A rice quality prediction method based on meteorological data and growth period

Abstract

The present invention provides a rice quality prediction method based on meteorological data and growing periods, comprising the following steps: obtaining historical meteorological data and historical quality data during the rice growth process, and dividing the growth process into multiple growing periods; constructing three quantitative evaluation models for temperature, precipitation, and sunshine based on the meteorological data within each growing period, and calculating three meteorological evaluation indices for temperature, precipitation, and sunshine; performing multiple linear regression analysis on the meteorological evaluation indices for each growing period and the corresponding historical quality data to obtain a quantitative relationship between each quality indicator in the quality data and the meteorological evaluation indices; and establishing a rice quality prediction model based on the quantitative relationship between the quality indicators and the meteorological evaluation indices, thereby providing decision support for improving rice quality. The present invention has the beneficial effect of providing guidance to agricultural departments on taking reasonable response measures and carrying out quality control and upgrading based on key time periods and key meteorological factors affecting rice quality, in combination with weather forecast products.

Description

Technical Field

[0001] The present invention belongs to the field of data prediction, and in particular relates to a rice quality prediction method based on meteorological data and growth period. Background Art

[0002] Rice quality is primarily influenced by both genetic characteristics and environmental meteorological conditions. When genetic characteristics remain stable, quality differences are primarily influenced by the meteorological conditions of the growing environment. Therefore, studying the impact of meteorological conditions on rice quality and predicting rice quality based on meteorological data during the rice growing season are crucial for the agricultural sector to implement effective quality control and upgrading measures.

[0003] Traditionally, rice quality is measured using instrumental testing. After harvest, rice is dried, hulled, and sampled for testing. This is inefficient, costly, and ineffective in stabilizing and improving rice quality. By studying the impact of environmental and meteorological conditions on rice quality, establishing a prediction model, and predicting rice quality based on meteorological data and growing seasons, and analyzing the key time periods and meteorological factors that influence quality indicators, and combining weather and climate forecast products to implement countermeasures, we can effectively mitigate the impact of adverse meteorological conditions on rice quality, providing technical support for upgrading rice quality and ensuring high-quality rice production. Summary of the Invention

[0004] In view of this, the present invention aims to provide a rice quality prediction method based on meteorological data and growth period, in order to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] In one aspect, the present invention provides a rice quality prediction method based on meteorological data and growth period, comprising:

[0007] Acquiring historical meteorological data and historical quality data during the rice growth process, and dividing the growth process into multiple growth periods;

[0008] Based on the meteorological data of each growth period, three quantitative evaluation models of temperature, precipitation and sunshine were constructed, and three meteorological evaluation indices of temperature, precipitation and sunshine were calculated;

[0009] The meteorological evaluation index of each growth period and the corresponding historical quality data were subjected to multiple linear regression analysis to obtain the quantitative relationship between each quality index in the quality data and the meteorological evaluation index;

[0010] Based on the quantitative relationship between quality indicators and meteorological evaluation indexes, a rice quality prediction model was established to provide decision-making support for improving rice quality.

[0011] Furthermore, the historical meteorological data includes: daily average temperature, precipitation, sunshine hours, and available sunshine hours;

[0012] The historical quality data include: amylose content, gel consistency, protein, and chalkiness;

[0013] The growth period includes: transplanting period, greening period, tillering period, jointing and ear formation period, heading and flowering period, and filling and maturity period.

[0014] Furthermore, the construction process of the temperature quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0015] For each growth period, record the daily average temperature of each day in the growth period, obtain the average of all daily average temperatures in the growth period, and obtain the average temperature of the growth period;

[0016] Each growth period is divided into several ten-day periods. For each ten-day period, the suitable temperature value is calculated based on the statistically fitted quadratic polynomial function. The suitable temperature values ​​of all ten-day periods in the growth period are averaged as the temperature reference value for the growth period.

[0017] Set the lower and upper temperature limits for the entire rice growth cycle. If the average temperature during the growth period is lower than the lower temperature limit or higher than the upper temperature limit, the temperature evaluation index is zero; otherwise, the temperature evaluation index is the ratio of the average temperature during the growth period to the temperature reference value.

[0018] Furthermore, the construction process of the precipitation quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0019] For each rice growing period, record the daily precipitation during the growing period and add them up to get the total precipitation during the growing period;

[0020] Each growing period is divided into several decades. A cubic polynomial function obtained through statistical fitting is used to describe the changes in rice water requirements within different decades. The ideal water requirement value of the crop in each decade is calculated. The water requirement values ​​of all decades within the growing period are added together to obtain the water requirement benchmark for that growing period.

[0021] The ratio of total precipitation during the growth period to the water requirement benchmark is calculated to obtain the precipitation evaluation index.

[0022] Furthermore, the construction process of the sunshine quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0023] For each growth period, record the actual sunshine hours and the theoretically available sunshine hours per day; add up the actual sunshine hours per day in each growth period to get the total sunshine hours for that growth period; add up the theoretically available sunshine hours per day to get the total available sunshine hours for that growth period;

[0024] The growing period is divided into several decades. For each decade, the reduction coefficient between the total sunshine hours and the total available sunshine hours is calculated based on the pre-fitted quadratic polynomial function. The product of the theoretical available sunshine hours in the growing period and the reduction coefficient is used to obtain the benchmark available sunshine hours in the growing period.

[0025] The sunshine evaluation index is obtained by using the ratio of the total sunshine hours during the growth period to the benchmark sunshine hours.

[0026] Furthermore, the rice quality prediction model is specifically as follows:

[0027] Amylose content =86.45-70.5 ;

[0028] Glue consistency =-1787.53+1916.44 -30.24 ;

[0029] protein =3.10+2.77 +2.23 ;

[0030] Chalky whiteness =4.05-3.10 -0.76 ;

[0031] in, is the temperature evaluation index during the jointing and booting stage, is the precipitation evaluation index during the heading and flowering period, is the temperature evaluation index during the heading and flowering period, is the sunshine evaluation index during the tillering period, It is the sunshine evaluation index during the grouting maturity period.

[0032] Furthermore, the decision support provided for improving rice quality is specifically:

[0033] Current weather data and weather forecast data are input, and a meteorological evaluation index is calculated. Based on the quantitative relationship between the quality index and the meteorological evaluation index, the quality prediction result of rice in the current growth period is calculated, and corresponding measures are taken according to the prediction result.

[0034] A second aspect of the present invention provides an electronic device, comprising a processor and a memory communicatively connected to the processor and used to store instructions executable by the processor, wherein the processor is used to execute the method described in the first aspect.

[0035] The third aspect of the present invention proposes a server, comprising at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to enable the at least one processor to perform the method described in the first aspect.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which implements the method described in the first aspect when executed by a processor.

[0037] Compared with the prior art, the rice quality prediction method based on meteorological data and growth period of the present invention has the following beneficial effects:

[0038] Based on the evaluation index of meteorological factors during the development period, a single rice quality index prediction model is established to predict the rice grade through quantitative forecast of quality indicators. At the same time, based on the prediction model, the key time periods and key meteorological factors affecting the quality are analyzed. Combined with weather forecast products, reasonable response measures are taken by the agricultural sector to provide guidance for quality control and upgrading. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0040] Figure 1 Schematic diagram of the flow of the prediction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0042] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0043] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0044] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0045] like Figure 1 A rice quality prediction method based on meteorological data and growth period is shown, comprising:

[0046] S1. Obtaining historical meteorological data and historical quality data during the rice growth process, and dividing the growth process into multiple growth periods;

[0047] S2. Construct three quantitative evaluation models of temperature, precipitation, and sunshine based on the meteorological data during each growth period, and calculate the three meteorological evaluation indices of temperature, precipitation, and sunshine;

[0048] S3. Perform multiple linear regression analysis on the meteorological evaluation index of each growth period and the corresponding historical quality data to obtain the quantitative relationship between each quality index in the quality data and the meteorological evaluation index;

[0049] S4. Based on the quantitative relationship between quality indicators and meteorological evaluation indexes, a rice quality prediction model is established to provide decision support for improving rice quality.

[0050] The historical meteorological data include: daily average temperature, precipitation, sunshine hours, and sunshine hours;

[0051] The historical quality data include: amylose content, gel consistency, protein, and chalkiness;

[0052] The growth period includes: transplanting period, greening period, tillering period, jointing and ear formation period, heading and flowering period, and filling and maturity period.

[0053] In some embodiments, the process of obtaining the above data is as follows:

[0054] Obtain daily average temperature, precipitation, sunshine hours, and sunshine hours data from national meteorological observation stations during the rice growing season from 2013 to 2022;

[0055] The growth period of rice is divided into 6 growth stages: transplanting stage, greening stage, tillering stage, jointing and heading stage, heading and flowering stage, and grain filling and maturity stage.

[0056] Transplanting period refers to the period when rice seedlings are transplanted from the nursery to the main field;

[0057] The greening period refers to the period when the seedlings turn from yellow to green after transplanting;

[0058] The tillering stage refers to the period when rice stems grow branches after turning green. These branches are called tillers.

[0059] The jointing and heading stage refers to the period after tillering stops, when the internodes at the base of the rice stalk begin to elongate, and the growth point at the top of the stem begins to differentiate into panicles;

[0060] Heading and flowering period refers to the period when rice ears emerge from leaf sheaths and spikelets on ears open;

[0061] The filling and maturity period refers to the period when the rice grains begin to accumulate dry matter after flowering, continue to fill up, swell and harden, and gradually form rice grains.

[0062] Obtain rice quality data from 2018 to 2022. The data was obtained by a professional testing company and includes four important indicators of rice quality: amylose content, gel consistency, protein, and chalkiness.

[0063] The construction process of the temperature quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0064] For each growth period, record the daily average temperature of each day in the growth period, obtain the average of all daily average temperatures in the growth period, and obtain the average temperature of the growth period;

[0065] Each growth period is divided into several ten-day periods. For each ten-day period, the suitable temperature value is calculated based on the statistical fitting of the quadratic polynomial function. The suitable temperature values ​​of all ten-day periods in the growth period are averaged as the temperature reference value for the growth period.

[0066] The lower and upper temperature limits of the entire rice growth cycle are set. If the average temperature during the growth period is lower than the lower limit or higher than the upper limit, the temperature evaluation index is zero; otherwise, the temperature evaluation index is the ratio of the average temperature during the growth period to the temperature reference value. In some embodiments, the specific calculation process of the temperature evaluation index is as follows:

[0067] The average temperature of each decade during the rice growing period from 2013 to 2022 was used in combination with the biological characteristics of rice to construct a suitable temperature curve, and the ratio of the average temperature during the growing period to the suitable temperature curve was used to construct a temperature quantitative evaluation model.

[0068] (1);

[0069] = (2);

[0070] in, represents the rice temperature evaluation index, 1 o'clock, take = Assignment; Indicates the order of ten days within the reproductive period; Indicates the number of ten days in each growth period, the curve =0.7996; The curve showing the suitable temperature change for each decade in the six growth periods; represents the average temperature during each growth period, =1, 2, 3, 4, 5, 6 represent the transplanting period, greening period, tillering period, jointing and booting period, heading and flowering period, and grain filling and maturity period respectively; Represents the daily average temperature during the growth period; Indicates the number of days in the reproductive period.

[0071] The construction process of the precipitation quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0072] For each rice growing period, record the daily precipitation during the growing period and add them up to get the total precipitation during the growing period;

[0073] Each growing period is divided into several decades. A cubic polynomial function obtained through statistical fitting is used to describe the changes in rice water requirements within different decades. The ideal water requirement value of the crop in each decade is calculated. The water requirement values ​​of all decades within the growing period are added together to obtain the water requirement benchmark for that growing period.

[0074] The ratio of total precipitation during the growth period to the water requirement benchmark is calculated to obtain the precipitation evaluation index.

[0075] In some embodiments, the specific calculation process of the above precipitation evaluation index is as follows:

[0076] The precipitation in each decade during the rice growing period from 2013 to 2022 was used to construct a water requirement curve combined with the water requirement law of rice growth, and a precipitation quantitative evaluation model was constructed using the ratio of precipitation during the growing period to the water requirement curve.

[0077] (3);

[0078] = (4);

[0079] in, represents the rice precipitation evaluation index, 1 o'clock, take = Assignment; Curve =0.695; The curve showing the change of water demand in each decade of the six growth periods; represents the precipitation in each growing period, Indicates daily precipitation during the growing period.

[0080] The construction process of the sunshine quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows:

[0081] For each growth period, record the actual sunshine hours and the theoretically available sunshine hours per day; add up the actual sunshine hours per day in each growth period to get the total sunshine hours for that growth period; add up the theoretically available sunshine hours per day to get the total available sunshine hours for that growth period;

[0082] The growing period is divided into several decades. For each decade, the reduction coefficient between the total sunshine hours and the total available sunshine hours is calculated based on the pre-fitted quadratic polynomial function. The product of the theoretical available sunshine hours in the growing period and the reduction coefficient is used to obtain the benchmark available sunshine hours in the growing period.

[0083] The sunshine evaluation index is obtained by using the ratio of the total sunshine hours during the growth period to the benchmark sunshine hours.

[0084] The sunshine hours refer to the maximum number of hours of sunshine in a region. Affected by various factors such as weather, the actual sunshine hours are lower than the sunshine hours. The ratio of the sunshine hours during the rice growing period to the revised sunshine hours change curve is used to construct a sunshine quantitative evaluation model.

[0085] = (5);

[0086] = , = (6);

[0087] in, represents the rice sunshine evaluation index, 1 o'clock, take =1 assignment; The reduction curve of the actual sunshine hours in each decade of the six growth periods compared with the available sunshine hours is shown in the figure. =0.6071; Indicates the number of sunshine hours in each growth period, Indicates the number of hours that can be taken during each growth period. Indicates the number of sunshine hours per day during the growth period. Indicates the number of hours of sunlight available each day during the reproductive period.

[0088] The rice quality prediction model is specifically as follows:

[0089] Amylose content =86.45-70.5 ;

[0090] Glue consistency =-1787.53+1916.44 -30.24 ;

[0091] protein =3.10+2.77 +2.23 ;

[0092] Chalky whiteness =4.05-3.10 -0.76 ;

[0093] in, is the temperature evaluation index during the jointing and booting stage, is the precipitation evaluation index during the heading and flowering period, is the temperature evaluation index during the heading and flowering period, is the sunshine evaluation index during the tillering period, It is the sunshine evaluation index during the grouting maturity period.

[0094] In some embodiments, the specific process of the rice quality prediction model is as follows:

[0095] The temperature, precipitation, and sunshine evaluation indexes of each rice growth period from 2018 to 2021 were used for regression analysis with the rice quality data from 2018 to 2021 to establish a rice quality prediction model with a single quality index. The model input is the meteorological evaluation index of each growth period, and the output is the rice quality index. The specific equation is shown in Table 1:

[0096] Table 1: Prediction model of single rice quality index

[0097]

[0098] : Amylose content, : glue consistency, :protein, : chalkiness; is the temperature evaluation index during the jointing and booting stage, is the precipitation evaluation index during the heading and flowering period, is the temperature evaluation index during the heading and flowering period, is the sunshine evaluation index during the tillering period, is the sunshine evaluation index during the grouting maturity period;

[0099] Among them, the regression equations of amylose content and protein passed the significance test of 0.1, the regression equation of gel consistency passed the significance test of 0.01, and the regression equation of chalkiness passed the significance test of 0.05. The linear relationship between rice quality indicators and meteorological factor evaluation index was significant.

[0100] The specific decision support for improving rice quality is as follows:

[0101] Current weather data and weather forecast data are input, and a meteorological evaluation index is calculated. Based on the quantitative relationship between the quality index and the meteorological evaluation index, the quality prediction result of rice in the current growth period is calculated, and corresponding measures are taken according to the prediction result.

[0102] In some embodiments, the above method is used to predict the rice quality in Baodi District, Tianjin in 2022, and the process is as follows:

[0103] 1) Using the daily average temperature, precipitation, sunshine hours, and available sunshine hours data of the national meteorological observation station during the tillering, jointing and booting, heading and flowering, and grain filling and maturity stages of rice in Baodi District, Tianjin in 2022, the calculations were performed according to formulas (2), (4), and (6). The average temperature during the jointing and booting stages and heading and flowering stages, the precipitation during the heading and flowering stage, and the sunshine hours and available sunshine hours during the tillering stage and grain filling and maturity stages were obtained, respectively.

[0104] 2) Use the data calculated in step 1) to enter equations (1), (3) and (5) for calculation, where the tillering period is The value is 3. The value is 3; jointing and booting stage The value is 6. The value is 3; heading and flowering period The value is 9. The value is 2; grouting maturity The value is 11. Taking the value as 5, we can get the temperature evaluation index of the jointing and booting stage and the heading and flowering stage, the precipitation evaluation index of the heading and flowering stage, and the sunshine evaluation index of the tillering stage and the grain filling and maturity stage, as shown in Table 2:

[0105] Table 2: Meteorological factors and evaluation indexes during the rice growing period in Baodi District in 2022

[0106]

[0107] 3) The calculation results of step 2) are introduced into the prediction model to calculate the rice quality index. The calculation results are compared and analyzed with the test results, as shown in Table 3:

[0108] Table 3: Model prediction test

[0109]

[0110] The maximum absolute value of the relative error of the model simulation is 10% for chalkiness, and the minimum is 0.2% for protein; the test results show that the rice quality prediction model constructed based on meteorological data during the growth period has a good prediction effect.

[0111] Amylose content, gel consistency, and chalkiness are important quality indicators for rice grade. Appropriate amylose content, high gel consistency, and slightly low protein content ensure the rice's soft, elastic, and sticky texture. Low chalkiness results in a clear, translucent, and lustrous appearance. Meteorological factors that significantly influence rice quality are temperature during the jointing and heading stages, as well as heading and flowering stages; precipitation during the heading and flowering stages; and sunshine during the tillering and grain-filling stages. Higher temperatures during the jointing and heading stages, slightly higher temperatures and frequent sunny days during the heading and flowering stages, and sufficient sunlight during the tillering and grain-filling stages are beneficial for improving rice quality. Rice enters its reproductive growth phase from the jointing and heading stage, and meteorological conditions during this reproductive growth period are crucial factors influencing rice quality.

[0112] A single rice quality index prediction model was established based on the meteorological factor evaluation index during the growth period to predict the rice grade through quantitative forecast of quality indexes.

[0113] Based on the prediction model, we analyze the key time periods and key meteorological factors that affect quality. Combined with weather forecast products, we provide guidance for the agricultural sector to take reasonable response measures and carry out quality control and upgrading.

[0114] An electronic device includes a processor and a memory connected to the processor for storing instructions executable by the processor, wherein the processor is used to execute the above-mentioned rice quality prediction method based on meteorological data and growth period.

[0115] A server includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to enable the at least one processor to execute a rice quality prediction method based on meteorological data and growth period.

[0116] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a rice quality prediction method based on meteorological data and growth period is implemented.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rice quality prediction method based on meteorological data and growth period, characterized in that: include: Acquiring historical meteorological data and historical quality data during the rice growth process, and dividing the growth process into multiple growth periods; Based on the meteorological data of each growth period, three quantitative evaluation models of temperature, precipitation and sunshine were constructed, and three meteorological evaluation indices of temperature, precipitation and sunshine were calculated; The meteorological evaluation index of each growth period and the corresponding historical quality data were subjected to multiple linear regression analysis to obtain the quantitative relationship between each quality index in the quality data and each meteorological evaluation index in the meteorological data; Based on the quantitative relationship between quality indicators and meteorological evaluation indexes, a rice quality prediction model was established to provide decision support for improving rice quality; The growth period includes: transplanting period, green period, tillering period, jointing and booting period, heading and flowering period, and filling and maturity period; The rice quality prediction model is specifically as follows: Amylose content Y s =86.45-70.5 T (4); Glue consistency Y g =-1787.53+1916.44 T (4)-30.24 R (5); protein Y p =3.10+2.77 T (5)+2.23 S (6); Chalky whiteness Y c =4.05-3.10 S (3)-0.76 S (6); in, T (4) is the temperature evaluation index during the jointing and booting stage, R (5) is the precipitation evaluation index during the heading and flowering period, T (5) is the temperature evaluation index during the heading and flowering period, S (3) is the sunshine evaluation index during the tillering period, S (6) is the sunshine evaluation index during the grouting maturity period; The construction process of the temperature quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows: For each growth period, record the daily average temperature of each day in the growth period, obtain the average of all daily average temperatures in the growth period, and obtain the average temperature of the growth period; Each growth period is divided into several ten-day periods. For each ten-day period, the suitable temperature value is calculated based on the statistically fitted quadratic polynomial function. The suitable temperature values ​​of all ten-day periods in the growth period are averaged as the temperature reference value for the growth period. Set the lower and upper temperature limits for the entire rice growth cycle. If the average temperature during the growth period is lower than the lower temperature limit or higher than the upper temperature limit, the temperature evaluation index is zero; otherwise, the temperature evaluation index is the ratio of the average temperature during the growth period to the temperature reference value.

2. The rice quality prediction method based on meteorological data and growth period according to claim 1, characterized in that: The historical meteorological data include: daily average temperature, precipitation, sunshine hours, and sunshine hours; The historical quality data include: amylose content, gel consistency, protein, and chalkiness.

3. The rice quality prediction method based on meteorological data and growth period according to claim 1, characterized in that: The construction process of the precipitation quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows: For each rice growing period, record the daily precipitation during the growing period and add them up to get the total precipitation during the growing period; Each growing period is divided into several decades. A cubic polynomial function obtained through statistical fitting is used to describe the changes in rice water requirements within different decades. The ideal water requirement value of the crop in each decade is calculated. The water requirement values ​​of all decades within the growing period are added together to obtain the water requirement benchmark for that growing period. The ratio of total precipitation during the growth period to the water requirement benchmark is calculated to obtain the precipitation evaluation index.

4. The rice quality prediction method based on meteorological data and growth period according to claim 1, characterized in that: The construction process of the sunshine quantitative evaluation model and the calculation process of the corresponding meteorological evaluation index are specifically as follows: For each growth period, record the actual sunshine hours and the theoretically available sunshine hours per day; add up the actual sunshine hours per day in each growth period to get the total sunshine hours for that growth period; add up the theoretically available sunshine hours per day to get the total available sunshine hours for that growth period; The growing period is divided into several decades. For each decade, the reduction coefficient between the total sunshine hours and the total available sunshine hours is calculated based on the pre-fitted quadratic polynomial function. The product of the theoretical available sunshine hours in the growing period and the reduction coefficient is used to obtain the benchmark available sunshine hours in the growing period. The sunshine evaluation index is obtained by using the ratio of the total sunshine hours during the growth period to the benchmark sunshine hours.

5. The rice quality prediction method based on meteorological data and growth period according to claim 1, characterized in that: The specific decision support for improving rice quality is as follows: Current weather data and weather forecast data are input, and based on the quantitative relationship between quality indicators and meteorological evaluation indexes, the quality prediction results of rice in the current growth period are calculated, and corresponding measures are taken according to the prediction results.

6. An electronic device comprising a processor and a memory in communication with the processor and configured to store instructions executable by the processor, wherein: The processor is configured to execute the method according to any one of claims 1 to 5.

7. A server, characterized in that: The invention comprises at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor so that the at least one processor performs the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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