CN109615150B - Method and system for determining rice meteorological output - Google Patents

Method and system for determining rice meteorological output Download PDF

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CN109615150B
CN109615150B CN201811646262.XA CN201811646262A CN109615150B CN 109615150 B CN109615150 B CN 109615150B CN 201811646262 A CN201811646262 A CN 201811646262A CN 109615150 B CN109615150 B CN 109615150B
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刘申
张彧豪
张虎成
彭远
董婷婷
张东晖
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Aisino Corp
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Abstract

The invention provides a method and a system for determining rice meteorological output. According to the method and the system, the weather index information of the rice in the current year is predicted through a weather index prediction model according to main weather index information which influences the growth of the crops in the current crop area and mainly comprises historical data and data of the known time in the current year, the weather biomass of the rice in each growth period in the current year is predicted through the weather index-weather biomass prediction model, and the weather yield of the rice in the current year is predicted through the weather biomass-weather yield prediction model. According to the method and the system for determining the rice meteorological output, the meteorological biomass prediction of the rice in each growth period can be realized by establishing the meteorological index-meteorological biomass prediction model of the rice in each growth period, so that the accuracy of the rice meteorological output prediction is improved, the dynamic release of the rice meteorological output is realized, and the technical support is provided for guaranteeing the balance of supply and demand of the rice market in China.

Description

Method and system for determining rice meteorological output
Technical Field
The present invention relates to the field of economic crop yield prediction, and more particularly, to a method and system for determining the meteorological yield of rice.
Background
Rice yield is generally divided into biological and economic yields. Biological yield, referred to as biomass for short, refers to the total amount of various organic substances produced and accumulated by photosynthesis and absorption, i.e., by transformation of matter and energy, in rice in each growth cycle, and usually does not include a root system when calculating biomass. Economic yield refers to the amount of harvested rice grain required for cultivation purposes, and is generally referred to as yield. Generally, the economic yield is proportional to the biomass.
The growth period of rice is mainly determined by the genetics of rice, and varies according to factors such as climatic conditions and cultivation techniques in cultivation areas. For example, the growth and development are slow and the growth period is longer due to low temperature during autumn sowing and winter sowing; the growth and development are fast and the growth period is short due to high temperature in spring sowing and summer sowing. The same variety is planted in different latitudes, and the growth period changes along with the difference of temperature and illumination.
Because the long-time yield fluctuation is not only related to meteorological indexes, but also closely related to rice variety updating, social and economic changes and the like, in the observation and statistics research of the relationship between the long-time sequential crop yield and the meteorological indexes, the yield of the rice is generally decomposed into a trend yield, a meteorological yield and a random error 3, wherein the trend yield is a long-period yield component reflecting the development level of the productivity in the historical period and is also called as a technical yield, and the meteorological yield is a fluctuation yield component influenced by a short-period change factor (mainly caused by agricultural climate disasters) mainly caused by the climate elements. Therefore, the rice meteorological output is the key point in the rice output prediction.
In the prior art, the prediction of the rice meteorological output only considers the change of the rice meteorological output in the full growth cycle, however, the requirements of the rice on the climatic conditions in different growth and development processes are different, the key periods and meteorological factors influencing the growth and development of crops in different regions are also different, and the rice meteorological output fluctuation under the climatic conditions cannot be timely and accurately predicted only by considering the influence of the full growth cycle climatic conditions on the rice meteorological output.
Therefore, there is a need for a technique for determining the meteorological production of rice by the change in the meteorological biomass at each growth period of rice, based on the difference in the meteorological biomass caused by the influence of weather conditions at different growth periods of rice.
Disclosure of Invention
In order to solve the technical problem that the influence of the climate conditions of the full growth cycle on the rice meteorological output cannot be timely and accurately predicted due to the fact that the influence of the climate conditions of the full growth cycle on the rice meteorological output is only considered in the prior art, the invention provides a method for determining the rice meteorological output, which comprises the following steps:
determining the data of the meteorological indexes of each growth period of the rice in the current year according to a set meteorological index prediction model based on the data of the meteorological indexes affecting the growth of the rice in the past n years and the data of the known time of the current year, wherein the meteorological indexes comprise daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height;
determining the meteorological biomass of the rice in each growth period in the current year according to the meteorological index-meteorological biomass prediction model of the rice in each growth period based on the data of the meteorological index of the rice in each growth period in the current year;
and determining the current-year meteorological output of the rice according to a rice meteorological biomass-meteorological output prediction model based on the current-year meteorological biomass of the rice in each growth period.
Further, the method further comprises the following steps of determining the data of the meteorological indexes of each growth period of the rice in the current year according to the set meteorological index prediction model based on the data of the meteorological indexes influencing the growth of the rice in the past n years and the data of the known time of the current year:
dividing the growth stage of the rice into a plurality of growth periods according to the growth characteristics of the rice;
collecting data of past n years of meteorological indexes influencing the growth of rice and data of known time of the year, data of past n years of biomass of each growth period, data of past n years of economic yield and historical data of start and end time of each growth period of rice;
determining the starting and ending time of each growth period in the current year according to the historical data of the starting and ending time of each growth period of rice;
determining data of the past n years of meteorological biomass of the rice in each breeding period based on the data of the past n years of biomass of the rice in each breeding period;
determining a meteorological index-meteorological biomass prediction model of the rice in each growth period based on data of the meteorological index in the past n years and data of the meteorological biomass in the past n years in each growth period;
determining the data of the rice meteorological output for the past n years based on the data of the rice economic output for the past n years;
and determining a rice meteorological biomass-meteorological yield prediction model based on the data of the meteorological biomass of the rice in each growth period in the past n years and the data of the rice meteorological yield in the past n years.
Further, the step of determining the meteorological index data of each growth period of the rice in the current year according to the set meteorological index prediction model based on the data of the past n years of meteorological indexes affecting the growth of the rice and the data of the known time of the current year comprises the following steps:
determining the meteorological index data of unknown time in the current year according to a set meteorological index prediction model based on the data of the past n years of meteorological indexes affecting the growth of rice, wherein:
the calculation formula of the daily average temperature prediction model is as follows:
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is greater than or equal to a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure BDA0001932118900000031
Figure BDA0001932118900000032
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure BDA0001932118900000033
Figure BDA0001932118900000034
in the formula, T nave Is the daily average temperature, T, of a day in the unknown time of the year hmin Is the minimum value of the daily minimum temperature, T, of the last n years for a certain day of the unknown time of the year hmax Is the maximum value of the highest daily temperature of the last n years for a certain day in the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the average of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, mu ave Is the mean of the average daily temperatures of the last n years of the month in which a day is located in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the day of the last n years in the month of a certain day in the unknown time of the year, sigma ave Is the standard deviation of the average daily temperature in the last n years in the month of a certain day of the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the daily minimum temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
T n min ＝μ min +σ min ×χ
Figure BDA0001932118900000041
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is smaller than a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure BDA0001932118900000042
Figure BDA0001932118900000043
in the formula, T n min Is the daily minimum temperature, T, of a certain day of the year's unknown time h max Is the maximum value of the highest daily temperature of the last n years for a certain day in the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest daily temperature in the last n years in the month of a certain day in the unknown time of the year, and χ is the standard normal deviation of the day generated according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the highest daily temperature prediction model is as follows:
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is greater than or equal to a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure BDA0001932118900000044
Figure BDA0001932118900000045
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is smaller than a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
T n max ＝μ max +σ max ×χ
Figure BDA0001932118900000051
in the formula, T n max Is the highest daily temperature, T, of a certain day in the unknown time of the year h min Is the minimum value of the daily minimum temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which the day is in the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the rice field solar water layer height prediction model is as follows:
H＝μ H +σ H ×χ
Figure BDA0001932118900000052
wherein H is the height of the water layer of the rice field on a certain day in the unknown time of the year, mu H Is the average value of the height of the rice field water layer in the last n years in the month of a certain day in the unknown time of the year, sigma G The standard deviation of the height of the water layer in the rice field in the past n years in the month of a certain day in the unknown time of the year, chi is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
and dividing the meteorological index data of the known time of the current year and the meteorological index data of the unknown time of the current year determined by the meteorological index prediction model according to the starting and ending time of each growth period of the rice to obtain the meteorological index data of each growth period of the rice.
Further, the data for determining the past n years of meteorological biomass for rice at each breeding season based on the data for the past n years of biomass for rice at each breeding season comprises:
generating biomass sequence data by using the data of the biomass of the rice in the past n years in each breeding period according to the time sequence;
taking i years as sliding step length, and performing statistical regression analysis on biomass of rice in each i years in each growth period by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining the simulation values of j biomass quantities of rice every year in each growth period based on j groups of unary linear regression equations;
determining the average value of the analog value of the biomass per year according to the analog values of j biomass per year in each breeding period of the rice, and taking the average value as the trend biomass per year in each breeding period of the rice;
and subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
Further, the determining a meteorological index-meteorological biomass prediction model for each growth period of rice based on the data of the meteorological index for the past n years and the data of the meteorological biomass for the past n years for each growth period of rice comprises:
determining a kernel function of each meteorological index and the meteorological biomass and the weight of each kernel function based on the data of the meteorological index in the past n years and the data of the meteorological biomass in the past n years in each growth period of the rice, and determining and solving the deviation value of the meteorological biomass according to the kernel function;
determining a meteorological index-meteorological biomass prediction model of each rice growth period based on the kernel function of each meteorological index and meteorological biomass, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure BDA0001932118900000061
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the year,
Figure BDA0001932118900000062
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure BDA0001932118900000063
And determining the deviation value of the meteorological biomass of the rice in the ith growth period of the current year.
Further, the data for determining the rice meteorological production for the past n years based on the data for the past n years of rice economic production comprises:
generating economic yield sequence data by data of the rice economic yield in the past n years according to a time sequence;
taking i years as sliding step length, carrying out statistical regression analysis on the economic yield of rice in each i years by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining j simulation values of economic yield of rice every year based on j sets of unary linear regression equations;
determining the average value of the annual economic yield simulation values according to the annual j economic yield simulation values of the rice, and taking the average value as the annual trend economic yield of the rice;
and subtracting the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
Further, the method for determining the rice meteorological biomass-meteorological yield prediction model based on the data of the rice meteorological biomass of the past n years in each growth period and the data of the rice meteorological yield of the past n years comprises the following steps:
determining a kernel function of the meteorological biomass and the meteorological output and the weight of each kernel function in each breeding period based on the data of the meteorological biomass in the past n years in each breeding period of the rice and the data of the meteorological output in the past n years, and determining and solving the deviation value of the meteorological output according to the kernel function;
determining a rice meteorological biomass-meteorological yield prediction model based on the kernel function of the meteorological biomass and the meteorological yield of the rice in each growth period, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure BDA0001932118900000071
wherein y is the annual meteorological production of rice,
Figure BDA0001932118900000072
is a kernel function of the meteorological biomass of the ith growth period of the rice in the current year, omega i Is the weight of the kernel function of the i-th growth period of the rice year, and b is based on the kernel function->
Figure BDA0001932118900000073
And determining the deviation value of the meteorological output of the rice in the current year.
According to another aspect of the present invention, there is provided a system for determining the meteorological output of rice, the system comprising:
the rice meteorological index unit is used for determining the data of the meteorological index of each growth period of the rice in the current year according to a set meteorological index prediction model based on the data of the meteorological index affecting the growth of the rice in the past n years and the data of the known time of the current year, wherein the meteorological index comprises daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height;
the rice meteorological biomass unit is used for determining the meteorological biomass of the rice in each growth period in the current year according to the meteorological index-meteorological biomass prediction model of the rice in each growth period based on the data of the meteorological index of the rice in each growth period in the current year;
and the rice meteorological output unit is used for determining the meteorological output of the rice in the current year according to the rice meteorological biomass-meteorological output prediction model based on the meteorological biomass of each growth period of the rice in the current year.
Further, the system further comprises:
the rice growth period dividing unit is used for dividing the growth period of the rice into a plurality of growth periods according to the growth characteristics of the rice;
a data acquisition unit for acquiring data of past n years of meteorological indexes and data of known time of the year, data of past n years of biomass of each breeding period, data of past n years of economic yield, and historical data of start and end time of each breeding period of rice, wherein the meteorological indexes influence the growth of the rice;
a growth period time determination unit for determining the start-stop time of each growth period of the year based on the history data of the start-stop time of each growth period of rice;
a first data unit for determining data of the past n years of meteorological biomass of rice per breeding season based on the data of the past n years of biomass of rice per breeding season;
a first model unit for determining a meteorological index-meteorological biomass prediction model for each growth period of rice based on data of the past n years of meteorological indexes and data of the past n years of meteorological biomass for each growth period of rice;
a second data unit for determining data of the past n years of rice meteorological production based on data of the past n years of rice economic production;
and a second model unit for determining a rice meteorological biomass-meteorological yield prediction model based on the data of the past n years of meteorological biomass and the data of the past n years of meteorological yield of rice for each breeding period of rice.
Further, the rice weather indicator unit includes:
and the unknown meteorological index unit is used for determining meteorological index data of unknown time in the current year according to the set meteorological index prediction model based on the data of the meteorological indexes affecting the growth of the rice in the past n years, wherein the calculation formula of the daily average temperature, the daily minimum temperature, the daily maximum temperature and the rice field daily water layer height prediction model is the same as that of the method for determining the rice meteorological output, and the description is omitted here.
And the index determining unit is used for dividing the meteorological index data of the known time of the current year and the meteorological index data of the unknown time of the current year determined by the meteorological index prediction model according to the starting and ending time of each growth period of the rice to obtain the meteorological index data of each growth period of the rice.
Further, the first data unit includes:
a first sequence unit for generating biomass sequence data chronologically from data of biomass of rice for past n years for each breeding season;
the first equation group unit is used for performing statistical regression analysis on biomass of rice in each growth period in each year by using the i year as a sliding step length and applying a linear sliding average method to obtain j groups of unary linear regression equations, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
a first analog value unit for determining analog values of j biomass of rice per year of each growth period based on j sets of one-dimensional linear regression equations;
a first trend value unit for determining an average value of the analog values of the biomass per year from the analog values of j biomass per year for each breeding period of rice, and taking the average value as the trend biomass per year for each breeding period of rice;
and the first result unit is used for subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
Further, the first model unit includes:
a first parameter unit for determining a kernel function of each meteorological index and the meteorological biomass and a weight of each kernel function based on data of the meteorological index of each growth period of rice in the past n years and data of the meteorological biomass in the past n years, and determining and solving a deviation value of the meteorological biomass according to the kernel function;
a first formula unit, which is used for determining a meteorological index-meteorological biomass prediction model of rice in each growth period based on a kernel function of each meteorological index and meteorological biomass, a weight of each kernel function and a deviation value, and the calculation formula is as follows:
Figure BDA0001932118900000091
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the year,
Figure BDA0001932118900000092
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure BDA0001932118900000093
And determining the deviation value of the meteorological biomass of the rice in the ith growth period of the current year.
Further, the second data unit includes:
a second sequence unit for generating economic yield sequence data by time-sequentially using data of the past n years of the economic yield of rice;
the second equation set unit is used for carrying out statistical regression analysis on the economic yield of the rice per i year by using a linear sliding average method with i year as a sliding step length to obtain j sets of unary linear regression equations, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
a second analog value unit for determining analog values of j economic yields of rice per year based on j sets of one-dimensional linear regression equations;
a second trend value unit for determining an average value of the simulated values of the economic yield per year from the simulated values of the j economic yields per year of the rice, and taking the average value as the trend economic yield per year of the rice;
and the second result unit is used for subtracting the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
Further, the second model unit includes:
a second parameter unit for determining a kernel function of the meteorological biomass and the meteorological output of each breeding period and the weight of each kernel function based on the data of the meteorological biomass of the rice in the past n years and the data of the meteorological output of the rice in the past n years in each breeding period, and determining an offset value of the meteorological output according to the kernel function;
a second formula unit, configured to determine a rice meteorological biomass-meteorological yield prediction model based on the kernel functions of the meteorological biomass and the meteorological yield of the rice in each growth period, the weight of each kernel function, and the deviation value, wherein the calculation formula is as follows:
Figure BDA0001932118900000101
wherein y is the current annual meteorological production of rice,
Figure BDA0001932118900000102
is a kernel function of meteorological biomass, omega, of the ith growth period of the rice in the current year i Is the weight of the kernel function of the i-th growth period of the rice year, and b is based on the kernel function->
Figure BDA0001932118900000103
And determining the deviation value of the current annual meteorological output of the rice.
According to the method and the system for determining the rice meteorological output, provided by the technical scheme, the rice is divided into a plurality of growth periods according to growth characteristics, a meteorological index-meteorological biomass prediction model is established respectively with the biomass in the same historical growth period in different growth periods by combining the meteorological index information of the historical main influence factors, and then the meteorological biomass-meteorological output prediction model is established by using the biomass in the same historical growth period and the historical meteorological output; and finally, forecasting the meteorological biomass of the rice in the current year in each growth period through a meteorological index-meteorological biomass forecasting model, and forecasting the meteorological yield of the rice in the current year through the meteorological biomass-meteorological yield forecasting model according to main meteorological index information which mainly comprises historical data and data of the known time in the current year and in which the current crop is located and influences the growth of the crops. The method and the system for determining the rice meteorological output have the following beneficial effects:
1. the meteorological biomass prediction model of each growth period of the rice is established, so that the meteorological biomass prediction of the rice in each growth period can be realized, and the accuracy of the rice meteorological output prediction is improved;
2. the results of a meteorological index prediction model, a meteorological index-meteorological biomass prediction model and a meteorological biomass-meteorological yield prediction model can be dynamically adjusted according to real-time updating of meteorological information, meteorological biomass and other data of rice in the current year, and dynamic release of rice meteorological yield is achieved;
3. the method can comprehensively, systematically and timely provide the rice meteorological output fluctuation process in China, provide intuitive and accurate rice meteorological output prediction results, and provide technical support for guaranteeing the balance of supply and demand of rice markets in China.
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Exemplary embodiments of the invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a method for determining the meteorological production of rice in accordance with a preferred embodiment of the present invention;
fig. 2 is a schematic configuration diagram of a system for determining the meteorological production of rice according to a preferred embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings are not intended to limit the present invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their context in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of a method for determining the meteorological production of rice in accordance with a preferred embodiment of the present invention. As shown in FIG. 1, a method 100 for determining the meteorological production of rice according to the preferred embodiment begins at step 101.
In step 101, the growth stage of rice is divided into several growth stages according to the growth characteristics of rice. In the preferred embodiment, the growth stage of rice is divided into 6 growth stages, including a seedling emergence stage, a transplanting stage, a tillering stage, a booting stage, a heading and flowering stage, and a grain filling stage.
In step 102, data of past n years of meteorological indexes affecting rice growth and data of known time of the year, data of past n years of biomass of each breeding period, data of past n years of economic yield, and historical data of starting and ending time of each breeding period of rice are collected.
In the preferred embodiment, the historical data is mainly obtained from the database of each large crop monitoring platform, the data of the current year at known time is mainly obtained by monitoring through a sensor, wherein the temperature is obtained by monitoring through a temperature sensor, the daily average temperature is calculated, and the height of the rice field daily water layer is obtained by monitoring through placing a pressure sensor on the surface layer of the rice field. In practice, the rice biomass refers to the constant weight of rice grown in each growth stage by low temperature drying.
In step 103, the starting and ending time of each growth period of the current year is determined according to the historical data of the starting and ending time of each growth period of the rice. In a preferred embodiment, the time of the rice having the largest number of times per starting and ending time of the growing season of the year is taken as the starting and ending time of the growing season of the year. When the number of times of two or more dates is the same, one of the dates is randomly selected.
At step 104, data for the past n years of meteorological biomass for the rice at each growing season is determined based on the data for the past n years of biomass for the rice at each growing season.
In step 105, a meteorological index-meteorological biomass prediction model for each growth period of rice is determined based on data of the past n years of meteorological indexes and data of the past n years of meteorological biomass for each growth period of rice.
At step 106, data for the past n years of rice meteorological production is determined based on the data for the past n years of rice economic production. In practice, the economic yield of rice refers to the dry matter weight of the rice as the main product harvested according to the purpose of rice cultivation.
In step 107, a rice meteorological biomass-meteorological yield prediction model is determined based on the data of the past n years of meteorological biomass and the data of the past n years of meteorological yield of rice for each breeding season of rice.
In step 108, data of the meteorological indexes of each growth period of the rice in the current year are determined according to a set meteorological index prediction model based on data of past n years of the meteorological indexes affecting the growth of the rice and data of known time of the current year, wherein the meteorological indexes comprise daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height.
In step 109, the meteorological biomass of the rice in each growth period in the current year is determined according to the meteorological index-meteorological biomass prediction model of the rice in each growth period based on the data of the meteorological index of the rice in each growth period in the current year.
In step 110, the meteorological output of the rice in the current year is determined according to the rice meteorological biomass-meteorological output prediction model based on the meteorological biomass of the rice in each growth period in the current year.
Preferably, the determining of the weather indicator data of each growth period of the rice in the current year according to the set weather indicator prediction model based on the data of the past n years of weather indicators affecting the growth of the rice and the data of the known time of the current year comprises:
determining the meteorological index data of unknown time in the current year according to a set meteorological index prediction model based on the data of the past n years of meteorological indexes affecting the growth of rice, wherein:
the calculation formula of the daily average temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day for the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day for the last n years:
Figure BDA0001932118900000131
Figure BDA0001932118900000132
when the standard deviation of the highest daily temperature determined from the highest daily temperature of the past n years of a certain day is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of the past n years of a certain day:
Figure BDA0001932118900000133
Figure BDA0001932118900000134
in the formula, T n ave Is the daily average temperature, T, of a day in the unknown time of the year h min Is the minimum value of the daily minimum temperature, T, of the last n years for a certain day of the unknown time of the year h max Is the maximum value of the highest daily temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the average of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, mu ave Is the mean of the average daily temperatures of the last n years of the month in which a day is located in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which a certain day in the unknown time of the year is located, sigma ave Is the standard deviation of the average daily temperature in the last n years in the month of a certain day of the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the daily minimum temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
T n min ＝μ min +σ min ×χ
Figure BDA0001932118900000141
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure BDA0001932118900000142
/>
Figure BDA0001932118900000143
in the formula, T n min Is the daily minimum temperature, T, of a certain day of the year's unknown time h max Is the maximum value of the highest daily temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the daily minimum temperature of the month in which a certain day in the unknown time of the year is in the past n years, sigma max Is the month of a certain day in the unknown time of the yearThe standard deviation of the highest temperature of the day in the last n years, χ being the standard normal deviation of the day produced, according to the two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the highest daily temperature prediction model is as follows:
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is greater than or equal to a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure BDA0001932118900000144
Figure BDA0001932118900000145
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is smaller than a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
T n max ＝μ max +σ max ×χ
Figure BDA0001932118900000151
in the formula, T n max Is the highest daily temperature, T, of a certain day in the unknown time of the year h min Is the minimum value of the daily minimum temperature of the last n years for a certain day of the unknown time of the year, mu min Is the mean value of the daily minimum temperature of the last n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the daily minimum temperature of the month in which a certain day in the unknown time of the year is in the past n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which the day is in the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the rice field solar water layer height prediction model is as follows:
H＝μ+σ H ×χ
Figure BDA0001932118900000152
wherein H is the height of the water layer of the rice field on a certain day in the unknown time of the year, mu H Is the average value of the height of the rice field water layer in the last n years in the month of a certain day in the unknown time of the year, sigma G The standard deviation of the height of the water layer in the rice field in the past n years in the month of a certain day in the unknown time of the year, chi is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
and dividing the meteorological index data of the known time of the current year and the meteorological index data of the unknown time of the current year determined by the meteorological index prediction model according to the starting and ending time of each growth period of the rice to obtain the meteorological index data of each growth period of the rice.
Preferably, the data for determining the past n years of meteorological biomass for rice at each breeding season based on the data for the past n years of biomass for rice at each breeding season comprises:
generating biomass sequence data by using the data of the biomass of the rice in the past n years in each breeding period according to the time sequence;
taking i years as sliding step length, and performing statistical regression analysis on biomass of rice in each i years in each growth period by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining the simulation values of j biomass quantities of rice every year in each growth period based on j groups of unary linear regression equations;
determining the average value of the analog values of the biomass per year according to the analog values of j biomass per year in each breeding period of the rice, and taking the average value as the trend biomass per year in each breeding period of the rice;
and subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
Preferably, the determining the rice growth period meteorological index-meteorological biomass prediction model based on the rice growth period meteorological index data of the past n years and the meteorological biomass data of the past n years comprises:
determining a kernel function of each meteorological index and the meteorological biomass and the weight of each kernel function based on the data of the meteorological index in the past n years and the data of the meteorological biomass in the past n years in each growth period of the rice, and determining and solving the deviation value of the meteorological biomass according to the kernel function;
determining a meteorological index-meteorological biomass prediction model of each rice growth period based on the kernel function of each meteorological index and meteorological biomass, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure BDA0001932118900000161
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the current year,
Figure BDA0001932118900000162
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure BDA0001932118900000163
And determining the deviation value of the meteorological biomass of the rice in the ith growth period of the current year.
In the preferred embodiment, the growth stage of rice is divided into 6 growth stages, including seedling emergence stage, transplanting stage, tillering stage, booting stage, heading and flowering stage, and grain filling stage. In order to make the meteorological index-meteorological biomass prediction model of each growth period more accurate, the lowest temperature value and the highest temperature value, the daily average temperature value and the rice field daily water layer height which are set according to historical experience are all divided into more specific intervals, specifically:
the calculation formula of the meteorological index-biomass prediction model in the rice seedling emergence stage is as follows:
Figure BDA0001932118900000164
in the formula, y bc For sowing and seedling emergence period meteorological biomass, BCTD L 、
Figure BDA0001932118900000165
Respectively the number of days with the lowest daily temperature less than 10 ℃, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCTD M 、/>
Figure BDA0001932118900000171
The number of days with the lowest daily temperature of 10-12 ℃ in the seedling emergence period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, BCTS (binary coded transmission System) L 、/>
Figure BDA0001932118900000172
Respectively the number of days with the average daily temperature of 12-20 ℃ in the seedling emergence period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCTS M 、/>
Figure BDA0001932118900000173
Respectively the number of days with the average daily temperature of 20-23 ℃ in the seedling emergence period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCTS H 、/>
Figure BDA0001932118900000174
The number of days with the daily average temperature of between 23 and 40 ℃ in the seedling emergence period, the kernel function of the meteorological index, the kernel function weight of the meteorological index and BCTG M 、/>
Figure BDA0001932118900000175
Figure BDA0001932118900000176
Respectively the number of days with the highest daily temperature of 40 ℃ in the seedling emergence period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCTG H 、/>
Figure BDA0001932118900000177
The number of days with the highest daily temperature of more than 40 ℃ in the seedling emergence period, the kernel function of the meteorological index, the kernel function weight of the meteorological index and BCSC L 、/>
Figure BDA0001932118900000178
Respectively the number of days that the height of the rice field water layer is lower than the shallow water layer threshold value in the seedling emergence period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCSC M 、/>
Figure BDA0001932118900000179
Respectively the number of days of the rice field in the seedling emergence period when the height of the rice field is between the height threshold of the shallow water layer and the height threshold of the wet layer, the kernel function of the meteorological index and the kernel function weight of the meteorological index, BCSC H 、/>
Figure BDA00019321189000001710
Respectively the number of days in the seedling emergence period when the height of the water layer of the rice field is greater than the height threshold of the wet layer, the kernel function of the meteorological index and the kernel function weight of the meteorological index, b bc Is a deviation.
The calculation formula of the meteorological index-biomass prediction model in the rice transplanting period is as follows:
Figure BDA00019321189000001711
in the formula, y yz For meteorological biomass during transplanting, YZTD L 、
Figure BDA00019321189000001712
Respectively the days with the lowest daily temperature less than 13 ℃, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTD M 、
Figure BDA00019321189000001713
Respectively the days with the daily minimum temperature of 13-15 ℃ in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTS L 、/>
Figure BDA0001932118900000181
Respectively the days with the average daily temperature of 15-25 ℃ in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTS M 、
Figure BDA0001932118900000182
Figure BDA0001932118900000183
Respectively the days with the daily average temperature of 25-30 ℃ in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTS H 、/>
Figure BDA0001932118900000184
Respectively the days with the average daily temperature of 30-35 ℃ in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTG M 、
Figure BDA0001932118900000185
The number of days with the highest daily temperature of 35 ℃ in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZTG H 、/>
Figure BDA0001932118900000186
The number of days with the highest daily temperature of more than 40 ℃, the kernel function of the meteorological index and the gas in the transplanting periodKernel function weight of image index, YZSC L 、
Figure BDA0001932118900000187
Respectively the number of days that the height of the water layer of the rice field is lower than the threshold value of the shallow water layer, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZSC M 、/>
Figure BDA0001932118900000188
Figure BDA0001932118900000189
Respectively the number of days of the rice field in the transplanting period when the height of the rice field is between the height threshold of the shallow water layer and the height threshold of the wet layer, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YZSC H 、/>
Figure BDA00019321189000001810
Respectively the number of days in which the height of the water layer of the rice field is greater than the height threshold of the wet layer in the transplanting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, b yz Is a deviation.
The calculation formula of the meteorological index-biomass prediction model at the rice tillering stage is as follows:
Figure BDA00019321189000001811
in the formula, y fn Is divided into tillering stage meteorological biomass and FNTD L 、
Figure BDA00019321189000001812
Respectively days with the lowest temperature of days less than 15 ℃, kernel function of the meteorological index and kernel function weight of the meteorological index, FNTD M 、
Figure BDA00019321189000001813
Respectively the days with the lowest temperature between 15 ℃ and 16 ℃ in the breeding period, the kernel function of the meteorological index and the gasKernel function weight, FNTS, of the image index L 、/>
Figure BDA00019321189000001814
Respectively the days with the average daily temperature between 16 ℃ and 23 ℃ in the stage of breeding, the kernel function of the meteorological index and the kernel function weight of the meteorological index, FNTS M 、/>
Figure BDA00019321189000001815
Figure BDA00019321189000001816
Respectively days with the daily average temperature between 23 ℃ and 32 ℃ in the breeding period, a kernel function of the meteorological index, the kernel function weight of the meteorological index, FNTS H 、/>
Figure BDA00019321189000001817
Respectively the days with the average daily temperature between 32 ℃ and 38 ℃ in the stage of breeding, the kernel function of the meteorological index and the kernel function weight of the meteorological index, FNTG M 、/>
Figure BDA0001932118900000191
Respectively the days with the highest temperature of 38-50 ℃ in the breeding period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, FNTG H 、/>
Figure BDA0001932118900000192
Respectively days with the highest temperature of days in the stage of breeding more than 50 ℃, a kernel function of the meteorological index, a kernel function weight of the meteorological index, and FNSC M 、/>
Figure BDA0001932118900000193
Figure BDA0001932118900000194
Respectively the days of the rice field in the stage of tillering between the height threshold of the shallow water layer and the height threshold of the wet layer, the kernel function of the meteorological index and the meteorological indexTarget Kernel function weight, b fn Is a deviation.
The calculation formula of the meteorological index-biomass prediction model of the rice booting stage is as follows:
Figure BDA0001932118900000195
in the formula, y yz Is meteorological biomass at booting stage, YSTD L 、
Figure BDA0001932118900000196
Respectively the days with the lowest daily temperature less than 15 ℃, the kernel function of the meteorological index and the kernel function weight of the meteorological index in the booting stage, and YSTD M 、
Figure BDA0001932118900000197
Respectively the days with the lowest daily temperature of 15 ℃ in the booting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YSTS L 、/>
Figure BDA0001932118900000198
Respectively the days with the daily average temperature between 15 ℃ and 25 ℃ in the booting period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, YSTS M 、
Figure BDA0001932118900000199
Respectively the days with the average daily temperature of 25-30 ℃ in the booting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, YSTS H 、/>
Figure BDA00019321189000001910
Respectively the days with the daily average temperature between 30 ℃ and 40 ℃ in the booting period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, YSTG M 、
Figure BDA00019321189000001911
The highest daily temperature in booting stageDays at 40 ℃, kernel function of the weather indicator and kernel function weight of the weather indicator, YSTG H 、/>
Figure BDA00019321189000001912
The days with the highest daily temperature of more than 40 ℃ in the booting stage, the kernel function of the meteorological index, the kernel function weight of the meteorological index and YSSC L 、
Figure BDA00019321189000001913
Respectively the number of days that the height of the rice field water layer is lower than the shallow water layer threshold value in the booting stage, the kernel function of the meteorological index, the kernel function weight of the meteorological index, YSSC M 、/>
Figure BDA00019321189000001914
Respectively the days of rice field in booting stage when the water layer height is between the shallow water layer height threshold and the wet layer height threshold, the kernel function of the meteorological index, the kernel function weight of the meteorological index, YSSC H 、/>
Figure BDA0001932118900000201
Respectively the number of days that the height of the rice field water layer is greater than the height threshold of the wet layer in the booting stage, the kernel function of the meteorological index and the kernel function weight of the meteorological index, b yc Is a deviation.
The calculation formula of the meteorological index-biomass prediction model in the heading and flowering period of the rice is as follows:
Figure BDA0001932118900000202
in the formula, y ck For heading and flowering phase meteorological biomass, CKTD L 、
Figure BDA0001932118900000203
Respectively the days with the lowest daily temperature less than 12 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index and CKTD M 、/>
Figure BDA0001932118900000204
Respectively the days with the lowest daily temperature of 12-15 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKTS L 、/>
Figure BDA0001932118900000205
Respectively the days with the average daily temperature of 15-25 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKTS M 、/>
Figure BDA0001932118900000206
Respectively the days with the average daily temperature of 25-32 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKTS H 、/>
Figure BDA0001932118900000207
Respectively the days with the average daily temperature of 32-40 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKTG M 、/>
Figure BDA0001932118900000208
Figure BDA0001932118900000209
Respectively the number of days with the highest daily temperature of 40-45 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKTG H 、
Figure BDA00019321189000002010
Respectively the number of days with the highest daily temperature of more than 45 ℃ in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index and CKSC L 、/>
Figure BDA00019321189000002011
Respectively at the heading and flowering stageThe number of days when the height of the water layer of the rice field is lower than the threshold value of the shallow water layer, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKSC M 、/>
Figure BDA00019321189000002012
Respectively the number of days of the rice field in the heading and flowering period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, CKSC H 、/>
Figure BDA00019321189000002013
Respectively the number of days that the height of the water layer of the rice field is greater than the height threshold of the wet layer in the heading and flowering period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, b ck Is a deviation.
The calculation formula of the meteorological index-biomass prediction model in the rice filling period is as follows:
Figure BDA0001932118900000211
in the formula, y gj For meteorological biomass in the grouting phase, GJTD L 、
Figure BDA0001932118900000212
The number of days with the daily minimum temperature of less than 18 ℃ in the grouting period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, GJTD M 、
Figure BDA0001932118900000213
The number of days with the lowest daily temperature of 18 ℃ in the grouting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, GJTS L 、/>
Figure BDA0001932118900000214
The number of days of the average daily temperature of 18-22 ℃ in the grouting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, GJTS M 、
Figure BDA0001932118900000215
The number of days with the daily average temperature between 22 ℃ and 28 ℃ in the grouting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, GJTS H 、/>
Figure BDA0001932118900000216
The number of days with the daily average temperature of 28-35 ℃ in the grouting period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, GJTG M 、
Figure BDA0001932118900000217
The number of days with the highest daily temperature of 35 ℃ in the grouting period, the kernel function of the meteorological index and the kernel function weight of the meteorological index, GJTG H GJ(YSTG H )、/>
Figure BDA0001932118900000218
The number of days with the highest daily temperature of more than 35 ℃ in the grouting period, the kernel function of the meteorological index, the kernel function weight of the meteorological index, and GJSC M 、
Figure BDA0001932118900000219
Respectively the number of days of the rice field in the filling period when the height of the rice field water layer is between the height threshold of the shallow water layer and the height threshold of the wet layer, the kernel function of the meteorological index and the kernel function weight of the meteorological index, b gj Is the deviation.
Preferably, the data for determining the past n years of meteorological yield of rice based on the data for the past n years of economic yield of rice comprises:
generating economic yield sequence data by data of the rice economic yield in the past n years according to the time sequence;
taking i years as sliding step length, carrying out statistical regression analysis on the economic yield of rice in each i years by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining j simulation values of economic yield of rice every year based on j sets of unary linear regression equations;
determining the average value of the annual economic yield simulation values according to the annual j economic yield simulation values of the rice, and taking the average value as the annual trend economic yield of the rice;
and subtracting the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
Preferably, the prediction model for determining the rice meteorological biomass-meteorological yield based on the data of the rice meteorological biomass for the past n years and the data of the rice meteorological yield for the past n years in each breeding period comprises:
determining a kernel function of the meteorological biomass and the meteorological output and the weight of each kernel function in each breeding period based on the data of the meteorological biomass in the past n years in each breeding period of the rice and the data of the meteorological output in the past n years, and determining and solving the deviation value of the meteorological output according to the kernel function;
determining a rice meteorological biomass-meteorological yield prediction model based on the kernel function of the meteorological biomass and the meteorological yield of the rice in each growth period, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure BDA0001932118900000221
wherein y is the current annual meteorological production of rice,
Figure BDA0001932118900000222
is a kernel function of the meteorological biomass of the ith growth period of the rice in the current year, omega i Is the weight of a kernel function in the i-th fertility stage of the rice year, b is based on the kernel function>
Figure BDA0001932118900000223
And determining the deviation value of the meteorological output of the rice in the current year.
In the preferred embodiment, the growth stage of rice is divided into 5 growth stages, including seeding stage, seedling stage, joint-pulling and booting stage, heading and flowering stage, and grain filling and maturation stage. Correspondingly, the calculation formula of the prediction model of the meteorological biomass and the meteorological yield of the rice in each growth period is as follows:
Figure BDA0001932118900000224
wherein z is rice meteorological output, y bc 、
Figure BDA0001932118900000225
Respectively are rice seeding emergence stage biomass, rice seeding emergence stage biomass kernel function and kernel function weight, y yz 、/>
Figure BDA0001932118900000226
Respectively are rice transplanting stage biomass, rice transplanting stage biomass kernel function and kernel function weight, y fn 、/>
Figure BDA0001932118900000227
w fn Respectively rice tillering stage biomass, rice tillering stage biomass kernel function and kernel function weight, y ys 、/>
Figure BDA0001932118900000228
Respectively rice booting stage biomass, rice booting stage biomass kernel function and kernel function weight, y ck 、/>
Figure BDA0001932118900000229
Respectively the biomass of the rice in the heading and flowering period, the kernel function and the kernel function weight of the biomass of the rice in the heading and flowering period, y gj 、/>
Figure BDA00019321189000002210
Respectively is the biomass of the rice filling stage, the kernel function of the biomass of the rice filling stage and the weight of the kernel function, and b is the deviation.
Fig. 2 is a schematic configuration diagram of a system for determining the meteorological production of rice according to a preferred embodiment of the present invention. As shown in fig. 2, the system 200 for determining the meteorological output of rice according to the preferred embodiment includes:
a rice growth period dividing unit 201 for dividing the growth period of rice into a plurality of growth periods according to the growth characteristics of rice;
a growth period time determination unit 202 for determining the start-stop time of each growth period of the year based on the history data of the start-stop time of each growth period of rice.
A data collecting unit 203 for collecting data of past n years of meteorological indexes affecting growth of rice and data of known time of the year, data of past n years of biomass per breeding season, and data of past n years of economic yield.
A first data unit 204 for determining data of the past n years of meteorological biomass for each breeding season of rice based on the data of the past n years of biomass for each breeding season of rice.
A first model unit 205 for determining a rice growth period meteorological index-meteorological biomass prediction model based on the rice growth period meteorological index data of the past n years and the meteorological biomass data of the past n years.
A second data unit 206 for determining data of the past n years of rice meteorological production based on data of the past n years of rice economic production.
A second model unit 207 for determining a rice meteorological biomass-meteorological-production prediction model based on the data of the rice meteorological biomass for the past n years and the data of the rice meteorological production for the past n years for each growth period.
And a rice weather indicator unit 208 for determining data of weather indicators for each growth period of the rice in the current year according to the set weather indicator prediction model based on data of weather indicators affecting growth of the rice in the past n years and data of known time of the current year, wherein the weather indicators include daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height.
And a rice meteorological biomass unit 209 for determining the meteorological biomass of the rice at each growth period of the current year according to the meteorological index-meteorological biomass prediction model at each growth period of the rice based on the data of the meteorological index at each growth period of the current year.
And the rice meteorological production unit 210 is used for determining the meteorological production of the rice in the current year according to the rice meteorological biomass-meteorological production prediction model based on the meteorological biomass of the rice in each growth period in the current year.
Preferably, the rice weather indicator unit 208 includes:
and an unknown weather indicator unit 281 for determining weather indicator data of unknown time in the current year according to a set weather indicator prediction model based on data of weather indicators influencing rice growth in the past n years, wherein a calculation formula of the daily average temperature, the daily minimum temperature, the daily maximum temperature and the rice field daily water layer height prediction model is the same as that of the method for determining rice weather yield, and details are not repeated here.
And an index determining unit 282 for dividing the weather index data of the known time of the year and the weather index data of the unknown time of the year determined by the weather index prediction model according to the starting and ending time of each growth period of the rice to obtain the weather index data of each growth period of the rice.
Preferably, the first data unit 204 comprises:
a first sequence unit 241 for generating biomass sequence data chronologically from data of the last n years of biomass for each breeding season of rice;
a first equation group unit 242, configured to perform statistical regression analysis on biomass of rice in each growth period in each year by using i year as a sliding step length and using a linear sliding average method to obtain j groups of unary linear regression equations, where i is greater than or equal to 1 and less than or equal to n, j is greater than or equal to 1 and less than or equal to i, and i, j, and n are natural numbers;
a first simulation value unit 243 for determining simulation values of j biomass quantities per year for each period of rice growth based on j sets of one-dimensional linear regression equations;
a first trend value unit 244 for determining an average value of the simulated values of the biomass per year from the simulated values of j biomass per year for each period of rice growth, and taking the average value as a trend biomass per year for each period of rice growth;
a first result unit 245, which is used for subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
Preferably, the first model unit 205 comprises:
a first parameter unit 251 for determining a kernel function of each meteorological index and meteorological biomass, a weight of each kernel function based on data of the meteorological index of each growth period of rice in the past n years and data of the meteorological biomass in the past n years, and determining a deviation value of the meteorological biomass according to the kernel function;
a first formula unit 252, configured to determine a meteorological index-meteorological biomass prediction model for each growth period of rice based on the kernel function of each meteorological index and meteorological biomass, the weight of each kernel function, and the deviation value, according to the following calculation formula:
Figure BDA0001932118900000241
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the year,
Figure BDA0001932118900000251
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure BDA0001932118900000252
And determining a deviation value of the meteorological biomass of the rice in the ith growth period of the current year.
Preferably, the second data unit 206 comprises:
a second sequence unit 261 for generating economic yield sequence data of rice from data of past n years of economic yield in chronological order;
a second equation set unit 262, configured to perform statistical regression analysis on the economic yield of rice in each i year by using a linear sliding average method with i year as a sliding step length to obtain j sets of unary linear regression equations, where i is greater than or equal to 1 and less than or equal to n, j is greater than or equal to 1 and less than or equal to i, and i, j, and n are natural numbers;
a second analog value unit 263 for determining analog values of j economic yields of rice per year based on j sets of one-dimensional linear regression equations;
a second trend value unit 264 for determining an average value of the simulated values of the economic yield per year from the simulated values of the j economic yields per year of the rice, and taking the average value as the trend economic yield per year of the rice;
the second result unit 265 subtracts the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
Preferably, the second model unit 207 includes:
a second parameter unit 271, for determining a kernel function of the meteorological biomass and the meteorological output, a weight of each kernel function, and an offset value of the meteorological output according to the kernel function determination, based on the data of the meteorological biomass of the rice in each breeding period in the past n years and the data of the meteorological output of the rice in the past n years;
a second formula unit 272, configured to determine a rice meteorological biomass-meteorological yield prediction model based on the kernel functions of the meteorological biomass and the meteorological yield of the rice in each growth period, the weight of each kernel function, and the deviation value, and the calculation formula is:
Figure BDA0001932118900000253
wherein y is the annual meteorological production of rice,
Figure BDA0001932118900000254
is a kernel function of the meteorological biomass of the ith growth period of the rice in the current year, omega i Is the weight of the kernel function of the i-th growth period of the rice year, and b is based on the kernel function->
Figure BDA0001932118900000261
And determining the deviation value of the meteorological output of the rice in the current year.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the ones disclosed above are equally possible within the scope of these appended patent claims, as these are known to those skilled in the art.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims (6)

1. A method for determining the meteorological output of rice, the method comprising:
dividing the growth stage of the rice into a plurality of growth periods according to the growth characteristics of the rice;
collecting data of meteorological indexes influencing the growth of rice in the past n years, data of known time of the year, data of biomass in the past n years of each growth period, data of economic yield in the past n years, and historical data of the starting time and the ending time of each growth period of the rice;
determining the starting and ending time of each growth period in the current year according to the historical data of the starting and ending time of each growth period of rice;
determining data of the past n years of meteorological biomass of the rice in each breeding period based on the data of the past n years of biomass of the rice in each breeding period;
the method for determining the meteorological index-meteorological biomass prediction model of each growth period of rice based on the data of the past n years of meteorological indexes and the data of the past n years of meteorological biomass of each growth period of rice comprises the following steps:
determining a kernel function of each meteorological index and the meteorological biomass and the weight of each kernel function based on the data of the meteorological index in the past n years and the data of the meteorological biomass in the past n years in each growth period of the rice, and determining and solving the deviation value of the meteorological biomass according to the kernel function;
determining a meteorological index-meteorological biomass prediction model of each rice growth period based on the kernel function of each meteorological index and meteorological biomass, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure FDA0004124531960000011
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the year,
Figure FDA0004124531960000012
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure FDA0004124531960000013
Determining a deviation value of meteorological biomass of the rice in the ith growth period in the current year;
determining the data of the rice meteorological output for the past n years based on the data of the rice economic output for the past n years;
the rice meteorological biomass-meteorological yield prediction model is determined based on the data of the past n years of meteorological biomass and the data of the past n years of meteorological yield of rice in each growth period of rice, and comprises the following steps:
determining a kernel function of the meteorological biomass and the meteorological yield of each growth period and the weight of each kernel function based on the data of the meteorological biomass of the rice in each growth period in the past n years and the data of the meteorological yield of the rice in the past n years, and determining and solving an offset value of the meteorological yield according to the kernel function;
determining a rice meteorological biomass-meteorological yield prediction model based on the kernel function of the meteorological biomass and the meteorological yield of the rice in each growth period, the weight of each kernel function and the deviation value, wherein the calculation formula is as follows:
Figure FDA0004124531960000021
wherein y is the annual meteorological production of rice,
Figure FDA0004124531960000022
is a kernel function of the meteorological biomass of the ith growth period of the rice in the current year, omega i Is the weight of the kernel function of the i-th growth period of the rice year, and b is based on the kernel function->
Figure FDA0004124531960000023
Determining a deviation value of the meteorological output of the rice in the current year;
the data of the meteorological indexes of each growth period of the rice in the current year are determined according to a set meteorological index prediction model based on the data of the past n years of the meteorological indexes affecting the growth of the rice and the data of the known time of the current year, and the data of the meteorological indexes comprise:
determining weather indicator data of unknown time in the current year based on data of weather indicators influencing the growth of rice in the past n years according to a set weather indicator prediction model, wherein the weather indicators comprise daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height, and the method comprises the following steps:
the calculation formula of the daily average temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure FDA0004124531960000024
Figure FDA0004124531960000025
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure FDA0004124531960000031
Figure FDA0004124531960000032
in the formula, T nave Is the daily average temperature, T, of a day in the unknown time of the year hmin Is the minimum value of the daily minimum temperature, T, of the last n years for a certain day of the unknown time of the year hmax Is the maximum value of the highest daily temperature of the last n years for a certain day of the unknown time of the year, mu min Is the mean value of the daily minimum temperature of the last n years in the month of a certain day in the unknown time of the year, mu max Is the average of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, mu ave Is the mean of the average daily temperatures of the last n years of the month in which a day is located in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which a certain day in the unknown time of the year is located, sigma ave Is the standard deviation of the average daily temperature in the past n years in the month of a certain day in the unknown time of the year, and χ is the standard normal deviation of the generated daily standard, according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the daily minimum temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
T nmin ＝μ min +σ min ×χ
Figure FDA0004124531960000033
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure FDA0004124531960000034
Figure FDA0004124531960000035
in the formula, T nmin Is the daily minimum temperature, T, of a certain day of the year's unknown time hmax Is the maximum value of the highest daily temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the daily minimum temperature of the month in which a certain day in the unknown time of the year is in the past n years, sigma max Is the standard deviation of the highest daily temperature in the last n years in the month of a certain day in the unknown time of the year, and χ is the standard normal deviation of the day generated according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the highest daily temperature prediction model is as follows:
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is greater than or equal to a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure FDA0004124531960000041
Figure FDA0004124531960000042
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
T nmax ＝μ max +σ max ×χ
Figure FDA0004124531960000043
in the formula, T nmax Is the highest daily temperature, T, of a certain day in the unknown time of the year hmin Is the minimum value of the daily minimum temperature of the last n years for a certain day of the unknown time of the year, mu min Is the mean value of the daily minimum temperature of the last n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which the day is in the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the rice field solar water layer height prediction model is as follows:
H＝μ H +σ H ×χ
Figure FDA0004124531960000044
wherein H is the height of the water layer of the rice field at a certain day in the unknown time of the year, mu H Is the average value of the water layer heights of the rice field in the last n years in the month of a certain day in the unknown time of the year, sigma G The standard deviation of the height of the water layer in the rice field in the past n years in the month of a certain day in the unknown time of the year, chi is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
dividing the meteorological index data of the known time of the current year and the meteorological index data of the unknown time of the current year determined by the meteorological index prediction model according to the starting and ending time of each growth period of the rice to obtain the meteorological index data of each growth period of the rice;
determining the meteorological biomass of the rice in each growth period in the current year according to the meteorological index-meteorological biomass prediction model of the rice in each growth period based on the data of the meteorological index of the rice in each growth period in the current year;
and determining the current-year meteorological output of the rice according to a rice meteorological biomass-meteorological output prediction model based on the current-year meteorological biomass of the rice in each growth period.
2. The method of claim 1, wherein determining the data for the past n years of meteorological biomass for rice for each growing period based on the data for the past n years of biomass for rice for each growing period comprises:
generating biomass sequence data by using the data of the biomass of the rice in the past n years in each breeding period according to the time sequence;
taking i years as sliding step length, and performing statistical regression analysis on biomass of rice in each i years in each growth period by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining the simulation values of j biomass quantities of rice every year in each growth period based on j groups of unary linear regression equations;
determining the average value of the analog value of the biomass per year according to the analog values of j biomass per year in each breeding period of the rice, and taking the average value as the trend biomass per year in each breeding period of the rice;
and subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
3. The method of claim 1, wherein determining the data for the past n years of rice meteorological production based on the data for the past n years of rice economic production comprises:
generating economic yield sequence data by data of the rice economic yield in the past n years according to the time sequence;
taking i years as sliding step length, carrying out statistical regression analysis on the economic yield of rice in each i years by using a linear sliding average method to obtain a j-group unary linear regression equation, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
determining j simulation values of economic yield of rice every year based on j sets of unary linear regression equations;
determining the average value of the annual economic yield simulation values according to the annual j economic yield simulation values of the rice, and taking the average value as the annual trend economic yield of the rice;
and subtracting the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
4. A system for determining the meteorological production of rice, the system comprising:
the rice growth period dividing unit is used for dividing the growth period of the rice into a plurality of growth periods according to the growth characteristics of the rice;
a data acquisition unit for acquiring data of past n years of meteorological indexes affecting the growth of rice and data of known time of the year, data of past n years of biomass of each growth period, data of past n years of economic yield, and historical data of start and end time of each growth period of rice;
a growth period time determination unit for determining the start-stop time of each growth period of the year based on the history data of the start-stop time of each growth period of rice;
a first data unit for determining data of the past n years of meteorological biomass for each breeding season of rice based on the data of the past n years of biomass for each breeding season of rice;
a first model unit for determining a rice growth period meteorological index-meteorological biomass prediction model based on rice growth period meteorological index data for the past n years and meteorological biomass data for the past n years, the first model unit comprising:
a first parameter unit for determining a kernel function of each meteorological index and the meteorological biomass and a weight of each kernel function based on data of the meteorological index of each growth period of rice in the past n years and data of the meteorological biomass in the past n years, and determining and solving a deviation value of the meteorological biomass according to the kernel function;
a first formula unit, which is used for determining a meteorological index-meteorological biomass prediction model of rice in each growth period based on a kernel function of each meteorological index and meteorological biomass, a weight of each kernel function and a deviation value, and the calculation formula is as follows:
Figure FDA0004124531960000061
in the formula, y i Is the meteorological biomass of the rice in the ith growth period of the year,
Figure FDA0004124531960000062
is a kernel function of the jth meteorological index of the ith growth period of the rice in the current year, omega ij Is the weight of the kernel function of the jth meteorological index of the ith growth period of the rice in the current year, b i Is based on a kernel function>
Figure FDA0004124531960000063
Determining a deviation value of meteorological biomass of the rice in the ith growth period of the current year;
a second data unit for determining data of the rice weather yield for the past n years based on data of the rice economic yield for the past n years;
a second model unit for determining a rice meteorological biomass-meteorological-production prediction model based on rice meteorological biomass data for the past n years and rice meteorological production data for the past n years for each breeding period, the second model unit comprising:
a second parameter unit for determining a kernel function of the meteorological biomass and the meteorological output of each breeding period and the weight of each kernel function based on the data of the meteorological biomass of the rice in the past n years and the data of the meteorological output of the rice in the past n years in each breeding period, and determining an offset value of the meteorological output according to the kernel function;
a second formula unit, configured to determine a rice meteorological biomass-meteorological yield prediction model based on the kernel functions of the meteorological biomass and meteorological yield of rice in each growth period, the weight of each kernel function, and the deviation value, wherein the calculation formula is as follows:
Figure FDA0004124531960000071
wherein y is the annual meteorological production of rice,
Figure FDA0004124531960000072
is a kernel function of the meteorological biomass of the ith growth period of the rice in the current year, omega i Is the weight of the kernel function of the i-th growth period of the rice year, and b is based on the kernel function->
Figure FDA0004124531960000073
Determining a deviation value of the meteorological output of the rice in the current year;
a rice weather indicator unit for determining data of weather indicators for each growth period of rice in the current year based on data of weather indicators affecting growth of rice in the past n years and data of known time of the current year, according to a set weather indicator prediction model, the rice weather indicator unit comprising:
an unknown weather indicator unit for determining weather indicator data of unknown time of the year based on data of past n years of weather indicators affecting rice growth according to a set weather indicator prediction model, the weather indicators including daily average temperature, daily minimum temperature, daily maximum temperature and rice field daily water layer height, wherein:
the calculation formula of the daily average temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day for the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day for the last n years:
Figure FDA0004124531960000081
Figure FDA0004124531960000082
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day for the last n years is smaller than the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day for the last n years:
Figure FDA0004124531960000083
Figure FDA0004124531960000084
in the formula, T nave Is the daily average temperature, T, of the same day in the unknown time of the year as in the data of the last n years hmin Is the minimum value, T, of the daily minimum temperatures of a certain day in the data of the past n years hmax Is the maximum daily maximum temperature of a certain day in the data of the past n years, mu min Is the average of the daily minimum temperatures, mu, of the months of a certain day in the data of the last n years max Is the average of the highest daily temperatures of the months of a certain day in the data of the past n years, mu ave Is the month of a certain day in the data of the last n yearsMean daily average temperature of parts, σ min Is the standard deviation of the daily minimum temperature of the month in which a certain day is located in the data of the last n years, sigma max Is the standard deviation of the highest daily temperature of the month in which a certain day is present in the data of the past n years, sigma ave Is the standard deviation of the daily average temperature of the month in which a certain day is located in the data of the last n years, and χ is the standard normal deviation of the generated day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the daily minimum temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
T nmin ＝μ min +σ min ×χ
Figure FDA0004124531960000085
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is smaller than a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
Figure FDA0004124531960000086
Figure FDA0004124531960000087
in the formula, T nmin Is the daily minimum temperature, T, of a certain day of the year's unknown time hmax Is the maximum value of the highest daily temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest daily temperature in the last n years in the month of a certain day in the unknown time of the year, and χ is the standard normal deviation of the day generated according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the daily maximum temperature prediction model is as follows:
when the standard deviation of the highest daily temperature determined from the highest daily temperature of a certain day in the last n years is greater than or equal to the standard deviation of the lowest daily temperature determined from the lowest daily temperature of a certain day in the last n years:
Figure FDA0004124531960000091
Figure FDA0004124531960000092
when a maximum daily temperature standard deviation determined from a maximum daily temperature of a certain day in the past n years is smaller than a minimum daily temperature standard deviation determined from a minimum daily temperature of a certain day in the past n years:
T nmax ＝μ max +σ max ×χ
Figure FDA0004124531960000093
in the formula, T nmax Is the highest daily temperature, T, of a certain day in the unknown time of the year hmin Is the minimum value of the daily minimum temperature of the last n years for a certain day of the unknown time of the year, mu min Is the average value of the lowest temperature of the days of the past n years in the month of a certain day in the unknown time of the year, mu max Is the mean value of the highest temperature of the days in the past n years in the month of a certain day in the unknown time of the year, sigma min Is the standard deviation of the lowest temperature of the day of the month in which a certain day in the unknown time of the year is in the last n years, sigma max Is the standard deviation of the highest temperature of the last n years of the day of the month in which the day is in the unknown time of the year, and χ is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the calculation formula of the rice field solar water layer height prediction model is as follows:
H＝μ H +σ H ×χ
Figure FDA0004124531960000094
wherein H is the height of the water layer of the rice field on a certain day in the unknown time of the year, mu H Is the average value of the water layer heights of the rice field in the last n years in the month of a certain day in the unknown time of the year, sigma G The standard deviation of the height of the water layer in the rice field in the past n years in the month of a certain day in the unknown time of the year, chi is the standard normal deviation generated per day according to two random numbers rnd 1 And rnd 2 Obtaining;
the index determining unit is used for dividing the meteorological index data of the known time of the current year and the meteorological index data of the unknown time of the current year determined by the meteorological index prediction model according to the starting and ending time of each growth period of the rice to obtain the meteorological index data of each growth period of the rice;
the rice meteorological biomass unit is used for determining the meteorological biomass of each growth period of the rice in the current year according to the meteorological index-meteorological biomass prediction model of each growth period of the rice based on the data of the meteorological index of each growth period of the rice in the current year;
and the rice meteorological output unit is used for determining the meteorological output of the rice in the current year according to the rice meteorological biomass-meteorological output prediction model based on the meteorological biomass of each growth period of the rice in the current year.
5. The system of claim 4, wherein the first data unit comprises:
a first sequence unit for generating biomass sequence data in chronological order from data of the past n years of biomass for each breeding season of rice;
the first equation group unit is used for performing statistical regression analysis on biomass of rice in each growth period in each year by using the i year as a sliding step length and applying a linear sliding average method to obtain j groups of unary linear regression equations, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
a first analog value unit for determining analog values of j biomass of rice per year of each growth period based on j sets of one-dimensional linear regression equations;
a first trend value unit for determining an average value of the analog values of the biomass per year from the analog values of j biomass per year for each breeding period of rice, and taking the average value as the trend biomass per year for each breeding period of rice;
and the first result unit is used for subtracting the annual biomass and the trend biomass of the rice in each breeding period to obtain the annual meteorological biomass of the rice in each breeding period.
6. The system of claim 4, wherein the second data unit comprises:
a second sequence unit for generating economic yield sequence data by time-sequentially using data of the past n years of the economic yield of rice;
the second equation set unit is used for carrying out statistical regression analysis on the economic yield of the rice in each i year by using the i year as a sliding step length and applying a linear sliding average method to obtain j sets of unary linear regression equations, wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to i, and i, j and n are natural numbers;
a second simulation value unit for determining simulation values of j economic yields of rice per year based on j sets of unary linear regression equations;
a second trend value unit for determining an average value of the simulated values of the economic yield per year from the simulated values of the j economic yields per year of the rice, and taking the average value as the trend economic yield per year of the rice;
and the second result unit is used for subtracting the annual economic yield and the trend economic yield of the rice to obtain the annual meteorological yield of the rice.
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Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110309969B (en) * 2019-06-28 2022-05-03 河南农业大学 Winter wheat late frost freezing damage monitoring and yield prediction method based on Internet of things and remote sensing inversion
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011102520A1 (en) * 2010-02-22 2011-08-25 株式会社パスコ Method of generating paddy rice crop yield forecasting model, and method of forecasting crop yield of paddy rice
CN107341577A (en) * 2017-07-25 2017-11-10 中国农业科学院农业信息研究所 A kind of crop yield Forecasting Methodology and system
CN107368687A (en) * 2017-07-25 2017-11-21 中国农业科学院农业信息研究所 A kind of method for optimizing and device of meteorological yield model
CN107392376A (en) * 2017-07-25 2017-11-24 中国农业科学院农业信息研究所 A kind of crops Meteorological Output Forecasting Methodology and system
CN108665107A (en) * 2018-05-15 2018-10-16 中国农业大学 Crop yield prediction technique and system
CN108921351A (en) * 2018-07-06 2018-11-30 北京兴农丰华科技有限公司 Crop production forecast method based on trend yield and Meteorological Output

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015051339A1 (en) * 2013-10-03 2015-04-09 Farmers Business Network, Llc Crop model and prediction analytics
KR20150096103A (en) * 2014-02-14 2015-08-24 한국전자통신연구원 Prediction Apparatus and Method for Yield of Agricultural Products
US10529036B2 (en) * 2016-01-22 2020-01-07 The Climate Corporation Forecasting national crop yield during the growing season using weather indices

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011102520A1 (en) * 2010-02-22 2011-08-25 株式会社パスコ Method of generating paddy rice crop yield forecasting model, and method of forecasting crop yield of paddy rice
CN107341577A (en) * 2017-07-25 2017-11-10 中国农业科学院农业信息研究所 A kind of crop yield Forecasting Methodology and system
CN107368687A (en) * 2017-07-25 2017-11-21 中国农业科学院农业信息研究所 A kind of method for optimizing and device of meteorological yield model
CN107392376A (en) * 2017-07-25 2017-11-24 中国农业科学院农业信息研究所 A kind of crops Meteorological Output Forecasting Methodology and system
CN108665107A (en) * 2018-05-15 2018-10-16 中国农业大学 Crop yield prediction technique and system
CN108921351A (en) * 2018-07-06 2018-11-30 北京兴农丰华科技有限公司 Crop production forecast method based on trend yield and Meteorological Output

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
帅细强 ; 王石立 ; 马玉平 ; 李迎春 ; .基于水稻生长模型的气象影响评价和产量动态预测.应用气象学报.2008,(第01期),全文. *
李红艳 ; 徐建强 ; 许甫金 ; 肖玉苹 ; 沈足金 ; 张乐平 ; 方明 ; .气象因素对水稻产量的影响及预测模型的建立.浙江农业科学.2018,(第07期),全文. *

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