Method, processor and apparatus for determining planting measures based on weather data

Abstract

The application relates to the technical field of intelligent agriculture, in particular to a method for determining planting measures based on meteorological data, a processor and an apparatus. The method comprises the following steps: obtaining historical crop parameters of multiple crops in a preset time period and historical meteorological data of a planting area where each crop is located; for any crop meeting a preset harvest quality, determining meteorological conditions of the planting area where the crop is located in each growth period according to the historical meteorological data; inputting a crop type of a to-be-processed crop, a current growth period, current meteorological data and meteorological forecast data into a crop quality prediction model to obtain a harvest quality prediction value of the to-be-processed crop; determining a climate quality of the planting area where the to-be-processed crop is located in a time period of the current growth period; and determining a planting measure for the to-be-processed crop according to the harvest quality prediction value and the climate quality, wherein the planting measure comprises at least one of fertilization, irrigation, drainage, sun-shading, sterilization and ventilation.

Description

Technical Field

[0001] The present application relates to the field of smart agricultural technology, and specifically to a method, processor, and device for determining planting measures based on meteorological data. Background Art

[0002] The yield and quality of grain crops are among the most crucial issues affecting national food security and people's well-being. With socioeconomic development and rising living standards, the quality of agricultural crops has become a growing concern. For example, climate, as an external factor influencing rice yield and quality, is closely linked to factors such as rice protein and fat content. However, in recent years, global climate change has been highly variable, with frequent extreme weather events and significant agrometeorological disasters. Agricultural production has been significantly impacted by climate change. For example, high temperatures, droughts, and continuous rains have severely impacted rice growth and quality. While existing studies have evaluated the climate quality of rice, these studies focus solely on climate and fail to correlate climate quality with specific rice cultivation practices. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, processor and device for determining planting measures based on meteorological data, which can determine corresponding planting measures based on meteorological data.

[0004] To achieve the above objectives, an embodiment of the present application provides a method for determining planting measures based on meteorological data, the method comprising:

[0005] Obtaining historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area of ​​each crop, wherein the crop parameters include at least crop type, crop growth period, starch content, and protein content;

[0006] Determine crops that meet pre-set harvest quality based on historical crop parameters;

[0007] For any crop that meets the preset harvest quality, determine the meteorological conditions of the crop's planting area during each growth period based on historical meteorological data;

[0008] Determine the current growing period of the crops to be treated;

[0009] Obtain the current weather data for the planting area of ​​the crops to be processed and the weather forecast data for the current growth period;

[0010] Inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed;

[0011] Determine the climate quality of the planting area where the crops to be treated are located during the current growth period based on current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be treated;

[0012] The planting measures for the crops to be treated are determined according to the harvest quality prediction value and the climate quality, and the planting measures include at least one of fertilization, irrigation, drainage, shading, sterilization and ventilation.

[0013] In an embodiment of the present application, the method also includes: before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crop to be processed, analyzing the historical crop parameters of multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located, and selecting historical crop parameters and historical meteorological data that meet the preset conditions as sample data; training the initial crop quality prediction model through the sample data to obtain a trained crop quality prediction model.

[0014] In an embodiment of the present application, selecting historical crop parameters and historical meteorological data that meet preset conditions as sample data includes: obtaining the starch content and protein content of the crops; determining the crops whose starch content and protein content both reach the corresponding preset content as sample crops that meet the preset harvest quality; determining the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops; and determining the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops as sample data.

[0015] In an embodiment of the present application, determining the climate quality of the planting area where the crops to be processed are located in the time period of the current growth period based on current meteorological data, meteorological forecast data and meteorological conditions corresponding to the current growth period of the crops to be processed includes: determining the predicted values ​​of the climate parameters of the planting area where the crops to be processed are located in the time period of the current growth period based on current meteorological data and meteorological forecast data, wherein the climate parameters include at least temperature, moisture and light; determining the reference value corresponding to each meteorological parameter in the meteorological conditions corresponding to the crops to be processed in the current growth period; for any climate parameter, comparing the predicted value and the reference value, and determining the climate quality of the planting area where the crops to be processed are located in the time period of the current growth period based on the comparison result.

[0016] A second aspect of the present application provides a device for determining planting measures based on meteorological data, the device comprising:

[0017] A historical data acquisition module is used to obtain historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area of ​​each crop, wherein the crop parameters include at least crop type, crop growth period, starch content and protein content;

[0018] A crop analysis module for determining crops that meet preset harvest quality based on historical crop parameters;

[0019] A meteorological data analysis module is used to determine the meteorological conditions of the crop's growing area during each growth period based on historical meteorological data for any crop that meets the preset harvest quality;

[0020] A growth period determination module determines the current growth period of the crops to be processed;

[0021] The meteorological data acquisition module obtains the current meteorological data of the planting area where the crops to be processed are located and the meteorological forecast data for the time period of the current growth period;

[0022] The crop quality prediction module is used to input the crop type, current growth period, current meteorological data, and meteorological forecast data of the crops to be processed into the crop quality prediction model to obtain a predicted value of the harvest quality of the crops to be processed;

[0023] A climate quality determination module is used to determine the climate quality of the planting area where the crops to be processed are located during the current growth period based on current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be processed;

[0024] The planting measure confirmation module is used to determine the planting measures for the crops to be processed according to the harvest quality prediction value and the climate quality. The planting measures include at least one of fertilization, irrigation, drainage, shading, sterilization and ventilation.

[0025] In an embodiment of the present application, the device includes: a historical data analysis module, which is used to analyze the historical crop parameters of multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crop to be processed, and select the historical crop parameters and historical meteorological data that meet the preset conditions as sample data; a model training module, which is used to train the initial crop quality prediction model through the sample data to obtain a trained crop quality prediction model.

[0026] In an embodiment of the present application, the historical data analysis module is also used to: obtain the starch content and protein content of crops; determine the crops whose starch content and protein content both reach the preset content as sample crops that meet the preset harvest quality; determine the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops; determine the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops as sample data.

[0027] In an embodiment of the present application, the climate quality determination module is also used to: determine the predicted value of the climate parameters of the planting area where the crops to be processed are located during the current growth period based on current meteorological data and meteorological forecast data, wherein the climate parameters include at least temperature, moisture and light; determine the reference value corresponding to each meteorological parameter in the meteorological conditions corresponding to the crops to be processed during the current growth period; for any climate parameter, compare the predicted value and the reference value, and determine the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison result.

[0028] A third aspect of the present application provides a processor configured to execute any one of the above methods for determining planting measures based on meteorological data.

[0029] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute any one of the above-mentioned methods for determining planting measures based on meteorological data.

[0030] Through the above technical scheme, the meteorological conditions of each growth period in the planting area where the crops reach the preset harvest quality are determined through historical meteorological data, and the climate quality of the planting area where the crops to be processed are located during the current growth period can be determined based on the current meteorological data and meteorological forecast data, and the quality prediction value of the crops to be processed determined based on the current meteorological data and the predicted meteorological data is determined, and then the planting measures for the crops to be processed are determined based on the quality prediction value and the climate quality, the harvest quality of the crops is predicted based on the meteorological data, and the planting measures for the crops in each growth period are determined based on the climate quality in that growth period, thereby reducing the impact of climate on crops.

[0031] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] 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 detailed description, 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:

[0033] Figure 1 The following schematically illustrates a flow chart of a method for determining planting measures based on meteorological data according to an embodiment of the present application;

[0034] Figure 2 The structure of the device for determining planting measures based on meteorological data is schematically shown;

[0035] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0036] The following describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present application and are not intended to limit the present application.

[0037] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0038] 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 for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0039] Figure 1 The flowchart of the method for determining planting measures based on meteorological data 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 planting measures based on meteorological data is provided, comprising the following steps:

[0040] Step 101, obtaining historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area of ​​each crop, wherein the crop parameters include at least crop type, crop growth period, starch content, and protein content;

[0041] Step 102, determining crops that meet a preset harvest quality based on historical crop parameters;

[0042] Step 103: for any crop meeting the preset harvest quality, determine the meteorological conditions of the planting area where the crop is located during each growth period based on historical meteorological data;

[0043] Step 104, determining the current growth period of the crop to be processed;

[0044] Step 105, obtaining current meteorological data of the planting area where the crops to be processed are located and meteorological forecast data for the time period of the current growth period;

[0045] Step 106 , inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed;

[0046] Step 107, determining the climate quality of the planting area where the crop to be processed is located during the current growth period based on the current meteorological data, the meteorological forecast data, and the meteorological conditions corresponding to the current growth period of the crop to be processed;

[0047] Step 108 : Determine planting measures for the crops to be processed based on the harvest quality prediction value and the climate quality. The planting measures include at least one of fertilizing, irrigation, drainage, shading, sterilization, and ventilation.

[0048] The processor can obtain historical crop parameters for a variety of crops within a preset time period and historical meteorological data for the planting area where each crop is located, wherein the crop parameters include at least the type of crop, the crop growth period, and the starch content and protein content of the crop. For example, the processor can set the preset time period to 30 years, and the processor can obtain historical meteorological data and crop parameters for the planting area where the crops are located over the past 30 years, wherein the historical meteorological data may include the temperature, precipitation, sunshine, relative humidity, carbon dioxide content, solar radiation, etc. of the crops during each growth period. The processor can determine crops that meet the preset harvest quality based on the historical crop parameters. That is, the processor can determine the starch content and protein content of the crops at harvest based on the historical crop parameters. The user can set the preset harvest quality of the crops according to the standards of high-quality crops, and the processor can select crops that meet this condition based on the set preset harvest quality. For any crop that meets the preset harvest quality, the processor can determine the meteorological conditions of the planting area where the crop is located during each growth period based on historical meteorological data. That is to say, after determining that the crop meets the preset harvest quality, the processor can determine the meteorological conditions of the crop during each growth period based on historical meteorological data, thereby determining the meteorological conditions during the growth period for the crop that meets the preset harvest quality.

[0049] The processor can determine the current growth period of the crops to be processed, and obtain the current meteorological data of the planting area where the crops to be processed are located, as well as the meteorological forecast data for the time period of the current growth period. The processor can input the crop type of the crops to be processed, the current growth period of the crops to be processed, the current meteorological data of the planting area where the crops to be processed are located, and the meteorological forecast data for the current growth period into a crop quality prediction model to obtain a predicted value of the harvest quality of the crops to be processed. Based on historical meteorological data and historical crop parameters, the meteorological conditions during the growth period of the crops to be processed that meet the preset harvest quality are determined. The processor can determine the climate quality of the planting area where the crops to be processed are located during the time period of the current growth period based on the current meteorological data in the planting area where the crops to be processed are located, the meteorological forecast data for the current growth period of the crops to be processed, and the meteorological conditions, wherein the climate quality may include an evaluation of temperature, moisture, and light.

[0050] After determining the harvest quality prediction value of the crop to be processed and the climate quality of the planting area where the crop to be processed is located during the current growth period, the processor can determine the planting measures for the crop to be processed based on the two, wherein the planting measures may include at least one of fertilization, irrigation, drainage, shading, sterilization, and ventilation. For example, assuming that the crop to be processed is early rice, and the current growth period of the crop to be processed is the seedling stage, the processor determines that the harvest quality prediction value of the early rice obtained by the crop quality prediction model is not enough to reach the preset harvest quality set by the processor, and the processor can generate the planting measures for the planting area where the early rice is located according to the climate quality of the planting area where the early rice is located during the seedling stage. For example, assuming that the climate quality is less light, the planting measures generated by the processor may be increasing the amount of irrigation, strengthening field management, increasing the amount of fertilizer, and increasing the amount of growth regulators.

[0051] In one embodiment, the method further includes: before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crop to be processed, analyzing the historical crop parameters of multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located, and selecting historical crop parameters and historical meteorological data that meet the preset conditions as sample data; training the initial crop quality prediction model through the sample data to obtain a trained crop quality prediction model.

[0052] In one embodiment, selecting historical crop agricultural parameters and historical meteorological data that meet preset conditions as sample data includes: obtaining the starch content and protein content of the crops; determining the crops whose starch content and protein content both reach the corresponding preset content as sample crops that meet the preset harvest quality; determining the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops; and determining the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops as sample data.

[0053] Before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain a predicted value for the harvest quality of the crop to be processed, the processor may train the crop quality prediction model. The processor may analyze the historical crop parameters of the various crops obtained within a preset time period and the historical meteorological data of the planting area where each crop is located, thereby selecting qualified sample data for model training. The processor may obtain the starch content and protein content of the crop based on the historical crop parameters. The processor may set preset contents corresponding to the starch content and protein content based on user input data. The processor may determine, based on the historical crop parameters, that the crops whose starch content and protein content both reach the corresponding preset contents are sample crops that meet the preset harvest quality. The processor may determine the climatic conditions of the planting area where the sample crop is located during the growth cycle of the sample crop, wherein the growth cycle may include multiple growth periods of the sample crop. The processor can determine sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions during the growth cycle of the sample crops as sample data. The processor can train an initial crop quality prediction model through the sample data. The processor can train the initial crop quality prediction model using the sample crops and the climatic conditions during the growth cycle of the sample crops as input data and the starch content and protein content corresponding to the sample crops as output data, thereby obtaining a trained crop quality prediction model.

[0054] In one embodiment, determining the climate quality of the planting area where the crops to be processed are located during the current growth period based on current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be processed includes: determining predicted values ​​of climate parameters of the planting area where the crops to be processed are located during the current growth period based on current meteorological data and meteorological forecast data, wherein the climate parameters include at least temperature, moisture, and light; determining reference values ​​corresponding to each meteorological parameter in the meteorological conditions corresponding to the current growth period of the crops to be processed; for any climate parameter, comparing the predicted value and the reference value, and determining the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison result.

[0055] The processor can determine the predicted value of the climate parameter of the planting area where the crop to be processed is located in the time period of the current growth period based on the current meteorological data and meteorological forecast data of the planting area where the crop to be processed is located, wherein the climate parameter can include at least one of atmosphere, moisture and light. The processor can determine the reference value corresponding to each meteorological parameter in the meteorological conditions corresponding to the crop to be processed in the current growth period based on historical meteorological data. For example, assuming that the crop to be processed is early rice, and the current growth period of the early rice is the seedling stage, the processor can determine the predicted value of the climate parameter of the early rice in the seedling stage based on the current meteorological data of the planting area where the early rice is located and the meteorological forecast data of the early rice in the seedling stage time period. The processor can also determine the meteorological data of the planting area where the early rice is located that meets the preset quality during the seedling stage based on the historical crop parameters and historical meteorological data, that is, if you want to obtain the early rice that meets the preset quality during the seedling stage, the meteorological conditions that the meteorological conditions in the planting area where the early rice is located must meet, and the processor can determine the reference value corresponding to each meteorological parameter in the meteorological conditions. For any climate parameter, the processor can compare the predicted value with the reference value, and thus determine the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison results, for example, whether the climate parameter temperature is higher or lower than the meteorological conditions, whether the moisture is more or less than the meteorological conditions, etc.

[0056] In one embodiment, Figure 2As shown, a device 200 for determining planting measures based on meteorological data is provided. The device 200 for determining planting measures based on meteorological data includes: a historical data acquisition module 201 for acquiring historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area where each crop is located, wherein the crop parameters include at least the crop type, crop growth period, starch content, and protein content; a crop analysis module 202 for determining crops that meet the preset harvest quality based on the historical meteorological data and historical crop parameters; a meteorological data analysis module 203 for determining the meteorological conditions of the planting area where the crop is located in each growth period for any crop that meets the preset harvest quality; a growth period determination module 204 for determining the current growth period of the crop to be processed; and a meteorological data acquisition module 205. , obtaining the current meteorological data of the planting area where the crops to be processed are located and the meteorological forecast data for the time period of the current growth period; the crop quality prediction module 206 is used to input the crop type, current growth period, current meteorological data, and meteorological forecast data of the crops to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crops to be processed; the climate quality determination module 207 is used to determine the climate quality of the planting area where the crops to be processed are located in the time period of the current growth period based on the current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be processed; the planting measure confirmation module 208 is used to determine the planting measures for the crops to be processed based on the harvest quality prediction value and the climate quality, and the planting measures include at least one of fertilization, irrigation, drainage, shading, sterilization, and ventilation.

[0057] The historical data acquisition module 201 can acquire historical crop parameters for a variety of crops over a preset time period, as well as historical meteorological data for the region where each crop is grown. The crop parameters include at least the type of crop, the crop's growth period, and the crop's starch and protein content. For example, the processor can set the preset time period to 30 years and acquire historical meteorological data and crop parameters for the crop's growth period over the past 30 years. The historical meteorological data can include temperature, precipitation, sunshine, relative humidity, carbon dioxide content, solar radiation, and other information for each crop's growth period. The crop analysis module 202 can determine crops that meet a preset harvest quality based on the historical crop parameters. Specifically, the crop analysis module 202 can determine the starch and protein content of the crops at harvest based on the historical crop parameters. The user can set a preset harvest quality for the crops based on the standards for high-quality crops, and the crop analysis module 202 can select crops that meet this preset harvest quality based on the set preset harvest quality. For any crop that meets the preset harvest quality, the meteorological data analysis module 203 can determine the meteorological conditions of the planting area where the crop is located during each growth period based on historical meteorological data. That is to say, after the crop analysis module 202 determines the crop that meets the preset harvest quality, the meteorological data analysis module 203 can determine the meteorological conditions of the crop during each growth period based on historical meteorological data, thereby determining the meteorological conditions during the growth period for the crop that meets the preset harvest quality.

[0058] The growth period determination module 204 can determine the current growth period of the crop to be processed, and the meteorological data acquisition module 205 can obtain current meteorological data for the planting area of ​​the crop to be processed and meteorological forecast data for the time period of the current growth period. The crop quality prediction module 206 can input the crop type, the current growth period of the crop to be processed, the current meteorological data for the planting area of ​​the crop to be processed, and the meteorological forecast data for the current growth period into the crop quality prediction model to obtain a predicted harvest quality value for the crop to be processed. The climate quality determination module 207 can obtain the meteorological conditions during the growth period of the crop to be processed that meet the preset harvest quality, as determined by the meteorological data analysis module 203 based on historical meteorological data and historical crop parameters. The climate quality determination module 207 can determine the climate quality of the planting area of ​​the crop to be processed during the time period of the current growth period based on the current meteorological data of the planting area of ​​the crop to be processed, the meteorological forecast data for the current growth period of the crop to be processed, and the meteorological conditions. The climate quality may include an assessment of temperature, moisture, and light.

[0059] After determining the harvest quality prediction value of the crops to be processed and the climate quality of the planting area where the crops to be processed are located during the current growth period, the planting measures confirmation module 208 can determine the planting measures for the crops to be processed based on the two. The planting measures may include at least one of fertilizing, irrigation, drainage, shading, sterilization and ventilation.

[0060] In one embodiment, Figure 2 The device 200 shown for determining planting measures based on meteorological data includes: a historical data analysis module 209, which is used to analyze the historical crop parameters of multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crop to be processed, and select the historical crop parameters and historical meteorological data that meet the preset conditions as sample data; a model training module 210, which is used to train the initial crop quality prediction model through sample data to obtain a trained crop quality prediction model.

[0061] In one embodiment, the historical data analysis module 209 is also used to obtain the starch content and protein content of crops; determine crops whose starch content and protein content both reach preset content as sample crops that meet the preset harvest quality; determine the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops; and determine the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops as sample data.

[0062] Before the crop quality prediction module 206 inputs the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain a predicted harvest quality value for the crop to be processed, the model training module 210 can train the crop quality prediction model. The historical data analysis module 209 can analyze the historical crop parameters obtained for multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located, thereby selecting qualified sample data for model training. The historical data analysis module 209 can obtain the starch content and protein content of the crop based on the historical crop parameters. The historical data analysis module 209 can set preset starch content and protein content corresponding to the starch content and protein content based on user input data. The historical data analysis module 209 can determine, based on the historical crop parameters, that crops whose starch content and protein content both reach the corresponding preset content are sample crops that meet the preset harvest quality. The historical data analysis module 209 can determine the climatic conditions of the planting area where the sample crop is located during the growth cycle of the sample crop, where the growth cycle can include multiple growth periods of the sample crop. The processor can determine the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions during the growth cycle of the sample crops as sample data. The model training module 210 can train the initial crop quality prediction model through the sample data. The model training module 210 can train the initial crop quality prediction model using the sample crops and the climatic conditions during the growth cycle of the sample crops as input data and the starch content and protein content corresponding to the sample crops as output data to obtain a trained crop quality prediction model.

[0063] In one embodiment, the climate quality determination module 207 is also used to: determine the predicted value of the climate parameters of the planting area where the crops to be processed are located during the current growth period based on current meteorological data and meteorological forecast data, wherein the climate parameters include at least temperature, moisture and light; determine the reference value corresponding to each meteorological parameter in the meteorological conditions corresponding to the crops to be processed during the current growth period; for any climate parameter, compare the predicted value and the reference value, and determine the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison result.

[0064] The climate quality determination module 207 can determine the predicted values ​​of the climate parameters of the planting area where the crop to be processed is located during the current growth period based on the current meteorological data and meteorological forecast data of the planting area where the crop to be processed is located, wherein the climate parameters may include at least one of atmosphere, moisture, and light. The climate quality determination module 207 can also determine the reference values ​​corresponding to each meteorological parameter in the meteorological conditions corresponding to the crop to be processed during the current growth period based on the historical meteorological data. For example, assuming that the crop to be processed is early rice and the early rice is currently in the seedling stage, the processor can determine the predicted values ​​of the climate parameters of the early rice during the seedling stage based on the current meteorological data of the planting area where the early rice is located and the meteorological forecast data for the time period when the early rice is in the seedling stage. The processor can also determine the meteorological data of the planting area where the early rice is located during the seedling stage that meets the preset quality based on the historical crop parameters and historical meteorological data. In other words, in order to obtain the meteorological conditions that the early rice planting area must meet during the seedling stage to meet the preset quality, the climate quality determination module 207 can determine the reference values ​​corresponding to each meteorological parameter in the meteorological conditions. For any climate parameter, the climate quality determination module 208 can compare the predicted value with the reference value, and thus determine the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison results, for example, whether the climate parameter temperature is higher or lower than the meteorological conditions, whether the moisture is more or less than the meteorological conditions, etc.

[0065] In one embodiment, a processor is provided, configured to execute any one of the above methods for determining planting measures based on meteorological data.

[0066] The above technical solution determines the meteorological conditions of each growth period in the planting area where the crops reach the preset harvest quality through historical meteorological data, and determines the climate quality of the planting area where the crops to be processed are located during the current growth period based on current meteorological data and meteorological forecast data, and determines the quality prediction value of the crops to be processed based on the current meteorological data and predicted meteorological data, and then determines the planting measures for the crops to be processed based on the quality prediction value and climate quality, predicts the harvest quality of the crops based on meteorological data, and determines the planting measures for the crops in each growth period based on the climate quality in that growth period, thereby reducing the impact of climate on crops.

[0067] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute any one of the above methods for determining planting measures based on meteorological data.

[0068] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the method for determining planting measures based on meteorological data according to any one of the above items.

[0069] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0070] 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 3 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 via 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 crop parameters and relevant data such as meteorological data of the planting area where the crops are located. 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 planting measures based on meteorological data is implemented.

[0071] Figure 1 FIG. 1 is a flow chart of a method for determining planting measures based on meteorological data 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. In addition, 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.

[0072] The 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 historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area where each crop is located, wherein the crop parameters include at least the crop type, the crop growth period, the starch content, and the protein content; determining crops that meet the preset harvest quality based on the historical crop parameters; for any crop that meets the preset harvest quality, determining the meteorological conditions of the planting area where the crop is located during each growth period based on the historical meteorological data; determining the current growth period of the crop to be processed; obtaining the crop to be processed; and Processing current meteorological data of the planting area where the crops are located and meteorological forecast data for the time period of the current growth period; inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crops to be processed into a crop quality prediction model to obtain a harvest quality prediction value of the crops to be processed; determining the climate quality of the planting area where the crops to be processed are located in the time period of the current growth period based on the current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be processed; determining planting measures for the crops to be processed based on the harvest quality prediction value and the climate quality, the planting measures including at least one of fertilizing, irrigation, drainage, shading, sterilization, and ventilation.

[0073] In one embodiment, the method further includes: before inputting the crop type, current growth period, current meteorological data, and meteorological forecast data of the crop to be processed into the crop quality prediction model to obtain the harvest quality prediction value of the crop to be processed, analyzing the historical crop parameters of multiple crops within a preset time period and the historical meteorological data of the planting area where each crop is located, and selecting historical crop parameters and historical meteorological data that meet the preset conditions as sample data; training the initial crop quality prediction model through the sample data to obtain a trained crop quality prediction model.

[0074] In one embodiment, selecting historical crop parameters and historical meteorological data that meet preset conditions as sample data includes: obtaining the starch content and protein content of the crops; determining the crops whose starch content and protein content both reach the corresponding preset content as sample crops that meet the preset harvest quality; determining the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops; and determining the sample crops, the starch content and protein content corresponding to the sample crops, and the climatic conditions of the planting area where the sample crops are located during the growth cycle of the sample crops as sample data.

[0075] In one embodiment, determining the climate quality of the planting area where the crops to be processed are located during the current growth period based on current meteorological data, meteorological forecast data, and meteorological conditions corresponding to the current growth period of the crops to be processed includes: determining predicted values ​​of climate parameters of the planting area where the crops to be processed are located during the current growth period based on current meteorological data and meteorological forecast data, wherein the climate parameters include at least temperature, moisture, and light; determining reference values ​​corresponding to each meteorological parameter in the meteorological conditions corresponding to the current growth period of the crops to be processed; for any climate parameter, comparing the predicted value and the reference value, and determining the climate quality of the planting area where the crops to be processed are located during the current growth period based on the comparison result.

[0076] 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 take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] 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 and 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 produce 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.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0081] 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.

[0082] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, 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 transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0084] The above are merely 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 modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining planting measures based on meteorological data, characterized in that: The method comprises: Obtaining historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area of ​​each crop, wherein the crop parameters include at least crop type, crop growth period, starch content, and protein content; determining crops meeting a preset harvest quality based on the historical crop parameters; For any crop that meets the preset harvest quality, determine the meteorological conditions of the planting area of ​​the crop in each growth period based on the historical meteorological data; Determine the current growing period of the crops to be treated; Obtaining current meteorological data of the planting area where the crop to be processed is located and meteorological forecast data for the time period of the current growth period; Inputting the crop type, the current growth period, the current meteorological data, and the meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed; Determining the climate quality of the planting area where the crop to be processed is located during the current growth period based on the current meteorological data, the meteorological forecast data, and meteorological conditions corresponding to the crop to be processed during the current growth period; Planting measures for the crops to be treated are determined according to the harvest quality prediction value and the climate quality, wherein the planting measures include at least one of fertilizing, irrigation, drainage, shading, sterilization and ventilation.

2. The method for determining planting measures based on meteorological data according to claim 1, characterized in that: The method further comprises: Before inputting the crop type, the current growth period, the current meteorological data, and the meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed, analyzing historical crop parameters of the multiple crops within a preset time period and historical meteorological data of the planting area where each crop is located, and selecting historical crop parameters and historical meteorological data that meet preset conditions as sample data; The initial crop quality prediction model is trained using the sample data to obtain a trained crop quality prediction model.

3. The method for determining planting measures based on meteorological data according to claim 2, characterized in that: The selection of historical crop parameters and historical meteorological data that meet preset conditions as sample data includes: obtaining the starch content and protein content of the crops; Determining the crops whose starch content and protein content both reach corresponding preset contents as sample crops meeting preset harvest quality; Determining the climatic conditions of the region where the sample crops are grown during the growth cycle of the sample crops; The sample crops, the starch content and protein content corresponding to the sample crops, and the climate conditions of the planting area where the sample crops are located during the growth period of the sample crops are determined as sample data.

4. The method for determining planting measures based on meteorological data according to claim 1, characterized in that: Determining the climate quality of the planting area where the crop to be processed is located during the current growth period according to the current meteorological data, the meteorological forecast data, and the meteorological conditions corresponding to the crop to be processed during the current growth period includes: Determining predicted values ​​of climate parameters of the planting area where the crop to be processed is located within the time period of the current growth period based on the current meteorological data and the meteorological forecast data, wherein the climate parameters include at least temperature, moisture, and light; Determining a reference value corresponding to each meteorological parameter in the meteorological conditions corresponding to the crop to be processed during the current growth period; For any climate parameter, the predicted value and the reference value are compared, and the climate quality of the planting area where the crop to be processed is located during the time period of the current growth period is determined based on the comparison result.

5. A device for determining planting measures based on meteorological data, characterized in that: The device comprises: A historical data acquisition module is used to obtain historical crop parameters of multiple crops within a preset time period and historical meteorological data of the planting area of ​​each crop, wherein the crop parameters include at least crop type, crop growth period, starch content and protein content; a crop analysis module, configured to determine crops meeting a preset harvest quality based on the historical crop parameters; A meteorological data analysis module is used to determine, for any crop meeting a preset harvest quality, the meteorological conditions of the planting area of ​​the crop during each growth period based on the historical meteorological data; A growth period determination module determines the current growth period of the crops to be processed; A meteorological data acquisition module is used to acquire the current meteorological data of the planting area where the crops to be processed are located and the meteorological forecast data of the time period of the current growth period; a crop quality prediction module, configured to input the crop type, the current growth period, the current meteorological data, and the meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed; a climate quality determination module, configured to determine the climate quality of the planting area where the crop to be processed is located during the current growth period based on the current meteorological data, the meteorological forecast data, and meteorological conditions corresponding to the crop to be processed during the current growth period; A planting measure confirmation module is used to determine the planting measures for the crops to be processed based on the harvest quality prediction value and the climate quality, and the planting measures include at least one of fertilizing, irrigation, drainage, shading, sterilization and ventilation.

6. The device for determining planting measures based on meteorological data according to claim 5, characterized in that: The device comprises: a historical data analysis module for analyzing historical crop parameters of the plurality of crops within a preset time period and historical meteorological data of the planting area of ​​each crop before inputting the crop type, the current growth period, the current meteorological data, and the meteorological forecast data of the crop to be processed into a crop quality prediction model to obtain a predicted value of the harvest quality of the crop to be processed, and selecting historical crop parameters and historical meteorological data that meet preset conditions as sample data; The model training module is used to train the initial crop quality prediction model using the sample data to obtain a trained crop quality prediction model.

7. The device for determining planting measures based on meteorological data according to claim 6, characterized in that: The historical data analysis module is also used to: obtaining the starch content and protein content of the crops; Determining the crops whose starch content and protein content both reach the preset content as sample crops meeting the preset harvest quality; Determining the climatic conditions of the region where the sample crops are grown during the growth cycle of the sample crops; The sample crops, the starch content and protein content corresponding to the sample crops, and the climate conditions of the planting area where the sample crops are located during the growth period of the sample crops are determined as sample data.

8. The device for determining planting measures based on meteorological data according to claim 5, characterized in that: The climate quality determination module is further configured to: Determining predicted values ​​of climate parameters for the planting area where the crop to be processed is located during the current growth period based on the current meteorological data and the meteorological forecast data, wherein the climate parameters include at least temperature, moisture, and light; and determining reference values ​​corresponding to each meteorological parameter in the meteorological conditions corresponding to the crop to be processed during the current growth period; For any climate parameter, the predicted value and the reference value are compared, and the climate quality of the planting area where the crop to be processed is located during the time period of the current growth period is determined based on the comparison result.

9. A processor, characterized in that: The method is configured to execute the method for determining planting measures based on meteorological data according to any one of claims 1 to 4.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the method for determining planting measures based on meteorological data according to any one of claims 1 to 4.

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