Disaster early warning method based on mathematical model of early warning of frost damage during flowering period of apple orchard

Abstract

The present invention discloses an apple orchard disaster warning method based on an apple flowering period frost damage warning model, which can predict and warn of flowering period frost damage before the apple flowering period arrives; and can also evaluate flowering period frost damage disasters in previous years based on the meteorological data of 25 days, including the peak flowering period date in previous years and 12 days before to 12 days after the peak flowering period, thereby achieving the effect of warning.

Description

Technical Field

[0001] The invention belongs to the technical field of meteorological disaster prediction, and in particular relates to an apple orchard disaster early warning method based on an apple flowering period frost damage early warning model. Background Art

[0002] Frost damage during the flowering period of apple orchards is one of the main meteorological disasters faced by apple production worldwide. It restricts the planting distribution of apples and affects their yield and quality. In temperate climate zones, the impact of frost damage during the flowering period in spring on deciduous fruit trees exceeds that of frost damage during the wintering period. my country is the world's largest apple producer, with a wide range of apple plantings and frequent cold air activities in spring. Frost damage during the flowering period occurs to varying degrees every year. As the global climate warms, the planting boundaries gradually move northward, and the apple flowering period is generally advanced, increasing the uncertainty of frost damage.

[0003] The frequency of frost damage during the apple flowering period varies in different cultivation areas. The degree of frost damage during the apple flowering period in my country generally shows a trend of gradually increasing from east to west and from south to north, especially in the marginal areas of the apple cultivation area, where the frequency of frost damage is high. For example, the production areas of the Northwest Loess Plateau and the Hexi region of Gansu are more prone to frost damage than the Sanmenxia production area of ​​Henan, and the frequency of frost damage in the Yellow River Basin of Ningxia is higher than that in the Guanzhong region of Shaanxi.

[0004] The severity of frost damage varies depending on the geographical environment of the orchard. The geographical location of the orchard is an important factor affecting frost damage during flowering. Generally, frost damage during flowering is mainly caused by frost, and most of them are advection frost. Low-lying areas are prone to frost damage during flowering, mainly because cold air easily accumulates in low-lying areas, resulting in long-term low temperatures in low-lying areas and severe frost damage; while on hillsides, due to the smooth air flow and the short residence time of cold air, frost damage is relatively mild.

[0005] -1.7℃ is usually used as the temperature index for freezing of apple flowers, and it is considered that frost damage occurs when the temperature remains at -2℃ for more than 0.5h. The following two situations also have a significant impact on apple production: (1) The temperature remains in the range of (-1.7,0] for a long time, resulting in damage to the quality of the flowers; (2) Although the minimum temperature is >0℃, the maximum temperature is <14℃, and bees and other pollinating insects are less active, resulting in a shortage of pollinated flowers. In addition, apple flowers in central and southern production areas of my country are less tolerant to low temperatures. For example, a low temperature of 0.6℃ on April 15, 2010 in Fengxian County, Jiangsu Province caused a 9% damage rate. It can be seen that under the trend of climate warming, a short-term strong drop in temperature will also have a certain impact on apple trees.

[0006] Usually, it is difficult for us to obtain the duration of low temperature that causes apple flowers to freeze in time, so in the study of apple flowering period frost damage forecast, not only the daily minimum temperature, but also the maximum temperature and average temperature should be considered. Normally, when the daily minimum temperature is the same, the maximum temperature and average temperature are higher, and the duration of low temperature is shorter, so it is believed that the duration of low temperature that causes apple flowers to freeze can be estimated by the maximum temperature and average temperature. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the apple orchard disaster warning method based on the apple flowering period frost damage warning model provided by the present invention realizes the prediction and warning of the apple orchard disaster loss rate caused by flowering period frost damage before the apple peak flowering period arrives.

[0008] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is: an apple orchard disaster warning method based on an apple flowering period frost damage warning model, comprising the following steps:

[0009] S1. Collect and analyze meteorological data from meteorological observation stations in the main apple producing areas, and obtain the index set X of the impact of flowering period frost damage on apple orchard production through the analytic hierarchy process;

[0010] S2, obtaining the observation matrix A according to the index set X and the meteorological observation data of the flowering period and the observation data of the degree of frost damage during the flowering period;

[0011] S3, based on the indicator variables in the observation matrix A, standardize them to obtain standardized indicator variables, and then obtain the main indicators of the impact of flowering period frost damage on apple yield through big data correlation analysis;

[0012] S4. Establish a frost damage model for the first apple flowering period according to the main indicators affecting apple yield, calculate the frost damage rate during the flowering period, and then obtain the parameters of the frost damage model for the first apple flowering period through nonlinear regression analysis and neural network regression, and improve the frost damage model for the first apple flowering period;

[0013] S5. establishing a second apple flowering period frost injury model based on the first apple flowering period frost injury model and the date when the apple flowering period was frozen;

[0014] S6. Based on the first apple flowering period frost damage model, the second apple flowering period frost damage model and different apple producing areas, a third apple flowering period frost damage model is established to conduct comprehensive prediction and early warning of apple flowering period frost damage and complete apple orchard disaster early warning.

[0015] Further: in step S1, the specific indicators of the impact of flowering frost damage on apple orchard production include the phenological period of extreme low temperature weather, surface temperature, surface humidity, air temperature, air humidity, precipitation, transpiration rate, wind speed and duration of extreme low temperature;

[0016] The step S1 is specifically as follows:

[0017] From the numerous meteorological data of meteorological observation stations in the main apple producing areas, a set of indicators X on the impact of low temperature weather during the apple flowering period on apple orchard production was selected.

[0018] The beneficial effect of the above further scheme is that by collecting and analyzing the meteorological data of the meteorological observation station, the specific indicators obtained by screening can be made more effective.

[0019] Further: the step S2 is specifically as follows:

[0020] According to the index set X that affects the production of apple orchards, m ), combined with the observation data of frost damage during flowering period, the observation matrix A is obtained; where m is the total number of specific indicators;

[0021]

[0022] Let the observation data in the observation matrix A be x ik , where i = 1, 2, ..., n, i is the ordinal number of the samples with frost damage during flowering, n is the total number of samples with frost damage during flowering; k = 1, 2, ..., m, k is the ordinal number of the specific indicator.

[0023] The beneficial effect of the above further scheme is that the establishment of the observation matrix A can more intuitively reflect the relationship between the specific indicators and the flowering period frost damage observation data, which is convenient for correlation analysis.

[0024] Further: the step S3 comprises the following sub-steps:

[0025] S31, according to the observation data in the observation matrix A is x ik , the indicator variable x is expressed as k Standardize to get the standardized indicator variable x′ k ;

[0026]

[0027] In the formula, x kmax is the maximum value of the kth indicator variable, x kmin is the minimum value of the kth indicator variable;

[0028] S32, standardize the indicator variable x′ by Pearson correlation coefficient, distance correlation coefficient, Mahalanobis distance and angle cosine k (k=1,2,…,m) to conduct big data correlation analysis and obtain the main indicators affecting apple orchard production, including daily minimum temperature, daily maximum temperature and daily average temperature.

[0029] The beneficial effect of the above further scheme is that the main indicators of the impact of frost damage during flowering on apple yield can be obtained through correlation analysis, thereby completing the establishment of an apple frost damage model during flowering.

[0030] Further: the step S4 includes the following sub-steps:

[0031] S41. Establishing a frost injury model for the first apple flowering period according to the daily minimum temperature and the daily average temperature;

[0032] Among them, the specific expression of the frost damage model during the first apple flowering period is:

[0033]

[0034] Where Y1 is the damage rate of apple frost damage on that day, x1 is the daily minimum temperature, x2 is the daily average temperature, a, b, c, f are all the parameters to be estimated for the first apple flowering period frost damage model; x1 can be replaced by x3 or x4, x3 is the daily maximum temperature, and x4 is the daily temperature difference;

[0035] S42, obtaining values ​​of parameters a, b, c, and f in the first apple flowering period frost injury model by performing nonlinear regression analysis and neural network regression on the first apple flowering period frost injury model;

[0036] S43, substituting the values ​​of parameters a, b, c, and f into the first apple flowering period frost damage model to improve the first apple flowering period frost damage model;

[0037] Among them, the specific expression of the improved first apple flowering period frost damage model is:

[0038]

[0039] The beneficial effects of the above further scheme are as follows: First, the apple flowering period frost damage model can predict the frost damage rate during the flowering period of the apple orchard.

[0040] Further, the step S5 is specifically as follows: according to the first apple flowering period freezing damage model, the freezing damage rate of the flowering period is obtained as y0, and then the second apple flowering period freezing damage model is established according to the freezing dates of the apple flowering period and the young fruit period; wherein the expression of the second apple flowering period freezing damage model is specifically as follows:

[0041]

[0042] In the formula, y d is the damage rate of frost damage during apple flowering period, and d is the number of days calculated from the 12th day before the peak flowering period of apple.

[0043] The beneficial effect of the above further scheme is that the second apple flowering period frost damage model can predict the frost damage rate on each day during the flowering period and young fruit period of the apple orchard.

[0044] Further: in step S6, the specific expression of the third apple flowering period frost damage model is:

[0045]

[0046] Where, 1≤d≤25, Y2 is the predicted value of the frost damage rate during the flowering period of apple;

[0047] According to the frost damage rate during flowering period in previous years in the main apple producing areas, the main apple producing areas with low frost damage rate during flowering period under the same extreme low temperature conditions are divided into the first main apple producing areas, and the rest of the main apple producing areas are divided into the second main apple producing areas.

[0048] The beneficial effect of the above further scheme is that the third apple blossom frost damage model can correct the prediction results of the apple blossom frost damage rate in the main apple orchard production areas, so that the prediction results are more in line with expectations.

[0049] The beneficial effects of the present invention are:

[0050] (1) The apple flowering period frost damage model of the present invention can evaluate the flowering period frost damage disaster of the apple orchard based on the meteorological data of the flowering period of the apple orchard in previous years, and provide a theoretical basis for disaster prevention and mitigation of the apple orchard.

[0051] (2) The apple flowering period frost damage model of the present invention can provide prediction and warning before the peak flowering period, and can provide prediction and warning as early as 19 days before the peak flowering period. Based on the warning results, anti-freeze preparations for the apple orchard can be made in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0053] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0054] In one embodiment of the present invention, the apple flowering period frost damage early warning model is used to predict the flowering period frost damage rate of 120 major apple producing counties in my country.

[0055] like Figure 1 As shown, the apple orchard disaster warning method based on the apple flowering period frost damage warning model includes the following steps:

[0056] S1. Collect meteorological data from meteorological observation stations in the main apple producing areas, and obtain the index set X of the impact of flowering period frost damage on apple orchard production through hierarchical analysis method;

[0057] S2, obtaining the observation matrix A according to the index set X and the meteorological observation data of the flowering period and the observation data of the degree of frost damage during the flowering period;

[0058] S3, based on the indicator variables in the observation matrix A, standardize them to obtain standardized indicator variables, and then obtain the main indicators of the impact of flowering period frost damage on apple yield through big data correlation analysis;

[0059] S4. Establish a frost damage model for the first apple flowering period according to the main indicators affecting apple yield, calculate the frost damage rate during the flowering period, and then obtain the parameters of the frost damage model for the first apple flowering period through nonlinear regression analysis and neural network regression, and improve the frost damage model for the first apple flowering period;

[0060] S5. establishing a second apple flowering period frost injury model based on the first apple flowering period frost injury model and the difference in days between the apple flowering period frost injury date and the peak flowering period;

[0061] S6. Based on the first apple flowering period frost damage model, the second apple flowering period frost damage model and different apple producing areas, a third apple flowering period frost damage model is established to conduct comprehensive prediction and early warning of apple flowering period frost damage and complete apple orchard disaster early warning.

[0062] In step S1, the specific indicators of the impact of flowering frost damage on apple orchard production include the phenological period of extreme low temperature weather, surface temperature, surface humidity, air temperature, air humidity, precipitation, transpiration rate, wind speed and duration of extreme low temperature;

[0063] The step S1 is specifically as follows:

[0064] From the numerous meteorological observation indicators of the meteorological observation stations in the main apple producing areas, the index set X of low temperature weather during apple flowering period affecting apple orchard production was screened out by combining the analytic hierarchy process.

[0065] In one embodiment of the present invention, meteorological data of 120 major apple-producing counties in my country from January 2011 to May 2021 are screened from meteorological data of 699 meteorological observation stations in my country, and an indicator set X affecting apple orchard production is obtained.

[0066] The meteorological data used in the present invention needs to be updated regularly, especially the weather forecast data of apple flowering period needs to be updated daily, so that the prediction accuracy of the model can be continuously improved when the flowering period is getting closer. In order to use the flowering period frost damage model to predict the frost damage of apple flowering period and young fruit period, it is necessary to add the predicted date of the peak flowering period of the year to the data file, and add the meteorological data of 28 typical main producing counties in my country (including the weather forecast data for the next 15 days) to the meteorological data file in time, and keep updating it daily during the early warning stage of frost damage during the apple flowering period.

[0067] The step S2 is specifically as follows:

[0068] According to the index set X that affects the production of apple orchards, m ), combined with the observation data of frost damage during flowering period, the observation matrix A is obtained; where m is the total number of specific indicators;

[0069]

[0070] Let the observation data in the observation matrix A be x ik , where i = 1, 2, ..., n, i is the ordinal number of the samples with frost damage during flowering, n is the total number of samples with frost damage during flowering; k = 1, 2, ..., m, k is the ordinal number of the specific indicator.

[0071] The step S3 comprises the following sub-steps:

[0072] S31, according to the indicator variable in the observation matrix A is x k , the indicator variable x is expressed as k Standardize to get the standardized indicator variable x′ k ;

[0073]

[0074] In the formula, x kmax is the maximum value of the kth indicator variable, x kmin is the minimum value of the kth indicator variable;

[0075] Since the units (scales) of specific indicators that affect apple orchard production are different, such as temperature, humidity, light, etc., their ranges of variation are significantly different, and there are differences in magnitude and dimension. Standardization can eliminate the adverse effects caused by unit differences between specific indicators.

[0076] S32, standardize the indicator variable x′ by Pearson correlation coefficient, distance correlation coefficient, Mahalanobis distance and angle cosine k (k=1,2,…,m) to conduct big data correlation analysis and obtain the main indicators affecting apple orchard production, including daily minimum temperature, daily maximum temperature and daily average temperature.

[0077] In this embodiment, a 10-year continuous investigation and analysis of frost damage during the flowering period of apple orchards in 28 major apple-producing counties in my country was conducted. Based on the high-precision prediction of apple flowering period using big data, the first apple flowering period frost damage model, i.e., the apple flowering period frost damage prediction model, was established.

[0078] The step S4 comprises the following sub-steps:

[0079] S41. Establishing a frost injury model for the first apple flowering period according to the daily minimum temperature and the daily average temperature;

[0080] Among them, the specific expression of the frost damage model during the first apple flowering period is:

[0081]

[0082] Where Y1 is the damage rate of apple frost damage on that day, x1 is the daily minimum temperature, x2 is the daily average temperature, a, b, c, f are all the parameters to be estimated for the first apple flowering period frost damage model; x1 can be replaced by x3 or x4, x3 is the daily maximum temperature, and x4 is the daily temperature difference;

[0083] S42, performing nonlinear regression analysis and neural network regression on the first apple flowering period frost injury model using SPSS 26.0 to obtain the values ​​of parameters a, b, c, and f in the first apple flowering period frost injury model;

[0084] S43, substituting the values ​​of parameters a, b, c, and f into the first apple flowering period frost damage model to improve the first apple flowering period frost damage model;

[0085] Among them, the specific expression of the improved first apple flowering period frost damage model is:

[0086]

[0087] The residual square sum Q of the first apple flowering period frost injury model of the present invention is 22.177, and the correlation coefficient square The significance probability P<0.05 can be used to predict frost damage during the flowering period.

[0088] The duration of apple flowering (from initial flowering to final flowering) is generally about 20 days. The present invention uses the lowest, highest and average temperatures of 25 days, including the peak flowering period and 12 days before and after, as sample data. The peak flowering period refers to the date when 50% of the flowers in the apple orchard are in full bloom. These 25 days basically cover the extreme low temperature weather during the entire flowering period and young fruit period of apples.

[0089] Based on the first apple flowering period frost injury model and the data on the variation of freezing degree of apple flowering period and young fruit period with date under the same low temperature conditions, a second apple flowering period frost injury model was established, which can incorporate the influence of date during the flowering process into the second apple flowering period frost injury model.

[0090] The step S5 is specifically as follows: according to the first apple flowering period freezing damage model, the freezing damage rate of the flowering period is obtained as y0, and then the second apple flowering period freezing damage model is established according to the freezing date of the apple flowering period; wherein the expression of the second apple flowering period freezing damage model is specifically as follows:

[0091]

[0092] In the formula, y d is the damage rate of frost damage during apple flowering period, and d is the number of days calculated from the 12th day before the peak flowering period of apple.

[0093] In the entire apple orchard flowering period, under the same extreme low temperature conditions, the later the date, the more serious the damage. Therefore, it is necessary to further establish a second apple flowering period frost damage model in which the frost damage rate changes with the date, and record the date numbers from 12 days before to 12 days after the peak flowering period as d = 1, 2, ..., 25 (the peak flowering period date number is d = 13).

[0094] According to the first apple flowering period frost injury model, the second apple flowering period frost injury model and different main apple producing areas, the third apple flowering period frost injury model is established, which can incorporate the influence of apple producing area factors on apple flowering period frost injury into the third apple flowering period frost injury model.

[0095] In step S6, the specific expression of the third apple flowering period frost damage model is:

[0096]

[0097] Where, 1≤d≤25, Y2 is the predicted value of the frost damage rate during the flowering period of apple;

[0098] The present invention integrates the first to third apple flowering period frost damage models to form a complete apple flowering period and young fruit period frost damage prediction and early warning model, achieving the goals of apple flowering period and young fruit period frost damage prediction and early warning in major apple producing areas, and achieving a high prediction accuracy.

[0099] In one embodiment of the present invention, according to the study of meteorological data of the main apple producing areas in my country, the damage rate of apple frost damage during flowering period in southern Liaoning, central Hebei, central Shanxi, northern Wei, southern Gansu and the areas north of them is smaller, and they are designated as the first main apple producing areas. The apple trees in these areas are more resistant to frost damage during flowering period, and the remaining main apple producing areas are classified as the second main apple producing areas.

[0100] The first to third apple flowering period frost damage models of the present invention can all be used to evaluate flowering period frost damage in previous years, and only require the peak flowering period date and 25 days of meteorological data from 12 days before to 12 days after the peak flowering period. Before the peak flowering period of apples, the flowering period frost damage can also be predicted and warned. According to the weather forecast data for the next 15 days, if the predicted date number of the peak flowering period is recorded as T1, the earliest date number that can be used by this model for effective real-time prediction is T2=T1-19.

[0101] The implementation process of the present invention is as follows: collecting meteorological data of the main apple producing areas and the predicted date of the peak flowering period of apple orchards in that year, and updating the meteorological data of the flowering period daily; screening the specific index X of the impact of flowering period frost damage on apple orchard production in the meteorological data of the main apple producing areas, and combining the observation data of flowering period frost damage for more than 10 years to obtain an observation matrix A; the indicator variable x in the observation matrix A is k Standardization is performed to obtain the standardized indicator variable x′ k , and use the Pearson correlation coefficient, distance correlation coefficient, Mahalanobis distance and angle cosine to standardize the indicator variable x′ k The correlation analysis of big data (including linear and nonlinear correlation and change dynamics) was carried out to obtain the main indicators affecting apple orchard production, including the daily minimum temperature, daily maximum temperature and daily average temperature. The first apple flowering period frost damage model was established based on the daily minimum temperature and daily average temperature, and nonlinear regression analysis and neural network regression were performed to improve the parameter values ​​of the first apple flowering period frost damage model; the first apple flowering period frost damage model can calculate the frost damage rate of apples in full flowering period; the second apple flowering period frost damage model established based on the frost damage rate in full flowering period and the date of occurrence of extreme low temperature weather can calculate the frost damage rate of apples in flowering period and young fruit period; for the regional climate specificity of apple orchards, the third apple flowering period frost damage model was established to revise the prediction results of apple flowering period frost damage rate in different regions, and finally complete the comprehensive prediction and early warning of apple flowering period frost damage.

[0102] The beneficial effects of the present invention are as follows: the apple flowering period frost damage model of the present invention can evaluate the flowering period frost damage disaster according to the meteorological data of the flowering period of the apple orchard in previous years, and the obtained evaluation results can be compared with the records of previous years, and the prediction accuracy of the model can be further improved through machine learning, which also provides a theoretical basis for disaster prevention and mitigation. The apple flowering period frost damage model can make predictions and warnings before the flowering period arrives, and can make effective predictions and warnings as early as 19 days before the peak flowering period, and the apple orchard can be prepared for frost prevention in advance based on the warning results.

[0103] In the description of the present invention, it is necessary to understand that the orientation or positional relationship indicated by the terms "center", "thickness", "upper", "lower", "horizontal", "top", "bottom", "inner", "outer", "radial", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used only for descriptive purposes, and cannot be understood as indicating or implying the relative importance or the number of implicitly specified technical features. Therefore, the features defined by "first", "second", and "third" may explicitly or implicitly include one or more of the features.

Claims

1. An apple orchard disaster warning method based on an apple flowering period frost damage warning model, characterized in that: The following steps are involved: S1. Collect and analyze meteorological data from meteorological observation stations in the main apple producing areas, and obtain the index set X of the impact of flowering period frost damage on apple orchard production through the analytic hierarchy process; S2, obtaining the observation matrix A according to the index set X and the meteorological observation data of the flowering period and the observation data of the degree of frost damage during the flowering period; S3, based on the indicator variables in the observation matrix A, standardize them to obtain standardized indicator variables, and then obtain the main indicators of the impact of flowering period frost damage on apple yield through big data correlation analysis; S4. Establishing a first apple flowering period frost injury model based on the main indicators affecting apple yield, and then obtaining the parameters of the first apple flowering period frost injury model through nonlinear regression analysis and neural network regression, and improving the first apple flowering period frost injury model; S5. establishing a second apple flowering period frost injury model based on the first apple flowering period frost injury model and the date when the apple flowering period was frozen; S6. Based on the first apple flowering period frost damage model, the second apple flowering period frost damage model and different apple main producing areas, a third apple flowering period frost damage model is established to conduct a comprehensive prediction and early warning of apple flowering period frost damage, and complete the apple orchard disaster early warning; The step S4 comprises the following sub-steps: S41. Based on the main indicators affecting apple orchard production, establish a frost damage model for the first apple flowering period and calculate the frost damage rate during the flowering period; Among them, the specific expression of the frost damage model during the first apple flowering period is: Where, Y1 is the damage rate of apple frost damage on that day, x1 is the daily minimum temperature, x2 is the daily average temperature, and a, b, c, and f are all parameters to be estimated for the frost damage model of the first apple flowering period; S42, obtaining the values ​​of parameters a, b, c, and f in the first apple flowering period frost damage model by performing neural network regression and nonlinear regression analysis on the first apple flowering period frost damage model; S43, substituting the values ​​of parameters a, b, c, and f into the first apple flowering period frost damage model to improve the first apple flowering period frost damage model; Among them, the specific expression of the improved first apple flowering period frost damage model is: The step S5 is specifically as follows: according to the first apple flowering period freezing damage model, the freezing damage rate of the flowering period is obtained as y0, and then the second apple flowering period freezing damage model is established according to the freezing dates of the apple flowering period and the young fruit period; wherein the expression of the second apple flowering period freezing damage model is specifically as follows: In the formula, y d is the damage rate of frost damage during apple flowering period, d is the number of days calculated from the 12th day before the peak flowering period of apple; In step S6, the specific expression of the third apple flowering period frost damage model is: Where, 1≤d≤25, Y2 is the predicted value of the frost damage rate during the flowering period of apple; According to the frost damage rate during flowering period in previous years in the main apple producing areas, the main apple producing areas with low frost damage rate during flowering period are divided into the first main apple producing areas, and the rest of the main apple producing areas are divided into the second main apple producing areas.

2. The apple orchard disaster warning method based on the apple flowering period frost damage warning model according to claim 1 is characterized in that: In step S1, the specific indicators of the impact of flowering frost damage on apple orchard production include the phenological period of extreme low temperature weather, surface temperature, surface humidity, air temperature, air humidity, precipitation, transpiration rate, wind speed and duration of extreme low temperature; The step S1 is specifically as follows: From the numerous meteorological data of meteorological observation stations in the main apple producing areas, a set of indicators X on the impact of low temperature weather during the apple flowering period on apple orchard production was selected.

3. The apple orchard disaster warning method based on the apple flowering period frost damage warning model according to claim 1 is characterized in that: The step S2 is specifically as follows: According to the index set X that affects the production of apple orchards, m ), combined with the observation data of frost damage during flowering period, the observation matrix A is obtained; where m is the total number of specific indicators; Note that the indicator variable x in the observation matrix A k The i-th observation of ik , where i = 1, 2, ..., n, i is the ordinal number of the samples with frost damage during flowering, n is the total number of samples with frost damage during flowering; k = 1, 2, ..., m, k is the ordinal number of the specific indicator.

4. The apple orchard disaster warning method based on the apple flowering period frost damage warning model according to claim 3 is characterized in that: The step S3 comprises the following sub-steps: S31, according to the observation data in the observation matrix A is x ik , the indicator variable x is expressed as k Standardization is performed to obtain the standardized indicator variable x′ k and its observation data x i ' k ; In the formula, x kmax is the maximum value of the kth indicator variable, x kmin is the minimum value of the kth indicator variable; S32, standardize the indicator variable x′ by Pearson correlation coefficient, distance correlation coefficient, Mahalanobis distance and angle cosine k (k=1,2,…,m) to conduct big data correlation analysis and obtain the main indicators affecting apple orchard production, including daily minimum temperature, daily maximum temperature and daily average temperature.

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