Winter wheat late frost prediction method and system and computer equipment

Abstract

The invention relates to the technical field of agricultural engineering, in particular to a winter wheat late frost prediction method and system and computer equipment. Judging whether establishment is established; if yes, substituting into a pre-constructed relation model between the lowest air temperature of the high place of the winter wheat farmland microclimate station in the target area and the lowest air temperature observed by an adjacent meteorological observation station for calculation, and obtaining the predicted lowest air temperature of the high place of the winter wheat farmland microclimate station in the future day; on the basis of the lowest temperature, whether winter wheat in the target area is frosted late in the next day or not is pre-judged. The method is used for improving the pre-judgment accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural engineering, in particular to a winter wheat late frost prediction method, system and computer equipment. BACKGROUND

[0002] After the spring warms up, some areas, such as Shandong, are often affected by the invasion of cold air from the north, and stage low temperature frequently occurs. At this time, winter wheat is in the jointing stage of rapid plant height growth, and the frost resistance is significantly reduced. The temperature of the soil or the surface of the plant and the near-surface air layer drops to 0℃ or below, resulting in frost damage to winter wheat plants and even death.

[0003] In recent years, with the continuous warming of the climate, the winter wheat green-up to jointing period has shown an advanced trend. Coupled with the frequent occurrence of extreme weather events, spring frost damage has become one of the most important agricultural meteorological disasters that threaten the stable and increased production of winter wheat.

[0004] Early prediction of spring frost risk is of great significance to food security. However, there are relatively few techniques for predicting winter wheat spring frost (also known as late frost) disasters. These prediction techniques for winter wheat spring frost disasters are often based on data from meteorological observation stations, and the accuracy is limited, which cannot meet the business service needs of actual production. SUMMARY

[0005] In view of the above problems, the present application provides a winter wheat late frost prediction method, system and computer equipment to solve at least one of the above problems.

[0006] In a first aspect, the present application provides a winter wheat late frost prediction method, which comprises: collecting future minimum temperature of the target area observed by the meteorological observation station , representing the minimum temperature of the future day; the target area is the area to be predicted for winter wheat late frost; determining whether the condition is met, representing the frost prediction temperature index; if the condition is met, then is substituted into the relationship model between the minimum temperature at high altitude of the target area winter wheat farmland microclimate station and the minimum temperature observed by the adjacent meteorological observation station, to obtain the predicted minimum temperature at high altitude of the target area winter wheat farmland microclimate station in the future day , Representative winter wheat farmland microclimate station in the target area High in the future The lowest temperature of the day, , cm; Based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a night frost?

[0007] Furthermore, based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a late frost, including: judge Whether it is valid, among which Winter wheat representing the target region Temperature indicators for late frost at high altitudes; like If established, it predicts that winter wheat in the target area will be harvested in the next [year / month / year]. There will be a late frost.

[0008] Furthermore, based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Whether a late frost will occur includes: like If this is not established, then it is predicted that winter wheat in the target area will be harvested in the next [year / month / year]. There will be no late frost. The method also includes: like If this is not established, then it is predicted that winter wheat in the target area will be harvested in the next [year / month / year]. There will be no night frost.

[0009] Furthermore, , =30cm, =60cm, =150cm.

[0010] Furthermore, the target area winter wheat farmland microclimate station The model relating the lowest temperature at a high altitude to the lowest temperature observed at a nearby meteorological station is as follows: , In the formula This represents the future forecast of the target area observed by the meteorological observation station. The lowest temperature of the day, represents the future Tianmubu area winter wheat farmland microclimate station The lowest temperature at higher altitudes , Represents the slope and intercept.

[0011] Furthermore, the relational model Training methods include: During the period of late frost occurrence for winter wheat in the target area, data were collected daily from the local microclimate stations in the winter wheat fields. The historical lowest temperature at higher elevations and the corresponding historical lowest temperature observed at nearby meteorological stations form a microclimate station for winter wheat farmland in the target area. Training set corresponding to high position ; training set Chinese data, substitute Then, the least squares method was used to calculate... and The value; Calculated and The value, substitute back into , to obtain a pre-constructed relational model .

[0012] Furthermore, The method for determining the value is as follows: Collection of training sets Samples from the middle of the region that experienced late frost Construct a temperature database of historical lowest temperatures at high altitudes ; Computational database The average of all temperatures in the range is . .

[0013] Furthermore, based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Whether a late frost will occur includes: Based on the lowest temperature Predicting the winter wheat in the target area in the next... When a late frost occurs, Add to temperature database ; Each time in Add to temperature database Then, calculate the database. The minimum value among all temperatures and The absolute value of the difference is used to calculate the database when the absolute value reaches a preset temperature difference threshold. The mean of all temperatures in the range is used to update the data. The value of .

[0014] In a second aspect, the present application provides a winter wheat late frost prediction system, the system comprising: a temperature collection module configured to collect future minimum temperature of a target area observed by a weather observation station representing the minimum temperature of the target area on the future day d; a judgment module configured to judge whether the following condition is met: representing a frost prediction temperature index; a calculation module configured to, when the condition is met, substitute the minimum temperature of the target area on the future day d into a pre-constructed relationship model between the minimum temperature of the target area on the future day d and the minimum temperature of a neighboring weather observation station, to obtain the predicted minimum temperature of the target area on the future day d; representing the minimum temperature of the target area on the future day d, cm; a late frost prediction module configured to predict whether the target area will experience late frost on the future day d based on the minimum temperature

[0015] In a third aspect, the present application provides a winter wheat late frost prediction computer device, comprising: a processor and a memory, wherein the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computer device executes the method of the above aspects.

[0016] As can be seen from the above technical solutions, the present application has the following advantages: ​​​​​​​​​​​​​​​​The present application provides a new winter wheat late frost prediction scheme, which is based on the relationship model between the minimum temperature at different levels of the established winter wheat farmland microclimate station and the minimum temperature observed by the meteorological observation station, uses the minimum temperature observed by the meteorological observation station to back-calculate the minimum temperature at different levels of the winter wheat farmland microclimate station, and then makes a late frost prediction for the target area based on the back-calculated minimum temperature at different levels of the winter wheat farmland microclimate station. The prediction result relies on the minimum temperature at different levels of the winter wheat farmland microclimate station, which to some extent helps to improve the prediction accuracy and meet the business service needs of actual production. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a schematic flow chart of the method of an embodiment of the present application.

[0019] Figure 2 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 30cm height of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station.

[0020] Figure 3 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 60cm height of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station.

[0021] Figure 4 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 150cm height of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station.

[0022] Figure 5 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 30cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station.

[0023] Figure 6 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 60cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station.

[0024] Figure 7 is a linear regression schematic diagram of the relationship model between the daily minimum temperature at 150cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station.

[0025] Figure 8 is a structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following detailed description of the application, specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, persons having ordinary skill in the art will readily recognize that the application can be practiced without these specific details. In other instances, methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the application.

[0027] It is to be understood that the terminology "includes", "comprises", "has", "contains", "involves", and variations thereof do not exclude the presence of other elements, steps, features, components, and / or groups it describes, but do not preclude the presence or addition of one or more other elements, steps, features, components, and / or groups thereof. The terminology "comprising", "comprises", "including", "includes", "containing", "contains" and variations thereof, do not exclude the presence of other elements, steps, features, components, and / or groups it describes, but do not preclude the presence or addition of one or more other elements, steps, features, components, and / or groups thereof.

[0028] The use of the terms "an embodiment" or "one embodiment" or "some embodiments" or "one specific embodiment" or "some specific embodiments" herein does not, unless otherwise specifically stated, necessarily refer to the same embodiment or specific embodiments. The terms "an embodiment" or "one embodiment" or "some embodiments" or "one specific embodiment" or "some specific embodiments" thus mean "one or more of the embodiments".

[0029] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application but not all of the embodiments. Based on the embodiments in the application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the application.

[0030] Figure 1 A winter wheat late frost prediction method provided in the embodiments of the application is shown in a flowchart. In the method, Figure 1 The execution subject can be a winter wheat late frost prediction system. The winter wheat late frost prediction method provided in the embodiments of the application is executed by a computer device, and accordingly, the winter wheat late frost prediction system runs in the computer device.

[0031] Please refer to Figure 1 The method includes the following steps 101 to 104.

[0032] Step 101, collecting future minimum temperature of the target area observed by a meteorological observation station representing the future first ​​​The minimum temperature of the day; the target area is the area where the late frost of winter wheat is to be predicted; Step 102, judging whether it is true, representing the frost prediction temperature index; Step 103, if is true, then is substituted into the relationship model between the high minimum temperature of the winter wheat farmland microclimate station in the target area and the minimum temperature observed by the adjacent weather observation station to calculate the predicted minimum temperature of the winter wheat farmland microclimate station in the target area on the future day, , representing the minimum temperature of the winter wheat farmland microclimate station in the target area on the future day, ,0 150 cm; Step 104, based on the minimum temperature , predicting whether the winter wheat in the target area will have late frost on the future day.

[0033] It can be understood that the adjacent weather observation station is the weather observation station closest to the winter wheat farmland microclimate station in the target area.

[0034] According to the meteorological standard "It is predicted that the ground minimum temperature will drop below 0°C within the next 24 hours, which will have an impact on agriculture, or has already dropped below 0°C, which has already had an impact on agriculture and may continue". According to the winter wheat growers, the minimum temperature below 4°C needs to be vigilant about the occurrence of frost. Therefore, The value range can be 4°C to 6°C. The value cannot be too large, otherwise it may appear that the subsequent relationship model calculation and late frost occurrence or not judgment are performed for the situation that does not need to be calculated and judged, wasting calculation resources. As a preferred, 5°C is taken.

[0035] In one embodiment, step 104 of predicting whether the winter wheat in the target area will have late frost on the future day based on the minimum temperature includes: judging whether it is true, wherein represents the temperature index of the late frost of the winter wheat in the target area; if is true, then it is predicted that the winter wheat in the target area will have late frost on the future late frost will occur in the target area on the future day j, .

[0036] In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j. If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j. If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j.

[0037] In another embodiment, the method further comprises: If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j. If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j. If the above condition is not met, it is predicted that a late frost will not occur in the target area on the future day j.

[0038] In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature , cm, cm, cm.

[0039] In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature

[0040] cm, cm and cm correspond to 30 cm, 60 cm, and 150 cm, which are three different heights for field microclimate observation at different growth stages of winter wheat. In the present application, the selection of the three levels is consistent with the observation business of the current farmland microclimate observation station.

[0041] In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature , In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature , In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature

[0042] In another embodiment, the step 104 of predicting whether a late frost will occur in the target area on the future day j based on the minimum temperature Training methods include: During the period of late frost occurrence for winter wheat in the target area (generally occurring from March to April each year, corresponding to the greening-jointing stage of winter wheat), data were collected daily from the winter wheat microclimate stations in the target area. The historical lowest temperature at higher elevations and the corresponding historical lowest temperature observed at nearby meteorological stations form a microclimate station for winter wheat farmland in the target area. Training set corresponding to high position ; training set Chinese data, substitute Then, the least squares method was used to calculate... and The value; Calculated and The value, substitute back into , to obtain a pre-constructed relational model .

[0043] Optionally, The method for determining the value is as follows: Collection of training sets Of all samples that experienced late frost Construct a temperature database of historical lowest temperatures at high altitudes ; Computational database The average of all temperatures in the range is... .

[0044] In one alternative implementation, based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Whether a late frost will occur includes: Based on the lowest temperature Predicting the winter wheat in the target area in the next... When a late frost occurs, Add to temperature database ; Each time in Add to temperature database Then, calculate the database. The minimum value among all temperatures and The absolute value of the difference is used to calculate the database when the absolute value reaches a preset temperature difference threshold. The mean of all temperatures in the range is used to update the data. The value of .

[0045] It can be understood that the preset temperature difference threshold value should not be too large, otherwise it may cause deviation in the prediction of late frost of winter wheat, and it should not be too small, otherwise it will trigger the update due to the slight change of temperature , which will lead to too frequent update and increase the consumption of computing resources. On this basis, the value range of the preset temperature difference threshold value can be 0.5℃-1.5℃. 0.5℃-1.5℃ can ensure timely update when the temperature change reaches a certain degree , and can avoid the problem of resource waste caused by frequent update. In specific implementation, the specific value of the preset temperature difference threshold value can be set by the person skilled in the art according to actual needs.

[0046] In order to facilitate the understanding of the present application, the winter wheat late frost prediction method of the present application is described further in combination with the process of predicting the winter wheat late frost in the embodiment.

[0047] Specifically, the winter wheat late frost prediction method comprises: (1) Data source In this embodiment, Shandong Province is taken as an example.

[0048] Shandong Province is located in the eastern coastal area and the lower reaches of the Yellow River in China, between 34°22.9′-38°24.01′N and 114°47.5′-122°42.3′E, with an average annual temperature of 13.0℃, an annual precipitation of 600-750mm, and a rainy and hot period. The main planted crops include winter wheat, corn, soybean, etc., among which winter wheat is mostly sown in early and middle October every year and harvested in June of the next year.

[0049] (2) Data collection (2.1) Observation data of meteorological station (i.e. meteorological observation station) The data comes from Shandong Province Meteorological Data Center, specifically the data of Shandong Province Meteorological Data Center meteorological basic observation station, including daily element data of minimum temperature in each geographical division of Shandong Province since 1981 (covering the spring frost occurrence period).

[0050] (2.2) Agricultural meteorological observation data The data comes from Shandong Province Meteorological Data Center, specifically the daily minimum temperature observation data of 30cm, 60cm and 150cm levels of each farmland microclimate station of Shandong Province Meteorological Data Center since the station was built.

[0051] There are 19 farmland microclimate stations in Shandong Province, which are divided into four modeling regions: Northwest of Shandong, Central of Shandong, South of Shandong and Peninsula. Among them, Northwest of Shandong includes four farmland microclimate stations: Boxing, Lingcheng, Qihex and Xinxian. Central of Shandong includes six farmland microclimate stations: Changle, Daiyue, Gaomi, Huantai, Jiyang and Tai'an. South of Shandong includes five farmland microclimate stations: Heze, Jiaxiang, Juxian, Lanling and Wenshang. Peninsula includes four farmland microclimate stations: Haiyang, Laian, Pingdu and Wendeng.

[0052] The Shandong Meteorological Data Center collects daily microclimate element observation data at three levels (30cm, 60cm, 150cm) from the 19 farmland microclimate stations since the establishment of the stations, including the occurrence period of spring frost (i.e. late frost).

[0053] (2.3) Disaster information The disaster information is derived from the county-level statistical data of Shandong Province and the Meteorological Disaster Management System of China Meteorological Administration, including data on disaster types, frost (late frost of winter wheat) occurrence period and disaster losses since 1981.

[0054] (2.4) Data extraction Based on the above collected data, the daily minimum temperature of the 30cm, 60cm and 150cm height levels of the farmland microclimate station and the corresponding daily minimum temperature observed by the adjacent meteorological observation station during the occurrence period of spring frost are sorted and summarized for each geographical region, including Boxing, Lingcheng, Qihex, Xinxian, Changle, Daiyue, Gaomi, Huantai, Jiyang, Tai'an, Heze, Jiaxiang, Juxian, Lanling, Wenshang, Haiyang, Laian, Pingdu and Wendeng. The data set for each geographical region is obtained. Each data in the data set is a sample.

[0055] During implementation, the occurrence of spring frost for each sample in the data set is also collected.

[0056] (3) Technical methods and theoretical basis (3.1) Data quality control (i.e. data quality control) In this embodiment, data quality control is to clean the data set, including: handling missing values, outliers.

[0057] During implementation, the temperature data value in the data set that is greater than 3 times the average value of the standard deviation is determined as an outlier. The method for determining whether each sample in the data set is an outlier includes: Calculate the average value (mean) of the samples in the data set; Calculate the standard deviation (standard deviation) of the samples in the data set; Determine the threshold: average value + 3 x standard deviation; Sample values greater than the threshold are outliers.

[0058] For missing values and outliers, according to statistical means, the average values of the sample values before and after the missing values and outliers are replaced.

[0059] (4) Regression modeling method Regression modeling method is a statistical method for determining the quantitative relationship between two or more variables. According to the number of variables, the relationship type between independent variables and dependent variables, etc., it can be divided into univariate and multivariate regression analysis, linear and nonlinear regression analysis, etc.

[0060] The main content of regression analysis includes: I. Determine the quantitative relationship between variables, and then use the least squares method to estimate the unknown parameters in the mathematical model between variables, and substitute the parameters into the quantitative relationship to obtain the constructed relationship model.

[0061] In this embodiment, Boxing and Gaomi are taken as examples, and other geographic divisions can refer to Boxing and Gaomi.

[0062] (4.1) The minimum temperature data of 30cm, 60cm and 150cm high of the farmland microclimate station in Boxing since the establishment of the station is constructed with the daily minimum temperature data of the meteorological observation station. The following zoning introduces the modeling results. In each formula, x represents the daily minimum temperature of the farmland microclimate station at different levels, and y represents the daily minimum temperature of the meteorological observation station.

[0063] Figure 2 、 Figure 3 、 Figure 4 The linear regression diagram of the relationship model between the daily minimum temperature of 30cm, 60cm and 150cm high of the farmland microclimate station in Boxing and the minimum temperature of the Boxing basic meteorological observation station is shown in the figure. The straight line composed of the virtual points in the figure is the corresponding relationship model of the corresponding area. The horizontal direction is the x-axis, and the vertical direction is the y-axis, based on Figure 2 、 Figure 3 、 Figure 4 It can be seen from this embodiment that: The relationship model between the daily minimum temperature of 30cm high of the farmland microclimate station in Boxing and the minimum temperature of the Boxing basic meteorological observation station (i.e. the neighboring meteorological observation station of the farmland microclimate station in Boxing) is: y = 1.0064x - 4.9299; The relationship model between the daily minimum temperature of 60cm high of the farmland microclimate station in Boxing and the minimum temperature of the Boxing basic meteorological observation station is: y = 0.9963x - 3.8623; The relationship model between the daily minimum temperature of 150cm high of the farmland microclimate station in Boxing and the minimum temperature of the Boxing basic meteorological observation station is: y = 0.9906x - 2.7532.

[0064] The relationship model of the daily minimum temperature at the height of 30 cm, 60 cm and 150 cm of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station was established, and the determination coefficient R square (R2) was used to measure the degree of explanation of the dependent variable (the value is 0-1). The closer to 1, the stronger the model's ability to explain the dependent variable.

[0065] Specifically, the relationship model of the daily minimum temperature at the height of 30 cm, 60 cm and 150 cm of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station was established, and the determination coefficient R square (R2) was used to measure the degree of explanation of the dependent variable (the value is 0-1). The values are 0.9335, 0.9472 and 0.9638, respectively.

[0066] .

[0067] In the formula, “ ” represents division.

[0068] In the construction of the relationship model, the relationship model of the daily minimum temperature at the height of 30 cm of the Boshan farmland microclimate station and the minimum temperature of the Boshan basic meteorological observation station was taken as an example. A database was formed by the daily minimum temperature of the farmland microclimate station that occurred late frost in the corresponding training set (i.e. the training set corresponding to the 30 cm high layer of the farmland microclimate station). Then the mean of all temperature data in the database was calculated, and the mean was taken as the temperature index of the 30 cm high layer of the winter wheat in the target area that occurred late frost.

[0069] (4.2) The relationship model was constructed by the daily minimum temperature data at the height of 30 cm, 60 cm and 150 cm of the farmland microclimate station since its establishment and the daily minimum temperature data of the meteorological observation station in the corresponding data set of Gaomi after data quality control. The modeling results are introduced in the following zones.

[0070] Figure 5 、 Figure 6 、 Figure 7 The linear regression diagram of the relationship model of the daily minimum temperature at the height of 30 cm, 60 cm and 150 cm of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station (the neighboring meteorological observation station of the Gaomi farmland microclimate station) is shown in the figure. The straight line composed of the virtual points is the corresponding relationship model of the corresponding area, and based on Figure 5 、 Figure 6 、 Figure 7 It can be seen in this embodiment that: The relationship model of the daily minimum temperature at the height of 30 cm of the farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station is y=0.9789x-4.7873. ​The relationship model of the daily minimum temperature at 60cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station is y=0.9642x-3.9503. The relationship model of the daily minimum temperature at 150cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station is y=0.9707x-2.7809.

[0071] The relationship model of the daily minimum temperature at 30cm, 60cm and 150cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station is measured by R square (R2) to measure the explanation degree of the dependent variable variation. ).

[0072] Specifically, the R2 of the relationship model of the daily minimum temperature at 30cm, 60cm and 150cm height of the Gaomi farmland microclimate station and the minimum temperature of the Gaomi basic meteorological observation station is 0.931, 0.9272 and 0.9602 respectively.

[0073] Based on the constructed relationship model, the minimum temperature of different levels of the farmland microclimate can be predicted by combining the minimum temperature result predicted by the intelligent grid forecast and other weather prediction products.

[0074] II. In order to further clarify the credibility of the mathematical model, the significance level of the above constructed mathematical relationship model is tested.

[0075] The present application adopts P test (i.e. judging the significance of the test result by P value) to test the significance level.

[0076] In the industry general standard, α=0.05 is usually taken as the main limit. If P 0.05, it indicates that the model is significant as a whole (statistically significant), and the independent variable has a significant effect on the dependent variable; if P≥0.05, it indicates that the model is not significant as a whole, and needs to be reconstructed. Specifically: If P 0.01, it passes the extremely significant level test, and the credibility is very high; If 0.01 P 0.05, it passes the significance test, and the credibility is relatively high; If 0.05 P 0.1, it passes the medium significant level test, and the credibility is relatively low; If P 0.1, it does not pass the significance test, and the credibility is very low.

[0077] The test shows that the relationship model constructed by the present application is reliable, and will not be described here. ​

[0078] (5) Forecast of late frost for winter wheat.

[0079] This embodiment takes Gaomi as an example to illustrate a method for predicting late frost damage to winter wheat, which includes the following steps: The future of Gaomi, as observed by the Gaomi meteorological observation station The lowest temperature of the day ; judge Whether it is valid, This represents a temperature index for predicting frost. like If established, then Substituting the values ​​into the pre-constructed model relating the minimum temperatures at 30cm, 60cm, and 150cm elevations of the Gaomi winter wheat farmland microclimate station to the predicted minimum temperatures from the Gaomi basic meteorological observation station, calculations were performed to obtain the predicted minimum temperatures at 30cm, 60cm, and 150cm elevations for the next three days at the Gaomi winter wheat farmland microclimate station. , Represents the predicted future Tiangaomi Winter Wheat Farmland Microclimate Station The lowest temperature at higher altitudes , cm, cm, cm; Based on the lowest temperature Predicting the future of high-density winter wheat Will there be a night frost?

[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0081] This invention provides a system for predicting late frost damage to winter wheat. Specifically, the system includes: The temperature acquisition module is used to collect future temperature data for the target area observed by the meteorological observation station. The lowest temperature of the day , , represents the future The lowest temperature of the day; the target area is the region where late frost forecasting for winter wheat is to be conducted; The judgment module is used to make judgments. Whether it is valid, This represents a temperature index for predicting frost. The calculation module is used for... When it is established, Substitute into the pre-constructed winter wheat farmland microclimate station of the target area A model was used to calculate the relationship between the lowest temperature at higher altitudes and the lowest temperature observed at nearby meteorological stations, resulting in a predicted microclimate for winter wheat farmland in the target area. High Place Future The lowest temperature of the day , Representative winter wheat farmland microclimate station in the target area High in the future The lowest temperature of the day, , cm; Late frost forecasting module, used to predict frost based on minimum temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a night frost?

[0082] This embodiment of the winter wheat late frost prediction system belongs to the same inventive concept as the winter wheat late frost prediction methods in the above embodiments. For details not described in detail in the embodiments of the winter wheat late frost prediction system, please refer to the embodiments of the winter wheat late frost prediction methods mentioned above.

[0083] Figure 8 This is a schematic diagram of a computer device 300 for predicting late frost in winter wheat, provided in an embodiment of the present invention. The computer device 300 can be used to execute the method provided in the embodiment of the present invention.

[0084] The computer device 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0085] The memory 320 can be used to store execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage computer device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the computer device 300 is able to perform some or all of the steps in the above method embodiments.

[0086] The processor 310 is the control center of the storage computer device, connects various parts of the entire electronic computer device by using various interfaces and lines, and performs various functions of the electronic computer device and / or processes data by running or executing software programs and / or modules stored in the memory 320 and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, can be composed of a single packaged IC, or can be composed of multiple packaged ICs connected together. For example, the processor 310 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single operation core or can include multiple operation cores.

[0087] The communication unit 330 is configured to establish a communication channel, so that the storage computer device can communicate with other computer devices. The communication unit 330 receives user data sent by other computer devices or sends user data to other computer devices.

[0088] In the present specification, the same or similar parts between various embodiments can be referred to each other. In particular, for system and computer device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between each other can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0090] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the present embodiment.

[0091] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0092] In the above embodiments of the present application, the description of each embodiment has a focus, and the part not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0093] In a specific implementation, if the target area does not have a winter wheat farmland microclimate station, a winter wheat farmland microclimate station can be constructed to collect data and build a corresponding relationship model; or a relationship model between the minimum temperature of different levels of the winter wheat farmland microclimate station and the minimum temperature observed by the meteorological observation station in the area where the winter wheat farmland microclimate station located nearest to the target area can be built, and then the relationship model is used as the relationship model of the target area for the late frost prediction of winter wheat in the target area.

[0094] The above description of disclosed embodiments enables those skilled in the art to carry out or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting late frost damage to winter wheat, characterized in that the method... include: The target area for collecting meteorological observations will be in the future. The lowest temperature of the day , , represents the future The lowest temperature of the day; the target area is the region where late frost forecasting for winter wheat will be conducted; judge Whether it is valid, This represents a temperature index for predicting frost. like If established, then Substitute into the pre-constructed winter wheat farmland microclimate station of the target area A model was used to calculate the relationship between the lowest temperature at higher altitudes and the lowest temperature observed at nearby meteorological stations, resulting in a predicted microclimate for winter wheat farmland in the target area. High Place Future The lowest temperature of the day , Representative winter wheat farmland microclimate station in the target area High in the future The lowest temperature of the day, 0 150cm; Based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a night frost? 2. The method for predicting late frost damage to winter wheat according to claim 1, characterized in that, Based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a late frost, including: judge Whether it is valid, among which Winter wheat representing the target region Temperature indicators for late frost at high altitudes; like If established, it predicts that winter wheat in the target area will be harvested in the next [year / month / year]. There will be a late frost.

3. The method for predicting late frost damage to winter wheat according to claim 2, characterized in that, Based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Whether a late frost will occur includes: like If this is not established, then it is predicted that winter wheat in the target area will be harvested in the next [year / month / year]. There will be no late frost. The method also includes: like If this is not established, then it is predicted that winter wheat in the target area will be harvested in the next [year / month / year]. There will be no night frost.

4. The method for predicting late frost damage to winter wheat according to any one of claims 1 to 3, characterized in that, , =30cm, =60cm, =150cm。 5. The method for predicting late frost damage to winter wheat according to claim 2, characterized in that, Winter wheat farmland microclimate stations in the target area The model relating the lowest temperature at a high altitude to the lowest temperature observed at a nearby meteorological station is as follows: ， In the formula This represents the future forecast of the target area observed by the meteorological observation station. The lowest temperature of the day, represents the future Tianmubu area winter wheat farmland microclimate station The lowest temperature at higher altitudes , Represents the slope and intercept.

6. The method for predicting late frost damage to winter wheat according to claim 5, characterized in that, relational model Training methods include: During the period of late frost occurrence for winter wheat in the target area, data were collected daily from the local microclimate stations in the winter wheat fields. The historical lowest temperature at higher elevations and the corresponding historical lowest temperature observed at nearby meteorological stations form a microclimate station for winter wheat farmland in the target area. Training set corresponding to high position ; training set Chinese data, substitute Then, the least squares method was used to calculate... and The value; Calculated and The value, substitute back into , to obtain a pre-constructed relational model .

7. The method for predicting late frost damage to winter wheat according to claim 6, characterized in that, The method for determining the value is as follows: Collection of training sets Samples from the middle of the region that experienced late frost Construct a temperature database of historical lowest temperatures at high altitudes ; Computational database The average of all temperatures in the range is . .

8. The method for predicting late frost damage to winter wheat according to claim 7, characterized in that, Based on the lowest temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Whether a late frost will occur includes: Based on the lowest temperature Predicting the winter wheat in the target area in the next... When a late frost occurs, Add to temperature database ; Each time in Add to temperature database Then, calculate the database. The minimum value among all temperatures and The absolute value of the difference is used to calculate the database when the absolute value reaches a preset temperature difference threshold. The mean of all temperatures in the range is used to update the data. The value of .

9. A system for predicting late frost damage to winter wheat, characterized in that, The system includes: The temperature acquisition module is used to collect future temperature data for the target area observed by the meteorological observation station. The lowest temperature of the day , , represents the future The lowest temperature of the day; the target area is the region where late frost forecasting for winter wheat will be conducted; The judgment module is used to make judgments. Whether it is valid, This represents a temperature index for predicting frost. The calculation module is used for... When it is established, Substitute into the pre-constructed winter wheat farmland microclimate station of the target area A model was used to calculate the relationship between the lowest temperature at higher altitudes and the lowest temperature observed at nearby meteorological stations, resulting in a predicted microclimate for winter wheat farmland in the target area. High Place Future The lowest temperature of the day , Representative winter wheat farmland microclimate station in the target area High in the future The lowest temperature of the day, , cm; Late frost forecasting module, used to predict frost based on minimum temperature Predicting that winter wheat in the target area will be harvested in the next [year / year]. Will there be a night frost? 10. A computer device for predicting late frost damage to winter wheat, characterized in that, include: processor; Memory used to store the processor's execution instructions; The processor is configured to execute the method for predicting late frost in winter wheat as described in any one of claims 1-8.

Images