Farmland drought dynamic partitioning and early warning method and system based on multi-source spatio-temporal data

Abstract

The invention relates to a farmland drought dynamic partitioning and early warning method and system based on multi-source spatio-temporal data. The method comprises the following steps: acquiring a monitoring range; wherein a farmland is included in the monitoring range; rasterizing the monitoring range to obtain a plurality of grids; determining the index value of the monitoring index of each grid; wherein the monitoring indexes comprise a topographic condition index, a soil moisture content index, a climate drought index, a historical disaster damage index and a crop vulnerability index; evaluating a drought risk index of each grid according to the index value of the monitoring index of each grid; and performing early warning according to the drought risk index of each grid. According to the method, the monitoring range is rasterized, each grid is monitored, personalized monitoring of different areas in one monitoring range can be achieved, and due to the fact that different grids in one monitoring range possibly have different prevention and control levels, the situation that the monitoring range is affected when the prevention and control area is large is avoided. And one prevention and control level does not meet the actual drought early warning requirement of the farmland.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart agriculture and disaster prevention and control technology, and particularly relates to a farmland drought dynamic zoning and early warning method and system based on multi-source spatio-temporal data. BACKGROUND

[0002] Farmland drought generally refers to a phenomenon that, during the growth period of crops, due to insufficient soil water supply, the supply and demand of farmland water are unbalanced, the soil water content is lower than the water requirement of crops, and thus the normal growth and development of crops are hindered.

[0003] Farmland drought early warning is an important way to reduce agricultural disaster losses and impacts. At present, farmland drought early warning is for the whole monitoring area, that is, the monitoring area assesses a control level, and early warning is performed based on the control level.

[0004] If the control area is large, one control level cannot meet the actual farmland drought early warning needs. SUMMARY

[0005] (I) Technical problems to be solved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present application provides a farmland drought dynamic zoning and early warning method and system based on multi-source spatio-temporal data.

[0007] (II) Technical solutions

[0008] In order to achieve the above-mentioned purposes, the main technical solutions adopted by the present application include:

[0009] In a first aspect, the present application provides a farmland drought dynamic zoning and early warning method based on multi-source spatio-temporal data, which comprises the following steps:

[0010] acquiring a monitoring range; wherein the monitoring range includes farmland;

[0011] rasterizing the monitoring range to obtain a plurality of grids;

[0012] determining the index values of monitoring indexes of each grid; wherein the monitoring indexes include: a terrain condition index, a soil moisture index, a climate drought index, a historical disaster loss index, and a crop vulnerability index;

[0013] evaluating the drought risk indexes of each grid according to the index values of the monitoring indexes of each grid;

[0014] performing early warning according to the drought risk indexes of each grid.

[0015] Optionally, the terrain condition index is a terrain humidity index.

[0016] The soil moisture index is an effective water holding capacity.

[0017] The climate drought index is a standardized precipitation evapotranspiration index;

[0018] The historical disaster loss index is a product of a drought frequency and an average yield reduction rate of a crop involved in the farmland within a preset first time length;

[0019] The crop vulnerability index is a water shortage sensitivity coefficient corresponding to a current growth period of the crop involved in the farmland.

[0020] Optionally, according to the index values of the monitoring indexes of the grids, the drought risk indexes of the grids are evaluated, including:

[0021] According to the index values of the monitoring indexes of the grids and the weights of the monitoring indexes, the drought risk indexes of the grids are evaluated by using an analytic hierarchy process;

[0022] The weight of the terrain condition index is 0.18;

[0023] The weight of the soil moisture index is 0.25;

[0024] The weight of the climate drought index is 0.30;

[0025] The weight of the historical disaster loss index is 0.15;

[0026] The weight of the crop vulnerability index is 0.12.

[0027] Optionally, according to the drought risk indexes of the grids, a warning is given, including:

[0028] The drought risk indexes of the grids are subjected to Gaussian filtering smoothing to obtain smoothed drought risk indexes;

[0029] If it is determined according to the index values of the monitoring indexes of the grids that an index value of the climate drought index within a preset second time length is not greater than a preset first threshold value, or it is determined according to the index values of the monitoring indexes of the grids that an index value of the soil moisture index is not greater than a preset second threshold value, or a change degree of a vegetation state index within the monitoring range is greater than a third threshold value, when there is a high-variation grid, the drought risk index of the high-variation grid is adjusted, and the warning is given according to the adjusted drought risk index; otherwise, the warning is given according to the smoothed drought risk indexes;

[0030] The high-variation grid is a grid satisfying a variation condition;

[0031] The variation condition is that a drought risk index change rate of the grid is greater than a fourth threshold value;

[0032] The drought risk index change rate of any grid is determined according to a drought risk index of any grid and a historical drought risk index of any grid;

[0033] Alternatively, the rate of change of the drought risk index of any grid is determined according to the drought risk indices of all grids in a region centered on the any grid.

[0034] Optionally, the initial value of the fourth threshold is 0.15.

[0035] When there is a grid whose index value of the climate drought index is not greater than the fifth threshold, the fourth threshold is adjusted to 0.08.

[0036] When the index values of the soil moisture index of all grids in a continuous preset third time period are all greater than the second threshold, the fourth threshold returns to the initial value.

[0037] Optionally, the drought risk index of the high-variation grid is adjusted, including:

[0038] determining the absolute value of the difference between the drought risk index of the high-variation grid and each surrounding grid; wherein the surrounding grid is a grid in a unit square region of the high-variation grid, and the surrounding grid is not the high-variation grid; the unit square region of the high-variation grid is a square region with three grids as the side length and centered on the high-variation grid;

[0039] If the number of surrounding grids whose absolute value of the difference is higher than the sixth threshold is not less than a preset first value, the variation degree of the high-variation grid is determined; a target set is determined according to the drought risk indices of the grids in a region centered on the high-variation grid; and the drought risk index of the high-variation grid is adjusted according to the target set.

[0040] Optionally, after determining the absolute value of the difference between the drought risk index of the high-variation grid and each surrounding grid, the method further includes:

[0041] If the number of surrounding grids whose absolute value of the difference is higher than the sixth threshold is less than the preset first value, the drought risk index of the high-variation grid is adjusted according to the drought risk index of the high-variation grid and the drought risk indices of the grids in a region centered on the high-variation grid.

[0042] Optionally, the warning is performed according to the drought risk indices of the grids, including:

[0043] determining the drought risk level of each grid according to the drought risk index of each grid and a preset corresponding relationship between the drought risk index and the drought risk level;

[0044] If there is an abnormal grid, topological optimization is performed, and the warning is performed based on the topologically optimized drought risk level of each grid; wherein the level difference between the maximum drought risk level and the minimum drought risk level of all grids in a unit square region of the abnormal grid is not less than a preset second value; the unit square region of any grid is a square region with three grids as the side length and centered on any grid.

[0045] If there is no abnormal grid, a warning is given based on the drought risk level of each grid.

[0046] Optionally, after the warning according to the drought risk index of each grid, the method further comprises:

[0047] executing a control strategy;

[0048] The control strategy is one or more of the following: emergency control, water control, observation control, normal control, and reserve control.

[0049] In a second aspect, an embodiment of the present application provides a farmland drought dynamic zoning and early warning system based on multi-source spatio-temporal data, which comprises a monitoring index collection module and a monitoring module.

[0050] The monitoring index collection module is configured to collect index values of monitoring indexes.

[0051] The monitoring module is configured to execute the method of the first aspect.

[0052] (Three) beneficial effects

[0053] The present application has the beneficial effects that: the present application relates to a farmland drought dynamic zoning and early warning method and system based on multi-source spatio-temporal data, the method comprising: obtaining a monitoring range; wherein the monitoring range comprises farmland; rasterizing the monitoring range to obtain a plurality of grids; determining index values of monitoring indexes of each grid; wherein the monitoring indexes comprise: terrain condition indexes, soil moisture condition indexes, climate drought indexes, historical disaster damage indexes, and crop vulnerability indexes; evaluating drought risk indexes of each grid according to the index values of the monitoring indexes of each grid; and giving a warning according to the drought risk indexes of each grid. The method of the present application rasterizes the monitoring range, monitors each grid, and can realize individualized monitoring of different regions in a monitoring range. Since different grids in a monitoring range may have different prevention and control levels, the problem that one prevention and control level does not meet the actual drought early warning needs of farmland when the area of a prevention and control region is large is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 A flowchart of a farmland drought dynamic zoning and early warning method based on multi-source spatio-temporal data provided by the present application;

[0055] Figure 2 A schematic diagram of a monitoring range provided by the present application;

[0056] Figure 3 A schematic diagram of a rasterized monitoring range provided by the present application;

[0057] Figure 4Another monitoring range schematic diagram provided by the present application;

[0058] Figure 5 Another grid monitoring range schematic diagram provided by the present application;

[0059] Figure 6 The third grid monitoring range schematic diagram provided by the present application. DETAILED DESCRIPTION

[0060] In order to better explain the present application, so as to be understood, the present application is described in detail by specific embodiments in combination with the accompanying drawings.

[0061] The early warning of farmland drought is an important way to reduce the loss and influence of agricultural disasters. At present, the farmland drought is a whole early warning for the monitoring area, that is, the monitoring area will evaluate a control level, and the early warning is based on the control level. If the control area is large, one control level cannot meet the actual drought early warning demand of the farmland.

[0062] Based on this, the present application relates to a farmland drought dynamic zoning and early warning method and system based on multi-source spatio-temporal data. The method comprises the following steps: acquiring a monitoring range; wherein the monitoring range comprises farmland; performing gridding on the monitoring range to obtain a plurality of grids; determining the index value of the monitoring index of each grid; wherein the monitoring index comprises a terrain condition index, a soil moisture index, a climate drought index, a historical disaster loss index and a crop vulnerability index; evaluating the drought risk index of each grid according to the index value of the monitoring index of each grid; and performing early warning according to the drought risk index of each grid. The method of the present application performs gridding on the monitoring range, monitors each grid, and can realize individualized monitoring of different regions in one monitoring range. Since different grids in one monitoring range may have different control levels, the problem that one control level cannot meet the actual drought early warning demand of the farmland when the control area is large is avoided.

[0063] Referring to Figure 1 The present embodiment provides a farmland drought dynamic zoning and early warning method based on multi-source spatio-temporal data. The implementation process of the method is as follows:

[0064] 101. Acquire a monitoring range.

[0065] The monitoring range comprises farmland. That is, the monitoring range is the area for drought early warning, which comprises the farmland for early warning.

[0066] The implementation process of step 101 is as follows: extract the crop (such as winter wheat / corn, etc. main food crops) planting structure vector boundary, and obtain the farmland for drought early warning from the vector boundary. Determine the range of the farmland as the monitoring range, or determine the minimum rectangle of the farmland as the monitoring range.

[0067] If there are multiple farmlands (i.e., there are multiple monitoring ranges), the drought warning of each monitoring range can be performed once by the farmland drought dynamic partitioning and early warning method based on multi-source spatio-temporal data provided in this embodiment.

[0068] 102, rasterize the monitoring range to obtain a plurality of grids.

[0069] When step 102 is performed, a pre-set grid length (such as 10 kilometers) can be pre-fetched, and the monitoring range is rasterized into a plurality of grids according to the grid length. For example, as shown in FIG. 1, a monitoring range is 15 kilometers long and 15 kilometers wide. Figure 2 As shown in FIG. 2, after rasterization, as shown in FIG. 3, 12 grids are obtained, each of which is a 10-kilometer-long square. Figure 3

[0070] In a specific implementation, it is possible that only part of the monitoring range is located in the grid, such as a 15-kilometer-long and 15-kilometer-wide monitoring range. If the upper left corner is started, a 10-kilometer-long and 10-kilometer-wide grid such as grid 1 in FIG. 4 will be obtained first. The remaining area cannot obtain a complete grid. At this time, the monitoring range can be expanded to obtain a complete grid, as shown in FIG. 5. Figure 4 Figure 5

[0071] 103, determine the index value of the monitoring index of each grid.

[0072] The monitoring index includes: terrain condition index, soil moisture condition index, climate drought index, historical disaster damage index, and crop vulnerability index.

[0073] 1. Terrain condition index

[0074] The terrain condition index is a topographic wetness index (TWI).

[0075] For example, 30-meter resolution DEM (Digital Elevation Model) data is obtained, and the topographic wetness index (TWI) is calculated.

[0076] 2. Soil moisture condition index

[0077] The soil moisture condition index is the effective water holding capacity.

[0078] For example, the soil type grid map (such as 1:500,000) is integrated, and the soil texture is reclassified into 5 levels of water holding capacity (AWC1-AWC5), and then the effective water holding capacity is obtained. The effective water holding capacity can be the effective water holding capacity of the 0-100 centimeter depth layer, and the unit of the effective water holding capacity can be millimeter. ​​​

[0079] 3. Climate drought index

[0080] The climate drought index is a standardized precipitation evaporation index (SPEI).

[0081] For example, access meteorological sites and CMORPH satellite precipitation data to calculate a standardized precipitation evaporation index (SPEI) per decade.

[0082] 4. Historical disaster loss index

[0083] The historical disaster loss index is the product of the drought frequency in a preset first time period and the average yield reduction rate of crops involved in farmland.

[0084] If the drought frequency in the preset first time period is DF and the average yield reduction rate of crops involved in farmland is RR, then the historical disaster loss index is DF x RR.

[0085] For example, collect county-level agricultural disaster reports to quantify yield reduction rates and economic losses. Integrate MODIS vegetation condition index (VCI) for spatial consistency verification to obtain the drought frequency DF and the average yield reduction rate RR of crops involved in farmland in the first time period, and then the historical disaster loss index is DF x RR.

[0086] In addition, the first time period is a preset value, which is determined according to actual conditions. For example, if the first time period is 10 years, the historical disaster loss index is the product of the drought frequency and the average yield reduction rate of crops involved in farmland in the past 10 years.

[0087] 5. Crop vulnerability index

[0088] The crop vulnerability index is the water shortage sensitivity coefficient corresponding to the current growth period of crops involved in farmland.

[0089] For example, the current growth period is determined according to the current season and crop growth rules, and then the water shortage sensitivity coefficient corresponding to the current growth period of crops involved in farmland is obtained.

[0090] 104. According to the index values of the monitoring indexes of each grid, the drought risk index of each grid is evaluated.

[0091] The drought risk index of each grid can be evaluated using the Analytic Hierarchy Process (AHP) according to the index values of the monitoring indexes of each grid and the weights of the monitoring indexes.

[0092] Among them, the weight of the terrain condition index is 0.18.

[0093] The weight of the soil moisture index is 0.25.

[0094] The weight of the climate drought index is 0.30.

[0095] The weight of the historical damage index is 0.15.

[0096] The weight of the crop vulnerability index is 0.12.

[0097] 105, issuing a warning according to the drought risk index of each grid.

[0098] The implementation process of step 105 is: issuing a warning according to the drought risk index of each grid obtained in step 104. Alternatively, the implementation process of step 105 is: smoothing the drought risk index of each grid obtained in step 104, and issuing a warning based on the smoothed drought risk index of each grid.

[0099] (I) Smoothing process

[0100] 201, performing Gaussian filter smoothing on the drought risk index of each grid to obtain a smoothed drought risk index.

[0101] For example, a two-dimensional Gaussian filter with a standard deviation of 1.0 km is used to perform convolution operation on the abrupt boundary to ensure that the risk level is continuously and gradually changed in space (gradient ≤ 1 level / km), and to eliminate jagged irregular boundaries.

[0102] 202, if it is determined according to the index value of the monitoring index of each grid that the index value of the climate drought index in a preset second time length is not greater than a preset first threshold value, or it is determined according to the index value of the monitoring index of each grid that the index value of the soil moisture index is not greater than a preset second threshold value, or the change degree of the vegetation state index in the monitoring range is greater than a third threshold value, then when there is a high-variation grid, the drought risk index of the high-variation grid is adjusted, and a warning is issued according to the adjusted drought risk index. Otherwise, a warning is issued according to the smoothed drought risk index.

[0103] The details of step 202 are described as follows:

[0104] 1. Triggering condition of adjustment

[0105] As long as one of the following conditions occurs, the drought risk index is adjusted when there is a high-variation grid.

[0106] (1) The index value of the climate drought index in a preset second time length is not greater than a preset first threshold value.

[0107] For example, the second time length is three months, and the first threshold value is -1.0, then the standardized precipitation evapotranspiration index (SPEI) in the last 3 months is ≤-1.0.

[0108] (2) the index value of the soil moisture index is not greater than a preset second threshold value.

[0109] For example, the second threshold value is 60% of the field capacity, and the effective water holding capacity of the plough layer (0-40 cm depth) is less than 60% of the field capacity.

[0110] (3) the change degree of the vegetation condition index in the monitoring range is greater than a third threshold value.

[0111] For example, the third threshold value is 20%, and the change degree is the inter-week drop, and the inter-week drop of the MODIS vegetation condition index (VCI) is greater than 20%.

[0112] 2. High-variation grid

[0113] The high-variation grid is a grid that meets the variation condition.

[0114] The variation condition is that the drought risk index change rate of the grid is greater than a fourth threshold value.

[0115] 1) Drought risk index change rate

[0116] There are multiple schemes for determining the drought risk index change rate, and two implementation schemes are exemplarily provided below. In actual implementation, either one of the two schemes can be selected.

[0117] ● First method for determining drought risk index change rate

[0118] The drought risk index change rate of any grid is determined according to the drought risk index of any grid and the historical drought risk index of any grid.

[0119] For example, the drought risk index change rate of any grid i is

[0120] Wherein, i is the grid identifier, R i is the drought risk index of the grid i, R' i is the historical drought risk index of the grid i (for example, R' i is the drought risk index of the grid i obtained by the most recent execution of the farmland drought dynamic partitioning and early warning method based on multi-source spatio-temporal data provided in this embodiment).

[0121] This calculation method determines the drought risk index change rate of any grid i from the time dimension, that is, the change degree of the currently obtained drought risk index and the last obtained drought risk index. The greater the value is, the greater the change is, and the greater the variation degree of any grid i is.

[0122] ● Second method for determining drought risk index change rate

[0123] The drought risk index change rate of any grid is determined according to the drought risk indexes of all grids in the region centered on the any grid.

[0124] For example, the drought risk index change rate of any grid i is

[0125] wherein SD is the standard sharpness, μ R is the mean of the drought risk indexes of all grids, σ R is the standard deviation of the drought risk indexes of all grids, I is the total number of grids, R i is the drought risk index of grid i. R i - μ R characterizes the deviation of the drought risk index of any grid i from the mean of the drought risk indexes, characterizes the mean of the deviations of the drought risk indexes of all grids from the mean of the drought risk indexes, σ R characterizes the fluctuation of the drought risk indexes of all grids, and thus SD characterizes the degree of dispersion per mean value, and the greater the value, the greater the difference between the drought risk indexes of the grids. SD determines the difference degree of the drought risk indexes of the grids from the spatial dimension.

[0126] SD' is the relative sharpness, μ' R is the mean of the historical drought risk indexes of all grids, σ' R is the standard deviation of the historical drought risk indexes of all grids. SD' determines the difference degree of the historical drought risk indexes of the grids from the spatial dimension. The historical drought risk index here is the drought risk index obtained by the most recent execution of the farmland drought dynamic zoning and early warning method based on multi-source spatio-temporal data provided in the embodiment.

[0127] SD" is the regional sharpness of any grid i, j is the identification of a grid in the unit square region of any grid i, the unit square region of any grid i is a square region with any grid i as the center and three grids as the side length, J is the total number of grids in the unit square region of any grid i, μ" R is the mean of the drought risk indexes of all grids in the unit square region of any grid i, σ' R is the standard deviation of the drought risk indexes of all grids in the unit square region of any grid i. For example, Figure 6 the gray region is the unit square region of any grid i, and thus J = 9. μ" R is the mean of the drought risk indexes of all grids in the gray region, σ' RSD is the standard deviation of the drought risk index of all grids in the unit square area of any grid i, which determines the degree of difference of the drought risk index of each grid in the unit square area of any grid i from the spatial dimension.

[0128] The degree of difference of the drought risk index of each grid in the unit square area of any grid i is characterized by the change of the degree of difference of the drought risk index of each grid. The degree of difference of the drought risk index of each grid is characterized by the change of the degree of difference of the historical drought risk index of each grid. The change of the degree of difference of the drought risk index of any grid i in the unit square area is characterized by the change of the degree of difference of the drought risk index relative to history as a change basis, The greater the value, the greater the change of the unit square area of any grid i relative to the change basis, and the greater the degree of variation of any grid i.

[0129] w i The weight of any grid i, The index value of the crop vulnerability index of any grid i, The degree of change of the vegetation state index of any grid i.w i The degree of variation of any grid i is characterized from the two dimensions of water shortage sensitivity and vegetation state.

[0130] This calculation method determines the change rate of the drought risk index of any grid i relative to other grids from the spatial dimension, i.e., the degree of change of the unit square area of any grid i relative to the change basis. The greater the value, the greater the change, and the greater the degree of variation of any grid i.

[0131] 2) Fourth threshold

[0132] To improve the boundary sensitivity under strong drought, the fourth threshold is adaptively adjusted.

[0133] For example, the initial value of the fourth threshold is 0.15. When there are grids whose index value of the climate drought index is not greater than the fifth threshold, the fourth threshold is adjusted to 0.08. When the index value of the soil moisture index of all grids in the continuous preset third time length is greater than the second threshold, the fourth threshold returns to the initial value.

[0134] Wherein, the fifth threshold value is -2.0 (or the threshold value corresponding to the high risk level), the third time length is 5 days, the second threshold value is 60% of the field water holding capacity, and the initial value of the fourth threshold value is 0.15. When there is an SPEI not greater than -2.0, the fourth threshold value is adjusted to 0.08. When the effective water holding capacity of all the grids in the 5 consecutive days is greater than 60% of the field water holding capacity, the fourth threshold value is restored to 0.15.

[0135] 3. The process of adjusting the drought risk index of the high-variation grid is a process of adjusting the drought risk index of the high-variation grid to approach the drought risk index of the surrounding grids. Specifically:

[0136] 1) Determine the absolute value of the difference between the drought risk index of the high-variation grid and each surrounding grid.

[0137] Wherein, the surrounding grid is a grid within the unit square region of the high-variation grid, and the surrounding grid is not a high-variation grid; the unit square region of the high-variation grid is a square region with the high-variation grid as the center and three grids as the side length.

[0138] If the high-variation grid is grid i, the unit square region of the high-variation grid is the gray area in Figure 6 , and the surrounding grid is the non-high-variation grid in the gray area in Figure 6 .

[0139] 2) If the number of surrounding grids with an absolute value of the difference higher than the sixth threshold value is not less than a preset first value, it means that the drought risk index of the high-variation grid is significantly different from that of the surrounding normal grids. For example, if the first value is 5, when the absolute value of the difference between the drought risk index of the high-variation grid and more than 5 (including 5) surrounding grids is greater than the sixth threshold value, the adjustment is made by the following method:

[0140] (1) Determine the variation degree of the high-variation grid.

[0141] Wherein, the variation degree of the high-variation grid is

[0142] R v is the drought risk index of the high-variation grid, is the minimum value of the drought risk index of the grids within the unit square region of the high-variation grid, is the maximum value of the drought risk index of the grids within the unit square region of the high-variation grid.

[0143] The variation degree V represents the position of the drought risk index of the high-variation grid within its unit square region. The larger the value, the more forward the position (i.e., the drought risk index of the high-variation grid is larger than that of the normal grids within its unit square region).

[0144] (2) Determine the target set according to the drought risk indexes of the grids in the region centered on the high-variation grid.

[0145] For example, the drought risk indexes of the grids in the unit square region of the high-variation grid are sorted from large to small, the top drought risk indexes form the first set, the bottom drought risk indexes form the third set, and the remaining drought risk indexes form the second set.

[0146] If , it means that the drought risk index of the high-variation grid is smaller in its unit square region, so the third set is determined as the target set. If , it means that the drought risk index of the high-variation grid is in the middle in its unit square region, so the second set is determined as the target set. If , it means that the drought risk index of the high-variation grid is larger in its unit square region, so the first set is determined as the target set.

[0147] Therefore, the target set is a set of other drought risk indexes that are similar to the drought risk index of the high-variation grid in the unit square region.

[0148] (3) Adjust the drought risk index of the high-variation grid according to the target set.

[0149] For example, the drought risk index of the high-variation grid is adjusted to

[0150] wherein is the mean of all elements in the target set, is the maximum value of all elements in the target set, is the minimum value of all elements in the target set.

[0151] The drought risk index of the high-variation grid differs greatly from that of the normal grid with more surrounding grids. If the high-variation grid is adjusted using the grids in its unit square region, the drought risk index of the high-variation grid will be greatly adjusted due to the large difference between the grids, which may result in over-adjustment. Therefore, the drought risk index of the high-variation grid is adjusted using other drought risk indexes that are similar to the drought risk index of the high-variation grid, which adjusts the drought risk index of the high-variation grid and avoids too much adjustment.

[0152] 3) If the number of surrounding rasters whose absolute difference exceeds the sixth threshold is less than the preset first value, it indicates that the drought risk index of the highly variable raster differs significantly from that of the surrounding normal rasters with fewer cells. For example, if the first value is 5, and the absolute difference between the drought risk index of the highly variable raster and 5 or fewer (excluding 5) surrounding rasters exceeds the sixth threshold, adjustments are made as follows:

[0153] The drought risk index of the highly variable raster is adjusted based on the drought risk index of the raster and the drought risk index of the raster within the region centered on the highly variable raster.

[0154] For example, when R v <μ v At that time, the drought risk index of highly variable raster was adjusted to When R v =μ v At that time, the drought risk index adjustment for highly variable rasters is not adjusted. When R v >μ v At that time, the drought risk index of highly variable raster was adjusted to

[0155] Among them, R v μ is a drought risk index for highly variable raster grids. v σ is the mean drought risk index of all grid cells within a unit square region of a highly variable raster. v The standard deviation of the drought risk index for all grids within a unit square region of a highly variable raster.

[0156] If the drought risk index of a highly variable raster differs significantly from that of surrounding normal rasteres with fewer cells, then the highly variable raster can be adjusted by modifying the raster within its unit square area. That is, if the drought risk index R of a highly variable raster is significantly higher... v The mean μ of the drought risk index for all cells within a unit square region smaller than the highly variable raster. v This indicates that the drought risk index of the highly variable raster is too low and needs to be adjusted upwards. Therefore, the drought risk index of the highly variable raster is adjusted to... If the drought risk index R of a highly variable raster v The drought risk index μ is equal to the mean of all grid cells within a unit square region of a highly variable grid. v This indicates that the drought risk index of the highly variable raster is already the mean and no adjustment is needed. If the drought risk index R of the highly variable raster... v The mean μ of the drought risk index for all grid cells within a unit square region larger than the highly variable grid. v This indicates that the drought risk index of highly variable rasters is too high and needs to be adjusted downwards. Therefore, the drought risk index of highly variable rasters will be adjusted to...

[0157] Through the above adjustment process, the drought risk index of the high-variation grid can be adjusted to the mean value, and the abnormal degree of the drought risk index of the high-variation grid is reduced.

[0158] 4. When the warning is performed according to the adjusted drought risk index, the adjusted drought risk index can be smoothed first, and the warning is performed based on the smoothed drought risk index of each grid.

[0159] (II) Warning process

[0160] The process includes: a process of performing warning according to the drought risk index of each grid obtained in step 104. The drought risk index of each grid obtained in step 104 is smoothed, and the warning is performed based on the smoothed drought risk index of each grid (specifically including: a process of performing warning according to the adjusted drought risk index, a process of performing warning according to the smoothed drought risk index).

[0161] Regardless of which warning, the implementation process is as follows:

[0162] 301. According to the drought risk index of each grid and the pre-set corresponding relationship between the drought risk index and the drought risk level, the drought risk level of each grid is determined.

[0163] For example, the pre-set corresponding relationship between the drought risk index and the drought risk level is: if the drought risk index R i ≥ 0.75 of any grid i, the drought risk level is high risk. If the drought risk index 0.45 ≤ R i < 0.75 of any grid i, the drought risk level is medium risk. If the drought risk index R i < 1.45 of any grid i, the drought risk level is low risk.

[0164] 302. If there is an abnormal grid, topological optimization is performed, and the warning is performed based on the topologically optimized drought risk level of each grid.

[0165] Among them, the level difference between the maximum drought risk level and the minimum drought risk level of all grids in the unit square area of the abnormal grid is not less than a pre-set second value.

[0166] The unit square area of any grid is a square area with three grids as the side length and any grid as the center.

[0167] If the second value is 2, the unit square area of grid i is Figure 6The abnormal grid is a grid i in the gray area, and the grade difference between the maximum drought risk level and the minimum drought risk level of all grids in the gray area is not less than 2, that is, the maximum drought risk level is high risk, and the minimum drought risk level is low risk. At this time, topology optimization is performed, and early warning is performed based on the drought risk level of each grid after topology optimization.

[0168] The topology optimization scheme is implemented by using an existing scheme.

[0169] 303, if there is no abnormal grid, early warning is performed based on the drought risk level of each grid.

[0170] For example, early warning is performed in one or more of the following ways:

[0171] 1. Real-time generation of a 0.01° resolution risk grid map (updated daily).

[0172] 2. According to the drought risk level of each grid, the drought risk area is statistically calculated according to township / watershed / crop type.

[0173] 3. According to the drought risk level of each grid, the drought migration rate is calculated based on cellular automata.

[0174] 4. According to the drought risk level of each grid, output the probability transition model of low risk→medium risk→high risk.

[0175] 5. Superimpose the drought heat map of risk level + crop water demand urgency.

[0176] 6. According to the drought risk level of each grid, associate the control strategy library to push drought-resistant measures.

[0177] 7. According to the drought risk level of each grid, the drought duration prediction yield reduction range.

[0178] In addition, after early warning according to the drought risk index of each grid, the control strategy is also executed.

[0179] Among them, the control strategy is one or more of the following: emergency control, water control, observation control, normal control, and reserve control.

[0180] For example, for the high-risk grid corresponding area, emergency control and / or water control are performed. The emergency control is, for example, starting water diversion and artificial rain enhancement, and the water control is, for example, rotation irrigation system and high water consumption crop restriction.

[0181] For the medium-risk grid corresponding area, observation control is performed. The observation control is, for example, soil moisture monitoring and water-saving equipment maintenance.

[0182] For the low-risk grid corresponding area, normal control is carried out. Among them, the normal control is like regular patrol and drought-resistant variety promotion.

[0183] For grids of various risk levels, reserve control can be carried out. Among them, the reserve control is like drought-resistant material preposition and water source engineering maintenance.

[0184] The method for dynamic zoning and early warning of farmland drought based on multi-source spatio-temporal data provided in this embodiment constructs a "three-level five-category" dynamic risk zoning model (topographic condition index, soil moisture index, climate drought index, historical disaster loss index, and crop vulnerability index), performs real-time coupling of multiple factors, and dynamically matches control strategies, thereby realizing spatio-temporal adaptive updating of the zoning boundary.

[0185] The method for dynamic zoning and early warning of farmland drought based on multi-source spatio-temporal data provided in this embodiment acquires a monitoring range; the monitoring range includes farmland; the monitoring range is rasterized to obtain a plurality of grids; the index values of monitoring indexes of each grid are determined; the monitoring indexes include topographic condition indexes, soil moisture indexes, climate drought indexes, historical disaster loss indexes, and crop vulnerability indexes; the drought risk indexes of each grid are evaluated according to the index values of the monitoring indexes of each grid; and early warning is performed according to the drought risk indexes of each grid. The method rasterizes the monitoring range, monitors each grid, and can realize individualized monitoring of different areas in a monitoring range. Since different grids in a monitoring range may have different prevention and control levels, the problem that one prevention and control level does not meet the actual drought early warning needs of farmland when the area of the prevention and control region is large is avoided.

[0186] Based on the same inventive concept as the method for dynamic zoning and early warning of farmland drought based on multi-source spatio-temporal data, this embodiment provides a system for dynamic zoning and early warning of farmland drought based on multi-source spatio-temporal data. The system includes a monitoring index acquisition module and a monitoring module.

[0187] 1. Monitoring index acquisition module

[0188] The monitoring index acquisition module is configured to acquire the index values of the monitoring indexes.

[0189] The monitoring indexes include topographic condition indexes, soil moisture indexes, climate drought indexes, historical disaster loss indexes, and crop vulnerability indexes.

[0190] 2. Monitoring module

[0191] The monitoring module is configured to perform the method described in the embodiment shown in Figure 1

[0192] For example, the monitoring module is configured to perform the following steps:

[0193] ​acquire a monitoring range, wherein the monitoring range comprises farmland,

[0194] grid the monitoring range to obtain a plurality of grids,

[0195] determine index values of monitoring indexes of the grids, wherein the monitoring indexes comprise a terrain condition index, a soil moisture index, a climate drought index, a historical disaster loss index, and a crop vulnerability index,

[0196] evaluate drought risk indexes of the grids according to the index values of the monitoring indexes of the grids,

[0197] issue a warning according to the drought risk indexes of the grids.

[0198] The terrain condition index is a terrain humidity index,

[0199] The soil moisture index is an effective water holding capacity,

[0200] The climate drought index is a standardized precipitation evapotranspiration index,

[0201] The historical disaster loss index is a product of a drought frequency and an average yield reduction rate of crops involved in the farmland within a preset first time length,

[0202] The crop vulnerability index is a water shortage sensitivity coefficient corresponding to a current growth period of the crops involved in the farmland.

[0203] The evaluating the drought risk indexes of the grids according to the index values of the monitoring indexes of the grids comprises:

[0204] adopting an analytic hierarchy process to evaluate the drought risk indexes of the grids according to the index values of the monitoring indexes of the grids and weights of the monitoring indexes,

[0205] The weight of the terrain condition index is 0.18,

[0206] The weight of the soil moisture index is 0.25,

[0207] The weight of the climate drought index is 0.30,

[0208] The weight of the historical disaster loss index is 0.15,

[0209] The weight of the crop vulnerability index is 0.12.

[0210] The issuing the warning according to the drought risk indexes of the grids comprises:

[0211] performing Gaussian filtering smoothing on the drought risk indexes of the grids to obtain smoothed drought risk indexes,

[0212] if the index value of the climate drought index in the preset second time period is determined according to the index value of the monitoring index of each grid, the index value of the soil moisture index is determined according to the index value of the monitoring index of each grid, or the change degree of the vegetation state index in the monitoring range is greater than the third threshold value, when the high-variation grid exists, the drought risk index of the high-variation grid is adjusted, the warning is performed according to the adjusted drought risk index, otherwise, the warning is performed according to the smoothed drought risk index,

[0213] wherein the high-variation grid is a grid satisfying a variation condition,

[0214] the variation condition is that the drought risk index change rate of the grid is greater than a fourth threshold value,

[0215] the drought risk index change rate of any grid is determined according to the drought risk index of any grid and the historical drought risk index of any grid,

[0216] or the drought risk index change rate of any grid is determined according to the drought risk index of all grids in the region centered on any grid.

[0217] wherein the initial value of the fourth threshold value is 0.15,

[0218] when there is a grid whose index value of the climate drought index is not greater than a fifth threshold value, the fourth threshold value is adjusted to 0.08,

[0219] when the index value of the soil moisture index of all grids in the continuous preset third time period is greater than the second threshold value, the fourth threshold value is restored to the initial value.

[0220] wherein the adjustment of the drought risk index of the high-variation grid comprises:

[0221] determining the absolute value of the difference between the drought risk index of the high-variation grid and each surrounding grid, wherein the surrounding grid is a grid in the unit square region of the high-variation grid, and the surrounding grid is not the high-variation grid, and the unit square region of the high-variation grid is a square region with the high-variation grid as the center and three grids as the side length,

[0222] if the number of surrounding grids whose absolute value of the difference is higher than the sixth threshold value is not less than a preset first value, the variation degree of the high-variation grid is determined, a target set is determined according to the drought risk index of the grids in the region centered on the high-variation grid, and the drought risk index of the high-variation grid is adjusted according to the target set.

[0223] wherein, after determining the absolute value of the difference between the drought risk index of the high-variation grid and each surrounding grid, it further comprises:

[0224] If the number of the peripheral grids whose absolute difference is higher than the sixth threshold value is less than the preset first value, the drought risk index of the high-variation grid is adjusted according to the drought risk index of the high-variation grid and the drought risk indexes of the grids in the region centered on the high-variation grid.

[0225] The warning is performed according to the drought risk indexes of the grids.

[0226] The drought risk levels of the grids are determined according to the drought risk indexes of the grids, and a preset corresponding relationship between the drought risk indexes and the drought risk levels.

[0227] If the abnormal grid exists, the topological optimization is performed, and the warning is performed according to the drought risk levels of the grids after the topological optimization, wherein the level difference between the maximum drought risk level and the minimum drought risk level of all the grids in the unit square region of the abnormal grid is not less than the preset second value, and the unit square region of any grid is a square region with the any grid as the center and three grids as the side length.

[0228] If the abnormal grid does not exist, the warning is performed according to the drought risk levels of the grids.

[0229] The warning is performed according to the drought risk indexes of the grids.

[0230] The control strategy is executed.

[0231] The control strategy is one or more of the following: emergency control, water control, observation control, normal control, and reserve control.

[0232] The system provided in the embodiment performs gridding on a monitoring range, monitors each grid, and can realize individualized monitoring of different regions in a monitoring range. Since different grids in a monitoring range can have different prevention and control levels, the problem that one prevention and control level cannot meet the actual drought warning needs of farmland when the area of a prevention and control region is large is avoided.

[0233] Since the system / device described in the above embodiments of the application is used to implement the method of the above embodiments of the application, the specific structure and modifications of the system / device can be understood by those skilled in the art based on the method described in the above embodiments of the application, and thus will not be described here. Any system / device used in the method of the above embodiments of the application belongs to the scope of the application.

[0234] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0235] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.

[0236] It should be noted that any reference numerals placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In claims that enumerate several means, several of these means may be embodied by the same hardware. The use of the terms first, second, third, etc., is merely for convenience of expression and does not indicate any order. These terms can be understood as part of the component names.

[0237] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0238] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the claims should be interpreted to include both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0239] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A farmland drought dynamic partitioning and early warning method based on multi-source spatio-temporal data, characterized in that, The method comprises: acquiring a monitoring range; wherein the monitoring range comprises farmland; rasterizing the monitoring range to obtain a plurality of grids; determining index values of monitoring indexes of the grids; wherein the monitoring indexes comprise a terrain condition index, a soil moisture index, a climate drought index, a historical disaster loss index, and a crop vulnerability index; evaluating drought risk indexes of the grids according to the index values of the monitoring indexes of the grids; warning according to the drought risk indexes of the grids.

2. The method of claim 1, wherein, The terrain condition index is a terrain humidity index. The soil moisture index is an effective water holding capacity. The climate drought index is a standardized precipitation evapotranspiration index. The historical disaster loss index is a product of a drought frequency in a preset first time length and an average yield reduction rate of crops involved in the farmland. The crop vulnerability index is a water shortage sensitivity coefficient corresponding to a current growth period of the crops involved in the farmland.

3. The method of claim 1, wherein, The evaluation of the drought risk indexes of the grids according to the index values of the monitoring indexes of the grids comprises: evaluating the drought risk indexes of the grids by using an analytic hierarchy process according to the index values of the monitoring indexes of the grids and weights of the monitoring indexes; The weight of the terrain condition index is 0.

18. The weight of the soil moisture index is 0.

25. The weight of the climate drought index is 0.

30. The weight of the historical disaster loss index is 0.

15. The weight of the crop vulnerability index is 0.

12.

4. The method of claim 1, wherein, The warning according to the drought risk indexes of the grids comprises: performing Gaussian filtering smoothing on the drought risk indexes of the grids to obtain smoothed drought risk indexes; if it is determined according to the index values of the monitoring indexes of the grids that an index value of the climate drought index in a preset second time length is not greater than a preset first threshold value, or it is determined according to the index values of the monitoring indexes of the grids that an index value of the soil moisture index is not greater than a preset second threshold value, or a change degree of a vegetation state index in the monitoring range is greater than a third threshold value, then when there are high-variation grids, adjusting drought risk indexes of the high-variation grids, and warning according to the adjusted drought risk indexes; otherwise, warning according to the smoothed drought risk indexes; The high-variation grids are grids that satisfy a variation condition. The variation condition is that a drought risk index change rate of a grid is greater than a fourth threshold value. The drought risk index change rate of any grid is determined according to a drought risk index of the any grid and a historical drought risk index of the any grid. Or, the drought risk index change rate of any grid is determined according to drought risk indexes of all grids in a region centered on the any grid.

5. The method of claim 4, wherein, An initial value of the fourth threshold value is 0.

15. When there are grids in which an index value of the climate drought index is not greater than a fifth threshold value, the fourth threshold value is adjusted to 0.

08. When index values of the soil moisture indexes of all grids in a continuous preset third time length are all greater than the second threshold value, the fourth threshold value is restored to the initial value.

6. The method of claim 4, wherein, The adjustment of the drought risk indexes of the high-variation grids comprises: determine an absolute value of a difference between the high-variation grid and a drought risk index of each surrounding grid, wherein the surrounding grid is a grid in a unit square region of the high-variation grid and is not a high-variation grid, and the unit square region of the high-variation grid is a square region with the high-variation grid as a center and three grids as a side length; if a number of surrounding grids with an absolute value of a difference higher than a sixth threshold value is not less than a preset first value, determine a variation degree of the high-variation grid, determine a target set according to drought risk indexes of grids in a region with the high-variation grid as a center, and adjust the drought risk index of the high-variation grid according to the target set.

7. The method of claim 6, wherein, After the determination of the absolute value of the difference between the high-variation grid and the drought risk index of each surrounding grid, the method further includes: if a number of surrounding grids with an absolute value of a difference higher than a sixth threshold value is less than a preset first value, adjust the drought risk index of the high-variation grid according to the drought risk index of the high-variation grid and the drought risk indexes of grids in a region with the high-variation grid as a center.

8. The method of claim 1, wherein, The prewarning according to the drought risk indexes of the grids includes: determining a drought risk level of each grid according to the drought risk index of each grid and a preset corresponding relationship between a drought risk index and a drought risk level; if there is an abnormal grid, performing topological optimization and prewarning based on a topologically optimized drought risk level of each grid, wherein a level difference between a maximum drought risk level and a minimum drought risk level of all grids in a unit square region of the abnormal grid is not less than a preset second value, and a unit square region of any grid is a square region with the any grid as a center and three grids as a side length; if there is no abnormal grid, prewarning based on the drought risk level of each grid.

9. The method of claim 1, wherein, After the prewarning according to the drought risk indexes of the grids, the method further includes: executing a control strategy; wherein the control strategy is one or more of the following: emergency control, water limit control, observation control, normal control, and reserve control.

10. A farmland drought dynamic partitioning and early warning system based on multi-source spatio-temporal data, characterized in that, The system includes a monitoring index collection module and a monitoring module. The monitoring index collection module is configured to collect an index value of a monitoring index. The monitoring module is configured to execute the method according to any one of claims 1-9.

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