A method for evaluating the degree of spring frost damage of winter wheat based on the WOFOST model

Abstract

The present invention relates to the technical field of winter wheat frost damage assessment, and in particular to a method for assessing the degree of winter wheat frost damage in spring based on the WOFOST model, the method comprising: data collection and curve drawing; determination of significant change points and abnormal growth points; calculation of correlation range and correlation coefficient; determination of model input parameters; and assessment of the degree of frost damage. The present invention provides accurate and dynamic monitoring of the impact of frost damage through correlation analysis of real-time meteorological data and growth data in frost damage assessment. By using the correlation determination of significant change points and abnormal growth points, the lowest temperature and the highest temperature are the main triggering factors of frost damage, wind speed and radiation will aggravate the impact of frost damage, and leaf area index and biomass reflect the growth status of winter wheat. The WOFOST model is used to quantify the difference in aboveground biomass under frost damage and non-frost damage conditions, which effectively solves the problems of inflexible application and poor real-time performance due to complex parameter selection.

Description

Technical Field

[0001] The invention relates to the technical field of winter wheat frost damage assessment, and in particular to a method for assessing the degree of winter wheat frost damage in spring based on a WOFOST model. Background Art

[0002] One of the main meteorological disasters during the growth and development of winter wheat is spring frost damage, with an occurrence frequency of 30% to 40%. As the global climate warms, extreme weather events are on the rise. At the same time, the winter wheat planting boundary moves northward and the main planting varieties become spring-oriented. The occurrence and harm of spring frost damage have shown a trend of gradually increasing, which has a great impact on the high and stable yield of winter wheat.

[0003] The patent document with publication number CN113218887A discloses a method for rapid monitoring and evaluation of the severity of wheat frost damage. The method includes: based on the correlation between canopy spectral parameters and chlorophyll fluorescence parameters and yield loss rate and the characteristics of their remote sensing information source, the canopy spectral parameters and fluorescence parameters are optimized and combined, with the yield loss rate as the dependent variable and the combined parameters as the independent variable, partial least squares method, neural network, and multivariate stepwise regression operations are used to select the best parameter combination, and a model is established. Each remote sensing parameter combination data set is subjected to partial least squares method, neural network, and multivariate stepwise regression operations, and the performance of the model results in the training set and the test set is further analyzed to establish an optimized multivariate regression equation, and rapid monitoring of the severity of wheat frost damage is obtained through the optimized multivariate regression equation.

[0004] It can be seen that the method for rapid monitoring and evaluation of the severity of wheat frost damage has the following problems: the method relies on a variety of regression operations (such as partial least squares, neural networks, multivariate stepwise regression, etc.), these calculation models require a large amount of training data, and adaptive training of different data sets is required, resulting in differences in applicability between different regions or years; the remote sensing data used needs to be obtained through satellites or drones, which is limited by weather, equipment accuracy and time delays in data processing, so real-time performance and accuracy are affected to a certain extent. Summary of the invention

[0005] To this end, the present invention provides a method for evaluating the degree of frost damage of winter wheat in spring based on the WOFOST model, so as to overcome the problems of inflexible application and poor real-time performance caused by complex parameter selection in the prior art.

[0006] To achieve the above object, the present invention provides a method for evaluating the degree of winter wheat frost damage in spring based on the WOFOST model, comprising:

[0007] Collect real-time meteorological data and real-time growth data of winter wheat on the day when frost damage occurs in the assessment area;

[0008] Drawing a plurality of meteorological curves corresponding to the real-time meteorological data and changing with time according to the real-time meteorological data;

[0009] Drawing a plurality of growth curves corresponding to the real-time growth data and changing with time according to the real-time growth data;

[0010] Determine a number of significant change points according to each of the meteorological curves;

[0011] Determining a number of abnormal growth points according to each of the growth curves;

[0012] Determine a number of association ranges according to each of the significant change points and the preset association duration;

[0013] Determine a number of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change point;

[0014] Determining model input parameters according to each of the correlation coefficients and a preset standard correlation coefficient;

[0015] The aboveground biomass of winter wheat under freezing conditions is simulated according to the model input parameters and the preset WOFOST model to form freezing biomass;

[0016] Obtaining an average value of all the real-time meteorological data corresponding to the model input parameters within a preset historical control period and the aboveground biomass of winter wheat simulated by the preset WOFOST model under non-freezing conditions to form a control biomass;

[0017] Determine the freezing damage loss grade according to the freezing damage biomass and the control biomass to form a freezing damage degree assessment result;

[0018] The real-time meteorological data include real-time minimum temperature, real-time maximum temperature, real-time average wind speed and real-time solar radiation; the real-time growth data include real-time leaf area index and real-time observed biomass of winter wheat; and the correlation coefficients include low temperature coefficient, high temperature coefficient, wind speed coefficient and radiation coefficient.

[0019] Furthermore, the meteorological curve includes a minimum temperature curve, a maximum temperature curve, an average wind speed curve and a solar radiation curve.

[0020] Further, the determining of a number of significant change points according to each of the meteorological curves includes:

[0021] Calculate the difference between the real-time minimum temperature at any moment on the minimum temperature curve and the previous moment to form a minimum temperature change value, and when the absolute value of the minimum temperature change value is greater than a preset standard temperature change value, determine that the point on the minimum temperature curve at the middle moment between the two moments is a temporary change point;

[0022] Calculate the difference between the real-time maximum temperature at any moment on the maximum temperature curve and the previous moment to form a maximum temperature change value, and when the absolute value of the maximum temperature change value is greater than a preset standard temperature change value, determine that the point on the maximum temperature curve at the middle moment between the two moments is a temporary change point;

[0023] Calculate the difference between the real-time average wind speed at any moment on the average wind speed curve and the previous moment to form an average wind speed change value, and when the wind speed change value is a positive number and the absolute value of the wind speed change value is greater than a preset standard wind speed change value, determine that the point on the average wind speed curve at the middle moment between the two moments is a temporary change point;

[0024] A number of significant change points are determined according to all the temporary change points and the solar radiation curve.

[0025] Further, the determining of a number of significant change points according to the temporary change point and the solar radiation curve comprises:

[0026] Calculate the difference between the real-time solar radiation at any moment on the solar radiation curve and the previous moment to form a solar radiation change value, and when the absolute value of the solar radiation change value is greater than a preset standard radiation change value, determine that the point on the solar radiation curve at the middle moment between the two moments is a frost damage meteorological determination point;

[0027] When the temporary change point coincides with the freezing weather determination point on the time axis, the temporary change point is determined to be the significant change point, forming a plurality of significant change points.

[0028] Furthermore, the growth curve includes a leaf area index curve and a biomass curve.

[0029] Furthermore, a number of abnormal growth points are determined according to each of the growth curves;

[0030] Calculating the difference between the real-time leaf area index at any moment on the leaf area index curve and the previous moment to form a leaf area change value, and when the leaf area change value is negative and the absolute value of the leaf area change value is greater than a preset standard leaf area change value, determining that the point on the leaf area index curve at the middle moment between the two moments is a temporary growth point;

[0031] Calculate the difference between the real-time observed biomass at any moment on the biomass curve and the previous moment to form a biomass change value, and when the biomass change value is a negative number and the absolute value of the biomass change value is greater than a preset standard biomass change value, or when the biomass change value is a positive number and the absolute value of the biomass change value is less than the preset standard biomass change value, determine the point on the biomass curve at the middle moment between the two moments as a frost damage growth determination point;

[0032] When the temporary growth point coincides with the frost damage growth determination point on the time axis, the temporary growth point is determined to be the abnormal growth point, forming a plurality of abnormal growth points.

[0033] Furthermore, the determining of a plurality of association ranges according to each of the significant change points and the preset association duration includes:

[0034] Calculate the difference between the moment corresponding to the significant change point and the preset association time to form a first moment, calculate the sum of the moment corresponding to the significant change point and the preset association time to form a second moment, select all moments between the first moment and the second moment as an association range, and form several association ranges.

[0035] Further, the determining of a number of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change point includes:

[0036] Calculate the Pearson correlation coefficient of the real-time minimum temperature corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point to form a low temperature coefficient;

[0037] Calculate the Pearson correlation coefficient of the real-time maximum temperature corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a high temperature coefficient;

[0038] Calculate the Pearson correlation coefficient of the real-time average wind speed corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a wind speed coefficient;

[0039] The Pearson correlation coefficient between the real-time solar radiation corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point is calculated to form a radiation coefficient.

[0040] Furthermore, the determining of the model input parameters according to each of the correlation coefficients and the preset standard correlation coefficient comprises:

[0041] When the low temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time minimum temperature as a model input parameter;

[0042] When the high temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time maximum temperature as a model input parameter;

[0043] When the wind speed coefficient is greater than the preset standard correlation coefficient, determining the real-time average wind speed as a model input parameter;

[0044] When the radiation coefficient is greater than the preset standard correlation coefficient, the real-time solar radiation is determined to be a model input parameter.

[0045] Furthermore, the step of determining the freezing damage loss grade based on the freezing damage biomass and the control biomass to form a freezing damage degree assessment result includes:

[0046] The relative deviation between the control biomass and the frost-damaged biomass is calculated to form a yield loss rate, and the frost damage loss grade is determined based on a comparison result between the yield loss rate and a preset standard loss rate range to form a frost damage degree assessment result.

[0047] Compared with the prior art, the beneficial effect of the present invention is that, in the frost damage assessment, accurate and dynamic monitoring of the impact of frost damage is provided through the correlation analysis of real-time meteorological data and growth data. By using the correlation judgment of significant change points and abnormal growth points, the minimum and maximum temperatures are the main triggering factors of frost damage, wind speed and radiation will aggravate the impact of frost damage, and leaf area index and biomass reflect the growth status of winter wheat, which can help identify the actual damage of frost damage to crops, achieve in-depth matching of meteorological conditions and growth status, and can identify the potential losses of winter wheat frost damage earlier and more accurately. The WOFOST model is used to quantify the difference in aboveground biomass under frost damage and non-frost damage conditions, ensuring that the assessment results are more scientific and comparative, reducing the deviation of human judgment and improving the automation level of data analysis to reduce frost damage losses, effectively solving the problems of inflexible application and poor real-time performance due to complex parameter selection.

[0048] Furthermore, by selecting parameters that are highly correlated with meteorological changes (minimum temperature, maximum temperature, average wind speed, solar radiation), we can more accurately capture the change points of elements in the meteorological system that have a significant impact on frost damage. When these parameters change significantly, they are often the manifestation of a sudden drop in temperature, a change in wind speed, or a decrease in solar radiation, which directly points to weather conditions that may cause frost damage. Selecting meteorological parameters that are highly correlated with frost damage also improves monitoring accuracy and can more effectively distinguish between real frost damage risks and ordinary temperature fluctuations. At the same time, after removing the interference of irrelevant factors, changes can be calculated and responded to efficiently.

[0049] Furthermore, by comprehensively considering the dynamic changes of temperature, wind speed and solar radiation, the significant change points with the greatest impact on crops are screened out. Doing so can ensure that the significant change points not only reflect the drastic changes in temperature and wind speed, but also match the sudden changes in solar radiation, so as to more accurately identify the key moments associated with frost damage. The reason for further judgment through sudden changes in solar radiation is that the rapid changes in solar radiation often work together with sudden drops in temperature and changes in wind speed, increasing the risk of frost damage. When solar radiation weakens rapidly at night or in the early morning, the ground temperature drops faster, especially on clear and windless nights, it is easier to form an inversion layer, causing the surface temperature to drop sharply, making crops more susceptible to frost damage. Therefore, combined with the sudden changes in solar radiation, it is possible to more accurately judge whether the environment has the meteorological conditions for frost damage.

[0050] Furthermore, by simultaneously analyzing the leaf area index and biomass changes, the plant growth abnormalities caused by frost damage can be accurately captured. The changes in leaf area or biomass alone may not be able to fully reflect the physiological changes of plants under the influence of low temperatures. By combining the overlap of the significant change points of the two, the actual extent and time of the impact of frost damage on plants can be more clearly confirmed, and abnormal growth points can be discovered and located in time.

[0051] Furthermore, by introducing a preset association duration, the time range of significant change points can be effectively narrowed or expanded, better capturing changes in environmental or growth factors related to these changes, avoiding reliance on burst data at a single moment, thereby improving the accuracy and timeliness of abnormal change identification and enabling a more comprehensive understanding of the actual impact of changes on the system.

[0052] Furthermore, by calculating the correlation coefficient, we can quantitatively analyze the impact of meteorological factors on crop growth, help more accurately identify and predict growth anomalies caused by meteorological changes, and by calculating the Pearson correlation coefficient, we can effectively distinguish the effects of various environmental factors.

[0053] Furthermore, through the correlation coefficient screening method, the model can focus on the meteorological factors that have a greater impact on crop growth, avoiding interference from irrelevant factors and improving the accuracy and efficiency of the model. It can ensure that the input parameters have a stronger correlation with the actual situation of crop growth.

[0054] Furthermore, by calculating the yield loss rate and comparing it with the standard loss rate range, the impact of frost damage on crops can be accurately assessed. This helps to timely detect and predict frost damage, help take effective prevention and control measures, and reduce the losses caused by frost damage. By comparing with the preset standard loss rate, the assessment results can be more objective and quantifiable, improving the accuracy and practicality of frost damage assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the method for evaluating the degree of winter wheat spring frost damage based on the WOFOST model in this embodiment;

[0056] Figure 2 A decision logic diagram for determining a temporary change point in this embodiment;

[0057] Figure 3 This is a decision logic diagram for determining significant change points in this embodiment;

[0058] Figure 4 This is a decision logic diagram for determining a temporary growth point in this embodiment. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0062] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] See also Figure 1 As shown, it is a flow chart of the method for evaluating the degree of spring frost damage of winter wheat based on the WOFOST model in this embodiment;

[0064] This embodiment provides a method for evaluating the degree of winter wheat frost damage in spring based on the WOFOST model, comprising:

[0065] Collect real-time meteorological data and real-time growth data of winter wheat on the day when frost damage occurs in the assessment area;

[0066] Drawing a plurality of meteorological curves corresponding to the real-time meteorological data and changing with time according to the real-time meteorological data;

[0067] Drawing a plurality of growth curves corresponding to the real-time growth data and changing with time according to the real-time growth data;

[0068] Determine a number of significant change points according to each of the meteorological curves;

[0069] Determining a number of abnormal growth points according to each of the growth curves;

[0070] Determine a number of association ranges according to each of the significant change points and the preset association duration;

[0071] Determine a number of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change point;

[0072] Determining model input parameters according to each of the correlation coefficients and a preset standard correlation coefficient;

[0073] The aboveground biomass of winter wheat under freezing conditions is simulated according to the model input parameters and the preset WOFOST model to form freezing biomass;

[0074] Obtaining an average value of all the real-time meteorological data corresponding to the model input parameters within a preset historical control period and the aboveground biomass of winter wheat simulated by the preset WOFOST model under non-freezing conditions to form a control biomass;

[0075] Determine the freezing damage loss grade according to the freezing damage biomass and the control biomass to form a freezing damage degree assessment result;

[0076] The real-time meteorological data include real-time minimum temperature, real-time maximum temperature, real-time average wind speed and real-time solar radiation; the real-time growth data include real-time leaf area index and real-time observed biomass of winter wheat; the correlation coefficients include low temperature coefficient, high temperature coefficient, wind speed coefficient and radiation coefficient; the real-time minimum temperature is the lowest temperature value observed in the time period from 0 o'clock to the current moment; the real-time maximum temperature is the highest temperature value observed in the time period from 0 o'clock to the current moment.

[0077] The preset association time refers to the time range used to determine the association between significant change points and abnormal growth points, which usually depends on the occurrence of frost damage and the reaction time of the crop, and is usually set between 2 hours and 6 hours; in this embodiment, it is set to 5 hours. This setting helps to quickly capture the immediate impact after the occurrence of frost damage, avoid reaction delays caused by too long a time range, and ensure timely and accurate judgment of the impact of frost damage.

[0078] The preset historical control period refers to the average value of historical meteorological data related to the model input parameters, which usually depends on the changes and accuracy of long-term climate patterns and is usually set between 10 and 35 years. In this embodiment, it is set to 30 years, which can provide sufficient historical meteorological data support to ensure that the evaluation model can reflect the long-term climate change trend, enhance the reliability and accuracy of frost damage loss assessment, and provide a more representative control, especially in the face of climate change.

[0079] The preset WOFOST model is a tool for simulating crop growth, especially for evaluating the growth of crops (such as winter wheat) under different climatic conditions. By inputting meteorological data and crop growth data, the model can simulate the changes in biomass under frost conditions and compare it with the control biomass under non-frost conditions to calculate the yield loss caused by frost. The WOFOST model can accurately predict the response of crops under different environmental conditions, provide a scientific basis, help evaluate the impact of frost damage, and provide decision support for agricultural production.

[0080] Based on the WOFOST model, by collecting real-time meteorological data (minimum temperature, maximum temperature, average wind speed, solar radiation) on the day of frost damage and real-time growth data of winter wheat (leaf area index and observed biomass), the meteorological and growth curves that change over time are drawn. The meteorological curve determines the significant change points and abnormal growth points in the growth curve, and the correlation range is determined by combining the correlation time, so as to calculate the correlation coefficient between the meteorological data and the growth data. After analyzing the comparison between each correlation coefficient and the preset standard, the appropriate model input parameters are selected, and the WOFOST model is used to simulate the aboveground biomass (frost damage biomass) under frost damage, and compared with the historical control biomass, the loss level and evaluation results of winter wheat spring frost damage are finally obtained.

[0081] In the frost damage assessment, the correlation analysis of real-time meteorological data and growth data provides accurate and dynamic monitoring of the impact of frost damage. Using the correlation between significant change points and abnormal growth points, the lowest and highest temperatures are the main triggering factors of frost damage, wind speed and radiation will aggravate the impact of frost damage, and leaf area index and biomass reflect the growth status of winter wheat, which can help identify the actual damage of frost damage to crops, achieve in-depth matching of meteorological conditions and growth status, and can identify the potential losses of winter wheat frost damage earlier and more accurately. The WOFOST model is used to quantify the difference in aboveground biomass under frost damage and non-frost damage conditions, ensuring that the assessment results are more scientific and comparative, reducing the deviation of human judgment and improving the automation level of data analysis to reduce frost damage losses, effectively solving the problems of inflexible application and poor real-time performance due to complex parameter selection.

[0082] Specifically, the meteorological curve includes a minimum temperature curve, a maximum temperature curve, an average wind speed curve and a solar radiation curve.

[0083] Please continue reading Figure 2 As shown, it is a decision logic diagram for determining a temporary change point in this embodiment;

[0084] Specifically, determining a number of significant change points according to each of the meteorological curves includes:

[0085] Calculate the difference between the real-time minimum temperature at any moment on the minimum temperature curve and the previous moment to form a minimum temperature change value, and when the absolute value of the minimum temperature change value is greater than a preset standard temperature change value, determine that the point on the minimum temperature curve at the middle moment between the two moments is a temporary change point;

[0086] Calculate the difference between the real-time maximum temperature at any moment on the maximum temperature curve and the previous moment to form a maximum temperature change value, and when the absolute value of the maximum temperature change value is greater than a preset standard temperature change value, determine that the point on the maximum temperature curve at the middle moment between the two moments is a temporary change point;

[0087] Calculate the difference between the real-time average wind speed at any moment on the average wind speed curve and the previous moment to form an average wind speed change value, and when the wind speed change value is a positive number and the absolute value of the wind speed change value is greater than a preset standard wind speed change value, determine that the point on the average wind speed curve at the middle moment between the two moments is a temporary change point;

[0088] A number of significant change points are determined according to all the temporary change points and the solar radiation curve.

[0089] The preset standard temperature change value is usually between 1°C and 5°C, depending on the critical temperature for freezing damage and the climate characteristics of the area, and is set to 2°C in this embodiment. This setting can effectively identify significant change points when the temperature fluctuates greatly, which helps to detect temperature changes that may cause freezing damage in a timely manner.

[0090] The preset standard wind speed change value is usually between 1m / s and 3m / s, depending on the local annual wind speed level and the impact of wind speed on ambient temperature. In this embodiment, it is set to 1.5m / s. This setting enables the system to mark the change point in time when the wind speed changes suddenly, helping to predict and prevent the impact of frost damage on crops.

[0091] First, based on the meteorological curves such as the minimum temperature, maximum temperature, average wind speed and solar radiation, the difference between any moment and the previous moment in each curve is calculated to form the minimum temperature change value, maximum temperature change value and average wind speed change value respectively. By comparing with the preset standard temperature change value and wind speed change value, the temporary change point in the meteorological curve is determined. Finally, based on these temporary change points and the solar radiation curve, the significant change points are further determined.

[0092] By selecting parameters that are highly correlated with meteorological changes (minimum temperature, maximum temperature, average wind speed, solar radiation), we can more accurately capture the change points of elements in the meteorological system that have a significant impact on frost damage. When these parameters change significantly, they are often the manifestation of a sudden drop in temperature, a change in wind speed, or a decrease in solar radiation, which directly points to weather conditions that may cause frost damage. Selecting meteorological parameters that are highly correlated with frost damage also improves monitoring accuracy and can more effectively distinguish between real frost damage risks and ordinary temperature fluctuations. At the same time, after removing the interference of irrelevant factors, changes can be calculated and responded to efficiently.

[0093] Please continue reading Figure 3 As shown, it is a decision logic diagram for determining significant change points in this embodiment;

[0094] Specifically, determining a number of significant change points according to the temporary change point and the solar radiation curve includes:

[0095] Calculate the difference between the real-time solar radiation at any moment on the solar radiation curve and the previous moment to form a solar radiation change value, and when the absolute value of the solar radiation change value is greater than a preset standard radiation change value, determine that the point on the solar radiation curve at the middle moment between the two moments is a frost damage meteorological determination point;

[0096] When the temporary change point coincides with the freezing weather determination point on the time axis, the temporary change point is determined to be the significant change point, forming a plurality of significant change points.

[0097] The preset standard radiation change value is a numerical threshold for determining a sudden change in solar radiation, which usually depends on historical meteorological data, geographical location, and critical conditions for the occurrence of frost damage. It is usually set between 50W / m² and 100W / m², and is set to 75W / m² in this embodiment, which can more keenly capture significant changes in solar radiation, effectively identify sudden changes related to frost damage conditions, and avoid missing early warnings of potential frost damage.

[0098] First, the solar radiation change value is formed by calculating the solar radiation difference between any moment and the previous moment in the solar radiation curve, and judging whether its absolute value exceeds the preset standard radiation change value, so as to mark the change point as the freezing damage meteorological judgment point. Then, all temporary change points are compared with the freezing damage meteorological judgment point on the time axis. If the two coincide, the temporary change point is determined as a significant change point, thereby obtaining several significant change points.

[0099] By comprehensively considering the dynamic changes of temperature, wind speed and solar radiation, the significant change points that have the greatest impact on crops are screened out. Doing so can ensure that the significant change points not only reflect the drastic changes in temperature and wind speed, but also match the sudden changes in solar radiation, so as to more accurately identify the key moments associated with frost damage. The reason for further judgment through sudden changes in solar radiation is that rapid changes in solar radiation often work together with sudden drops in temperature and changes in wind speed, increasing the risk of frost damage. When solar radiation weakens rapidly at night or in the early morning, the ground temperature drops faster, especially on clear and windless nights, it is easier to form an inversion layer, causing the surface temperature to drop sharply, making crops more susceptible to frost damage. Therefore, combined with the sudden changes in solar radiation, it is possible to more accurately judge whether the environment has the meteorological conditions for frost damage.

[0100] Specifically, the growth curve includes a leaf area index curve and a biomass curve.

[0101] Please continue reading Figure 4 As shown, it is a determination logic diagram for determining a temporary growth point in this embodiment;

[0102] Specifically, the determining of a number of abnormal growth points according to each of the growth curves;

[0103] Calculating the difference between the real-time leaf area index at any moment on the leaf area index curve and the previous moment to form a leaf area change value, and when the leaf area change value is negative and the absolute value of the leaf area change value is greater than a preset standard leaf area change value, determining that the point on the leaf area index curve at the middle moment between the two moments is a temporary growth point;

[0104] Calculate the difference between the real-time observed biomass at any moment on the biomass curve and the previous moment to form a biomass change value, and when the biomass change value is a negative number and the absolute value of the biomass change value is greater than a preset standard biomass change value, or when the biomass change value is a positive number and the absolute value of the biomass change value is less than the preset standard biomass change value, determine the point on the biomass curve at the middle moment between the two moments as a frost damage growth determination point;

[0105] When the temporary growth point coincides with the frost damage growth determination point on the time axis, the temporary growth point is determined to be the abnormal growth point, forming a plurality of abnormal growth points.

[0106] The preset standard leaf area change value is set according to the law of crop growth and climatic conditions, and depends on the growth stage and environmental conditions of the crop. It is usually set between 0.2 and 2.0, and is set to 1.0 in this embodiment, which can effectively reflect the significant changes in the leaf area of ​​the crop, especially when encountering abnormal climate such as frost damage, it can accurately capture the negative changes in the leaf area and issue early warnings in time so that preventive measures can be taken to ensure the healthy growth of crops.

[0107] The preset standard biomass change value is a parameter used to measure the normal fluctuation range of crop biomass within a certain period of time, which depends on the growth cycle of the crop and environmental factors, and is usually set between 0.5 and 5.0. In this embodiment, it is set to 1.5, aiming to effectively distinguish between normal fluctuations and abnormal fluctuations in crop biomass. Through this standard value, abnormal situations with large biomass changes, especially the impact of frost damage on biomass, can be effectively identified, and timely warnings and corresponding measures can be taken to optimize crop management and increase yields.

[0108] The leaf area index curve and biomass curve of the plant are analyzed. By calculating the leaf area index and biomass change values ​​at adjacent moments, the leaf area index change points (temporary growth points) with significant declines and abnormal biomass change points (freezing growth judgment points) are identified. When the temporary growth point and the freezing growth judgment point coincide on the time axis, the point is identified as an abnormal growth point, thereby identifying possible freezing damage.

[0109] By simultaneously analyzing the leaf area index and biomass changes, we can accurately capture plant growth abnormalities caused by frost damage. Changes in leaf area or biomass alone may not be able to fully reflect the physiological changes of plants under the influence of low temperatures. By combining the overlap of the significant change points of the two, we can more clearly confirm the actual extent and time of the impact of frost damage on plants, and promptly discover and locate abnormal growth points.

[0110] Specifically, the determining of a number of association ranges according to each of the significant change points and the preset association duration includes:

[0111] Calculate the difference between the moment corresponding to the significant change point and the preset association time to form a first moment, calculate the sum of the moment corresponding to the significant change point and the preset association time to form a second moment, select all moments between the first moment and the second moment as an association range, and form several association ranges.

[0112] According to the significant change point and the preset association duration, the first moment and the second moment are determined, and then all moments between the first moment and the second moment are selected as the association range to form several association ranges. In this way, the moment of significant change can be associated with a certain length of time period, ensuring the complete capture of the change process and providing more accurate data support for subsequent analysis.

[0113] By introducing a preset correlation duration, the time range of significant change points can be effectively narrowed or expanded, and changes in environmental or growth factors related to these changes can be better captured, avoiding reliance on burst data at a single moment, thereby improving the accuracy and timeliness of abnormal change identification and enabling a more comprehensive understanding of the actual impact of changes on the system.

[0114] Specifically, determining a number of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change point includes:

[0115] Calculate the Pearson correlation coefficient of the real-time minimum temperature corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point to form a low temperature coefficient;

[0116] Calculate the Pearson correlation coefficient of the real-time maximum temperature corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a high temperature coefficient;

[0117] Calculate the Pearson correlation coefficient of the real-time average wind speed corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a wind speed coefficient;

[0118] The Pearson correlation coefficient between the real-time solar radiation corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point is calculated to form a radiation coefficient.

[0119] According to the significant change points and the abnormal growth points within the associated range, the Pearson correlation coefficient between the real-time minimum temperature, maximum temperature, average wind speed and solar radiation corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point is calculated, thereby obtaining the low temperature coefficient, high temperature coefficient, wind speed coefficient and radiation coefficient. These coefficients reflect the relationship between environmental factors (such as temperature, wind speed, solar radiation) and biomass changes, and are used to analyze the impact of different environmental conditions on crop growth.

[0120] By calculating the correlation coefficient, we can quantitatively analyze the impact of meteorological factors on crop growth, help more accurately identify and predict growth anomalies caused by meteorological changes, and by calculating the Pearson correlation coefficient, we can effectively distinguish the effects of various environmental factors.

[0121] Specifically, determining the model input parameters according to the correlation coefficients and the preset standard correlation coefficients includes:

[0122] When the low temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time minimum temperature as a model input parameter;

[0123] When the high temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time maximum temperature as a model input parameter;

[0124] When the wind speed coefficient is greater than the preset standard correlation coefficient, determining the real-time average wind speed as a model input parameter;

[0125] When the radiation coefficient is greater than the preset standard correlation coefficient, the real-time solar radiation is determined to be a model input parameter.

[0126] The preset standard correlation coefficient is a set threshold value used to determine whether the correlation between meteorological factors and crop growth is significant, which depends on the characteristics of the data, the needs of the research object, and the accuracy requirements of the prediction model. In practical applications, the preset standard correlation coefficient is usually set between 0.5 and 0.8, and an appropriate value is selected according to the actual situation. In this embodiment, it is set to 0.7 to ensure that only meteorological factors with a strong correlation with crop growth are used as model input parameters, and those factors with weaker correlation can be excluded, ensuring that the input data of the model is more accurate, and improving the prediction accuracy and effectiveness of the model.

[0127] According to each correlation coefficient and the preset standard correlation coefficient, it is judged whether each correlation coefficient is greater than the preset standard correlation coefficient. If the low temperature coefficient, high temperature coefficient, wind speed coefficient or radiation coefficient is greater than the preset standard correlation coefficient, the corresponding meteorological factor (real-time minimum temperature, real-time maximum temperature, real-time average wind speed or real-time solar radiation) is selected as the input parameter of the model. This process helps to screen meteorological variables that have a significant impact on model prediction.

[0128] Through the correlation coefficient screening method, the model can focus on the meteorological factors that have a greater impact on crop growth, avoiding interference from irrelevant factors and improving the accuracy and efficiency of the model. It can ensure that the input parameters have a stronger correlation with the actual situation of crop growth.

[0129] Specifically, determining the freezing damage loss grade according to the freezing damage biomass and the control biomass to form the freezing damage degree assessment result includes:

[0130] Calculating the relative deviation between the control biomass and the frost-damaged biomass to form a yield loss rate, determining the frost damage loss grade according to a comparison result between the yield loss rate and a preset standard loss rate range, and forming a frost damage degree assessment result, wherein when the yield loss rate is less than the minimum value of the preset standard loss rate range, the frost damage loss grade is determined to be mild frost damage;

[0131] When the yield loss rate is within the preset standard loss rate range, the freezing damage loss level is determined to be moderate freezing damage;

[0132] When the yield loss rate is greater than the maximum value of the preset standard loss rate range, the freezing damage loss level is determined to be severe freezing damage.

[0133] The preset standard loss rate range refers to a loss rate range that is pre-set based on factors such as the frost resistance of different crops, historical frost damage data, meteorological conditions, etc. It is used to judge the degree of frost damage and is determined by agricultural experts or climate models based on historical frost damage data and the performance of different crops. Generally speaking, it is usually set between 10% and 50%. In this embodiment, the range is set to 20% to 40%. Taking into account the loss range of moderately frost-resistant crops when suffering from mild frost damage, it can ensure that the assessment of the frost damage loss level is more accurate and scientific, avoid underestimation or overestimation of the impact of frost damage on crops, and provide an effective basis for subsequent agricultural intervention.

[0134] First, the relative deviation between the frozen biomass and the control biomass was calculated to obtain the yield loss rate. Then, the yield loss rate was compared with the preset standard loss rate range to determine the freezing loss grade, thus forming the freezing damage degree assessment result.

[0135] By calculating the yield loss rate and comparing it with the standard loss rate range, the impact of frost damage on crops can be accurately assessed. This helps to timely detect and predict frost damage, help take effective prevention and control measures, and reduce the losses caused by frost damage. By comparing with the preset standard loss rate, the assessment results can be more objective and quantifiable, improving the accuracy and practicality of frost damage assessment.

[0136] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the degree of winter wheat frost damage in spring based on the WOFOST model, characterized in that: include: Collect real-time meteorological data and real-time growth data of winter wheat on the day when frost damage occurs in the assessment area; Drawing a plurality of meteorological curves corresponding to the real-time meteorological data and changing with time according to the real-time meteorological data; Drawing a plurality of growth curves corresponding to the real-time growth data and changing with time according to the real-time growth data; Determine a number of significant change points according to each of the meteorological curves; Determining a number of abnormal growth points according to each of the growth curves; Determining a number of association ranges according to each of the significant change points and the preset association duration; Determine a number of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change point; Determining model input parameters according to each of the correlation coefficients and a preset standard correlation coefficient; The aboveground biomass of winter wheat under freezing conditions is simulated according to the model input parameters and the preset WOFOST model to form freezing biomass; Obtaining an average value of all the real-time meteorological data corresponding to the model input parameters within a preset historical control period and the aboveground biomass of winter wheat simulated by the preset WOFOST model under non-freezing conditions to form a control biomass; Determine the freezing damage loss grade according to the freezing damage biomass and the control biomass to form a freezing damage degree assessment result; The real-time meteorological data include the real-time minimum temperature, the real-time maximum temperature, the real-time average wind speed and the real-time solar radiation; the real-time growth data include the real-time leaf area index and the real-time observed biomass of winter wheat; and the correlation coefficients include the low temperature coefficient, the high temperature coefficient, the wind speed coefficient and the radiation coefficient; The preset correlation time refers to the time range used to determine the correlation between significant change points and abnormal growth points.

2. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 1, characterized in that: The meteorological curves include a minimum temperature curve, a maximum temperature curve, an average wind speed curve and a solar radiation curve.

3. The method for evaluating the degree of winter wheat frost damage in spring based on the WOFOST model according to claim 2, characterized in that: Determining a number of significant change points according to each of the meteorological curves comprises: Calculate the difference between the real-time minimum temperature at any moment on the minimum temperature curve and the previous moment to form a minimum temperature change value, and when the absolute value of the minimum temperature change value is greater than a preset standard temperature change value, determine that the point on the minimum temperature curve at the middle moment between the two moments is a temporary change point; Calculate the difference between the real-time maximum temperature at any moment on the maximum temperature curve and the previous moment to form a maximum temperature change value, and when the absolute value of the maximum temperature change value is greater than a preset standard temperature change value, determine that the point on the maximum temperature curve at the middle moment between the two moments is a temporary change point; Calculate the difference between the real-time average wind speed at any moment on the average wind speed curve and the previous moment to form an average wind speed change value, and when the wind speed change value is a positive number and the absolute value of the wind speed change value is greater than a preset standard wind speed change value, determine that the point on the average wind speed curve at the middle moment between the two moments is a temporary change point; A number of significant change points are determined according to all the temporary change points and the solar radiation curve.

4. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 3, characterized in that: Determining a number of significant change points according to the temporary change point and the solar radiation curve comprises: Calculate the difference between the real-time solar radiation at any moment on the solar radiation curve and the previous moment to form a solar radiation change value, and when the absolute value of the solar radiation change value is greater than a preset standard radiation change value, determine that the point on the solar radiation curve at the middle moment between the two moments is a frost damage meteorological determination point; When the temporary change point coincides with the freezing weather determination point on the time axis, the temporary change point is determined to be the significant change point, forming a plurality of significant change points.

5. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 4, characterized in that: The growth curve includes a leaf area index curve and a biomass curve.

6. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 5, characterized in that: Determining a number of abnormal growth points according to each of the growth curves comprises: Calculating the difference between the real-time leaf area index at any moment on the leaf area index curve and the previous moment to form a leaf area change value, and when the leaf area change value is negative and the absolute value of the leaf area change value is greater than a preset standard leaf area change value, determining that the point on the leaf area index curve at the middle moment between the two moments is a temporary growth point; Calculate the difference between the real-time observed biomass at any moment on the biomass curve and the previous moment to form a biomass change value, and when the biomass change value is a negative number and the absolute value of the biomass change value is greater than a preset standard biomass change value, or when the biomass change value is a positive number and the absolute value of the biomass change value is less than the preset standard biomass change value, determine the point on the biomass curve at the middle moment between the two moments as a frost damage growth determination point; When the temporary growth point coincides with the frost damage growth determination point on the time axis, the temporary growth point is determined to be the abnormal growth point, forming a plurality of abnormal growth points.

7. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 6, characterized in that: Determining a number of association ranges according to each of the significant change points and the preset association duration includes: Calculate the difference between the moment corresponding to the significant change point and the preset association time to form a first moment, calculate the sum of the moment corresponding to the significant change point and the preset association time to form a second moment, select all moments between the first moment and the second moment as an association range, and form several association ranges.

8. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 7, characterized in that: The determining of a plurality of correlation coefficients according to each of the significant change points and the abnormal growth points within the associated range corresponding to the significant change points comprises: Calculate the Pearson correlation coefficient of the real-time minimum temperature corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point to form a low temperature coefficient; Calculate the Pearson correlation coefficient of the real-time maximum temperature corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a high temperature coefficient; Calculate the Pearson correlation coefficient of the real-time average wind speed corresponding to each significant change point and the real-time observed biomass corresponding to the abnormal growth point to form a wind speed coefficient; The Pearson correlation coefficient between the real-time solar radiation corresponding to each of the significant change points and the real-time observed biomass corresponding to the abnormal growth point is calculated to form a radiation coefficient.

9. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 8, characterized in that: Determining the model input parameters according to the correlation coefficients and the preset standard correlation coefficients includes: When the low temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time minimum temperature as a model input parameter; When the high temperature coefficient is greater than the preset standard correlation coefficient, determining the real-time maximum temperature as a model input parameter; When the wind speed coefficient is greater than the preset standard correlation coefficient, determining the real-time average wind speed as a model input parameter; When the radiation coefficient is greater than the preset standard correlation coefficient, the real-time solar radiation is determined to be a model input parameter.

10. The method for evaluating the degree of winter wheat freezing damage in spring based on the WOFOST model according to claim 9, characterized in that: Determining the freezing damage loss grade according to the freezing damage biomass and the control biomass to form the freezing damage degree assessment result comprises: The relative deviation between the control biomass and the frost-damaged biomass is calculated to form a yield loss rate, and the frost damage loss grade is determined based on a comparison result between the yield loss rate and a preset standard loss rate range to form a frost damage degree assessment result.

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