Composite drought monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to a composite drought monitoring method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed data set, wherein the to-be-processed data set comprises drought index data of each monitoring area within a target duration; based on the drought monitoring requirement information, constructing an association relationship of the drought index data on a preset time scale and / or a space scale; and based on a preset composite drought monitoring algorithm and the association relationship, determining a composite drought monitoring result corresponding to each monitoring area within the target duration. By adopting the method, the monitoring accuracy and comprehensiveness of the composite drought monitoring result can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and particularly to a composite drought monitoring method, apparatus, computer device, and storage medium. Background Art

[0002] Drought monitoring technology refers to the technology of real-time or semi-real-time monitoring of various factors such as meteorological conditions, soil humidity, vegetation growth conditions, and hydrological conditions in a vast geographical area. This technology has important application values in aspects such as agricultural management, water resource management, environmental conservation, and disaster prevention and mitigation.

[0003] In the current traditional technology, drought events are usually monitored in a single dimension, for example, in a single region or at a single moment. Taking agricultural drought as an example, agricultural drought index data in the monitored area is collected through various types of sensor devices, and then, based on the agricultural drought index data, it is determined whether an agricultural drought event occurs in the monitored area, that is, the monitoring result of the agricultural drought event is obtained.

[0004] However, in the traditional technology, when monitoring drought events in a single time or space dimension, the considered factors are relatively single, and the drought event scenarios have limitations, which cannot meet the current technical requirements of drought monitoring. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a composite drought monitoring method, apparatus, computer device, and storage medium that can expand the spatio-temporal dimension and event scenarios of drought monitoring and improve the monitoring accuracy and comprehensiveness of composite drought monitoring results.

[0006] A composite drought monitoring method, the method includes:

[0007] Obtain a dataset to be processed, where the dataset to be processed contains drought index data of each monitored area within a target time period;

[0008] Based on the drought monitoring requirement information, construct the correlation relationships of each of the drought index data on a preset time scale and / or space scale;

[0009] Based on a preset composite drought monitoring algorithm and the correlation relationships, determine the composite drought monitoring results corresponding to each of the monitored areas within the target time period.

[0010] In one embodiment, the obtaining the dataset to be processed includes:

[0011] Obtain the meteorological and hydrological data of each monitored area within the target time period;

[0012] Based on the meteorological and hydrological data and a preset time period, calculate the drought index data corresponding to each monitoring area within each time period to obtain a dataset to be processed.

[0013] In one embodiment, constructing the correlation relationships of the drought index data at a preset time scale and / or spatial scale based on the drought monitoring requirement information includes:

[0014] Based on the drought monitoring requirement information, determine the time condition correlation relationships of various types of drought index data within the monitoring area in consecutive time periods;

[0015] Determining the compound drought monitoring results corresponding to each monitoring area within the target duration based on the preset compound drought monitoring algorithm and the correlation relationships includes:

[0016] Based on the conditional compound drought monitoring algorithm, determine whether the drought index data with the time condition correlation relationships in consecutive time periods meet the preset drought judgment conditions;

[0017] When the preset drought judgment conditions are met, obtain the conditional compound drought monitoring results corresponding to the monitoring area.

[0018] In one embodiment, constructing the correlation relationships of the drought index data at a preset time scale and / or spatial scale based on the drought monitoring requirement information includes:

[0019] Based on the drought monitoring requirement information, determine the time synchronization correlation relationships among various types of drought index data within the monitoring area in the same time period;

[0020] Determining the compound drought monitoring results corresponding to each monitoring area within the target duration based on the preset compound drought monitoring algorithm and the correlation relationships includes:

[0021] Divide multiple drought index data with the time synchronization correlation relationships into the same drought data index group;

[0022] Based on the preset multi-variable compound drought monitoring algorithm and the drought data index group corresponding to each monitoring area, construct a multivariate distribution function;

[0023] Based on the multivariate distribution function, determine the multi-variable compound drought monitoring results corresponding to each monitoring area.

[0024] In one embodiment, constructing the correlation relationships of the drought index data at a preset time scale and / or spatial scale based on the drought monitoring requirement information includes:

[0025] Based on the drought monitoring requirement information, determine the time - continuous association relationship of drought index data of the same type within the same monitoring area over a continuous time period;

[0026] Based on the preset composite drought monitoring algorithm and the association relationship, determine the composite drought monitoring results corresponding to each of the monitoring areas within the target duration, including:

[0027] Based on the preset time - composite drought monitoring algorithm, perform a weighted summation calculation on the drought index data with a time - continuous association relationship within the continuous time period to obtain the time - composite drought monitoring results of the monitoring area within the continuous time period.

[0028] In one embodiment, the constructing the association relationship of each drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information includes:

[0029] Based on the drought index data within the same time period, screen target areas within each of the monitoring areas;

[0030] Based on the drought monitoring requirement information, construct the regional association relationship between the drought index data of the target areas;

[0031] Based on the preset composite drought monitoring algorithm and the association relationship, determine the composite drought monitoring results corresponding to each of the monitoring areas within the target duration, including:

[0032] Based on the preset space - composite drought monitoring algorithm and the regional association relationship between the drought index data of the target areas, determine the space - composite drought monitoring results between the target areas.

[0033] In one embodiment, the method further includes:

[0034] Based on the composite drought monitoring results corresponding to each of the monitoring areas, determine the drought control measures corresponding to the composite drought monitoring results;

[0035] Based on the preset visualization display strategy, display the composite drought monitoring results corresponding to each of the monitoring areas and the drought control measures corresponding to the composite drought monitoring results.

[0036] A composite drought monitoring device, the device includes:

[0037] An acquisition module, configured to acquire a dataset to be processed, where the dataset to be processed contains drought index data of each monitoring area within a target duration;

[0038] A construction module, configured to construct the association relationship of each drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information;

[0039] The first determination module is configured to determine the composite drought monitoring results corresponding to each of the monitoring regions within the target duration based on a preset composite drought monitoring algorithm and the association relationship.

[0040] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] Obtain a dataset to be processed, where the dataset to be processed contains drought index data of each monitoring region within the target duration;

[0042] Based on the drought monitoring requirement information, construct the association relationship of each drought index data on a preset time scale and / or space scale;

[0043] Based on a preset composite drought monitoring algorithm and the association relationship, determine the composite drought monitoring results corresponding to each of the monitoring regions within the target duration.

[0044] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain a dataset to be processed, where the dataset to be processed contains drought index data of each monitoring region within the target duration;

[0046] Based on the drought monitoring requirement information, construct the association relationship of each drought index data on a preset time scale and / or space scale;

[0047] Based on a preset composite drought monitoring algorithm and the association relationship, determine the composite drought monitoring results corresponding to each of the monitoring regions within the target duration.

[0048] For the above composite drought monitoring method, device, computer device and storage medium, obtain a dataset to be processed, where the dataset to be processed contains drought index data of each monitoring region within the target duration; based on the drought monitoring requirement information, construct the association relationship of each drought index data on a preset time scale and / or space scale; based on a preset composite drought monitoring algorithm and the association relationship, determine the composite drought monitoring results corresponding to each of the monitoring regions within the target duration. By using this method, through constructing the association relationship of each drought index data on a preset time scale and / or space scale, comprehensive analysis of each drought index data in the time dimension or space dimension is realized, and then combined with the composite drought monitoring algorithm, the composite drought monitoring results of each monitoring region are determined, expanding the monitoring scenarios of the composite drought monitoring results and improving the monitoring accuracy and comprehensiveness of the composite drought monitoring results. Description of the Drawings

[0049] Figure 1 It is a schematic flow chart of the composite drought monitoring method in an embodiment;

[0050] Figure 2 It is a schematic flow chart of the step of obtaining the dataset to be processed in an embodiment;

[0051] Figure 3 It is a schematic flow chart of the step of determining the composite drought monitoring result based on the conditional composite drought monitoring algorithm in an embodiment;

[0052] Figure 4 It is a schematic flow chart of the step of determining the composite drought monitoring result based on the multivariate composite drought monitoring algorithm in an embodiment;

[0053] Figure 5 It is a schematic flow chart of the step of determining the composite drought monitoring result based on the time composite drought monitoring algorithm in an embodiment;

[0054] Figure 6 It is a schematic flow chart of the step of determining the composite drought monitoring result based on the spatial composite drought monitoring algorithm in an embodiment;

[0055] Figure 7 It is a schematic flow chart of the step of visualizing the composite drought monitoring result and drought control measures in an embodiment;

[0056] Figure 8 It is a structural block diagram of the composite drought monitoring device in an embodiment;

[0057] Figure 9 It is an internal structure diagram of a computer device in an embodiment. Specific embodiments

[0058] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] In one embodiment, as Figure 1 shown, a composite drought monitoring method is provided. Taking the application of this method to a terminal as an example, it includes the following steps:

[0060] Step 102, obtain the dataset to be processed.

[0061] Among them, the dataset to be processed contains drought index data of each monitoring area within the target duration.

[0062] In practice, drought monitoring is the process of evaluating and tracking drought conditions and their impacts, which usually involves the collection and analysis of meteorological, agricultural, hydrological and socio-economic data, and is mainly divided into meteorological drought monitoring, agricultural drought monitoring, hydrological drought monitoring, etc. When monitoring various types of droughts, various types of sensors (for example, rainfall sensors, soil sensors, satellites, drones, etc.) are usually used to collect monitoring data of each monitoring area within the target time. Then, the terminal obtains the monitoring data collected by each sensor, processes each monitoring data, and obtains the drought index data of each monitoring area within the target time. In this way, the terminal obtains the data set to be processed for monitoring each monitoring area based on the drought index data of each monitoring area.

[0063] Optionally, the data set to be processed can be drought index data obtained from real-time monitoring data (i.e., meteorological and hydrological data) or semi-real-time monitoring data collected from each monitoring area, or drought index data obtained based on historical monitoring data of each monitoring area, so as to estimate and monitor the drought conditions in each monitoring area in the current period based on the drought index data of the historical period.

[0064] Optionally, the drought index data included in the data set to be processed may include, but are not limited to: Meteorological Drought Index (MDI), Agricultural Drought Index (ADI), Standardized Groundwater Drought Index (SGI) data, etc. Among them, the meteorological drought index MDI may be Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Evapotranspiration Deficit Index (EDI), etc. The agricultural drought index ADI may be Standardized Soil Moisture Index (SSI), Soil Moisture Percentile (SMP), etc. The present disclosure does not limit the types of drought index data.

[0065] Optionally, the types of drought index data calculated for each monitoring area can be determined based on the actual drought monitoring requirement information, and the drought index data for different monitoring areas can be different. For example, in some areas with good irrigation conditions, precipitation may not reflect the drought situation at this time. Therefore, another type of drought index data may be used, or multiple types of drought index data may be used. In some areas (such as arid areas) without irrigation conditions, a meteorological drought index based solely on precipitation can well monitor drought, so only one type of drought index data can be used. Therefore, the embodiments of the present disclosure do not limit the types and quantities of drought index data for each monitoring area.

[0066] Step 104: Based on the drought monitoring requirement information, construct the correlation relationship between each drought index data on a preset time scale and / or space scale.

[0067] In implementation, different drought monitoring tasks correspond to different drought monitoring requirements. Therefore, when performing composite drought monitoring on each monitoring area, the terminal constructs the correlation relationship of the drought index data for each monitoring area within the target duration according to the drought monitoring requirement information included in the received drought monitoring task. Specifically, the terminal establishes the correlation relationship between each drought index data on the time scale and / or space scale through the target duration covered by each drought index data and each monitoring area involved in the values of each drought monitoring data index, so as to comprehensively analyze each drought index data from the time and / or space perspectives through this correlation relationship, and further obtain the composite drought monitoring result comprehensively analyzed from the time and / or space perspectives.

[0068] Step 106: Based on the preset composite drought monitoring algorithm and the correlation relationship, determine the composite drought monitoring results corresponding to each monitoring area within the target duration.

[0069] In implementation, different composite drought monitoring algorithms are pre-integrated in the terminal. Different composite drought monitoring algorithms are used to implement the analysis and monitoring requirements in different drought monitoring requirement information, that is, for each determined type (time scale and / or space scale) of correlation relationship, there is a corresponding composite drought monitoring algorithm to analyze and process each drought index data, so as to obtain the composite drought monitoring results corresponding to each drought index data under this type of correlation relationship. Therefore, when one or more types of correlation relationships existing between each drought index data are determined based on the drought monitoring requirement information, the terminal processes the drought index data for each monitoring area within the target duration included in the dataset to be processed based on the pre-integrated composite drought monitoring algorithm, and obtains the composite drought monitoring result. This composite drought monitoring result is used to reflect the comprehensive drought situation of each monitoring area, and determine characteristics such as drought intensity and drought scope from a global perspective.

[0070] In the above composite drought monitoring method, a data set to be processed is obtained, and the data set to be processed contains drought index data of each monitoring area within a target time period; based on the drought monitoring requirement information, the correlation relationship of each drought index data on a preset time scale and / or spatial scale is constructed; based on the preset composite drought monitoring algorithm and the correlation relationship, the composite drought monitoring results corresponding to each monitoring area within the target time period are determined. By using this method, through constructing the correlation relationship of each drought index data on a preset time scale and / or spatial scale, the comprehensive analysis of each drought index data in the time dimension or the spatial dimension is realized, and then combined with the composite drought monitoring algorithm, the composite drought monitoring results of each monitoring area are determined, the monitoring scenario of the composite drought monitoring results is expanded, and the monitoring accuracy and comprehensiveness of the composite drought monitoring results are improved.

[0071] In one embodiment, as Figure 2 shown, the specific processing process of step 102 includes:

[0072] Step 201, obtain the meteorological and hydrological data of each monitoring area within the target time period.

[0073] In implementation, when conducting the composite drought monitoring of each monitoring area, the terminal obtains the meteorological and hydrological data of each monitoring area within the target time period. Among them, the meteorological and hydrological data may include the meteorological, agricultural, hydrological and socioeconomic data of each monitoring area, etc. Specifically, when the terminal obtains the meteorological and hydrological data of each monitoring area within the target time period, the target time period may be the current period (within 1 year), or it may be a historical period. For example, the previous T (T≥2) years of the current year, or a certain historical time period, for example, from 1960 to 2020. The target time period can be adjusted based on the drought monitoring requirement information, and the specific time range of the target time period is not limited in the embodiments of the present disclosure.

[0074] Step 202, based on the meteorological and hydrological data and the preset time period, calculate the drought index data corresponding to each monitoring area within each time period to obtain the data set to be processed.

[0075] In implementation, after the terminal obtains the meteorological and hydrological data of each monitoring area, it can calculate the drought index data corresponding to each monitoring area within each time period based on the meteorological and hydrological data and a preset time period. Among them, the terminal can divide the target duration into different time scales for the analysis of drought index data. For example, the target duration can be divided into different time periods according to years, months or days. For different drought monitoring requirements, different time scales (i.e., different time periods) are adopted to calculate the drought index data of each monitoring area. For example, taking the current year as the Tth year, for the meteorological water level data of the historical target duration (the previous 1 to T years), with months as the time period, a drought index threshold t0 is set. Through this drought index threshold t0, the drought index data included in the meteorological and hydrological data of month M (0 < M ≤ 12) in the 1 to T years is determined. Thus, when the drought index data of each monitoring area within each time period is determined, the terminal obtains a dataset to be processed for composite drought monitoring.

[0076] Optionally, different drought index thresholds can be set for each monitoring area based on factors such as the geographical attributes of each monitoring area, etc., for judging the drought index data of each monitoring area. The embodiments of the present disclosure do not limit this.

[0077] In this embodiment, by obtaining the meteorological and hydrological data of each monitoring area within the target duration, the climate and water resource conditions of different regions can be comprehensively understood. Furthermore, by comprehensively analyzing the meteorological and hydrological data and calculating the drought index, the degree and influence range of drought in each monitoring area can be more accurately evaluated.

[0078] Four composite drought monitoring algorithms are provided in the present disclosure, including: conditional composite drought monitoring algorithm, multivariate composite drought monitoring algorithm, temporal composite drought monitoring algorithm, and spatial composite drought monitoring algorithm. These four composite drought algorithms are based on the correlation relationships (time conditional correlation relationship, time synchronous correlation relationship, time continuous correlation relationship, and regional correlation relationship respectively) among various drought index data on the time scale and / or spatial scale, and perform composite drought assessment and monitoring on each monitoring area. According to the four composite drought monitoring algorithms, four composite drought indices can be calculated. For each composite drought index, a preset composite drought index threshold (generally a negative value) is used to determine the composite drought event. When the value of the composite drought index is less than or equal to the composite drought index threshold, it is considered that a composite drought event has occurred (for example, when it is less than the threshold of -0.5, it can be determined as a mild composite drought event, and when it is less than the threshold of -2, it can be determined as a severe composite drought event). Among them, the composite drought index threshold can be determined based on the attribute characteristics of different regions and different departments, as well as the influencing data, etc. The composite drought index thresholds corresponding to different regions and different departments (agriculture, water resources, hydropower, etc.) can be different. For example, in regions or departments with higher vulnerability (such as agricultural production areas with poor irrigation conditions, water use areas with less per capita water resources, and hydropower stations lacking multi-year regulation capacity), the composite drought index threshold can be selected as a relatively higher value (that is, a mild composite drought may cause serious impacts). In this way, when conducting composite drought monitoring, users can select one or more target composite drought monitoring algorithms that meet the drought monitoring requirements from the four composite drought monitoring algorithms based on the drought monitoring requirement information of different monitoring areas, different departments, etc. The following embodiments respectively introduce these four composite drought monitoring algorithms:

[0079] Method 1. In one embodiment, as Figure 3 shown, the specific processing procedure of step 104 includes:

[0080] Step 301, based on the drought monitoring requirement information, determine the time conditional correlation relationship of various types of drought index data in the monitoring area within a continuous time period.

[0081] In implementation, the drought monitoring requirements information includes the conditional information of various types of drought index data within a continuous time period. For example, the conditional information is the conditional information between the first type of drought index data and the second type of drought index data, that is, the reduction in precipitation last month (a time period) (meteorological drought index value) leads to agricultural drought this month (agricultural drought index value). Or, the reduction in snow cover in spring (a time period) (snow drought data index value) leads to the reduction in summer runoff (hydrological drought data). In this way, based on the conditional information between various types of drought index data included in the drought monitoring requirements information, the terminal determines the time - condition correlation relationship of various types of drought index data within a continuous time period in the same monitoring area. For example, meteorological drought generally occurs before agricultural drought. Therefore, in a specified monitoring area or grid (regional grid), taking the meteorological drought index (MDI) data in month M - 1 and the agricultural drought index (ADI) data in month M as an example, the terminal determines the time - condition correlation relationship between the meteorological drought index data in month M - 1 and the agricultural drought index data in month M.

[0082] Then the specific processing process of step 106 includes:

[0083] Step 302, based on the conditional composite drought monitoring algorithm, determine whether the drought index data with a time - condition correlation relationship within a continuous time period meets the preset drought judgment condition.

[0084] In implementation, the conditional composite drought monitoring algorithm includes preset drought judgment conditions. Therefore, after determining the time - condition correlation relationship existing between drought index data of each dimension for each monitoring area, the terminal constructs a composite drought index value for the drought index data with a time - condition correlation relationship within a continuous time period based on the conditional composite drought monitoring algorithm. Furthermore, the terminal determines whether the composite drought index meets the preset drought judgment condition. Specifically, the specific formula for constructing the conditional composite drought monitoring index based on the drought index data with a time - condition correlation relationship is as follows:

[0085] (1)

[0086] Among them, and are single - drought (MDI, ADI) index thresholds, represents that the meteorological drought index data (MDI) in month M - 1 is less than its index threshold (i.e., there is meteorological drought in month M - 1), and the agricultural drought index data (ADI) in month M is less than or equal to its index threshold (i.e., there is agricultural drought in month M). represents the composite drought index value in month M, where, and is a weighting coefficient. Moreover, the preset drought determination condition includes a preset composite drought index threshold (c1). Therefore, the terminal compares the composite drought index value with the composite drought index threshold. If is also less than or equal to the composite drought index threshold ( ), it indicates that the preset drought determination condition is satisfied. Here In addition to using a linear combination, other combination methods can also be used for index composition to obtain the composite drought index result. For example, non-linear combination methods (polynomial regression), etc. The embodiments of the present disclosure do not limit the combination method for index composition.

[0087] Step 303, when the preset drought determination condition is satisfied, obtain the conditional composite drought monitoring result corresponding to the monitoring area.

[0088] In implementation, for each monitoring area, if there is drought index data in the monitoring area that satisfies the preset drought determination condition, the terminal can obtain the conditional composite drought monitoring result corresponding to the monitoring area based on the preset drought determination condition (i.e., the conditional composite drought discrimination condition). It can be known that the process of conditional composite drought discrimination performed by the terminal in each monitoring area is similar, and the embodiments of the present disclosure will not elaborate one by one. Then, the terminal can display the conditional composite drought monitoring results of each monitoring area obtained through a preset visualization strategy, and at the same time, can give an analysis of the conditional composite drought situation.

[0089] In this embodiment, by determining the time condition correlation relationship between the drought index data of each dimension in the monitoring area within a continuous time period, it is possible to comprehensively and systematically analyze the multi-faceted impacts of drought, predict future drought trends, and improve the accuracy of the composite drought monitoring results.

[0090] Method 2, in an exemplary embodiment, as Figure 4 shown, the specific processing process of step 104 includes:

[0091] Step 401, based on the drought monitoring requirement information, determine the time synchronization correlation relationship between the drought index data of each type in the monitoring area within the same time period.

[0092] In implementation, the correlation among various drought index data may jointly result in a greater impact. For example, a decrease in soil moisture and high temperature may further lead to the simultaneous occurrence of agricultural drought and an increase in the vapor pressure deficit. Furthermore, drought monitoring requires that the information can include the collaborative information of various types of drought data indicators within the same time period. Furthermore, based on this collaborative information, the terminal determines the time synchronization correlation relationship among the various types of drought index data included in the same monitoring area within the same time period. For example, based on the collaborative information, the terminal determines that some drought index data such as soil moisture content, soil temperature, air temperature, vapor pressure deficit, evapotranspiration deficit, etc. have a time synchronization correlation relationship.

[0093] Then the specific processing process of step 106 includes:

[0094] Step 402, divide multiple drought index data with a time synchronization correlation relationship into the same drought data index group.

[0095] In implementation, the terminal divides multiple drought index data with time synchronization correlation information within the same monitoring area into the same drought data index group. Furthermore, the terminal can monitor the comprehensive impact of different drought types on a certain department (such as agriculture, meteorology, etc.) by monitoring the different drought index data included in this drought data index group. For example, the multiple drought index data included in a certain drought data index group can be: meteorological drought index (MDI) data, evapotranspiration deficit drought index (EDI, representing the water loss situation, and can also be represented by the vapor pressure deficit, etc.) data. Therefore, the embodiments of the present disclosure do not limit the types and quantities of the multiple drought data indicators included in the drought data index group.

[0096] Step 403, construct a multivariate distribution function based on a preset multivariate composite drought monitoring algorithm and the drought data index group corresponding to each monitoring area.

[0097] In implementation, for the multivariate collaborative monitoring requirements within the same monitoring area included in the drought monitoring requirement information, the terminal performs data processing on the drought data index group corresponding to each pre-constructed monitoring area based on a preset multivariate composite drought monitoring algorithm, and constructs a multivariate distribution function to comprehensively analyze the joint impact of multiple variables (i.e., multiple drought index data) on the drought situation. Taking the multiple drought index data included in the drought data index group as the meteorological drought index (MDI) and the evapotranspiration deficit drought index (EDI) as an example for illustration, the specific formula of the multivariate composite drought index based on the multivariate distribution function is as follows:

[0098] CDI m (2)

[0099] Where is a function, generally a multivariate distribution function, and N is the standard normal distribution.

[0100] Step 404: Based on the multivariate distribution function, determine the multivariate composite drought monitoring results corresponding to each monitoring area.

[0101] In implementation, for each monitoring area among the monitoring areas, the terminal, based on the constructed multivariate distribution function, solves and calculates the composite drought index value CDI of the drought data index group corresponding to the multivariate distribution function. m . Meanwhile, a composite drought index threshold c2 is preset in the terminal. In this way, the terminal compares the composite drought index threshold c2 with the composite drought index value CDI m to determine the drought result of the composite drought index corresponding to the monitoring area, that is, to obtain the multivariate composite drought monitoring result. Specifically, if CDI m ≤c2, the terminal determines that there is a multivariate composite drought in the monitoring area; if CDI m >c2, the terminal determines that there is no multivariate composite drought in the monitoring area. In this way, until the terminal completes the comparison and monitoring of the composite drought indexes of each monitoring area, the terminal obtains the multivariate composite drought monitoring results corresponding to each monitoring area.

[0102] In this embodiment, by determining the time-synchronous correlation relationship among the drought index data of different types within the monitoring area in the same time period, the mutual influence and correlation among different types of droughts (such as atmospheric drought, meteorological drought, agricultural drought, ecological drought, groundwater drought, and hydrological drought) can be comprehensively evaluated, so as to more comprehensively monitor the overall drought situation through the obtained multivariate composite drought monitoring results.

[0103] Method 3: In an exemplary embodiment, as Figure 5 shown, the specific processing procedure of step 104 includes:

[0104] Step 501: Based on the drought monitoring requirement information, determine the time-continuous correlation relationship of the drought index data of the same type within the same monitoring area in consecutive time periods.

[0105] In implementation, since consecutive drought events may lead to greater impacts. For example, in the case of consecutive droughts over multiple years, the first-year drought causes a decrease in groundwater. If a drought occurs again in the second year, there is no precipitation recharge, which further reduces the groundwater, and may thus lead to more severe impacts. Therefore, it is also very necessary to monitor compound droughts with continuous time periods. Furthermore, monitoring requirements regarding the continuity of time periods are set in the drought monitoring requirements information. In this way, based on this drought monitoring requirements information, the terminal determines the time continuous correlation relationship of drought index data of the same type within the same monitoring area over continuous time periods. For example, there is a drought situation in month M of year T-1, and a drought still occurs in month M of year T. Expressed by the formula as , is the preset single drought index threshold, then the drought index value in month M of year T-1 and the drought index value in month M of year T have a time continuous correlation relationship. Furthermore, through the drought index value in month M of year T-1 and the drought index value in month M of year T with this time continuous correlation relationship, the drought situations in year T-1 and year T of month M in this area can be comprehensively monitored and analyzed.

[0106] Then the specific processing process of step 106 includes:

[0107] Step 502, based on the preset time compound drought monitoring algorithm, perform a weighted summation calculation on the drought index data with a time continuous correlation relationship within continuous time periods to obtain the time compound drought monitoring result for the continuous time periods within the monitoring area.

[0108] In implementation, for the time continuous correlation relationship of the drought data indicators of the same type within the same monitoring area determined, the terminal, based on the preset time compound drought monitoring algorithm, performs a weighted summation calculation on the drought index data with a time continuous correlation relationship within continuous time periods to obtain the time compound drought index data, which is used to comprehensively evaluate the time compound drought situation in the monitoring area over continuous time periods. Specifically, taking a certain drought index (DI) in the scenario of consecutive droughts over multiple years as an example, the specific formula for the constructed time compound drought index is as follows:

[0109] (3)

[0110] Among them, θ and γ are weighting coefficients, is the time compound drought data index in month M of year T, and are the single drought indices in month M of year T-1 and year T respectively.

[0111] In this way, based on the obtained time compound drought index data , and the preset compound drought index threshold Compare to determine the time - composite drought monitoring results for the monitored area within a continuous time period. That is, if when, the terminal determines that there is a time - composite drought in the monitored area; if > when, the terminal determines that there is no time - composite drought in the monitored area. In this way, until the terminal completes the comparison and monitoring of the composite drought indicators for each monitored area, the terminal obtains the time - composite drought monitoring results of the corresponding drought indicator data for each monitored area. , Other combination methods can also be used for index composition to obtain the composite drought index results. For example, non - linear combination methods (polynomial regression), etc. The embodiments of the present disclosure do not limit the combination methods for index composition.

[0112] Then, the terminal can, through a preset visualization strategy, mark the monitored areas with drought (single drought index value ) in month M of year T and the monitored areas with drought (single drought index data ) in month M of year T - 1, and display the time - composite drought monitoring results of the marked monitored areas, and at the same time, a time - composite drought situation analysis can be given to achieve the purpose of monitoring time - composite drought.

[0113] In this embodiment, by determining the time - continuous correlation relationship of the same - type drought index data within the same monitored area in a continuous time period, the time trend and dynamic characteristics of drought changes can be captured, the time resolution of drought monitoring can be improved. By using the preset time - composite drought monitoring algorithm to perform weighted summation calculation on the drought index data with time - continuous correlation relationship within a continuous time period, the cumulative effect and historical impact of drought can be more accurately reflected, and the accuracy of the composite drought monitoring results can be improved.

[0114] Method Four, in an exemplary embodiment, as Figure 6 shown, the specific processing procedure of step 104 includes:

[0115] Step 601, based on the drought index data within the same time period, screen the target areas in each monitored area.

[0116] In implementation, since drought may also occur simultaneously in multiple regions (such as major crop production areas, water source areas and water receiving areas of water diversion projects, and multiple hydropower generation areas), furthermore, simultaneous drought in multiple regions may lead to greater impacts. Therefore, it is also very necessary for spatial compound drought monitoring. Therefore, the terminal screens target regions with monitoring correlation relationships within each monitoring region based on drought index data within the same time period. For example, for multiple different monitoring regions (taking five regions as an example, represented by A1, A2, A3, A4, and A5 respectively), among these five monitoring regions, if three regions (i.e., A1, A2, and A3) are all in drought during the same time period M, determine these three regions as target regions, and construct the corresponding drought index data for these three monitoring regions (represented by D1, D2, and D3 respectively), so as to simultaneously monitor the drought situation in the three regions.

[0117] Step 602: Based on the drought monitoring requirement information, construct the regional correlation relationship between the drought index data of the target regions.

[0118] In implementation, for the drought index data of each determined target region, the terminal constructs the regional correlation relationship of each target region based on the monitoring requirements for spatial compound drought events within the same time period in the drought monitoring requirement information.

[0119] Optionally, the determined target regions may originally have a geographical attribute correlation relationship, or a resource transportation correlation relationship. Or, except for the common feature of simultaneous occurrence of drought events, there is no other any correlation relationship between the target regions, and they are completely independent regions. Therefore, the present disclosure embodiment does not limit the regional correlation relationship between the target regions constructed during the spatial compound drought monitoring process.

[0120] Then the specific processing process of step 106 includes:

[0121] Step 603: Based on the preset spatial compound drought monitoring algorithm and the regional correlation relationship between the drought index data of the target regions, determine the spatial compound drought monitoring result between the target regions.

[0122] In implementation, for the determined target regions with regional correlation relationships, the terminal performs data processing on each drought index data included in the target regions based on the preset spatial compound drought monitoring algorithm. Among them, the spatial compound drought monitoring algorithm can be a linear function or a non-linear function. The linear function can be a linear regression algorithm, an average value algorithm. The non-linear function can be an algorithm such as a multivariate distribution function, etc. The present disclosure embodiment does not limit the specific algorithm of the spatial compound drought monitoring algorithm. Specifically, taking the drought index (DI) of three monitoring regions as an example, the specific formula of the constructed spatial compound drought index is as follows:

[0123] (4)

[0124] Among them, CDIs is the spatial compound drought index, and the function G is a function for combining different drought index data, which can be a linear function (average value algorithm, linear regression algorithm) or a non-linear function (multivariate distribution function, etc.). In this way, the terminal processes the drought index data included in the target area to obtain the compound drought index value corresponding to the spatial compound drought event. Furthermore, based on this compound drought index value, the spatial compound drought monitoring result between the target areas is determined. Specifically, taking the average value algorithm in the linear function algorithm of this spatial compound drought monitoring algorithm as an example, the weighted average of the drought index values of the selected target areas is calculated, and the weighted average of the drought index values of the three target areas can be used as the compound drought index value of the spatial compound drought event to characterize the drought intensity of the spatial compound drought. The smaller this compound drought index value (less than the preset compound drought index threshold) indicates that droughts occur simultaneously in the three areas and the drought situation is relatively serious. Thus, the spatial compound drought monitoring result between the three target areas is obtained.

[0125] In this embodiment, by screening target areas in each monitoring area and constructing the regional association relationship of the drought index data between these areas, the drought distribution and comprehensive impact in space can be comprehensively considered, so as to more comprehensively understand the spatial characteristics of drought. Using the preset spatial compound drought monitoring algorithm and combining the association relationship between regions, the impact range and intensity of drought can be more accurately evaluated, and the accuracy and reliability of drought monitoring can be improved.

[0126] In an exemplary embodiment, as Figure 7 shown, the method further includes:

[0127] Step 701, based on the compound drought monitoring results corresponding to each monitoring area, determine the drought control measures corresponding to the compound drought monitoring results.

[0128] In implementation, the corresponding relationship between various compound drought monitoring results and drought control measures is pre-stored in the terminal. In this way, after the terminal monitors the compound drought monitoring results of each monitoring area, the drought control measures corresponding to the current compound monitoring results of each monitoring area are determined in the pre-stored corresponding relationships. This drought control measure can be used as the guiding opinion for subsequent drought prevention and control to guide the development of drought prevention and control work.

[0129] Step 702, based on the preset visualization display strategy, display the compound drought monitoring results corresponding to each monitoring area and the drought control measures corresponding to the compound drought monitoring results.

[0130] In implementation, the terminal also includes a visualization display strategy, which includes but is not limited to strategies for displaying content in forms such as combining text with pictures, charts, etc. Moreover, the terminal opens a visualization configuration interface, so that users can customize visualization metrics and their display forms according to the needs of drought task monitoring. Therefore, the embodiments of the present disclosure do not limit the visualization display strategy. On this basis, for the visualization form configured by the user, the determined composite drought monitoring results and the drought control measures corresponding to the composite drought monitoring results are dynamically displayed on the display page of the terminal.

[0131] In this embodiment, based on the pre-stored correspondence between the composite drought monitoring results and the drought control measures, the drought control measures corresponding to the current composite drought monitoring results are determined, and the composite drought monitoring results and the corresponding control measures are dynamically displayed through a preset visualization display strategy, realizing real-time monitoring of the composite drought monitoring results in each monitoring area and enhancing the intuitiveness of the composite drought monitoring content.

[0132] It should be understood that although Figures 1 to 7 the steps in the flowchart of Figures 1 to 7 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover,

[0133] In one embodiment, as Figure 8 shown, a composite drought monitoring device 800 is provided, including: an acquisition module 801, a construction module 802, and a first determination module 803, where:

[0134] The acquisition module 801 is configured to acquire a dataset to be processed, and the dataset to be processed contains drought index data of each monitoring area within a target time period;

[0135] The construction module 802 is configured to construct the correlation relationship of each drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information;

[0136] The first determination module 803 is configured to determine the composite drought monitoring results corresponding to each monitoring area within the target time period based on a preset composite drought monitoring algorithm and the correlation relationship.

[0137] In one embodiment, the obtaining module 801 is specifically configured to obtain meteorological and hydrological data of each monitoring area within a target duration;

[0138] Based on the meteorological and hydrological data and a preset time period, calculate the drought index data corresponding to each monitoring area within each time period to obtain a dataset to be processed.

[0139] In one embodiment, the constructing module 802 is specifically configured to determine the time condition correlation relationship of various types of drought index data within the monitoring area in continuous time periods based on the drought monitoring requirement information;

[0140] The first determining module 803 is specifically configured to determine whether the drought index data with a time condition correlation relationship in continuous time periods meets the preset drought judgment condition based on the conditional composite drought monitoring algorithm;

[0141] When the preset drought judgment condition is met, obtain the conditional composite drought monitoring result corresponding to the monitoring area.

[0142] In one embodiment, the constructing module 802 is specifically configured to determine the time synchronization correlation relationship between various types of drought index data within the monitoring area in the same time period based on the drought monitoring requirement information;

[0143] The first determining module 803 is specifically configured to divide multiple drought index data with a time synchronization correlation relationship into the same drought data index group;

[0144] Based on the preset multi-variable composite drought monitoring algorithm and the drought data index group corresponding to each monitoring area, construct a multivariate distribution function;

[0145] Based on the multivariate distribution function, determine the multi-variable composite drought monitoring result corresponding to each monitoring area.

[0146] In one embodiment, the constructing module 802 is specifically configured to determine the time continuity correlation relationship of the drought index data of the same type within the same monitoring area in continuous time periods based on the drought monitoring requirement information;

[0147] The first determining module 803 is specifically configured to perform a weighted sum calculation on the drought index data with a time continuity correlation relationship in continuous time periods based on the preset time composite drought monitoring algorithm to obtain the time composite drought monitoring result of the continuous time period within the monitoring area.

[0148] In one embodiment, the constructing module 802 is specifically configured to screen target areas within each monitoring area based on the drought index data in the same time period; based on the drought monitoring requirement information, construct the regional correlation relationship between the drought index data of the target areas;

[0149] The first determination module 803 is specifically configured to determine the spatial composite drought monitoring result between target regions based on the regional association relationship between the preset spatial composite drought monitoring algorithm and the drought index data of the target region.

[0150] In one embodiment, the composite drought monitoring device 800 further includes:

[0151] The second determination module is configured to determine the drought control measures corresponding to the composite drought monitoring result based on the composite drought monitoring results corresponding to each monitoring region;

[0152] The display module is configured to display the composite drought monitoring results corresponding to each monitoring region and the drought control measures corresponding to the composite drought monitoring results based on the preset visualization display strategy.

[0153] For the specific limitations of the composite drought monitoring device, reference can be made to the limitations of the composite drought monitoring method in the above text, which will not be elaborated here. Each module in the above composite drought monitoring device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0154] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a composite drought monitoring method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0155] Those skilled in the art can understand, Figure 9The structure shown is only a block diagram of some of the structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0156] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0157] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0158] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0160] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0161] The embodiments described above merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A composite drought monitoring method, characterized in that, The method includes: Obtaining a dataset to be processed, where the dataset to be processed contains drought index data of each monitoring area within a target duration; Based on drought monitoring requirement information, constructing the correlation relationship of each of the drought index data on a preset time scale and / or space scale; Based on a preset composite drought monitoring algorithm and the correlation relationship, determining the composite drought monitoring results corresponding to each of the monitoring areas within the target duration.

2. The method according to claim 1, characterized in that, The obtaining of the dataset to be processed includes: Obtaining the meteorological and hydrological data of each monitoring area within the target duration; Based on the meteorological and hydrological data and a preset time period, calculating the drought index values corresponding to each of the monitoring areas within each of the time periods to obtain the dataset to be processed.

3. The method according to claim 1, wherein The constructing of the correlation relationship of each of the drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information includes: Based on the drought monitoring requirement information, determining the time condition correlation relationship of each type of drought index data within the monitoring area in consecutive time periods; The determining of the composite drought monitoring results corresponding to each of the monitoring areas within the target duration based on the preset composite drought monitoring algorithm and the correlation relationship includes: Based on a conditional composite drought monitoring algorithm, determining whether the drought index data with the time condition correlation relationship in consecutive time periods meets the preset drought judgment condition; When the preset drought judgment condition is met, obtaining the conditional composite drought monitoring result corresponding to the monitoring area.

4. The method according to claim 1, wherein The constructing of the correlation relationship of each of the drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information includes: Based on the drought monitoring requirement information, determining the time synchronization correlation relationship between each type of drought index data within the same monitoring area in the same time period; The determining of the composite drought monitoring results corresponding to each of the monitoring areas within the target duration based on the preset composite drought monitoring algorithm and the correlation relationship includes: Dividing multiple drought index data with the time synchronization correlation relationship into the same drought data index group; Based on a preset multivariate composite drought monitoring algorithm and the drought data index group corresponding to each of the monitoring areas, constructing a multivariate distribution function; Based on the multivariate distribution function, determining the multivariate composite drought monitoring results corresponding to each of the monitoring areas.

5. The method according to claim 1, characterized in that The constructing of the correlation relationship of each of the drought index data on a preset time scale and / or space scale based on the drought monitoring requirement information includes: Based on the drought monitoring requirement information, determining the time continuity correlation relationship of the drought index data of the same type within the same monitoring area in consecutive time periods; The determining of the composite drought monitoring results corresponding to each of the monitoring areas within the target duration based on the preset composite drought monitoring algorithm and the correlation relationship includes: Based on a preset time composite drought monitoring algorithm, performing a weighted summation calculation on the drought index data with the time continuity correlation relationship in the consecutive time periods to obtain the time composite drought monitoring result of the consecutive time periods within the monitoring area.

6. The method according to claim 1, characterized in that, Constructing the correlation relationships of the drought index data at a preset time scale and / or space scale based on the drought monitoring requirement information, including: Based on the drought index data within the same time period, screening target areas in each of the monitoring areas; Constructing the regional correlation relationships among the drought index data of the target areas based on the drought monitoring requirement information; Determining the composite drought monitoring results corresponding to each of the monitoring areas within the target duration based on the preset composite drought monitoring algorithm and the correlation relationships, including: Determining the spatial composite drought monitoring results among the target areas based on the preset spatial composite drought monitoring algorithm and the regional correlation relationships among the drought index data of the target areas.

7. The method according to claim 1, wherein The method further includes: Determining the drought control measures corresponding to the composite drought monitoring results based on the composite drought monitoring results corresponding to each of the monitoring areas; Displaying the composite drought monitoring results corresponding to each of the monitoring areas and the drought control measures corresponding to the composite drought monitoring results based on the preset visualization display strategy.

8. A composite drought monitoring device, characterized in that, The device includes: An acquisition module, configured to acquire a dataset to be processed, where the dataset to be processed contains the drought index data of each monitoring area within the target duration; A construction module, configured to construct the correlation relationships of the drought index data at a preset time scale and / or space scale based on the drought monitoring requirement information; A first determination module, configured to determine the composite drought monitoring results corresponding to each of the monitoring areas within the target duration based on the preset composite drought monitoring algorithm and the correlation relationships.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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