Agricultural drought risk assessment method and system

Abstract

The invention discloses an agricultural drought risk assessment method. The method comprises the following steps: dividing a target area into a plurality of assessment units according to geographic space raster data of the target area; agricultural disaster damage data of each evaluation unit is collected, and a quantitative agricultural loss rate is obtained by adopting a K-Means classification method and through moisture-yield coefficient correction; the total output value of the target area and the agricultural output value and the regional output value of each evaluation unit are extracted, an agricultural exposure degree model is constructed, and the agricultural exposure degree of the target area is obtained; constructing an agricultural drought risk assessment model by using the drought risk, the agricultural loss rate and the agricultural exposure degree, and obtaining agricultural drought risk pattern grid data of each assessment unit by using a neighborhood spatial analysis-main filtering method and a spatial difference method; and distributing the agricultural drought risk pattern grid data of each evaluation unit in a target area according to geographic space grid data of the target area to obtain an agricultural drought risk pattern of the target area. The agricultural drought risk is accurately quantified, high-vulnerability and high-risk regions are defined, and a scientific basis is provided for agricultural resource allocation optimization, disaster prevention and reduction strategy formulation and drought emergency management.

Description

Technical Field

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

[0002] Agricultural drought is the main disaster affecting agricultural production under climate change, with the characteristics of high frequency, wide impact range, long duration and great harm. Traditional agricultural drought risk assessment methods are difficult to meet the needs of refined management due to insufficient spatial resolution, lack of dynamics and comprehensiveness, limited model applicability, and lack of risk classification and response guidance. Therefore, a new method based on multi-source data fusion, dynamic analysis and high-resolution spatial assessment is urgently needed to accurately identify the distribution pattern of drought risks, support regional disaster prevention and mitigation and optimal allocation of agricultural resources, and provide technical support for scientific decision-making. Summary of the invention

[0003] In view of 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 an agricultural drought risk assessment method, 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 K-Means classification method and correcting by moisture-yield coefficient to obtain 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 use 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 in the historical time period of the evaluation unit, calculating the meteorological drought comprehensive index (MCI) based on the historical precipitation and the historical temperature data, identifying drought processes according to the national standard (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, 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 factor.

[0018] Furthermore, when a certain assessment unit experiences mild drought or above for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate drought 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 drought-affected, disaster-stricken, and crop-completed areas in the assessment unit, set the loss weights through expert scoring method, and calculate the agricultural loss rate L of each assessment unit as shown in the following formula:

[0021] L=I 3 ×90%+(I 2 -I 3 )×55%+(I 1 -I 2 )×20% (3)

[0022] In formula (3): L is the comprehensive reduction rate (%), I 1 The proportion of crop area with a production reduction of more than 10% to the sown area; 2 The proportion of crop area with a production reduction of more than 30% to the sown area; 3 To reduce the proportion of crop area producing more than 80% to the sown area;

[0023] S2.2. The sample space composed of all the assessment units in the entire target area is composed of agricultural loss rate sample values ​​of each assessment unit; all agricultural loss rate sample values ​​are classified according to the 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 ;

[0024] S2.3. Further correct 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 agricultural comprehensive loss rate; K y is the crop water-yield coefficient.

[0027] Further, 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 the local noise in the agricultural drought risk pattern raster data of the target area based on the geospatial correlation between each assessment unit in the target area through neighborhood spatial analysis-primary filtering method;

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

[0036] Further, 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 comprises 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] The quantitative assessment data of agricultural drought risk in the filter were statistically analyzed and the mode was calculated. If the grid data in 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 in 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] On the other hand, 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 at least one processor, implements the steps of the above method.

[0045] The beneficial technical effects of the present invention are as follows: based on multi-source data fusion, neighborhood spatial analysis-main filtering method, etc., accurate quantification of agricultural drought risk and identification of high-risk areas are achieved, providing data support for the scientific formulation of regional disaster prevention and mitigation strategies. With the assistance of information technology, pre-disaster defense, post-disaster emergency management and adaptive capacity building are effectively carried out, the efficiency of drought emergency management is improved, the allocation of agricultural resources is optimized, and sustainable agricultural development and regional disaster resistance capacity building are promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 :A flow chart of the agricultural drought risk assessment scheme 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 for an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following examples further illustrate the content of 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 all fall within the scope of the present invention.

[0050] It should be noted that the original data acquisition method of the following implementation method is based on the county-level annual scale drought disaster data collected by the National Disaster Reduction Center of the Ministry of Emergency Management, mainly including: drought-affected area of ​​crops, disaster-stricken area, crop failure area, direct economic losses, etc. The annual value statistical data of agriculture in various provinces (autonomous regions, municipalities directly under the central government) and cities over the same period were also collected, mainly including: total sown area of ​​crops, local gross domestic product (gross domestic product, GDP) and consumer price index (consumer price index, CPI, last year = 100), etc. The statistical data comes from the National Bureau of Statistics of the People's Republic of China "China Statistical Yearbook" (http: / / www.stats.gov.cn / tjsj / ndsj / ) and the National Data Query System (http: / / data.stats.gov.cn).

[0051] Some of the schemes in the following implementation methods use the station data such as precipitation and temperature provided by the China Meteorological Data Network "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 solutions of the following implementation modes 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 the invention belongs.

[0054] Some solutions 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 K-Means classification method and correcting by moisture-yield coefficient to obtain 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 use 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 solutions in Figure 1 Based on the steps shown in the figure, 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] In a more specific scheme, the meteorological drought comprehensive index (MCI) is calculated according to the following formula:

[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 Index;

[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 factor.

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

[0070] Table 1. Classification standards of meteorological drought comprehensive index

[0071]

[0072] When a certain assessment unit experiences mild drought or above for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate drought 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] On the basis of the above scheme, some schemes collect agricultural disaster loss data of each assessment unit, adopt K-Means classification method and correct it through moisture-yield coefficient to obtain quantitative agricultural loss rate, which specifically includes the following steps:

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

[0075] L=I 3 ×90%+(I 2 -I 3 )×55%+(I 1 -I 2 )×20% (3)

[0076] In formula (3): L is the comprehensive reduction rate (%), I 1 The proportion of crop area with a production reduction of more than 10% to the sown area; 2 The proportion of crop area with a production reduction of more than 30% to the sown area; 3 To reduce the proportion of crop area producing more than 80% to the sown area;

[0077] S2.2. The sample space composed of all the assessment units in the entire target area, the agricultural loss rate L of each assessment unit is 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 central 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 by 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 crop loss rate; K y is the crop water-yield coefficient.

[0081] Crop moisture-yield coefficient K yThe calculation method is an existing technology disclosed in the literature, such as 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] On the basis of 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, agricultural loss rate and agricultural exposure to build an agricultural drought risk assessment model, and adopt neighborhood spatial analysis-main filtering 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 factor;

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

[0090] S4.3. Using a spatial difference method based on spatial standard deviation, the spatial distribution data of agricultural drought risk is processed 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] In the formula, 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] The quantitative assessment data of agricultural drought risk in the filter were statistically analyzed and the mode was calculated. If the grid data in 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 in 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 above scheme, the agricultural drought risk in southwest China is assessed. From 1991 to 2020, the drought risk in the Sichuan Basin, Guizhou and southeastern Yunnan is low, the drought risk in most areas of Guangxi is moderate, and the drought risk in western Sichuan and Yunnan is high. The spatial distribution characteristics of agricultural drought vulnerability are as follows: the vulnerability is high in parts of the northeast, southeast and central regions, and low in the northwest. High vulnerability (level 4 and 5) areas are mainly concentrated in the Sichuan Basin, southeastern Guangxi, northwestern Guizhou and eastern Yunnan. High-risk areas for agricultural drought are mainly distributed in the central Sichuan Basin, central Yunnan, southern Guangxi and northeastern China.

[0100] An agricultural drought risk assessment system, the system is as follows 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 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 above. 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 a data processing device or to control the operation of a data processing device.

[0102] Embodiments of the subject matter described in this specification may be implemented in a computing system including a back-end component such as a data server, or an intermediate component such as an application server. Alternatively or in addition, program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated as encoded information for transmission to a suitable receiver device executed by a data processing device. The computer storage medium may 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 above devices.

[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] Specific implementations of the subject matter have been described. Other implementations 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 described in the accompanying drawings do not necessarily require the specific order or sequential order shown in order to achieve the desired results. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A method for assessing agricultural drought risk, characterized in that: The following steps are involved: S1, dividing the target area into a number of evaluation units according to its geospatial raster data; S2, collecting agricultural disaster loss data of each assessment unit, using K-Means classification method and correcting by moisture-yield coefficient to obtain 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. Construct an agricultural drought risk assessment model using drought hazard, the agricultural loss rate and the agricultural exposure, and use 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.

2. The method according to claim 1, characterized in that The method also includes the steps of constructing a meteorological drought disaster risk assessment model to assess the drought risk, specifically including: 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; 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 identifier of the drought level, i = 1, 2, 3, 4….

3. The method according to claim 2, 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, 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 factor.

4. The method according to claim 2, characterized in that: When a certain assessment unit experiences mild drought or above for 15 consecutive days or more during the historical time period, and the drought intensity reaches moderate drought 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.

5. The method according to claim 2, characterized in that: The method of S2 specifically comprises the following steps: S2.

1. Collect the drought-affected, disaster-stricken, and crop-completed areas in the assessment unit, set the loss weights through expert scoring method, and calculate the agricultural loss rate L of 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 composed of all the assessment units in the entire target area, the agricultural loss rate L of each assessment unit is 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 central 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 agricultural comprehensive loss rate; K y is the crop water-yield coefficient.

6. The method according to claim 5, characterized in that 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.

7. The method according to claim 6, characterized in that 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 factor; S4.2, smoothing the local noise in the agricultural drought risk pattern raster data of the target area based on the geospatial correlation between each assessment unit in the target area through neighborhood spatial analysis-primary filtering method; S4.

3. Using a spatial difference method based on spatial standard deviation, the spatial distribution data of agricultural drought risk are processed into the spatial distribution of agricultural drought risk levels in the target area as the agricultural drought risk pattern.

8. The method according to claim 7, 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.

9. The method according to claim 7, 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; The quantitative assessment data of agricultural drought risk in the filter were statistically analyzed and the mode was calculated. If the grid data in 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 in 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.

10. An agricultural drought risk assessment system, characterized in that: The system comprises 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 9.

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