Drought disaster dynamic assessment method based on space-time big data analysis

Abstract

The invention discloses a time-space big data analysis-based drought disaster dynamic assessment method, and relates to the technical field of drought disaster assessment, and the method comprises the following steps: carrying out the region division of a to-be-assessed region, setting a plurality of sampling points in each region, collecting the soil humidity and precipitation at each sampling point, and obtaining the time-space data of the sampling points; acquiring a drought sensitivity index and a time stability index of each sampling point to obtain sampling point index data; sampling points are analyzed and evaluated, and monitoring points of drought disasters are dynamically selected from the sampling points; collecting the relative humidity of soil at each monitoring point, and evaluating the drought disaster condition of each region of the to-be-evaluated region; the method is used for solving the problem that when an existing drought disaster assessment technology is used for assessing the agricultural drought condition of a region, a monitoring point of the relative humidity of soil cannot be dynamically selected according to the sensitivity of different places of the region to drought and the stability of the soil humidity, and the drought disaster cannot be assessed.

Description

Technical Field

[0001] This invention relates to the field of drought disaster assessment technology, specifically a dynamic assessment method for drought disasters based on spatiotemporal big data analysis. Background Technology

[0002] Drought disaster assessment technology refers to a technical system that aims to reveal the spatiotemporal evolution of drought disasters, quantify the degree of disaster impact, and determine the risk level. It integrates multi-source data collection, indicator system construction, model algorithm analysis, and spatiotemporal visualization to conduct systematic, dynamic, and multi-dimensional quantitative analysis and comprehensive evaluation of the precursors, development process, scope of impact, degree of loss, and disaster reduction effects of drought disasters.

[0003] Current drought disaster assessment technologies typically use soil relative humidity as a core indicator when assessing agricultural drought conditions in a region. However, the selection of monitoring points for soil relative humidity is often arbitrary or uniform. The same region may contain various soil types, such as sandy soil, loam, and clay. Even within the same soil type, the water retention capacity and drought sensitivity of soils at different locations can vary significantly. Furthermore, water evaporation and runoff loss differ significantly between slopes, plains, and depressions. Arbitrary or uniform selection of monitoring points cannot accurately distinguish these areas and may lead to misclassification. Including data from insufficient monitoring points in the assessment leads to inadequate accuracy and reliability in drought disaster assessment. For example, uniform distribution of monitoring points may result in the same or similar number of points in areas with strong water retention capacity and drought-prone areas, diluting the data in drought-prone areas and distorting the assessment results. On the other hand, random selection of points may happen to select drought-blind areas such as low-lying areas, directly underestimating the degree of drought in the region. Therefore, existing drought disaster assessment technologies cannot dynamically select soil relative humidity monitoring points based on the drought sensitivity and soil moisture stability of different locations in a region when assessing agricultural drought conditions in a region, and thus cannot assess drought disaster. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in the prior art. It involves dividing the area to be assessed into regions, setting multiple sampling points in each region, and collecting soil moisture and precipitation data at each sampling point to obtain spatiotemporal data. The invention also acquires drought sensitivity and temporal stability indicators for each sampling point, resulting in sampling point indicator data. Furthermore, it analyzes and evaluates these sampling points, dynamically selecting monitoring points for drought disasters from among them. Finally, it collects soil relative humidity data at each monitoring point and assesses the drought disaster situation in each region of the area to be assessed. This addresses the problem that existing drought disaster assessment technologies, when assessing agricultural drought conditions in a region, cannot dynamically select soil relative humidity monitoring points based on the drought sensitivity and soil moisture stability of different locations within the region, and thus cannot assess drought disasters accordingly.

[0005] To achieve the above objectives, this application provides a method for dynamic assessment of drought disasters based on spatiotemporal big data analysis, comprising the following steps: The area to be evaluated was divided into regions, and multiple sampling points were set up in each region. Soil moisture and precipitation were collected at each sampling point to obtain spatiotemporal data of the sampling points. Based on the spatiotemporal data of the sampling points, drought sensitivity indicators and temporal stability indicators of each sampling point are obtained to obtain the sampling point indicator data; Based on the index data of the sampling points, the sampling points are analyzed and evaluated, and monitoring points for drought disasters are dynamically selected from the sampling points. Based on the selected drought disaster monitoring points, soil relative humidity was collected at each monitoring point, and the drought disaster situation in each area of ​​the region to be assessed was evaluated.

[0006] Furthermore, the area to be evaluated is divided into regions, and multiple sampling points are set up in each region. Soil moisture and precipitation are collected at each sampling point to obtain spatiotemporal data, including the following sub-steps: Any area to be assessed for drought disaster is designated as the first drought area. The first drought area is divided into multiple regions based on the soil type of the first drought area, and each region is designated as a drought area. Any one of the drought areas is designated as the first drought area. Multiple sampling points were evenly set up in each arid area and denoted as sampling points, with any one of the sampling points designated as the first sampling point.

[0007] Furthermore, the area to be evaluated is divided into regions, and multiple sampling points are set up in each region. Soil moisture and precipitation are collected at each sampling point to obtain the spatiotemporal data of the sampling points. This process also includes the following sub-steps: The first duration is set to t1. With t1 as the period, soil moisture and rainfall for each rainfall event are periodically collected at the first collection point. The collection time and rainfall time for each rainfall event are recorded as the spatiotemporal information of the first collection point. The data is collected synchronously at each collection point to obtain the spatiotemporal information of each collection point, which is recorded as the spatiotemporal data of the sampling point.

[0008] Furthermore, based on the spatiotemporal data of the sampling points, drought sensitivity indicators and temporal stability indicators are obtained for each sampling point. The resulting sampling point indicator data includes the following sub-steps: For the first sampling point, the portion collected in the most recent second time period is extracted from the corresponding spatiotemporal information and recorded as the most recent spatiotemporal data of the first sampling point; any rainfall event of the first sampling point within the most recent second time period is recorded as the first rainfall event; where the second time period is t2; The precipitation amount of the first rainfall event is denoted as AP. If AP is less than k1, the first rainfall event is recorded as invalid rainfall; otherwise, it is recorded as valid rainfall. Here, k1 is the set precipitation amount. If the first rainfall event is valid rainfall, then obtain the soil moisture collected k1 times before the start of the first rainfall event, and record it as the pre-rain moisture set of the first rainfall event; and obtain the soil moisture collected k2 times after the end of the first rainfall event, and record it as the post-rain moisture set of the first rainfall event, where k2 is the set number; Calculate the average value AE of the pre-rain humidity set and the average value BE of the post-rain humidity set, and calculate (BE-AE) / AP, which is denoted as the humidity response coefficient YR of the first rainfall event; repeat the humidity response coefficients of all effective rainfalls within the most recent first time period to obtain the rainfall response coefficient set.

[0009] Furthermore, obtaining drought sensitivity and temporal stability indices for each sampling point based on spatiotemporal data, and acquiring the sampling point index data, also includes the following sub-steps: Based on the most recent spatiotemporal data, the most recent first duration is divided into multiple time periods. Any time period is designated as the first period. If rainfall occurs within the first period, it is designated as an invalid period; otherwise, it is designated as a valid period. If the first period is an effective period, the soil moisture collected in the first period is counted. The soil moisture collected for the first and last time in the first period is recorded as QP and HP respectively. QP-HP is calculated and recorded as the humidity response coefficient XR for the first period. The humidity response coefficients of all effective periods are obtained by repeating the process to obtain the drought response coefficient set. The coefficient of variation of soil moisture in the first period is calculated and denoted as the coefficient of variation of moisture in the first period (CV). The coefficients of variation of moisture in all valid periods are obtained repeatedly to obtain the set of coefficients of variation.

[0010] Further, obtaining the drought sensitivity index and time stability index for each sampling point based on the spatio-temporal data of the sampling points, the steps for obtaining the sampling point index data further include the following sub-steps: Denote |BE - AE| as the weight base of YR, and denote |BE - AE|×YR as the weighted response coefficient corresponding to YR; denote |QP - HP| as the weight base of XR, and |QP - HP|×XR as the weighted response coefficient corresponding to XR; According to the 3σ principle, remove the outliers in the rainfall response coefficient set and the drought response coefficient set respectively, and obtain the rainfall standard set and the drought standard set respectively; Repeatedly obtain all the corresponding weighted response coefficients and weight bases in the rainfall standard set and the drought standard set, and sum them up respectively to obtain the sum of response coefficients HR and the sum of weight bases HQ, and calculate HR / HQ, which is denoted as the drought sensitivity index of the first sampling point.

[0011] Further, obtaining the drought sensitivity index and time stability index for each sampling point based on the spatio-temporal data of the sampling points, the steps for obtaining the sampling point index data further include the following sub-steps: For the first period, taking the first period as the end point, count the number of consecutive time periods without rain and denote it as the number of rainless periods WG in the first period; If WG < k3, set the weight CQ of CV to 1; if k3 ≤ WG ≤ k4, set the weight CQ of CV to 1.2; if k4 < WG, set the weight CQ of CV to 1.5, where k3 and k4 are set thresholds; calculate CQ×CV, which is denoted as the weighted coefficient of the humidity variation coefficient CV; [[ID=,15]]According to the 3σ principle, remove the outliers in the variation coefficient set to obtain the variation coefficient standard set; repeatedly obtain all the weighted coefficients of the humidity variation coefficients in the variation coefficient standard set and the corresponding weights, and sum them up respectively to obtain the sum of weighted coefficients VH and the corresponding sum of weights CH; calculate VH / CH, which is denoted as the time stability index of the first sampling point; Repeatedly obtain the drought sensitivity index and time stability index of all sampling points to obtain the sampling point index data.

[0012] Further, conduct sampling point analysis and evaluation based on the sampling point index data, and dynamically select the monitoring points for drought disasters from the sampling points, which includes the following sub-steps: Set the third time length as t3, periodically obtain the sampling point index data according to t3, denote the currently obtained sampling point index data as the current index data, and according to the current index data, denote the sampling points with a drought sensitivity index less than e1 or a time stability index not within [e2, e3] as unqualified points, and denote the other sampling points as qualified points, where e1, e2, and e3 are set thresholds; Based on the drought sensitivity index and time stability index of all qualified points, the Min-Max standardization method was used to standardize each drought sensitivity index and time stability index to obtain qualified point index data. If the first sampling point is a qualified point, the drought sensitivity index and time stability index of the first sampling point after standardization are recorded as AX and BX respectively in order; calculate q1×AX+q2×BX, which is recorded as the comprehensive index score of the first sampling point, and repeat to obtain the comprehensive index scores of all qualified points, where q1 and q2 are the set weights.

[0013] Furthermore, based on the sampling point index data, the sampling points are analyzed and evaluated. Dynamically selecting monitoring points for drought disasters from the sampling points includes the following sub-steps: For the first drought region, the number of monitoring points in the first drought region is set to n1; the qualified points in the first drought region are arranged from largest to smallest according to the comprehensive index score, and recorded as the qualified point sequence; For any qualified point, the corresponding monitoring area is a circular area with the qualified point as the center and a radius of R1, where R1 is the set radius; The first qualified point in the qualified point sequence is recorded as a monitoring point. Starting from the second qualified point, if there is no monitoring point in the monitoring area of ​​the second qualified point, the second qualified point is recorded as a monitoring point; otherwise, it is recorded as a redundant point. The selection is carried out sequentially according to the qualified point sequence until the n1 monitoring point is selected as the monitoring point of the first arid region. All monitoring points in all areas of the first arid region are obtained repeatedly.

[0014] Furthermore, based on the selected drought monitoring points, soil relative humidity was collected at each monitoring point, and the drought situation in each area of ​​the region to be assessed was evaluated, including the following sub-steps: For the first arid region, soil relative humidity was collected at all monitoring points in the first arid region to obtain relative humidity distribution data for the first arid region; the range of soil relative humidity values ​​was divided into multiple intervals, and a corresponding drought level was set for each interval; Based on the relative humidity distribution data of the first arid region, a soil relative humidity distribution map of the first arid region is generated by spatial interpolation, and colors are divided according to the corresponding drought level to obtain the drought disaster distribution map of the first arid region, which is recorded as the drought disaster assessment result of the first arid region; Repeatedly obtain the drought disaster assessment results of all areas in the first arid region and combine them to form a drought disaster distribution map of the arid region to obtain the drought disaster assessment results of the first arid region.

[0015] The beneficial effects of this invention are as follows: This invention divides the area to be evaluated into regions and sets up multiple sampling points in each region. Soil moisture and precipitation are collected at each sampling point to obtain spatiotemporal data. Based on the spatiotemporal data, drought sensitivity indicators and time stability indicators are obtained for each sampling point to obtain sampling point indicator data. Sampling point indicator data are analyzed and evaluated, and monitoring points for drought disasters are dynamically selected from the sampling points. Based on the selected monitoring points for drought disasters, soil relative humidity is collected at each monitoring point, and the drought disaster situation in each region of the area to be evaluated is assessed. When assessing the agricultural drought situation in a region, monitoring points for soil relative humidity can be dynamically selected according to the drought sensitivity and soil moisture stability of different locations in the region, thereby improving the accuracy and reliability of drought disaster assessment. This invention uses humidity variation as a weighting base and multiplies it by the humidity response coefficient. This amplifies the impact of events with large fluctuations on the final sensitivity index, thereby avoiding the dominance of numerous small noises in the results and improving the accuracy of drought sensitivity indicators. Adjusting the weight of the humidity variation coefficient with the number of drought duration cycles allows for the reasonable amplification of humidity fluctuations caused by long-term drought, facilitating the discovery of long-term stability of sampling points and avoiding the influence of short-term fluctuations. The drought sensitivity index reflects the strength of the sampling point's response to rainfall or drought conditions. It measures the sensitivity of the sampling point's response to drought and rainfall, while humidity temporal stability reflects the volatility of humidity over time. It measures the long-term stability and reliability of soil moisture data from sampling points. Combining these two aspects can improve the quality of selected monitoring points and enhance the accuracy and reliability of the assessment. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the method of the present invention; Figure 2 This is a flowchart of the drought sensitivity index acquisition process of the present invention; Figure 3 This is a drought disaster distribution map for the present invention; Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1As shown, this application provides a method for dynamic assessment of drought disasters based on spatiotemporal big data analysis, including the following steps: Step S1 involves dividing the area to be evaluated into regions, setting up multiple sampling points in each region, and collecting soil moisture and precipitation data at each sampling point to obtain spatiotemporal data. Step S1 includes the following sub-steps: Step S101: Any area to be assessed for drought disaster is designated as the first drought area. The first drought area is divided into multiple regions based on soil type, and each region is designated as a drought region. Any one of these drought regions is designated as the first drought area. Different soil types have vastly different water retention capacities and drought sensitivities. Dividing regions by soil type can prevent data from different regions from influencing each other and causing errors in the assessment results. Furthermore, dividing regions can prevent the subsequent selection of monitoring points from being concentrated in a small number of areas. Moreover, different soil types may require different monitoring densities. For example, sandy soil is sensitive to rainfall but has high spatial heterogeneity, so it may require a denser distribution of monitoring points. Dividing the regions makes it easier to develop differentiated sampling plans and budget allocations. In step S102, multiple sampling points are evenly set up in each arid area and denoted as sampling points, with any one of these sampling points designated as the first sampling point. The initially set sampling points serve as candidate monitoring points, from which monitoring points will be dynamically selected subsequently. A sufficient number and evenly distributed sampling points ensure that the selected monitoring points are representative and have spatial coverage.

[0019] Step S103: Set the first duration to t1. With t1 as the period, periodically collect soil moisture and rainfall for each rainfall at the first collection point, and record the collection time and rainfall time for each rainfall, which are recorded as the spatiotemporal information of the first collection point. Soil moisture, or soil water content, is a physical quantity that represents the degree of dryness or wetness of the soil, and is expressed as the percentage of soil water content to dry soil weight. In this embodiment, t1 = 1 day, that is, soil moisture is collected once a day, which can be flexibly set according to actual application needs. Step S104: Repeat the synchronous collection at each collection point to obtain the spatiotemporal information of each collection point, which is recorded as the spatiotemporal data of the sampling point; In the specific implementation process, if there is only one type of soil in the area to be evaluated, the area to be evaluated can be divided into grids, that is, divided into multiple uniform grid areas.

[0020] Step S2 involves obtaining drought sensitivity and temporal stability indices for each sampling point based on the spatiotemporal data, thus acquiring the sampling point index data. Step S2 includes the following sub-steps: For step S201, please refer to... Figure 2As shown, for the first sampling point, the portion collected in the most recent second duration is extracted from the corresponding spatiotemporal information and recorded as the most recent spatiotemporal data of the first sampling point; any rainfall event of the first sampling point within the most recent second duration is recorded as the first rainfall event; where the second duration is t2, and in this embodiment, t2 = one year, that is, the data of the most recent year is used for analysis and processing; Step S202: The precipitation of the first rainfall event is denoted as AP. If AP is less than k1, the first rainfall event is recorded as invalid rainfall; otherwise, it is recorded as valid rainfall. Here, k1 is the set precipitation amount. In this embodiment, k1=2mm, which can be flexibly set to exclude tiny rainfalls to avoid affecting subsequent calculations. Drizzle with too little precipitation will be consumed by surface evaporation or soil adsorption and cannot effectively replenish soil moisture. Its response coefficient in subsequent calculations has no practical significance. Step S203: If the first rainfall event is effective rainfall, then obtain the soil moisture collected k1 times before the start of the first rainfall event, and record it as the pre-rain moisture set of the first rainfall event; and obtain the soil moisture collected k2 times after the end of the first rainfall event, and record it as the post-rain moisture set of the first rainfall event, where k2 is the set number; in this embodiment, k2=3, which can be set flexibly, and is generally [3, 5]; Step S204: Calculate the average value AE of the pre-rain humidity set and the average value BE of the post-rain humidity set. Using the average values ​​of soil moisture from several times before and after rainfall can smooth out instantaneous fluctuations, measurement errors, and instrument noise, making the response coefficient statistics more stable. Calculate (BE-AE) / AP, denoted as the humidity response coefficient YR of the first rainfall event. Repeat the humidity response coefficients of all effective rainfall within the most recent first time period to obtain the rainfall response coefficient set. The humidity response coefficient of effective rainfall represents the change in soil relative humidity caused by a unit change in precipitation, used to quantify the sensitivity of the water replenishment stage. The larger the coefficient, the more soil moisture content can be increased by a unit of precipitation, and the more sensitive the soil is to rainfall.

[0021] Step S205: Based on the most recent spatiotemporal data, the most recent first duration is divided into multiple time periods. Any time period is recorded as the first period. If rainfall occurs within the first period, it is recorded as an invalid period; otherwise, it is recorded as a valid period. In this embodiment, 7 days is one time period, that is, one week is one time period. The time period can be divided according to the actual application scenario, generally 7 to 15 days. Step S206: If the first period is an effective period, the soil moisture collected in the first period is counted. The soil moisture collected for the first and last time in the first period is recorded as QP and HP respectively. QP-HP is calculated and recorded as the humidity response coefficient XR of the first period. The humidity response coefficients of all effective periods are obtained repeatedly to obtain the drought response coefficient set. The humidity response coefficient of the time period quantifies the sensitivity of the water loss stage. The larger the XR, the faster the soil moisture loss during the rainless period and the more sensitive the response to drought. Step S207, and calculate the coefficient of variation of soil moisture in the first period, denoted as the coefficient of variation of moisture in the first period CV; repeat to obtain the coefficient of variation of moisture in all valid periods to obtain the set of coefficients of variation. The coefficient of variation of moisture represents the degree of fluctuation and dispersion of soil moisture in a rainless time period. The smaller the value, the more stable the soil moisture.

[0022] Step S208: Denote |BE-AE| as the weight base of YR, and |BE-AE|×YR as the weighted response coefficient corresponding to YR; denote |QP-HP| as the weight base of XR, and |QP-HP|×XR as the weighted response coefficient corresponding to XR; the weight base is essentially the degree of influence of the event on soil moisture. The change in soil moisture content directly reflects the magnitude of the change in soil moisture state caused by the event. The change caused by heavy rain is large, indicating that the event has a large impact, and its response coefficient should contribute more to the sensitivity. The change caused by long-term drought without rain is large, indicating that the soil loses a lot of water, and its response coefficient can better reflect the sensitivity of the sampling point to drought, and should be given a higher weight. Step S209: According to the 3σ principle, outliers in the rainfall response coefficient set and the drought response coefficient set are removed respectively to obtain the rainfall standard set and the drought standard set; removing a few outlier data ensures that the subsequent weighted average is not dominated by a few extreme events.

[0023] Step S210: Repeatedly obtain all corresponding weighted response coefficients and weight bases in the rainfall standard set and drought standard set, and sum them uniformly to obtain the sum of response coefficients HR and the sum of weight bases HQ. Calculate HR / HQ and record it as the drought sensitivity index of the first sampling point. Drought sensitivity indexes characterize the extent to which changes in soil relative humidity at a sampling point can be triggered by changes in unit precipitation and rainless periods. They are core indicators for measuring the sensitivity of a sampling point to drought and precipitation. The higher the drought sensitivity index, the more sensitive the sampling point is to precipitation and drought. Highly sensitive sampling points will immediately and synchronously decrease their soil relative humidity as soon as there is less precipitation in the region, reflecting the initial signal of drought in the first instance. As long as there is a small amount of rainfall, the humidity will also rise synchronously, accurately reflecting the degree of drought relief, making them suitable as monitoring points. For example, at a certain sampling point, there are five effective rainfall events and two effective cycles. The corresponding humidity response coefficients are {0.008, 0.012, 0.005, 0.015, 0.006, 0.07, 0.09}, and the corresponding changes in soil humidity, i.e., the weight bases, are {0.04, 0.06, 0.02, 0.08, 0.03, 0.07, 0.09}. Then the corresponding weighted response coefficients are {0.00032, 0.00072, 0.00010, 0.0012, 0.00018, 0.0049, 0.00810}. Then HR = 0.01552, HQ = 0.39, and the drought sensitivity index is HR / HQ = 0.04.

[0024] Step S211: For the first cycle, taking the first cycle as the end point, count the number of consecutive non-rainy time periods and record it as the non-rainy cycle number WG of the first cycle. Step S212: If WG < k3, set the weight CQ of CV to 1; if k3 ≤ WG ≤ k4, set the weight CQ of CV to 1.2; if k4 < WG, set the weight CQ of CV to 1.5, where k3 and k4 are set thresholds; calculate CQ × CV and record it as the weighted coefficient of the humidity coefficient of variation CV. In this embodiment, k3 = 2 and k4 = 3, which can be flexibly set; the larger WG is, the longer the non-rainy time is, and the more important the impact of long-term drought is. The larger WG is, the higher the weight, amplifying the importance of fluctuations under long-term non-rainy conditions; this can make the time stability index more sensitively reflect the soil humidity volatility of the sampling point under long-term drought; Step S213: According to the 3σ principle, eliminate the outliers in the coefficient of variation set to obtain the standard coefficient of variation set; repeatedly obtain the weighted coefficients and corresponding weights of all humidity coefficients of variation in the standard coefficient of variation set, and sum them respectively to obtain the sum of weighted coefficients VH and the corresponding sum of weights CH; calculate VH / CH and record it as the time stability index of the first sampling point. The time stability index represents the degree of fluctuation and dispersion of the relative soil humidity data at a sampling point during consecutive monitoring periods, measuring the long-term stability and reliability of the soil humidity data at this sampling point. The monitoring points to be evaluated are those that can sensitively reflect drought but whose data do not fluctuate randomly. Therefore, if the time stability index is too high or too low, it is not suitable as a monitoring point; Step S214: Repeatedly obtain the drought sensitivity indices and time stability indices of all sampling points to obtain the sampling point index data. In the specific implementation process, the weight of the humidity coefficient of variation CV can be flexibly set according to actual application requirements.

[0025] Step S3 involves analyzing and evaluating the sampling points based on their index data, and dynamically selecting monitoring points for drought disasters from the sampling points. Step S3 includes the following sub-steps: Step S301: Set the third time period to t3. Periodically acquire sampling point index data based on t3. Record the currently acquired sampling point index data as the current index data. Based on the current index data, record sampling points with drought sensitivity index less than e1 or time stability index not located in [e2, e3] as unqualified points, and record other sampling points as qualified points. Here, e1, e2, and e3 are set thresholds. In this embodiment, t3 = 3 months, that is, sampling point index data is acquired every 3 months, i.e., monitoring points are reselected; e1 = 0.03, e2 = 5%, e3 = 40%. Step S302: Based on the drought sensitivity index and time stability index of all qualified points, the Min-Max standardization method is used to standardize each drought sensitivity index and time stability index to obtain qualified point index data. When standardizing the time stability index, [e2, e3] should be used as the benchmark. The closer the standardized time stability index is to 1, the better the stability. When standardizing the drought sensitivity index, the largest drought sensitivity index should be used as the benchmark. The closer the standardized drought sensitivity index is to 1, the better the sensitivity. Step S303: If the first sampling point is a qualified point, the drought sensitivity index and time stability index of the first sampling point after standardization are recorded as AX and BX respectively in order; calculate q1×AX+q2×BX, which is recorded as the comprehensive index score of the first sampling point. Repeat the acquisition of the comprehensive index scores of all qualified points, where q1 and q2 are set weights. In this embodiment, q1=0.6, q2=0.4, which can be flexibly set, and q1+q2=1.

[0026] Step S304: For the first drought area, set the number of monitoring points in the first drought area to n1; n1 can be set according to actual needs. Arrange the qualified points in the first drought area from largest to smallest according to the comprehensive index score, and record it as the qualified point sequence. Step S305: For any qualified point, a circular area with the qualified point as the center and a radius of R1 is recorded as the corresponding monitoring area, where R1 is the set radius; R1 can be set according to the actual monitoring needs. In this embodiment, R1 = 1.5km. Step S306: Record the first qualified point in the qualified point sequence as a monitoring point, and starting from the second qualified point, if there is no monitoring point in the monitoring area of ​​the second qualified point, then record the second qualified point as a monitoring point; otherwise, record it as a redundant point. Select points sequentially according to the qualified point sequence until n1 monitoring points are selected as the monitoring points of the first arid region. Repeat this process to obtain all monitoring points in all areas of the first arid region. In the specific implementation process, if only the top n1 sampling points are selected as monitoring points based on the comprehensive index score, the selected monitoring points may be concentrated in a certain area. Using a radius R1 to limit the distribution of monitoring points can make the spatial distribution of monitoring points more balanced, avoid all monitoring points being concentrated, and improve the reliability of drought disaster assessment.

[0027] Step S4 involves collecting soil relative humidity data at each selected drought monitoring point and assessing the drought situation in each area of ​​the region to be evaluated. Step S4 includes the following sub-steps: Step S401: For the first arid region, soil relative humidity is collected at all monitoring points in the first arid region to obtain the relative humidity distribution data of the first arid region; the range of soil relative humidity values ​​is divided into multiple intervals, and each interval is set with a corresponding drought level; soil relative humidity refers to the percentage of soil water content to field capacity; for example, soil relative humidity greater than 60% represents no drought, (50%, 60%) represents mild drought, (40%, 50%) represents moderate drought, (30%, 40%) represents severe drought, and not greater than 30% represents extremely severe drought; Step S402: Based on the relative humidity distribution data of the first arid region, a soil relative humidity distribution map of the first arid region is generated through spatial interpolation. The map is then color-coded according to the corresponding drought level to obtain a drought disaster distribution map of the first arid region, which is recorded as the drought disaster assessment result for the first arid region. For example, black represents extremely severe drought, dark gray represents severe drought, gray represents moderate drought, light gray represents mild drought, and white represents no drought. Please refer to [link to relevant documentation]. Figure 3 As shown; Step S403: Repeatedly obtain the drought disaster assessment results of all areas in the first arid region and combine them into a drought disaster distribution map of the arid region to obtain the drought disaster assessment results of the first arid region; In practice, drought levels can be classified based on soil relative humidity, which can map continuous soil relative humidity into a limited risk level, making assessment easier.

[0028] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps such as those in a dynamic drought disaster assessment method based on spatiotemporal big data analysis to achieve the following functions: dividing the area to be assessed into regions, setting multiple sampling points in each region, and collecting soil moisture and precipitation data at each sampling point to obtain spatiotemporal data; obtaining drought sensitivity and time stability indicators for each sampling point based on the spatiotemporal data to obtain sampling point indicator data; performing sampling point analysis and assessment based on the sampling point indicator data, and dynamically selecting drought disaster monitoring points from the sampling points; and based on the selected drought disaster monitoring points, collecting soil relative humidity at each monitoring point and assessing the drought disaster situation in each region of the area to be assessed.

[0029] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0030] Example 3: This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs steps as described in the method for dynamic assessment of drought disasters based on spatiotemporal big data analysis, to achieve the following functions: dividing the area to be assessed into regions, setting multiple sampling points in each region, and collecting soil moisture and precipitation at each sampling point to obtain spatiotemporal data of the sampling points; obtaining drought sensitivity indicators and time stability indicators for each sampling point based on the spatiotemporal data of the sampling points to obtain sampling point indicator data; performing sampling point analysis and assessment based on the sampling point indicator data, and dynamically selecting monitoring points for drought disasters from the sampling points; and based on the selected monitoring points for drought disasters, collecting soil relative humidity at each monitoring point and assessing the drought disaster situation in each region of the area to be assessed.

[0031] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0032] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A dynamic assessment method for drought disasters based on spatiotemporal big data analysis, characterized in that, Includes the following steps: The area to be evaluated was divided into regions, and multiple sampling points were set up in each region. Soil moisture and precipitation were collected at each sampling point to obtain spatiotemporal data of the sampling points. Based on the spatiotemporal data of the sampling points, drought sensitivity indicators and temporal stability indicators of each sampling point are obtained to obtain the sampling point indicator data; Based on the index data of the sampling points, the sampling points are analyzed and evaluated, and monitoring points for drought disasters are dynamically selected from the sampling points. Based on the selected drought disaster monitoring points, soil relative humidity was collected at each monitoring point, and the drought disaster situation in each area of ​​the region to be assessed was evaluated.

2. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 1, characterized in that, The area to be evaluated is divided into regions, and multiple sampling points are set up in each region. Soil moisture and precipitation are collected at each sampling point to obtain spatiotemporal data, including the following sub-steps: Any area to be assessed for drought disaster is designated as the first drought area. The first drought area is divided into multiple regions based on the soil type of the first drought area, and each region is designated as a drought area. Any one of the drought areas is designated as the first drought area. Multiple sampling points were evenly set up in each arid area and denoted as sampling points, with any one of the sampling points designated as the first sampling point.

3. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 2, characterized in that, The process of dividing the area to be evaluated into regions, setting up multiple sampling points in each region, and collecting soil moisture and precipitation data at each sampling point to obtain spatiotemporal data also includes the following sub-steps: The first duration is set to t1. With t1 as the period, soil moisture and rainfall for each rainfall event are periodically collected at the first collection point. The collection time and rainfall time for each rainfall event are recorded as the spatiotemporal information of the first collection point. The data is collected synchronously at each collection point to obtain the spatiotemporal information of each collection point, which is recorded as the spatiotemporal data of the sampling point.

4. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 3, characterized in that, Based on the spatiotemporal data of the sampling points, the drought sensitivity index and temporal stability index of each sampling point are obtained. The process of obtaining the sampling point index data includes the following sub-steps: For the first sampling point, the portion collected in the most recent second time period is extracted from the corresponding spatiotemporal information and recorded as the most recent spatiotemporal data of the first sampling point; any rainfall event of the first sampling point within the most recent second time period is recorded as the first rainfall event; where the second time period is t2; The precipitation amount of the first rainfall event is denoted as AP. If AP is less than k1, the first rainfall event is recorded as invalid rainfall; otherwise, it is recorded as valid rainfall. Here, k1 is the set precipitation amount. If the first rainfall event is valid rainfall, then obtain the soil moisture collected k1 times before the start of the first rainfall event, and record it as the pre-rain moisture set of the first rainfall event; and obtain the soil moisture collected k2 times after the end of the first rainfall event, and record it as the post-rain moisture set of the first rainfall event, where k2 is the set number; Calculate the average value AE of the pre-rain humidity set and the average value BE of the post-rain humidity set, and calculate (BE-AE) / AP, which is denoted as the humidity response coefficient YR of the first rainfall event; repeat the humidity response coefficients of all effective rainfalls within the most recent first time period to obtain the rainfall response coefficient set.

5. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 4, characterized in that, Obtaining the drought sensitivity index and time stability index for each sampling point according to the spatio-temporal data of the sampling points, and obtaining the sampling point index data further includes the following sub-steps: Based on the recent spatio-temporal data, divide the recent first time period into multiple time cycles, denote any one time cycle as the first cycle. If rainfall occurs within the first cycle, it is denoted as an invalid cycle; otherwise, it is denoted as a valid cycle. If the first cycle is a valid cycle, count the soil moisture collected within the first cycle. Denote the soil moisture collected for the first time and the last time within the first cycle as QP and HP in sequence, and calculate QP - HP, which is denoted as the humidity response coefficient XR of the first cycle. Repeat to obtain the humidity response coefficients of all valid cycles to get the drought response coefficient set. And calculate the coefficient of variation of the soil moisture in the first cycle, which is denoted as the humidity coefficient of variation CV of the first cycle. Repeat to obtain the coefficients of variation of all valid cycles to get the coefficient of variation set.

6. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 5, characterized in that, Obtaining the drought sensitivity index and time stability index for each sampling point according to the spatio-temporal data of the sampling points, and obtaining the sampling point index data further includes the following sub-steps: Denote |BE - AE| as the weight base of YR, and denote |BE - AE|×YR as the weighted response coefficient corresponding to YR; denote |QP - HP| as the weight base of XR, and |QP - HP|×XR as the weighted response coefficient corresponding to XR. Remove the outliers from the rainfall response coefficient set and the drought response coefficient set respectively according to the 3σ principle to obtain the rainfall standard set and the drought standard set respectively. Repeatedly obtain all the corresponding weighted response coefficients and weight bases in the rainfall standard set and the drought standard set, and sum them up respectively to obtain the sum of response coefficients HR and the sum of weight bases HQ. Calculate HR / HQ, which is denoted as the drought sensitivity index of the first sampling point.

7. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 6, characterized in that, Obtaining the drought sensitivity index and time stability index for each sampling point according to the spatio-temporal data of the sampling points, and obtaining the sampling point index data further includes the following sub-steps: For the first cycle, with the first cycle as the end point, count the number of consecutive non-rainy time cycles, which is denoted as the non-rainy cycle number WG of the first cycle. If WG < k3, set the weight CQ of CV to 1; if k3 ≤ WG ≤ k4, set the weight CQ of CV to 1.2; if k4 < WG, set the weight CQ of CV to 1.5, where k3 and k4 are set thresholds. Calculate CQ×CV, which is denoted as the weighted coefficient of the humidity coefficient of variation CV. Remove the outliers from the coefficient of variation set according to the 3σ principle to obtain the coefficient of variation standard set; repeatedly obtain all the weighted coefficients of the humidity coefficients of variation in the coefficient of variation standard set and the corresponding weights, and sum them up respectively to obtain the sum of weighted coefficients VH and the corresponding sum of weights CH; calculate VH / CH, which is denoted as the time stability index of the first sampling point. Repeatedly obtain the drought sensitivity index and time stability index of all sampling points to obtain the sampling point index data.

8. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 7, characterized in that, Conduct sampling point analysis and evaluation according to the sampling point index data, and dynamically select the monitoring points for drought disasters from the sampling points, which includes the following sub-steps: The third time period is set to t3. The sampling point index data is periodically acquired based on t3. The index data of the currently acquired sampling point is recorded as the current index data. Based on the current index data, the sampling points with drought sensitivity index less than e1 or time stability index not located in [e2, e3] are recorded as unqualified points, and the other sampling points are recorded as qualified points. Here, e1, e2 and e3 are the set thresholds. Based on the drought sensitivity index and time stability index of all qualified points, the Min-Max standardization method was used to standardize each drought sensitivity index and time stability index to obtain qualified point index data. If the first sampling point is a qualified point, the drought sensitivity index and time stability index of the first sampling point after standardization are recorded as AX and BX respectively in order; calculate q1×AX+q2×BX, which is recorded as the comprehensive index score of the first sampling point, and repeat to obtain the comprehensive index scores of all qualified points, where q1 and q2 are the set weights.

9. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 8, characterized in that, Based on the sampling point index data, the sampling point analysis and evaluation, and the dynamic selection of monitoring points for drought disaster from the sampling points, include the following sub-steps: For the first drought region, the number of monitoring points in the first drought region is set to n1; the qualified points in the first drought region are arranged from largest to smallest according to the comprehensive index score, and recorded as the qualified point sequence; For any qualified point, the corresponding monitoring area is a circular area with the qualified point as the center and a radius of R1, where R1 is the set radius; The first qualified point in the qualified point sequence is recorded as a monitoring point. Starting from the second qualified point, if there is no monitoring point in the monitoring area of ​​the second qualified point, the second qualified point is recorded as a monitoring point; otherwise, it is recorded as a redundant point. The selection is carried out sequentially according to the qualified point sequence until the n1 monitoring point is selected as the monitoring point of the first arid region. All monitoring points in all areas of the first arid region are obtained repeatedly.

10. The method for dynamic assessment of drought disasters based on spatiotemporal big data analysis according to claim 9, characterized in that, Based on the selected drought monitoring points, soil relative humidity was collected at each monitoring point, and the drought situation in each area of ​​the region to be assessed was evaluated, including the following sub-steps: For the first arid region, soil relative humidity was collected at all monitoring points in the first arid region to obtain relative humidity distribution data for the first arid region; the range of soil relative humidity values ​​was divided into multiple intervals, and a corresponding drought level was set for each interval; Based on the relative humidity distribution data of the first arid region, a soil relative humidity distribution map of the first arid region is generated by spatial interpolation, and colors are divided according to the corresponding drought level to obtain the drought disaster distribution map of the first arid region, which is recorded as the drought disaster assessment result of the first arid region; Repeatedly obtain the drought disaster assessment results of all areas in the first arid region and combine them to form a drought disaster distribution map of the arid region to obtain the drought disaster assessment results of the first arid region.

Images