A field-scale tea garden drought and heat disaster monitoring and early warning method and system

Abstract

The application discloses a kind of field scale tea garden drought-heat disaster monitoring and early warning method and system, comprising the following steps: obtaining tea garden climate environment data by field weather station;Tea garden drought index is calculated based on tea garden climate environment data;Establish the simulation model between tea tree canopy leaf temperature and dominant environmental variable;Tea tree canopy leaf temperature simulation value of monitoring point is calculated by simulation model;According to tea garden drought index, obtain the degree of risk of tea garden drought disaster;According to tea tree canopy leaf temperature simulation value of monitoring point, obtain the degree of risk of tea garden heat disaster;According to the degree of risk of tea garden drought disaster and the degree of risk of tea garden heat disaster, drought-heat disaster monitoring and early warning are carried out.The application improves the accuracy of real-time judgment whether to suffer drought-heat disaster and the degree of drought-heat disaster by using agricultural internet of things equipment;Meanwhile, compared with remote sensing monitoring method, the economic efficiency and timeliness of field scale tea garden drought-heat disaster monitoring and early warning are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of tea garden drought and heat disaster monitoring and early warning, in particular to a field scale tea garden drought and heat disaster monitoring and early warning method and system. BACKGROUND

[0002] In the tea area of the Yangtze River Basin, from July to August every year, the temperature is high, the sunshine intensity is large, and the air humidity is small, and drought and heat disaster often occurs, which seriously threatens the growth of tea trees. Accurate drought and heat disaster monitoring and early warning can help tea farmers take effective measures in time to reduce disaster losses. The commonly used drought and heat disaster monitoring and early warning methods include remote sensing monitoring and weather forecasting.

[0003] The advantage of remote sensing monitoring is that it can obtain remote sensing index related to temperature and moisture of tea tree canopy in large area tea garden, and through the construction of drought and heat disaster identification model, it is more applied in identifying and evaluating the area and severity of drought and heat disaster in tea garden. However, due to the difficulty in obtaining remote sensing images, especially the fact that tea gardens are mostly distributed in mountainous areas with more cloud cover, the technical requirements for remote sensing image acquisition and interpretation are high, and there are disadvantages such as high cost, time lag and poor practicability. At the same time, this method has limitations in drought and heat disaster early warning.

[0004] The method of weather forecasting is to rely on the weather forecast information released by local meteorological bureaus to guide the disaster reduction and prevention of tea garden drought and heat disaster. It is commonly used for regional scale large range extreme weather disaster prediction and forecasting, but due to the small number of automatic weather stations of local meteorological bureaus and uneven distribution of station locations, the accuracy of field scale tea garden drought and heat disaster early warning is not high, especially in the mountainous area where the climate changes greatly with the terrain. At the same time, there are limitations in monitoring the degree of drought and heat disaster.

[0005] The rapid development of agricultural Internet of Things technology promotes the large-area use of Internet of Things devices, and the Internet of Things devices for field scale can realize all-weather collection and recording of field microclimate environment. Farmers can understand the tea garden environment information at any time and anywhere through mobile phone applets or APP. However, to accurately and reliably monitor and early warn the drought and heat disaster of field scale tea garden, it is necessary to build an analysis method and system based on environmental data. However, relying on only a single indicator such as temperature for monitoring and early warning has low accuracy and certain limitations. SUMMARY

[0006] In view of the above shortcomings in the prior art, the field scale tea garden drought and heat disaster monitoring and early warning method and system provided by the present application solves the problems of lagging, poor practicability and low accuracy of the existing tea garden drought and heat disaster monitoring and early warning information.

[0007] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0008] A field scale tea garden drought and heat disaster monitoring and early warning method is provided, which comprises the following steps:

[0009] S1. Obtain climate and environmental data of tea gardens through field weather stations;

[0010] S2. Calculate the drought index of tea gardens based on tea garden climate and environmental data;

[0011] S3. Establish a simulation model of the relationship between tea canopy leaf temperature and dominant environmental variables;

[0012] S4. Calculate the simulated leaf temperature of the tea tree canopy at the monitoring point using a simulation model;

[0013] S5. Obtain the drought risk level of tea gardens based on the tea garden drought index; obtain the heat damage risk level of tea gardens based on the simulated leaf temperature values ​​of tea tree canopies at monitoring points.

[0014] S6. Conduct drought and heat damage monitoring and early warning based on the degree of drought risk and heat damage risk in tea gardens.

[0015] Furthermore, the specific method for step S1 is as follows:

[0016] Centered on the field weather station, select at least 5 monitoring points within a radius of 200m to collect environmental data of the tea tree canopy within this radius;

[0017] The data collected by the field meteorological stations include hourly temperature, hourly relative humidity, hourly wind speed, hourly rainfall, hourly light intensity, and soil volumetric moisture content; the data collected by the field meteorological stations must be collected continuously for at least 5 days.

[0018] Furthermore, the specific method of step S2 includes the following sub-steps:

[0019] S2-1. Calculate the average data of the field meteorological stations over the past 5 days;

[0020] S2-2, According to the formula:

[0021]

[0022] Calculate the soil moisture content W_SoilWater; where ThetaV_SoilWater hour ROU_P represents the average soil volumetric moisture content over the past 5 days; ROU_P represents the average soil bulk density of the tea garden over the past 5 days.

[0023] S2-3, According to the formula:

[0024] V soil =10×20×ROU_P×(W_SoilWater-Wk) / 100

[0025] Calculate the effective soil moisture storage V over the past 5 days. soil; wherein Wk is the wilting coefficient of the soil;

[0026] S2-4, according to the formula:

[0027]

[0028] obtain a tea garden drought index K; wherein E t is the evapotranspiration of the tea garden in the last 5 days; RainFall is the cumulative value of the rainfall in the last 5 days.

[0029] Further, the specific method of step S3 includes the following sub-steps:

[0030] S3-1, respectively using the average air temperature x1, the average air relative humidity value x2, the average wind speed value x3, the average rainfall x4, the average light intensity x5, the average soil volume water content x6 at different positions of the tea garden at a moment and the tea tree crown layer leaf temperature data y to establish the following one-variable regression equation:

[0031] y = B i + A i x i + ε

[0032] wherein A i , B i are constants; x i ∈{x1,x2,x3,x4,x5,x6}; the tea tree crown layer leaf temperature data is the dependent variable; and ε is the residual value;

[0033] S3-2, respectively calculating the test statistic value F i of the regression coefficient of the single variable x i , and recording the maximum test statistic value of the single variable x i as F ii ;

[0034] S3-3, given that the significance level value is 0.05, recording the corresponding critical value as F, judging whether F ii is greater than or equal to F, if yes, then the corresponding variable x i is selected into the variable set;

[0035] S3-4, establishing the regression model of the dependent variable y and the variables in the jth subset of the variable set, and calculating the corresponding maximum test statistic value F jj ;

[0036] S3-5, given that the significance level value is 0.05, recording the current corresponding critical value as F', judging whether the current F jj is greater than or equal to the current F', if yes, then the corresponding regression model is added to the model set; otherwise, the corresponding regression model is discarded;

[0037] S3-6, for the regression model in the model set, respectively, import the validation data to obtain the simulation value, and the simulation value is correlated with the corresponding measured value, and the correlation coefficient and the mean absolute error are obtained;

[0038] S3-7, the regression model with correlation coefficient square less than or equal to 0.9 or mean absolute error greater than or equal to 0.5 is discarded, and the highest precision is selected from the remaining regression model as the simulation model between tea tree canopy leaf temperature and dominant environmental variables.

[0039] Further, the expression of the simulation model between tea tree canopy leaf temperature and dominant environmental variables in step S3-7 is:

[0040] y = -4.45105 + 1.08969x1 + 0.0534008x5.

[0041] Further, the specific method of step S5 is:

[0042] If the tea garden drought index is less than 1.5, it is determined that there is no drought risk, and the corresponding tea garden drought risk degree value is set to 0;

[0043] If the tea garden drought index is greater than or equal to 1.5 and less than 2, it is determined that the drought risk is low, and the corresponding tea garden drought risk degree value is set to 0.25;

[0044] If the tea garden drought index is greater than or equal to 2 and less than 3, it is determined that the drought risk is medium, and the corresponding tea garden drought risk degree value is set to 0.5;

[0045] If the tea garden drought index is greater than or equal to 3 and less than 4, it is determined that the drought risk is high, and the corresponding tea garden drought risk degree value is set to 0.75;

[0046] If the tea garden drought index is greater than or equal to 4, it is determined that the drought risk is serious, and the corresponding tea garden drought risk degree value is set to 1;

[0047] If the monitoring point tea tree canopy leaf temperature simulation value is less than or equal to 30℃, it is determined that there is no heat damage risk, and the corresponding tea garden heat damage risk degree value is set to 0;

[0048] If the monitoring point tea tree canopy leaf temperature simulation value is greater than 30℃ and less than or equal to 34℃, it is determined that the heat damage risk is low, and the corresponding tea garden heat damage risk degree value is set to 0.25;

[0049] If the monitoring point tea tree canopy leaf temperature simulation value is greater than 34℃ and less than or equal to 39℃, it is determined that the heat damage risk is medium, and the corresponding tea garden heat damage risk degree value is set to 0.5;

[0050] If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 39℃ and less than or equal to 42℃, it is determined that the risk of heat damage is high, and the corresponding tea garden heat damage risk degree value is set to 0.75;

[0051] If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 42℃, it is determined that the risk of heat damage is serious, and the corresponding tea garden heat damage risk degree value is set to 1.

[0052] Further, the specific method of step S6 includes the following sub-steps:

[0053] S6-1, determining whether the tea garden drought risk degree value is 0, if yes, it is determined that there is no drought heat damage; otherwise, step S6-2 is entered;

[0054] S6-2, obtaining the sum WT_Value of the tea garden drought risk degree value and the tea garden heat damage risk degree value;

[0055] S6-3, if WT_Value is greater than 0 and less than or equal to 0.5, it is determined that the risk of drought heat damage of the tea garden is low;

[0056] If WT_Value is greater than 0.5 and less than or equal to 1, it is determined that the risk of drought heat damage of the tea garden is medium;

[0057] If WT_Value is greater than 1 and less than or equal to 1.5, it is determined that the risk of drought heat damage of the tea garden is high;

[0058] If WT_Value is greater than 1.5, it is determined that the risk of drought heat damage of the tea garden is serious.

[0059] A field scale tea garden drought heat damage monitoring and early warning system is provided, which comprises:

[0060] A basic data acquisition module for acquiring tea garden climate environment data through a field meteorological station;

[0061] A tea garden drought index calculation module for calculating a tea garden drought index based on tea garden climate environment data;

[0062] A simulation model establishment module for establishing a simulation model between tea tree canopy leaf temperature and dominant environmental variables;

[0063] A tea tree canopy leaf temperature simulation value acquisition module for calculating the simulated value of the tea tree canopy leaf temperature at the monitoring point through the simulation model;

[0064] A risk acquisition module for acquiring the tea garden drought risk degree according to the tea garden drought index and acquiring the tea garden heat damage risk degree according to the simulated value of the tea tree canopy leaf temperature at the monitoring point;

[0065] A drought heat damage early warning module for monitoring and early warning of drought heat damage according to the tea garden drought risk degree and the tea garden heat damage risk degree.

[0066] The beneficial effects of the present application are:

[0067] 1、The present application collects tea garden environment information in all-weather real-time, couples the requirements of tea tree key growth period on temperature, illumination, relative humidity, etc., studies the characteristics of tea garden microclimate environment and tea tree canopy and tea shoots when drought and heat damage occurs, constructs a tea garden drought and heat damage monitoring and early warning method and system, and realizes the purpose of monitoring and early warning of tea garden drought and heat damage by using Internet of Things devices and their management platform.

[0068] 2、The present application simultaneously considers the water condition of tea garden and the growth condition of tea tree (tea tree canopy leaf temperature), compares the existing monitoring and early warning method using only tea garden average temperature and maximum temperature, improves the accuracy of real-time judgment of whether to suffer from drought and heat damage and the degree of drought and heat damage by using agricultural Internet of Things devices; and significantly improves the economy and timeliness of field scale tea garden drought and heat damage monitoring and early warning compared with remote sensing monitoring method. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 is a flowchart of the present method;

[0070] Figure 2 is a correlation analysis diagram of leaf temperature measurement values and simulation values in the embodiment;

[0071] Figure 3 is a schematic diagram of input parameters of the tea garden drought and heat damage monitoring and early warning using API interface program method in the embodiment;

[0072] Figure 4 is a warning result diagram of the tea garden drought and heat damage monitoring and early warning using API interface program method. DETAILED DESCRIPTION

[0073] The specific embodiments of the present application are described below to facilitate those skilled in the art to understand the present application, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0074] As Figure 1 shown, the field scale tea garden drought and heat damage monitoring and early warning method includes the following steps:

[0075] S1, obtaining tea garden climate environment data through a field weather station;

[0076] S2, calculating a tea garden drought index based on the tea garden climate environment data;

[0077] S3, establishing a simulation model between tea tree canopy leaf temperature and dominant environmental variables;

[0078] S4, calculating the monitoring point tea tree canopy leaf temperature simulation value through the simulation model;

[0079] S5, obtaining the tea garden drought risk degree according to the tea garden drought index, and obtaining the tea garden heat damage risk degree according to the monitoring point tea tree canopy leaf temperature simulation value;

[0080] S6, conducting drought and heat damage monitoring and early warning according to the tea garden drought risk degree and the tea garden heat damage risk degree.

[0081] The specific method of step S1 is: taking a field weather station as the center, selecting at least 5 monitoring points within a radius of 200 m, and collecting tea tree canopy environment data within the radius; wherein the data collected by the field weather station includes hourly air temperature value Tem hour (℃), hourly air relative humidity value RH hour (%), hourly wind speed value WS hour (m / s), hourly rainfall value PRE hour mm, hourly light intensity value SSD hour (lux), soil volume water content ThetaV_SoilWater hour (%). The above data time needs to be continuous for more than 5 days (i.e. more than one candidate).

[0082] In addition, in order to accurately conduct early warning, the following data also needs to be obtained: latitude value LAT (°) of the field weather station, altitude Height (m); date DATE (year-month-day) of monitoring and early warning; tea garden soil bulk density value ROU_P (g / cm 3 ) and soil wilting coefficient value Wk (%).

[0083] The specific method of step S2 includes the following sub-steps:

[0084] S2-1, calculating the average data of the field weather station in the latest 5 days, as shown in Table 1;

[0085] S2-2, calculating the soil quality water content W_SoilWater according to the formula:

[0086]

[0087] ThetaV_SoilWater hour is the average value of soil volume water content in the latest 5 days; ROU_P is the average value of tea garden soil bulk density in the latest 5 days;

[0088] S2-3, calculating the soil quality water content W_SoilWater according to the formula:

[0089] V soil = 10 x 20 x ROU_P x (W_SoilWater-Wk) / 100

[0090] Calculate the soil available water storage V of the last 5 days soil ; wherein Wk is the soil wilting coefficient;

[0091] S2-4, according to the formula:

[0092]

[0093] Obtain the tea garden drought index K; wherein E t is the tea garden evapotranspiration of the last 5 days, the value of which is calculated by referring to the FAO Penman-Monteith equation; RainFall is the cumulative value of the rainfall of the last 5 days.

[0094] Table 1: average data of the last 5 days (one candidate) of the field meteorological station

[0095]

[0096] In the specific implementation process, the specific method of step S3 includes the following sub-steps:

[0097] S3-1, respectively using the average air temperature x1, the average air relative humidity value x2, the average wind speed value x3, the average rainfall x4, the average light intensity x5, the average soil volume water content x6 and the tea tree canopy leaf temperature data y at different positions of the tea garden at a moment to establish the following simple regression equation:

[0098] y = B i + A i x i + ε

[0099] wherein A i , B i are constants; x i ∈{x1,x2,x3,x4,x5,x6}; the tea tree canopy leaf temperature data is the dependent variable; ε is the residual value; the tea tree canopy leaf temperature data y used to construct the regression equation is collected by a temperature sensor;

[0100] S3-2, respectively calculate the test statistic value F i of the corresponding regression coefficient of a single variable x i , and record the maximum test statistic value of the corresponding single variable x i as F ii ;

[0101] S3-3, given the significance level value is 0.05, record the corresponding critical value as F, judge whether F ii is greater than or equal to F, if yes, the corresponding variable x i is selected into the variable set;

[0102] S3-4, establish a regression model of dependent variable y and variables in the jth subset of the variable set, and calculate the corresponding maximum test statistic value F jj ;

[0103] S3-5, given a significance level value of 0.05, record the current critical value as F', determine whether the current F jj is greater than or equal to the current F', if yes, add the corresponding regression model to the model set; otherwise, discard the corresponding regression model;

[0104] S3-6, for the regression models in the model set, respectively import the validation data to obtain the simulation values, and perform correlation analysis on the simulation values and the corresponding measured values to obtain the correlation coefficient and the mean absolute error;

[0105] S3-7, discard the regression models with a correlation coefficient squared less than or equal to 0.9 or a mean absolute error greater than or equal to 0.5, and select the highest precision among the remaining regression models as the simulation model between the tea tree canopy leaf temperature and the dominant environmental variable.

[0106] In the specific implementation process, steps S3-3 and S3-5 use the F test method, in which a significance level value of 0.05 means that 95% of the sample size is significant, and the corresponding F value is searched in the F test table. If the single variable x i corresponds to the maximum test statistic value F ii (or F jj ) is greater than F (or F'), it can be considered that the correlation is significant and meets the requirements; otherwise, it is considered that the correlation is not significant and does not meet the requirements.

[0107] In this embodiment, the variables selected into the variable set in step S3-3 are x1, x2 and x5, so there are 7 subsets in step S3-4, which are {x1}, {x2}, {x5}, {x1, x2}, {x2, x5}, {x1, x5} and {x1, x2, x5}. Through the processing of steps S3-5 to S3-7, the regression model constructed by the combination of air temperature x1 and light intensity x5 is the regression model with the highest precision, that is, the fitting expression of the simulation model between the tea tree canopy leaf temperature and the dominant environmental variable in step S3-7 is: y = -4.45105 + 1.08969x1 + 0.0534008x5. The values of the simulated leaf temperature in the tea garden and the sensor collected leaf temperature corresponding to the simulation model are shown in Table 2.

[0108] Table 2: Values of simulated leaf temperature in tea garden and sensor collected leaf temperature corresponding to the simulation model

[0109]

[0110] As Figure 2As shown, the 20% validation data of leaf temperature measured by the sensor is taken as the abscissa, and the simulated leaf temperature data calculated by the above formula model is taken as the ordinate to draw a scatter plot, and the correlation coefficient square reaches 0.9849, R 2 > 90%, and is significantly correlated at the 1% level (p<0.01), which is extremely significant correlation. As shown in Table 2, the average absolute error is within 0.5℃, and it can be seen that the simulated leaf temperature value of the model can well reflect the tea garden canopy leaf temperature value in the test tea garden.

[0111] It should be particularly pointed out that the combination of air temperature x1 and light intensity x5 obtained in the embodiment is the regression model with the highest accuracy, and does not mean that the regression model is applicable to all regions. For different regions, only the steps S3-1 to S3-7 proposed in the method need to be performed to construct a model to obtain a simulation model suitable for different regions between the tea canopy leaf temperature and the dominant environmental variables.

[0112] In the specific implementation process, the specific method of step S5 is:

[0113] If the tea garden drought index is less than 1.5, it is determined that there is no drought risk, and the corresponding tea garden drought risk degree value is defined as 0;

[0114] If the tea garden drought index is greater than or equal to 1.5 and less than 2, it is determined that the drought risk is low, and the corresponding tea garden drought risk degree value is defined as 0.25;

[0115] If the tea garden drought index is greater than or equal to 2 and less than 3, it is determined that the drought risk is medium, and the corresponding tea garden drought risk degree value is defined as 0.5;

[0116] If the tea garden drought index is greater than or equal to 3 and less than 4, it is determined that the drought risk is high, and the corresponding tea garden drought risk degree value is defined as 0.75;

[0117] If the tea garden drought index is greater than or equal to 4, it is determined that the drought risk is serious, and the corresponding tea garden drought risk degree value is defined as 1;

[0118] If the simulated value of the tea tree canopy leaf temperature of the monitoring point is less than or equal to 30℃, it is determined that there is no heat damage risk, and the corresponding tea garden heat damage risk degree value is defined as 0;

[0119] If the simulated value of the tea tree canopy leaf temperature of the monitoring point is greater than 30℃ and less than or equal to 34℃, it is determined that the heat damage risk is low, and the corresponding tea garden heat damage risk degree value is defined as 0.25;

[0120] If the simulated value of the tea tree canopy leaf temperature of the monitoring point is greater than 34℃ and less than or equal to 39℃, it is determined that the heat damage risk is medium, and the corresponding tea garden heat damage risk degree value is defined as 0.5;

[0121] If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 39℃ and less than or equal to 42℃, it is determined that the risk of heat damage is high, and the corresponding tea garden heat damage risk degree value is set to 0.75;

[0122] If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 42℃, it is determined that the risk of heat damage is serious, and the corresponding tea garden heat damage risk degree value is set to 1.

[0123] The specific method of step S6 includes the following sub-steps:

[0124] S6-1, determine whether the tea garden drought risk degree value is 0, if yes, determine that there is no drought heat damage; otherwise, go to step S6-2;

[0125] S6-2, obtain the sum WT_Value of the tea garden drought risk degree value and the tea garden heat damage risk degree value;

[0126] S6-3, if WT_Value is greater than 0 and less than or equal to 0.5, it is determined that the risk of drought heat damage in the tea garden is low;

[0127] If WT_Value is greater than 0.5 and less than or equal to 1, it is determined that the risk of drought heat damage in the tea garden is medium;

[0128] If WT_Value is greater than 1 and less than or equal to 1.5, it is determined that the risk of drought heat damage in the tea garden is high;

[0129] If WT_Value is greater than 1.5, it is determined that the risk of drought heat damage in the tea garden is serious.

[0130] The corresponding early warning of different tea garden drought heat damage risks can adopt the mode shown in Table 3.

[0131] Table 3: Corresponding early warning of different tea garden drought heat damage risks

[0132]

[0133] In the specific implementation process, the field scale tea garden drought heat damage monitoring and early warning system comprises:

[0134] A basic data acquisition module for acquiring tea garden climate environment data through a field weather station;

[0135] A tea garden drought index calculation module for calculating a tea garden drought index based on tea garden climate environment data;

[0136] A simulation model establishment module for establishing a simulation model between tea tree canopy leaf temperature and dominant environmental variables;

[0137] A tea tree canopy leaf temperature simulation value acquisition module for calculating the simulated value of the tea tree canopy leaf temperature at the monitoring point through the simulation model;

[0138] The risk obtaining module is used for obtaining a drought disaster risk degree of the tea garden according to the tea garden drought index and obtaining a heat disaster risk degree of the tea garden according to the monitored tea tree crown layer leaf temperature simulation value;

[0139] The drought and heat disaster early warning module is used for monitoring and early warning of drought and heat disaster according to the drought disaster risk degree of the tea garden and the heat disaster risk degree of the tea garden.

[0140] In an embodiment of the present application, as shown in Figure 3 and Figure 4 The present system encapsulates the field scale tea garden drought and heat disaster monitoring and early warning method into an API interface program to realize intelligent monitoring and early warning of the tea garden drought and heat disaster.

[0141] In summary, the present application considers the moisture condition of the tea garden and the growth condition of the tea tree at the same time. Compared with the prior art monitoring and early warning method using only the average air temperature and the maximum air temperature of the tea garden, the present application improves the accuracy of real-time judgment of whether the drought and heat disaster is suffered and the degree of the drought and heat disaster suffered by using the agricultural internet of things device. Meanwhile, compared with the remote sensing monitoring method, the present application significantly improves the economy and timeliness of the field scale tea garden drought and heat disaster monitoring and early warning.

Claims

1. A field-scale tea garden drought and heat stress monitoring and early warning method, characterized in that, The method comprises the following steps: S1, obtaining tea garden climate environment data through a field meteorological station; S2, calculating a tea garden drought index based on the tea garden climate environment data; S3, establishing a simulation model between tea tree canopy leaf temperature and dominant environmental variables; S4, calculating a monitoring point tea tree canopy leaf temperature simulation value through the simulation model; S5, obtaining a tea garden drought risk degree according to the tea garden drought index; obtaining a tea garden heat damage risk degree according to the monitoring point tea tree canopy leaf temperature simulation value; S6, conducting drought and heat damage monitoring and early warning according to the tea garden drought risk degree and the tea garden heat damage risk degree; The specific method of step S1 is: Select at least 5 monitoring points within a radius of 200m around the field meteorological station, and collect tea tree canopy environment data within the radius; The data collected by the field meteorological station includes hourly air temperature value, hourly air relative humidity value, hourly wind speed value, hourly rainfall value, hourly light intensity value, and soil volume water content; The data collected by the field meteorological station is continuously collected for at least 5 days; The specific method of step S2 comprises the following sub-steps: S2-1, calculating the average data of the field meteorological station for the latest 5 days; S2-2, according to the formula: Calculating the soil quality moisture content ; wherein is the average of the soil volume moisture content of the last 5 days; is the average of the tea field soil bulk density of the last 5 days; S2-3, according to the formula: Calculate the available soil water storage for the last 5 days ; wherein is the soil wilting coefficient; S2-4, according to the formula: obtaining a tea field drought index K ; wherein is the cumulative value of the evapotranspiration of the tea field for the last 5 days; is the cumulative value of the rainfall for the last 5 days; The specific method of step S3 comprises the following sub-steps: S3-1, respectively using the average air temperature of the tea garden at different positions at a certain moment , the average air relative humidity value , the average wind speed value , the average rainfall , the average light intensity , the average soil volume water content The following linear regression equation is established with the tea tree canopy leaf temperature data y: wherein , are constants; ; Camellia sinensis canopy leaf temperature data is the dependent variable; is the residual value; S3-2, calculate individual variable corresponding regression coefficient test statistics value and individual variable corresponding maximum test statistics value is recorded as ; S3-3, given a significance level value of 0.05, record the corresponding critical value as F, determine whether it is greater than or equal to F, if so, select the corresponding variable into the variable set; ​​ S3-4, establish a regression model of dependent variable y and variables in the first subset of the variable set, and calculate the corresponding maximum test statistic value j ;​ S3-5, given a significance level value of 0.05, record the current corresponding critical value as , determine whether the current is greater than or equal to the current , if yes, add the corresponding regression model to the model set; otherwise, discard the corresponding regression model; S3-6, for the regression model in the model set, respectively import the verification data to obtain the simulation value, and perform correlation analysis on the simulation value and the corresponding measured value to obtain the correlation coefficient and the mean absolute error; S3-7, discard the regression model with a correlation coefficient square less than or equal to 0.9 or a mean absolute error greater than or equal to 0.5, and select the highest precision from the remaining regression models as the simulation model between the tea tree canopy leaf temperature and the dominant environmental variables.

2. The field-scale tea garden drought-heat disaster monitoring and early warning method according to claim 1, characterized in that, The expression of the simulation model between the tea tree canopy leaf temperature and the dominant environmental variables in step S3-7 is: 。 3. The field-scale tea garden drought-heat disaster monitoring and early warning method according to claim 1, characterized in that, The specific method of step S5 is: If the tea garden drought index is less than 1.5, it is determined that there is no drought risk, and the corresponding tea garden drought risk degree value is set to 0; If the tea garden drought index is greater than or equal to 1.5 and less than 2, it is determined that the drought risk is low, and the corresponding tea garden drought risk degree value is set to 0.25; If the tea garden drought index is greater than or equal to 2 and less than 3, it is determined that the drought risk is medium, and the corresponding tea garden drought risk degree value is set to 0.5; If the tea garden drought index is greater than or equal to 3 and less than 4, it is determined that the drought risk is high, and the corresponding tea garden drought risk degree value is set to 0.75; If the tea garden drought index is greater than or equal to 4, it is determined that the drought risk is serious, and the corresponding tea garden drought risk degree value is set to 1; If the monitoring point tea tree canopy leaf temperature simulation value is less than or equal to 30℃, it is determined that there is no heat damage risk, and the corresponding tea garden heat damage risk degree value is set to 0; If the monitoring point tea tree canopy leaf temperature simulation value is greater than 30℃ and less than or equal to 34℃, it is determined that the heat damage risk is low, and the corresponding tea garden heat damage risk degree value is set to 0.25; If the monitoring point tea tree canopy leaf temperature simulation value is greater than 34℃ and less than or equal to 39℃, it is determined that the heat damage risk is medium, and the corresponding tea garden heat damage risk degree value is set to 0.5; If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 39℃ and less than or equal to 42℃, it is determined that the heat damage risk is high, and the corresponding tea garden heat damage risk degree value is set to 0.75; If the simulated value of the tea tree canopy leaf temperature at the monitoring point is greater than 42℃, it is determined that the heat damage risk is serious, and the corresponding tea garden heat damage risk degree value is set to 1.

4. The field-scale tea garden drought-heat disaster monitoring and early warning method according to claim 3, characterized in that, The specific method of step S6 includes the following sub-steps: S6-1, determining whether the tea garden drought risk degree value is 0, if yes, determining no drought heat damage; otherwise, entering step S6-2; S6-2, obtaining the sum WT_Value of the tea garden drought risk degree value and the tea garden heat damage risk degree value; S6-3, if WT_Value is greater than 0 and less than or equal to 0.5, it is determined that the drought heat damage risk of the tea garden is low; If WT_Value is greater than 0.5 and less than or equal to 1, it is determined that the drought heat damage risk of the tea garden is medium; If WT_Value is greater than 1 and less than or equal to 1.5, it is determined that the drought heat damage risk of the tea garden is high; If WT_Value is greater than 1.5, it is determined that the drought heat damage risk of the tea garden is serious.

5. A system based on the field-scale tea garden drought and heat disaster monitoring and early warning method according to any one of claims 1-4, characterized in that, It includes: A basic data acquisition module for acquiring tea garden climate environment data through a field meteorological station; A tea garden drought index calculation module for calculating a tea garden drought index based on tea garden climate environment data; A simulation model establishment module for establishing a simulation model between tea tree canopy leaf temperature and dominant environmental variables; A tea tree canopy leaf temperature simulation value acquisition module for calculating the simulated value of the tea tree canopy leaf temperature at the monitoring point through the simulation model; A risk acquisition module for acquiring the tea garden drought risk degree according to the tea garden drought index and acquiring the tea garden heat damage risk degree according to the simulated value of the tea tree canopy leaf temperature at the monitoring point; A drought heat damage early warning module for monitoring and early warning of drought heat damage according to the tea garden drought risk degree and the tea garden heat damage risk degree.

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