity index for the past 30 days. 90 、SPI 150 are the 90-day and 150-day standardized precipitation indices respectively; b, c, and d are weight coefficients adjusted according to the region. In the north and west, a, b, c, and d are 0.3, 0.5, 0.3, and 0.2 respectively; in the south, a, b, c, and d are 0.5, 0.6, 0.2, and 0.1 respectively. K a is the seasonal adjustment coefficient.

3. The method according to claim 1, characterized in that When a certain assessment unit experiences mild or above drought for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate or above on at least one day, it is identified as a drought process, and the highest drought level in the drought process is set as the drought level of the drought process.

4. The method according to claim 1, wherein In S3, the method for constructing the agricultural exposure model is as follows: In formula (5), E is the exposure to agricultural drought; V is the proportion of the total agricultural output value of the assessment unit to the total output value of the assessment unit. GDP is the total output value of the target area.

5. The method according to claim 4, wherein The specific steps of S4 include: S4.

1. Construct an agricultural drought risk assessment model as follows: Risk=H×L c ×E×η (6) In formula (6), Risk is the agricultural drought risk, H is the drought hazard, L c is the corrected agricultural loss rate, E is the exposure to agricultural drought, and η is the unit conversion coefficient; S4.

2. Smoothing local noise in the agricultural drought risk pattern raster data of the target area using a neighborhood spatial analysis-primary filtering method based on the geospatial correlations between each assessment unit in the target area; S4.

3. Use the spatial difference method based on the spatial standard deviation to process the spatial distribution data of agricultural drought risk into the spatial distribution of agricultural drought risk levels in the target area as the agricultural drought risk pattern.

6. The method according to claim 5, characterized in that The spatial standard deviation described in S4.3 is calculated as follows: In formula (7), δ is the spatial standard deviation of agricultural drought disaster risk, x j is the agricultural drought disaster risk of the jth sample, is the average agricultural drought disaster risk, and n is the total number of samples.

7. The method according to claim 5, characterized in that The neighborhood space analysis-main filtering method comprises the following steps: Determine raster data for quantitative assessment of agricultural drought risk in target areas; Taking the target element grid as the center, its four orthogonal adjacent element grids are used as the range of the main filter; Statistical analysis was performed on the quantitative assessment data of agricultural drought risk within the filter, and the mode was calculated. If the grid data within the filter met any of the following conditions, the value of the target meta-grid was adjusted: the mode of the agricultural drought risk data within the filter existed and was unique; at least half of the adjacent meta-grids had the same quantitative assessment data of agricultural drought risk; When the above conditions are met, the agricultural drought risk raster data of the target element grid is modified to the mode value within the filter.

8. An agricultural drought risk assessment system, characterized in that: The system includes at least one processor; and a memory storing instructions, which, when executed by the at least one processor, implement the steps of the method according to any one of claims 1 to 7.

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