Method and device for determining extreme weather occurrence index in crop growth process

Abstract

The embodiment of the invention provides a method and device for determining an extreme weather occurrence index in a crop growth process. The method comprises the following steps: acquiring various historical meteorological data of each historical date of a historical crop in a historical growth period; for each kind of historical meteorological data, preprocessing the historical meteorological data of each historical date to obtain preprocessed historical meteorological data; inputting all the preprocessed historical meteorological data into a crop growth model, so that the crop growth model outputs target meteorological data and growth characteristic data of the historical crops in each growth period; determining a generalized extreme value distribution model of corresponding extreme weather triggered by historical meteorological data according to the target meteorological data and the growth characteristic data of all the growth periods; according to the generalized extreme value distribution model, the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops is determined, and the accuracy of determining the extreme weather occurrence index is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, storage medium and processor for determining an extreme weather occurrence index during crop growth. Background Art

[0002] Extreme weather is a general term for rare weather phenomena that are destructive to human society and the ecosystem. If extreme weather occurs during the growth of crops, it may cause agricultural meteorological disasters to the crops, resulting in the obstruction of the crop growth process and the subsequent reduction of yield and quality. In the existing technology, the extreme weather occurrence index of crops is determined by manual statistical analysis, which may produce certain errors, low reliability and insufficient accuracy. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, storage medium and processor for determining the extreme weather occurrence index during crop growth, so as to solve the problem of inaccurate determination of the extreme weather occurrence index in the prior art.

[0004] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining an extreme weather occurrence index during crop growth, comprising:

[0005] Obtain various historical meteorological data for each historical date during the historical growing period of historical crops;

[0006] For each type of historical meteorological data, preprocess the historical meteorological data of each historical date to obtain preprocessed historical meteorological data;

[0007] For each type of historical meteorological data, all pre-processed historical meteorological data are input into the crop growth model, so that the crop growth model outputs the target meteorological data and growth characteristic data of the historical crops in each growth period;

[0008] For each type of historical meteorological data, generalized extreme value distribution fitting is performed on the target meteorological data and growth characteristic data of all growth periods to obtain the generalized extreme value distribution parameters corresponding to the historical meteorological data;

[0009] For each type of historical meteorological data, a generalized extreme value distribution model of the corresponding extreme weather caused by the historical meteorological data is constructed according to the generalized extreme value distribution parameters;

[0010] For each type of historical meteorological data, the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growing period of historical crops is determined based on the generalized extreme value distribution model.

[0011] In the embodiment of the present application, the generalized extreme value distribution parameters include location parameters, scale parameters and shape parameters, and the generalized extreme value distribution model is defined by formula (1):

[0012]

[0013] Among them, F(x) refers to the cumulative distribution function of any kind of historical meteorological data, x refers to any kind of historical meteorological data under all historical dates, β is the location parameter, α is the scale parameter, and k refers to the shape parameter.

[0014] In an embodiment of the present application, for each type of historical meteorological data, determining the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops according to the generalized extreme value distribution model includes: solving the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model, the upper tail probability refers to the ratio between the frequency of occurrence of historical meteorological data that meets the corresponding extreme weather occurrence conditions and the total number of historical meteorological data; transforming the upper tail probability to obtain the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops.

[0015] In an embodiment of the present application, the method also includes: determining a planting plan for crops to be planted that are the same as historical crop varieties based on the extreme weather occurrence index; wherein the planting plan includes irrigation management time and pest and disease management time for the crops to be planted.

[0016] In an embodiment of the present application, the method further includes: determining the disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period based on the extreme weather occurrence index.

[0017] In the embodiment of the present application, the preprocessing method includes at least one of removing outliers and data interpolation.

[0018] In the embodiment of the present application, the historical crop is rice.

[0019] A second aspect of the present application provides a device for determining an extreme weather occurrence index during crop growth, the device comprising:

[0020] a memory configured to store instructions; and

[0021] The processor is configured to call instructions from the memory and implement the above-mentioned method for determining the extreme weather occurrence index during the crop growth process when executing the instructions.

[0022] A third aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned method for determining an extreme weather occurrence index during crop growth.

[0023] A fourth aspect of the present application provides a processor configured to execute the above-mentioned method for determining the extreme weather occurrence index during crop growth.

[0024] Through the above technical scheme, a variety of historical meteorological data of each historical date in the historical growth period of historical crops are obtained; for each historical meteorological data, the historical meteorological data of each historical date are preprocessed to obtain the preprocessed historical meteorological data; for each historical meteorological data, all the preprocessed historical meteorological data are input into the crop growth model, so that the crop growth model outputs the target meteorological data and growth characteristic data of the historical crops in each growth period; for each historical meteorological data, the target meteorological data and growth characteristic data of all growth periods are fitted with generalized extreme value distribution to obtain the generalized extreme value distribution parameters corresponding to the historical meteorological data; for each historical meteorological data, a generalized extreme value distribution model of the corresponding extreme weather caused by the historical meteorological data is constructed according to the generalized extreme value distribution parameters; for each historical meteorological data, the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops is determined according to the generalized extreme value distribution model, so that the extreme weather occurrence index of the crop in each growth period during the growth period can be determined, and the determined extreme weather occurrence index is more objective, avoiding errors caused by human participation, and improving the accuracy of determining the extreme weather occurrence index.

[0025] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0027] Figure 1 A schematic diagram of a process for determining an extreme weather occurrence index during crop growth according to an embodiment of the present application is shown;

[0028] Figure 2 The internal structure of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0030] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0031] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0032] Figure 1 The flowchart of the method for determining the extreme weather occurrence index during crop growth according to an embodiment of the present application is schematically shown. Figure 1 As shown, in one embodiment of the present application, a method for determining an extreme weather occurrence index during crop growth is provided, comprising the following steps:

[0033] Step 101: Acquire a variety of historical meteorological data for each historical date during the historical growing period of the historical crop.

[0034] Historical crops refer to crops that have been planted. In an embodiment of the present application, historical crops may be rice. Rice may include japonica rice and indica rice. The historical growth period may refer to the growth period of the historical crop from the tillering stage to the filling and flowering stage. The processor may obtain a variety of historical meteorological data for each historical date of the historical crop during the historical growth period. Among them, there are various types of historical meteorological data, which may include precipitation, temperature, relative humidity, air pressure, wind speed, etc. Historical meteorological data may be obtained through meteorological observation stations, satellite remote sensing, meteorological radar, and data assimilation.

[0035] Step 102: For each type of historical meteorological data, preprocess the historical meteorological data of each historical date to obtain preprocessed historical meteorological data.

[0036] For each type of historical meteorological data, the processor may preprocess the historical meteorological data of each historical date to obtain preprocessed historical meteorological data.

[0037] In the embodiment of the present application, the preprocessing method includes at least one of removing outliers and data interpolation.

[0038] The acquired historical meteorological data may have differences in accuracy, completeness, and vertical continuity. Therefore, the historical meteorological data may be processed to remove outliers to control the quality of the acquired historical meteorological data. The historical meteorological data may be interpolated to make the acquired historical meteorological data more complete and continuous in space. In one embodiment, the data interpolation may be performed using Kriging interpolation.

[0039] Step 103: For each type of historical meteorological data, all pre-processed historical meteorological data are input into the crop growth model, so that the crop growth model outputs the target meteorological data and growth characteristic data of the historical crops in each growth period.

[0040] For each type of historical meteorological data, the processor can input all the pre-processed historical meteorological data into the crop growth model, so that the crop growth model outputs the target meteorological data and growth characteristic data of the historical crops in each growth period. Among them, the crop growth model refers to a model used to simulate and predict the growth process, growth period and yield of crops under different meteorological conditions. Specifically, the crop growth model can be a DSSAT model, and the growth characteristic data can refer to the leaf index and cumulative temperature of the crop in each growth period, etc., which can reflect the crop growth process.

[0041] Step 104: For each type of historical meteorological data, generalized extreme value distribution fitting is performed on the target meteorological data and growth characteristic data of all growth periods to obtain generalized extreme value distribution parameters corresponding to the historical meteorological data.

[0042] For each type of historical meteorological data, the processor can perform generalized extreme value distribution fitting on the target meteorological data and growth characteristic data of all growth periods to obtain generalized extreme value distribution parameters corresponding to the historical meteorological data.

[0043] Step 105: For each type of historical meteorological data, a generalized extreme value distribution model of the corresponding extreme weather caused by the historical meteorological data is constructed according to the generalized extreme value distribution parameters.

[0044] Among them, extreme weather refers to a general term for weather phenomena that are rare and destructive to human society and the ecosystem. In one embodiment, extreme weather refers to the weather phenomenon corresponding to the meteorological data when the meteorological data of each historical date exceeds a preset threshold. For example, for any historical date, the highest temperature or average temperature of the historical date is greater than the preset temperature. At this time, it can be determined that extreme high temperature weather occurred on the historical date. In one embodiment, extreme weather can also refer to the weather phenomenon corresponding to the meteorological data when the number of times the meteorological data exceeds the preset threshold within a preset time period is greater than the preset value. For example, the temperature of 20 days in 30 days exceeds the preset threshold. At this time, it can be determined that extreme high temperature weather occurred in the 30 days.

[0045] For each type of historical meteorological data, the processor can construct a generalized extreme value distribution model of the corresponding extreme weather caused by the historical meteorological data according to the generalized extreme value distribution parameters. For example, for precipitation, it can construct a generalized extreme value distribution model of extreme drought weather or extreme precipitation weather caused by precipitation. For example, for temperature, it can construct a generalized extreme value distribution model of extreme high temperature or extreme low temperature caused by temperature. For another example, for wind speed, it can construct a generalized extreme value distribution model of tornado or tropical cyclone caused by wind speed.

[0046] In the embodiment of the present application, the generalized extreme value distribution parameters include location parameters, scale parameters and shape parameters, and the generalized extreme value distribution model is defined by formula (1):

[0047]

[0048] Among them, F(x) refers to the cumulative distribution function of any kind of historical meteorological data, x refers to any kind of historical meteorological data under all historical dates, β is the location parameter, α is the scale parameter, and k is the shape parameter. Among them, the optimal value range of k can be (-0.5, 0.5). For example, let the extreme precipitation once in T years be xp, and the corresponding extreme value occurrence frequency p = 1 / T, that is, the probability. When the parameter (-1 / k) is greater than 0, the generalized extreme value distribution is right-skewed, that is, the tail of the generalized extreme value distribution is thicker. When the parameter (-1 / k) is less than 0, the generalized extreme value distribution is left-skewed, that is, the tail of the generalized extreme value distribution is thinner. When the parameter (-1 / k) is approximately 0, the generalized extreme value distribution is an exponential distribution.

[0049] Step 106: For each type of historical meteorological data, determine the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops according to the generalized extreme value distribution model.

[0050] For each type of historical meteorological data, the processor can determine the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops according to the generalized extreme value distribution model. The extreme weather occurrence index refers to the intensity or frequency of extreme weather.

[0051] In an embodiment of the present application, for each type of historical meteorological data, determining the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops according to the generalized extreme value distribution model includes: solving the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model, the upper tail probability refers to the ratio between the frequency of occurrence of historical meteorological data that meets the corresponding extreme weather occurrence conditions and the total number of historical meteorological data; transforming the upper tail probability to obtain the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops.

[0052] The processor can solve the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model. The upper tail probability refers to the ratio between the frequency of occurrence of historical meteorological data that meets the corresponding extreme weather occurrence conditions and the total number of historical meteorological data. The processor can transform the upper tail probability to obtain the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops.

[0053] In an embodiment of the present application, the method also includes: determining a planting plan for crops to be planted that are the same as historical crop varieties based on the extreme weather occurrence index; wherein the planting plan includes irrigation management time and pest and disease management time for the crops to be planted.

[0054] The processor can determine a planting plan for the crops to be planted that are the same as the historical crop varieties according to the extreme weather occurrence index, wherein the planting plan includes the irrigation management time and the pest and disease management time of the crops to be planted.

[0055] For example, when crops enter the tillering stage, it is necessary to promote effective tillering of crops and control ineffective tillering. If extreme precipitation or extreme drought weather occurs during this period, it may lead to the loss of soil nutrients or applied fertilizers in the planting area, thereby reducing the effective tillering of crops. Therefore, the irrigation management time can be formulated in advance for the crops to be planted when they enter the tillering stage to ensure the normal growth of crops.

[0056] The above scheme determines the crop planting plan based on the extreme weather occurrence index, and can make adaptive adjustments to the crop in terms of farmland management strategies, irrigation arrangements, crop planting, and pest and disease prevention, which can reduce the adverse effects of extreme weather on crop growth.

[0057] In an embodiment of the present application, the method further includes: determining the disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period based on the extreme weather occurrence index.

[0058] The processor can determine the disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period according to the extreme weather occurrence index. The greater the extreme weather occurrence index, the higher the corresponding disaster risk level.

[0059] Through the above technical scheme, a variety of historical meteorological data of each historical date in the historical growth period of historical crops are obtained; for each historical meteorological data, the historical meteorological data of each historical date are preprocessed to obtain the preprocessed historical meteorological data; for each historical meteorological data, all the preprocessed historical meteorological data are input into the crop growth model, so that the crop growth model outputs the target meteorological data and growth characteristic data of the historical crops in each growth period; for each historical meteorological data, the target meteorological data and growth characteristic data of all growth periods are fitted with generalized extreme value distribution to obtain the generalized extreme value distribution parameters corresponding to the historical meteorological data; for each historical meteorological data, a generalized extreme value distribution model of the corresponding extreme weather caused by the historical meteorological data is constructed according to the generalized extreme value distribution parameters; for each historical meteorological data, the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops is determined according to the generalized extreme value distribution model, and the extreme weather occurrence index of the crop in each growth period during the growth period can be determined according to each historical meteorological data in the crop growth period, and the determined extreme weather occurrence index is more objective, avoiding errors caused by human participation, and improving the accuracy of determining the extreme weather occurrence index.

[0060] Figure 1 FIG. 1 is a flow chart of a method for determining an extreme weather occurrence index during crop growth in one embodiment. Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0061] In one embodiment, a device for determining an extreme weather occurrence index during crop growth is provided, comprising:

[0062] a memory configured to store instructions; and

[0063] The processor is configured to call instructions from the memory and implement the above-mentioned method for determining the extreme weather occurrence index during the crop growth process when executing the instructions.

[0064] In one embodiment, a storage medium is provided, on which a program is stored, and when the program is executed by a processor, the method for determining the extreme weather occurrence index during crop growth is implemented.

[0065] In one embodiment, a processor is provided, and the processor is used to run a program, wherein when the program is run, the above method for determining the extreme weather occurrence index during crop growth is executed.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as extreme weather occurrence index. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for determining the extreme weather occurrence index during crop growth is implemented.

[0067] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0068] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining a plurality of historical meteorological data of each historical date in a historical growth period of a historical crop; for each type of historical meteorological data, preprocessing the historical meteorological data of each historical date to obtain preprocessed historical meteorological data; for each type of historical meteorological data, inputting all preprocessed historical meteorological data into a crop growth model, so that the crop growth model outputs target meteorological data and growth characteristic data of the historical crop in each growth period; for each type of historical meteorological data, performing generalized extreme value distribution fitting on the target meteorological data and growth characteristic data of all growth periods to obtain generalized extreme value distribution parameters corresponding to the historical meteorological data; for each type of historical meteorological data, constructing a generalized extreme value distribution model of corresponding extreme weather caused by the historical meteorological data according to the generalized extreme value distribution parameters; for each type of historical meteorological data, determining an extreme weather occurrence index of corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crop according to the generalized extreme value distribution model.

[0069] In the embodiment of the present application, the generalized extreme value distribution parameters include location parameters, scale parameters and shape parameters, and the generalized extreme value distribution model is defined by formula (1):

[0070]

[0071] Among them, F(x) refers to the cumulative distribution function of any kind of historical meteorological data, x refers to any kind of historical meteorological data under all historical dates, β is the location parameter, α is the scale parameter, and k refers to the shape parameter.

[0072] In an embodiment of the present application, for each type of historical meteorological data, determining the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growing period of historical crops according to the generalized extreme value distribution model includes solving the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model, and the upper tail probability refers to the ratio between the frequency of occurrence of historical meteorological data that meets the corresponding extreme weather occurrence conditions and the total number of historical meteorological data; transforming the upper tail probability to obtain the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growing period of historical crops.

[0073] In an embodiment of the present application, the method also includes: determining a planting plan for crops to be planted that are the same as historical crop varieties based on the extreme weather occurrence index; wherein the planting plan includes irrigation management time and pest and disease management time for the crops to be planted.

[0074] In an embodiment of the present application, the method further includes: determining the disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period based on the extreme weather occurrence index.

[0075] In the embodiment of the present application, the preprocessing method includes at least one of removing outliers and data interpolation.

[0076] In the embodiment of the present application, the historical crop is rice.

[0077] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: obtaining multiple historical meteorological data for each historical date during the historical growth period of historical crops; for each type of historical meteorological data, preprocessing the historical meteorological data for each historical date to obtain preprocessed historical meteorological data; for each type of historical meteorological data, inputting all preprocessed historical meteorological data into a crop growth model, so that the crop growth model outputs target meteorological data and growth characteristic data of the historical crops in each growth period; for each type of historical meteorological data, performing generalized extreme value distribution fitting on the target meteorological data and growth characteristic data of all growth periods to obtain generalized extreme value distribution parameters corresponding to the historical meteorological data; for each type of historical meteorological data, constructing a generalized extreme value distribution model for the corresponding extreme weather caused by the historical meteorological data based on the generalized extreme value distribution parameters; for each type of historical meteorological data, determining the extreme weather occurrence index for the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops based on the generalized extreme value distribution model.

[0078] In the embodiment of the present application, the generalized extreme value distribution parameters include location parameters, scale parameters and shape parameters, and the generalized extreme value distribution model is defined by formula (1):

[0079]

[0080] Among them, F(x) refers to the cumulative distribution function of any kind of historical meteorological data, x refers to any kind of historical meteorological data under all historical dates, β is the location parameter, α is the scale parameter, and k refers to the shape parameter.

[0081] In an embodiment of the present application, for each type of historical meteorological data, determining the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops according to the generalized extreme value distribution model includes: solving the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model, the upper tail probability refers to the ratio between the frequency of occurrence of historical meteorological data that meets the corresponding extreme weather occurrence conditions and the total number of historical meteorological data; transforming the upper tail probability to obtain the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of historical crops.

[0082] In an embodiment of the present application, the method also includes: determining a planting plan for crops to be planted that are the same as historical crop varieties based on the extreme weather occurrence index; wherein the planting plan includes irrigation management time and pest and disease management time for the crops to be planted.

[0083] In an embodiment of the present application, the method further includes: determining the disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period based on the extreme weather occurrence index.

[0084] In the embodiment of the present application, the preprocessing method includes at least one of removing outliers and data interpolation.

[0085] In the embodiment of the present application, the historical crop is rice.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0092] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0094] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining the occurrence index of extreme weather during crop growth, It is characterized in that The method comprises: Obtain various historical meteorological data for each historical date during the historical growing period of historical crops; For each type of historical meteorological data, preprocessing the historical meteorological data of each historical date to obtain preprocessed historical meteorological data; For each type of historical meteorological data, all pre-processed historical meteorological data are input into a crop growth model, so that the crop growth model outputs target meteorological data and growth characteristic data of the historical crop in each growth period; For each type of historical meteorological data, generalized extreme value distribution fitting is performed on the target meteorological data and growth characteristic data of all growth periods to obtain generalized extreme value distribution parameters corresponding to the historical meteorological data; For each type of historical meteorological data, a generalized extreme value distribution model of corresponding extreme weather caused by the historical meteorological data is constructed according to the generalized extreme value distribution parameters; For each type of historical meteorological data, the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crop is determined according to the generalized extreme value distribution model.

2. The method for determining the extreme weather occurrence index during crop growth according to claim 1, It is characterized in that The generalized extreme value distribution parameters include location parameters, scale parameters and shape parameters. The generalized extreme value distribution model is defined by formula (1): Among them, F(x) refers to the cumulative distribution function of any kind of historical meteorological data, x refers to any kind of historical meteorological data under all historical dates, β is the location parameter, α is the scale parameter, and k refers to the shape parameter.

3. The method for determining the occurrence index of extreme weather during crop growth according to claim 1, It is characterized in that For each type of historical meteorological data, determining the extreme weather occurrence index of the corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crop according to the generalized extreme value distribution model includes: Solving the generalized extreme value distribution model to obtain the upper tail probability of the generalized extreme value distribution model, wherein the upper tail probability refers to the ratio between the frequency of occurrence of the historical meteorological data that meets the corresponding extreme weather occurrence condition and the total number of the historical meteorological data; The upper tail probability is transformed to obtain an extreme weather occurrence index of corresponding extreme weather caused by the historical meteorological data in each growth period of the historical crops.

4. The method for determining the extreme weather occurrence index during crop growth according to claim 1, It is characterized in that The method further comprises: Determining a planting plan for crops to be planted that are the same as the historical crop varieties according to the extreme weather occurrence index; The planting plan includes the irrigation management time and the pest and disease management time of the crops to be planted.

5. The method for determining the extreme weather occurrence index during crop growth according to claim 1, It is characterized in that The method further comprises: The disaster risk level of the crops to be planted that are the same as the historical crop varieties during the corresponding growth period is determined based on the extreme weather occurrence index.

6. The method for determining the extreme weather occurrence index during crop growth according to claim 1, It is characterized in that The preprocessing method includes at least one of removing outliers and data interpolation.

7. The method for determining the occurrence index of extreme weather during crop growth according to any one of claims 1 to 6, It is characterized in that The historical crop is rice.

8. A device for determining the occurrence index of extreme weather during crop growth, It is characterized in that The device comprises: A memory configured to store instructions; and a processor configured to call the instructions from the memory and implement the method for determining the extreme weather occurrence index during crop growth according to any one of claims 1 to 7 when executing the instructions.

9. A machine-readable storage medium, It is characterized in that The machine-readable storage medium stores instructions for causing the machine to execute the method for determining an extreme weather occurrence index during crop growth according to any one of claims 1 to 7.

10. A processor, It is characterized in that The method is configured to execute the method for determining the extreme weather occurrence index during crop growth according to any one of claims 1 to 7.

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