Corn high-temperature drought disaster risk assessment system and method

Abstract

The invention discloses a corn high-temperature drought disaster risk assessment system and method, and relates to the technical field of high-temperature disaster assessment, and the method comprises the steps: firstly obtaining past corn growth data of a target region, carrying out the processing and analysis of the past corn growth data, obtaining corn crop data, meteorological data, soil data, and a mapping relation between the data, and carrying out the calculation of the data; then determining the high-temperature drought sensitivity of corn growth to obtain a corn high-temperature drought sensitivity model, then obtaining real-time data of corn crops, real-time data of meteorology, real-time data of soil and a mapping relation thereof in a target region, and inputting the data into the corn high-temperature drought sensitivity model for evaluation. And finally, performing risk assessment on the corn high-temperature drought disaster according to an evaluation result. According to the invention, the risk assessment of the high-temperature drought disaster of the corn is realized, and the accuracy of the risk assessment of the high-temperature drought disaster of the corn is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-temperature disaster risk assessment, and particularly relates to a corn high-temperature drought disaster risk assessment system and method. BACKGROUND

[0002] As a major agricultural disaster under the background of global climate change, high-temperature drought poses a serious threat to corn production. Soil drought inhibits root penetration and lateral root development, resulting in reduced water absorption capacity. At the same time, high temperature and water deficit directly slow down cell division and elongation, causing plants to be short and internodes to be shortened, and leaf curling and drooping to reduce transpiration, and leaf area to be reduced or even withered and fallen. In some areas as the main production area of high-quality corn, in recent years, due to climate warming and reduced precipitation, the groundwater level has decreased, and high-temperature drought has seriously affected the stability of corn yield due to long duration and large scope.

[0003] High-temperature drought disaster risk assessment can directly serve regional agricultural planning, crop layout optimization and agricultural technology popularization. However, the traditional risk assessment method only relies on a single meteorological index, and lacks accuracy. Therefore, there is an urgent need for a corn high-temperature drought disaster risk assessment system and method. SUMMARY

[0004] The purpose of the present application is to provide a corn high-temperature drought disaster risk assessment system and method, which monitors the occurrence of corn high-temperature drought disasters by constructing a corn high-temperature drought sensitivity model, and realizes risk assessment of corn high-temperature drought disasters.

[0005] The present application provides a corn high-temperature drought disaster risk assessment method, comprising the following steps:

[0006] S1, obtaining past corn growth data of a target region, and processing and analyzing the past corn growth data to obtain corn crop data, meteorological data, soil data, and a mapping relationship of the corn crop data, the meteorological data and the soil data;

[0007] S2, determining corn growth high-temperature drought sensitivity according to the corn crop data, the meteorological data, the soil data, and the mapping relationship of the corn crop data, the meteorological data and the soil data, to obtain a corn high-temperature drought sensitivity model;

[0008] S3, obtaining real-time corn crop data, real-time meteorological data, real-time soil data, and a mapping relationship of the real-time corn crop data, the real-time meteorological data and the real-time soil data of the target region;

[0009] S4, inputting the real-time corn crop data, the real-time meteorological data, the real-time soil data, and the mapping relationship of the real-time corn crop data, the real-time meteorological data and the real-time soil data into the corn high-temperature drought sensitivity model for evaluation to obtain an evaluation result.

[0010] S5, performing risk assessment on the corn high-temperature drought disaster according to the evaluation result.

[0011] Preferably, the corn growth data of the target region in the past is acquired in S1, and the corn growth data in the past is processed and analyzed to obtain the specific content of the corn crop data, the meteorological data, the soil data, and the mapping relationship of the corn crop data, the meteorological data, and the soil data, which includes:

[0012] The corn growth data of the target region in the past is acquired to construct a corn growth database;

[0013] The corn crop data, the meteorological data, and the soil data in the corn growth database are extracted;

[0014] The corn crop data, the meteorological data, and the soil data are respectively subjected to feature extraction to obtain the corn growth period and the corn growth days, the daily maximum temperature, and the soil water content;

[0015] The corn growth days, the daily maximum temperature, and the soil water content are subjected to numerical extraction to obtain the corn growth numerical value, the daily maximum temperature value, and the soil water content value;

[0016] Based on the corn growth period, the time reference line is established in sequence for the corn growth numerical value, the daily maximum temperature value, and the soil water content value, and the corn-meteorological-soil data dynamic curve is generated with the time reference line as the horizontal coordinate to obtain the mapping relationship of the corn growth numerical value, the daily maximum temperature value, and the soil water content value, i.e., the mapping relationship of the corn crop data, the meteorological data, and the soil data.

[0017] Preferably, the corn growth high-temperature drought sensitivity is determined according to the corn crop data, the meteorological data, the soil data, and the mapping relationship of the corn crop data, the meteorological data, and the soil data in S2 to obtain the specific content of the corn high-temperature drought sensitivity model, which includes:

[0018] Based on the corn growth days and the daily maximum temperature, the corn growth high-temperature sensitivity is obtained according to the corn growth critical temperature function and the high-temperature sensitivity index segmented response function;

[0019] Based on the corn growth days and the soil water content, the corn growth drought sensitivity is obtained according to the soil effective water content correction function and the drought sensitivity index segmented response function;

[0020] Based on the mapping relationship of the corn crop data, the meteorological data, and the soil data, the corn growth high-temperature drought sensitivity matrix is constructed according to the corn growth high-temperature sensitivity and the corn growth drought sensitivity to obtain the corn high-temperature drought sensitivity model.

[0021] Preferably, in S4, the corn crop real-time data, the meteorological real-time data, the soil real-time data, and the mapping relationship among the corn crop real-time data, the meteorological real-time data, and the soil real-time data are input into the corn high-temperature drought sensitivity model for evaluation, and the specific content of the evaluation result includes:

[0022] The corn crop real-time data, the meteorological real-time data, the soil real-time data, and the mapping relationship among the corn crop real-time data, the meteorological real-time data, and the soil real-time data are input into the corn high-temperature drought sensitivity model to obtain a corn growth high-temperature drought sensitivity real-time matrix;

[0023] The corn growth high-temperature drought sensitivity real-time matrix is subjected to sensitivity comprehensive evaluation;

[0024] A sensitivity comprehensive evaluation standard is defined;

[0025] A current corn growth high-temperature sensitivity value is obtained, a high-temperature sensitivity emphasis conversion coefficient is configured to the current corn growth high-temperature sensitivity value according to the high-temperature sensitivity emphasis requirement in the sensitivity comprehensive evaluation, and a high-temperature sensitivity evaluation result is obtained;

[0026] A current corn growth drought sensitivity value is obtained, a drought sensitivity emphasis conversion coefficient is configured to the current corn growth drought sensitivity value according to the drought sensitivity emphasis requirement in the sensitivity comprehensive evaluation, and a drought sensitivity evaluation result is obtained;

[0027] The high-temperature sensitivity evaluation result and the drought sensitivity evaluation result are respectively configured with a weight coefficient to obtain a sensitivity comprehensive evaluation value, i.e., an evaluation result.

[0028] Preferably, the expression of the sensitivity comprehensive evaluation value is:

[0029] H = a (a x T) + b (b x D);

[0030] wherein H is the sensitivity comprehensive evaluation value, a is the high-temperature sensitivity weight coefficient, a is the high-temperature sensitivity emphasis conversion coefficient, T is the current corn growth high-temperature sensitivity value, b is the drought sensitivity value weight coefficient, b is the drought sensitivity value emphasis conversion coefficient, and D is the current corn growth drought sensitivity value.

[0031] Preferably, in S5, the specific content of the risk assessment of the corn high-temperature drought disaster according to the evaluation result includes:

[0032] If the sensitivity comprehensive evaluation value is equal to the sensitivity comprehensive evaluation standard, the risk level of the corn high-temperature drought disaster is a medium risk level;

[0033] If the sensitivity comprehensive evaluation value is higher than the sensitivity comprehensive evaluation standard, the risk level of the corn high-temperature drought disaster is a high risk level;

[0034] If the sensitivity comprehensive evaluation value is higher than the sensitivity comprehensive evaluation standard, the risk grade of the corn high-temperature drought disaster is a low risk grade.

[0035] Preferably, the specific content of the risk assessment of the corn high-temperature drought disaster according to the evaluation result in S5 further comprises:

[0036] The time reference line is established based on the sensitivity comprehensive evaluation standard and the sensitivity comprehensive evaluation value in sequence, and a standard value-evaluation value dynamic curve is generated with the time reference line as the horizontal coordinate.

[0037] According to the standard value-evaluation value dynamic curve, the risk grade of the corn high-temperature drought disaster is marked in real time.

[0038] The present application provides a corn high-temperature drought disaster risk assessment system, comprising:

[0039] The data acquisition module is used for acquiring the past corn growth data, real-time corn crop data, real-time meteorological data, real-time soil data, and the mapping relationship of the real-time corn crop data, real-time meteorological data and real-time soil data in the target region.

[0040] The data analysis module is used for processing and analyzing the past corn growth data to obtain the corn crop data, meteorological data, soil data, and the mapping relationship of the corn crop data, meteorological data and soil data.

[0041] The model construction module is used for determining the corn growth high-temperature drought sensitivity according to the mapping relationship of the corn crop data, meteorological data, soil data, and the corn crop data, meteorological data and soil data, and obtaining a corn high-temperature drought sensitivity model.

[0042] The evaluation analysis module is used for inputting the mapping relationship of the real-time corn crop data, real-time meteorological data, real-time soil data, and the real-time corn crop data, real-time meteorological data and real-time soil data into the corn high-temperature drought sensitivity model for evaluation, and obtaining an evaluation result.

[0043] The risk assessment module is used for risk assessment of the corn high-temperature drought disaster according to the evaluation result.

[0044] The present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program in the memory to realize the content of the above-mentioned corn high-temperature drought disaster risk assessment method.

[0045] The present application provides a storage medium, wherein the storage medium stores computer executable instructions, and the computer executable instructions are loaded and executed by a processor to realize the content of the above-mentioned corn high-temperature drought disaster risk assessment method.

[0046] In summary, the corn high-temperature drought disaster risk assessment system and method provided by this invention have the following advantages compared with traditional technologies: By collaboratively analyzing corn crop data, meteorological data, and soil data in the corn crop-meteorology-soil system, a corn high-temperature drought sensitivity model is constructed, and then an assessment is conducted to determine the occurrence of corn high-temperature drought disasters. This achieves risk assessment of corn high-temperature drought disasters, effectively improves the accuracy of corn high-temperature drought disaster risk assessment, and provides timely and accurate response strategies to achieve the purpose of disaster prevention and mitigation.

[0047] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the steps of a method for assessing the risk of high-temperature drought disasters in maize according to the present invention.

[0049] Figure 2 This is a block diagram of a corn high-temperature drought disaster risk assessment system according to the present invention. Detailed Implementation

[0050] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0051] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0052] Techniques, systems, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the instruction manual.

[0053] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0054] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0055] like Figure 1 As shown, the present invention provides a method for assessing the risk of high-temperature drought disasters in maize, comprising:

[0056] Step S1, obtain the past corn growth data of the target region, and process and analyze the past corn growth data to obtain corn crop data, weather data, soil data, and the mapping relationship of the corn crop data, weather data, and soil data.

[0057] Further, step S1 can be replaced by steps S101-S105:

[0058] Step S101, obtain the past corn growth data of the target region, and construct a corn growth database.

[0059] Step S102, extract the corn crop data, weather data, and soil data in the corn growth database.

[0060] Step S103, respectively extract the features of the corn crop data, weather data, and soil data to obtain the corn growth period, corn growth days, daily maximum temperature, and soil moisture content. The corn growth period includes sowing period, emergence period, seedling period, jointing period, earing period, silking period, and maturation period. The corn growth days represent the growth days of corn in the above-mentioned growth period.

[0061] Step S104, extract the numerical values of the corn growth days, daily maximum temperature, and soil moisture content to obtain the corn growth day value, daily maximum temperature value, and soil moisture content value.

[0062] Step S105, based on the corn growth period, sequentially establish time reference lines for the corn growth day value, daily maximum temperature value, and soil moisture content value, and generate a corn-weather-soil data dynamic curve with the time reference line as the horizontal coordinate to obtain the mapping relationship of the corn growth day value, daily maximum temperature value, and soil moisture content value, i.e., the mapping relationship of the corn crop data, weather data, and soil data. The soil moisture content value includes multi-layer soil moisture content. The layer height value is related to the crop root zone.

[0063] Step S2, determine the corn growth high-temperature drought sensitivity based on the corn crop data, weather data, soil data, and the mapping relationship of the corn crop data, weather data, and soil data to obtain a corn high-temperature drought sensitivity model.

[0064] Further, step S2 specifically includes the following steps:

[0065] Step S201, based on the corn growth days and daily maximum temperature, obtain the corn growth high-temperature sensitivity according to the corn growth critical temperature function and the high-temperature sensitivity index piecewise response function.

[0066] The expression of the corn growth critical temperature function is:

[0067] T critical = Tbase + ΔT stage + αDAS norm ;

[0068] wherein, T critical is the critical temperature value for corn growth, T base is the basic heat tolerance threshold of the current corn variety, α is the growth progress coefficient of the current corn variety, DAS norm is the corn growth days.

[0069] The expression of the high-temperature sensitivity index segmented response function is:

[0070]

[0071] wherein, HTS is the high-temperature sensitivity of corn growth, β is the sensitive coefficient of the growth period of the current corn variety, T max is the daily maximum temperature, T critical is the critical high-temperature threshold of the current corn variety.

[0072] In step S202, based on the corn growth days and the soil water content, the soil effective water content correction function and the drought sensitivity index segmented response function are used to obtain the corn growth drought sensitivity.

[0073] In step S203, based on the mapping relationship of the corn crop data, the meteorological data and the soil data, the corn growth high-temperature drought sensitivity matrix is constructed according to the corn growth high-temperature sensitivity and the corn growth drought sensitivity, and the corn high-temperature drought sensitivity model is obtained.

[0074] wherein, the expression of the soil effective water content correction function is:

[0075]

[0076] wherein, AWC adj is the soil effective water content, θ i is the soil water content of the i-th layer, i is the soil layer number, i = 1, 2, …, n, θ PWP is the permanent wilting point of corn, RDF i is the root distribution weight of the current corn variety, DAS factor is the root growth dynamic coefficient of the current corn variety.

[0077] The expression of the drought sensitivity index segmented response function is:

[0078]

[0079] wherein, DSI is the corn growth drought sensitivity, θ FC is the soil water holding capacity, θ C is the critical soil water content.

[0080] Step S3, obtaining real-time data of corn crops in the target region, real-time meteorological data, real-time soil data, and mapping relationship of the real-time data of corn crops, real-time meteorological data, and real-time soil data.

[0081] Step S4, inputting the real-time data of corn crops, real-time meteorological data, real-time soil data, and mapping relationship of the real-time data of corn crops, real-time meteorological data, and real-time soil data into a corn high-temperature drought sensitivity model for evaluation, to obtain an evaluation result.

[0082] Further, the specific content of step S4 includes:

[0083] Step S401, inputting the real-time data of corn crops, real-time meteorological data, real-time soil data, and mapping relationship of the real-time data of corn crops, real-time meteorological data, and real-time soil data into a corn high-temperature drought sensitivity model, to obtain a real-time matrix of corn growth high-temperature drought sensitivity.

[0084] Step S402, performing a sensitivity comprehensive evaluation on the real-time matrix of corn growth high-temperature drought sensitivity.

[0085] Step S403, defining a sensitivity comprehensive evaluation standard.

[0086] Step S404, obtaining a current corn growth high-temperature sensitivity value, configuring a high-temperature sensitivity emphasis conversion coefficient to the current corn growth high-temperature sensitivity value according to a high-temperature sensitivity emphasis requirement in the sensitivity comprehensive evaluation, to obtain a high-temperature sensitivity evaluation result.

[0087] Step S405, obtaining a current corn growth drought sensitivity value, configuring a drought sensitivity emphasis conversion coefficient to the current corn growth drought sensitivity value according to a drought sensitivity emphasis requirement in the sensitivity comprehensive evaluation, to obtain a drought sensitivity evaluation result.

[0088] Step S406, respectively configuring a weight coefficient to the high-temperature sensitivity evaluation result and the drought sensitivity evaluation result, to obtain a sensitivity comprehensive evaluation value, i.e., an evaluation result.

[0089] The expression of the sensitivity comprehensive evaluation value is:

[0090] H = a (a x T) + b (b x D);

[0091] wherein, H is the sensitivity comprehensive evaluation value, a is the high-temperature sensitivity weight coefficient, a is the high-temperature sensitivity emphasis conversion coefficient, T is the current corn growth high-temperature sensitivity value, b is the drought sensitivity value weight coefficient, b is the drought sensitivity value emphasis conversion coefficient, and D is the current corn growth drought sensitivity value.

[0092] Step S5, according to the evaluation result, the risk of corn high temperature drought disaster is evaluated.

[0093] Further, step S5 specifically includes the following contents:

[0094] If the sensitivity comprehensive evaluation value is equal to the sensitivity comprehensive evaluation standard, the risk level of corn high temperature drought disaster is medium risk level.

[0095] If the sensitivity comprehensive evaluation value is higher than the sensitivity comprehensive evaluation standard, the risk level of corn high temperature drought disaster is high risk level.

[0096] If the sensitivity comprehensive evaluation value is higher than the sensitivity comprehensive evaluation standard, the risk level of corn high temperature drought disaster is low risk level.

[0097] It can be understood that according to the risk level of corn high temperature drought disaster, timely and accurate coping strategies can be given to achieve the purpose of disaster prevention and reduction, such as: low risk level, field routine management, medium risk level, supplementary irrigation, high risk level, emergency drought resistance.

[0098] The specific content of step S5 further includes:

[0099] Based on the sensitivity comprehensive evaluation standard and the sensitivity comprehensive evaluation value, a time reference line is established in sequence, and a standard value-evaluation value dynamic curve is generated with the time reference line as the horizontal coordinate.

[0100] According to the standard value-evaluation value dynamic curve, the risk level of corn high temperature drought disaster is marked in real time.

[0101] It can be understood that since the mapping relationship of the obtained corn crop data, meteorological data and soil data is based on the background of corn growth period, and corn is in different growth periods, the corn high temperature drought sensitivity is different. Therefore, the corn high temperature drought sensitivity model under different growth periods is constructed, and the risk evaluation of corn high temperature drought disaster under different growth periods of corn can be realized.

[0102] The application also provides a corn high temperature drought disaster risk assessment system, as shown in Figure 2 The system comprises:

[0103] A data acquisition module is configured to acquire corn growth data, real-time corn crop data, real-time meteorological data, real-time soil data, and a mapping relationship between the real-time corn crop data, the real-time meteorological data and the real-time soil data in a target region.

[0104] A data analysis module is configured to process and analyze the past corn growth data to obtain corn crop data, meteorological data, soil data, and a mapping relationship between the corn crop data, the meteorological data and the soil data.

[0105] A model construction module is configured to determine a corn growth high-temperature drought sensitivity according to the corn crop data, the meteorological data, the soil data, and a mapping relationship among the corn crop data, the meteorological data, and the soil data, and obtain a corn high-temperature drought sensitivity model.

[0106] An evaluation analysis module is configured to input the corn crop real-time data, the meteorological real-time data, the soil real-time data, and a mapping relationship among the corn crop real-time data, the meteorological real-time data, and the soil real-time data into the corn high-temperature drought sensitivity model for evaluation, and obtain an evaluation result.

[0107] A risk assessment module is configured to perform a risk assessment on the corn high-temperature drought disaster according to the evaluation result.

[0108] The application further provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor realizing the content of the corn high-temperature drought disaster risk assessment method when calling the computer program in the memory.

[0109] The application further provides a storage medium storing computer executable instructions, the computer executable instructions being loaded and executed by a processor to realize the content of the corn high-temperature drought disaster risk assessment method.

[0110] Finally, it should be noted that: the above examples are only used to illustrate the technical method of the application rather than limit it, although the application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical method of the application, and these modifications or equivalent replacements cannot make the modified technical method deviate from the spirit and scope of the technical method of the application.

Claims

1. A method for assessing risk of high temperature drought disaster of corn, characterized in that, The method comprises the following steps: S1, obtaining past corn growth data of a target region, and processing and analyzing the past corn growth data to obtain corn crop data, meteorological data, soil data, and a mapping relationship among the corn crop data, the meteorological data, and the soil data; S2, determining corn growth high-temperature drought sensitivity according to the corn crop data, the meteorological data, the soil data, and the mapping relationship among the corn crop data, the meteorological data, and the soil data, and obtaining a corn high-temperature drought sensitivity model; S3, obtaining real-time corn crop data, real-time meteorological data, real-time soil data, and a mapping relationship among the real-time corn crop data, the real-time meteorological data, and the real-time soil data of the target region; S4, inputting the real-time corn crop data, the real-time meteorological data, the real-time soil data, and the mapping relationship among the real-time corn crop data, the real-time meteorological data, and the real-time soil data into the corn high-temperature drought sensitivity model for evaluation, and obtaining an evaluation result; S5, performing risk assessment on corn high-temperature drought disasters according to the evaluation result.

2. The method according to claim 1, wherein, In S1, the specific content of obtaining past corn growth data of a target region and processing and analyzing the past corn growth data to obtain corn crop data, meteorological data, soil data, and a mapping relationship among the corn crop data, the meteorological data, and the soil data comprises: obtaining past corn growth data of a target region, and constructing a corn growth database; extracting corn crop data, meteorological data, and soil data in the corn growth database; performing feature extraction on the corn crop data, the meteorological data, and the soil data respectively to obtain corn growth period, corn growth days, daily maximum temperature, and soil water content; performing numerical extraction on the corn growth days, the daily maximum temperature, and the soil water content to obtain corn growth day values, daily maximum temperature values, and soil water content values; based on the corn growth period, time reference lines are established in sequence for the corn growth day values, the daily maximum temperature values, and the soil water content values, and corn-meteorology-soil data dynamic curves are generated with the time reference lines as the horizontal coordinates to obtain the mapping relationship among the corn growth day values, the daily maximum temperature values, and the soil water content values, i.e. the mapping relationship among the corn crop data, the meteorological data, and the soil data.

3. The method according to claim 2, wherein, In S2, the specific content of determining corn growth high-temperature drought sensitivity according to the corn crop data, the meteorological data, the soil data, and the mapping relationship among the corn crop data, the meteorological data, and the soil data, and obtaining a corn high-temperature drought sensitivity model comprises: based on corn growth days and daily maximum temperature, corn growth high-temperature sensitivity is obtained according to a corn growth critical temperature function and a high-temperature sensitivity index segmented response function; based on corn growth days and soil water content, corn growth drought sensitivity is obtained according to a soil effective water content correction function and a drought sensitivity index segmented response function; based on the mapping relationship among the corn crop data, the meteorological data, and the soil data, a corn growth high-temperature drought sensitivity matrix is constructed according to the corn growth high-temperature sensitivity and the corn growth drought sensitivity, and a corn high-temperature drought sensitivity model is obtained.

4. The method according to claim 3, wherein, In S4, the corn crop real-time data, meteorological real-time data, soil real-time data, and the mapping relationship of the corn crop real-time data, meteorological real-time data, and soil real-time data are input into the corn high-temperature drought sensitivity model for evaluation. The specific content of the evaluation result includes: The corn crop real-time data, meteorological real-time data, soil real-time data, and the mapping relationship of the corn crop real-time data, meteorological real-time data, and soil real-time data are input into the corn high-temperature drought sensitivity model to obtain a corn growth high-temperature drought sensitivity real-time matrix. The corn growth high-temperature drought sensitivity real-time matrix is subjected to sensitivity comprehensive evaluation. The sensitivity comprehensive evaluation standard is defined. The current corn growth high-temperature sensitivity value is obtained, and a high-temperature sensitivity emphasis conversion coefficient is configured for the current corn growth high-temperature sensitivity value according to the high-temperature sensitivity emphasis requirement in the sensitivity comprehensive evaluation to obtain a high-temperature sensitivity evaluation result. The current corn growth drought sensitivity value is obtained, and a drought sensitivity emphasis conversion coefficient is configured for the current corn growth drought sensitivity value according to the drought sensitivity emphasis requirement in the sensitivity comprehensive evaluation to obtain a drought sensitivity evaluation result. The high-temperature sensitivity evaluation result and the drought sensitivity evaluation result are respectively configured with weight coefficients to obtain a sensitivity comprehensive evaluation value, i.e., the evaluation result.

5. The method according to claim 4, wherein, The expression of the sensitivity comprehensive evaluation value is: H=α(a×T)+β(b×D); wherein, H is the sensitivity comprehensive evaluation value, α is the high-temperature sensitivity weight coefficient, a is the high-temperature sensitivity emphasis conversion coefficient, T is the current corn growth high-temperature sensitivity value, β is the drought sensitivity value weight coefficient, b is the drought sensitivity value emphasis conversion coefficient, and D is the current corn growth drought sensitivity value.

6. The method according to claim 4, wherein, In S5, the specific content of the risk assessment of the corn high-temperature drought disaster based on the evaluation result includes: If the sensitivity comprehensive evaluation value is equal to the sensitivity comprehensive evaluation standard, the risk level of the corn high-temperature drought disaster is the medium risk level; If the sensitivity comprehensive evaluation value is higher than the sensitivity comprehensive evaluation standard, the risk level of the corn high-temperature drought disaster is the high risk level; If the sensitivity comprehensive evaluation value is lower than the sensitivity comprehensive evaluation standard, the risk level of the corn high-temperature drought disaster is the low risk level.

7. The method according to claim 4, wherein the method is characterized by, The specific content of the risk assessment of the corn high-temperature drought disaster based on the evaluation result in S5 also includes: Based on the sensitivity comprehensive evaluation standard and the sensitivity comprehensive evaluation value, a time reference line is established in sequence, and a standard value-evaluation value dynamic curve is generated with the time reference line as the horizontal coordinate; According to the standard value-evaluation value dynamic curve, the risk level of the corn high-temperature drought disaster is labeled in real time.

8. A corn high temperature drought disaster risk assessment system, characterized in that, The system includes: A data acquisition module for acquiring past corn growth data, corn crop real-time data, meteorological real-time data, soil real-time data, and the mapping relationship of the corn crop real-time data, meteorological real-time data, and soil real-time data in a target region; A data analysis module for processing and analyzing the past corn growth data to obtain corn crop data, meteorological data, soil data, and the mapping relationship of the corn crop data, meteorological data, and soil data; A model construction module is configured to determine corn growth high-temperature drought sensitivity according to corn crop data, meteorological data, soil data, and a mapping relationship among the corn crop data, the meteorological data, and the soil data, and obtain a corn high-temperature drought sensitivity model; An evaluation analysis module is configured to input corn crop real-time data, meteorological real-time data, soil real-time data, and a mapping relationship among the corn crop real-time data, the meteorological real-time data, and the soil real-time data into the corn high-temperature drought sensitivity model for evaluation, and obtain an evaluation result; A risk assessment module is configured to perform risk assessment on corn high-temperature drought disasters according to the evaluation result.

9. An electronic device, comprising: A memory and a processor are included, the memory has a computer program stored therein, and the processor, when calling the computer program in the memory, implements the content of the corn high-temperature drought disaster risk assessment method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium has computer executable instructions stored therein, and the computer executable instructions, when loaded and executed by a processor, implement the content of the corn high-temperature drought disaster risk assessment method according to any one of claims 1 to 7.

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