CN109615149A - A kind of method and system of determining beet Meteorological Output - Google Patents
A kind of method and system of determining beet Meteorological Output Download PDFInfo
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Abstract
The present invention provides a kind of method and system of determining beet Meteorological Output.Described method and system is according to the main meteorological indication information for influencing plant growth when preceding crop region, main includes the data of historical data and current year known time, pass through meteorological index prediction model, predict current year beet meteorological index information, the meteorological biomass for predicting beet current year each growthdevelopmental stage by meteorological index-meteorology biomass prediction model again, predicts current year beet Meteorological Output by meteorological biomass-Meteorological Output prediction model.The method and system of determining beet Meteorological Output of the present invention is by establishing meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, it can be realized the meteorological biomass prediction of each growthdevelopmental stage of beet, to increase the accuracy of beet Meteorological Output prediction, the dynamic release of beet Meteorological Output is realized, to ensure that the beet market supply and demand balance in China provides technical support.
Description
Technical field
The present invention relates to yield of commercial crops to predict field, and more particularly, to a kind of determining beet Meteorological Output
Method and system.
Background technique
Sugarbeet Yield is generally divided into biological yield and economic flow rate.Biological yield abbreviation biomass, refers to beet each
By photosynthesis and absorption in breeding cycle, i.e., produces and accumulate various organic by the conversion of matter and energy
The total amount of object does not usually include root system when calculating biomass.Economic flow rate refers to the harvest yield of beet grain required for cultivation purpose,
I.e. general signified yield.In general, the height of economic flow rate is directly proportional to biomass height.
The length of beet breeding time, in addition to the heredity for depending mainly on beet, due also to the weather conditions in cultivation area
With the factors such as cultivation technique and it is variant.Because temperature is low when such as autumn sowing, winter sowing, growth and development is slow, and breeding time is longer；Sow in spring,
Because of temperature height when summer sowing, growth and development is fast, and breeding time is shorter.Same kind is planted in different latitude area, due to temperature, illumination
Difference, breeding time also changes therewith.
Since prolonged output fluctuation is not only related with meteorological index, also updated with beet variety, socioeconomic transition
Etc. closely related, so in the crop yield of long-term sequence and the observation statistical research of meteorological index relationship, generally sweet tea
The yield of dish is decomposed into 3 part of trend yield, Meteorological Output and random error, and trend yield is reflecting history period productivity hair
Horizontal long period yield component is opened up, also referred to as technical production, Meteorological Output are the variation of short period based on climate element
The fluctuating yield component that the factor (based on agroclimate disaster) influences.Therefore beet Meteorological Output is the weight in Sugarbeet Yield prediction
Point.
The full breeding cycle weather conditions for only accounting for beet to the prediction of beet Meteorological Output in the prior art change, so
And requirement of the beet in different growth and development processes to weather conditions is different, different geographical influences the pass of crop growth
Key period and meteorologic factor are also different, only consider influence of the full breeding cycle weather conditions to beet Meteorological Output can not and
When, beet Meteorological Output fluctuates under Accurate Prediction weather conditions.
Therefore, it is necessary to a kind of technology, can according to the influence of the different growthdevelopmental stage climate condition of beet and caused by
The difference of meteorological biomass determines the Meteorological Output of beet by each growthdevelopmental stage meteorology biomass variety of beet.
Summary of the invention
It can not in order to solve the influence only considered in the prior art full breeding cycle weather conditions to beet Meteorological Output
In time, the technical issues of beet Meteorological Output fluctuates under Accurate Prediction weather conditions, it is meteorological that the present invention provides a kind of determining beet
The method of yield, which comprises
The data of data and current year known time based on the meteorological index past n for influencing beet growth, according to setting
Meteorological index prediction model, determine the data of the meteorological index of beet current year each growthdevelopmental stage, wherein the meteorological index
Including Daily minimum temperature, max. daily temperature, soil moisture and wind speed；
The data of meteorological index based on beet current year each growthdevelopmental stage, refer to according to the meteorology of each growthdevelopmental stage of beet
Mark-meteorology biomass prediction model determines the meteorological biomass of beet current year each growthdevelopmental stage；
Based on the meteorological biomass of beet current year each growthdevelopmental stage, predicted according to beet meteorology biomass-Meteorological Output
Model determines the Meteorological Output of beet current year.
Further, the method is gone over known to data and the current year of n based on the meteorological index for influencing beet growth
The data of time determine the number of the meteorological index of beet current year each growthdevelopmental stage according to the meteorological index prediction model of setting
According to before further include:
According to the fertility feature of beet, the growth stage of beet is divided into several growthdevelopmental stages；
Acquisition influences data, each fertility of the data and current year known time of the meteorological index past n of beet growth
The data of biomass past n, the data of economic flow rate past n and beet each growthdevelopmental stage beginning and ending time in period
Historical data；
The beginning and ending time of current year each growthdevelopmental stage is determined according to the historical data of beet each growthdevelopmental stage beginning and ending time；
Determine that the meteorology of each growthdevelopmental stage of beet is raw based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of object amount past n；
The data of meteorological index past n based on each growthdevelopmental stage of beet and the data of meteorological biomass past n
Determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet；
The data of beet Meteorological Output past n are determined based on the data of beet economic flow rate past n；
The data and beet Meteorological Output of meteorological biomass past n based on each growthdevelopmental stage of beet go over n's
Data determine meteorological biomass-Meteorological Output prediction model of beet.
Further, the data of data and current year known time based on the meteorological index past n for influencing beet growth,
According to the meteorological index prediction model of setting, determine that the meteorological index data of beet current year each growthdevelopmental stage include:
Based on the data for the meteorological index past n for influencing beet growth, according to the meteorological index prediction model of setting, really
The meteorological index data of settled unknown time in year, in which:
The calculation formula of Daily minimum temperature prediction model are as follows:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain
It is in the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
Tnmin=μmin+σmin×χ
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in mistake
When the Daily minimum temperature standard deviation for going the Daily minimum temperature of n to determine:
In formula, TnminIt is certain day Daily minimum temperature in the current year unknown time, ThmaxIt is certain day in the current year unknown time
Maximum value in the max. daily temperature of past n, μminIt is month where certain day in the current year unknown time in past n
The mean value of Daily minimum temperature, μmaxMonth where certain day in unknown time current year past n max. daily temperature it is equal
Value, σminBe where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxBe current year not
Month where knowing certain day in the time, χ was that the daily standard normal of generation is inclined in the standard deviation of the max. daily temperature of past n
Difference, according to two random number rnd1And rnd2It obtains；
The calculation formula of max. daily temperature prediction model are as follows:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain
It is in the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in mistake
When the Daily minimum temperature standard deviation for going the Daily minimum temperature of n to determine:
Tnmax=μmax+σmax×χ
In formula, TnmaxIt is certain day max. daily temperature in the current year unknown time, ThminIt is certain day in the current year unknown time
Minimum value in the Daily minimum temperature of past n, μminIt is month where certain day in the current year unknown time in past n
The mean value of Daily minimum temperature, μmaxMonth where certain day in unknown time current year past n max. daily temperature it is equal
Value, σminBe where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxBe current year not
Month where knowing certain day in the time, χ was that the daily standard normal of generation is inclined in the standard deviation of the max. daily temperature of past n
Difference, according to two random number rnd1And rnd2It obtains；
The calculation formula of soil moisture prediction model are as follows:
RHUmon=RHmon+(1-RHmon)×exp(RHmon-1)
RHLmon=RHmon×(1-exp(-RHmon))
WhenWhen:
RH=RHLmon+[rnd1×(RHUmon-RHLmon)×(RHmon-RHLmon)]0.5
WhenWhen:
In formula, RHIt is certain day per day relative humidity in the current year unknown time, rnd1It is a random number, RHmonIt is
Average value of the month in the per day relative humidity of past n, R where certain day in the unknown time for the yearHUmonIt is that current year is unknown
Maximum value of the month in the per day relative humidity of past n, R where certain day in timeHLmonIt is in the current year unknown time
Certain day where month minimum value in the per day relative humidity of past n；
The calculation formula of forecasting wind speed model are as follows:
In formula, u is certain day wind speed in the current year unknown time, μuIt is to exist in month where certain day in unknown time current year
Past n day wind speed mean value, σuMonth where certain day in unknown time current year past n day wind speed standard
Difference, ξ be month where certain day in unknown time current year past n day wind speed the coefficient of skewness, χ is the daily mark of generation
Quasi- normal deviate, according to two random number rnd1And rnd2It obtains；
By the meteorological index data of current year known time and the current year unknown time determining by meteorological index prediction model
Meteorological index data divided according to the beginning and ending time of each growthdevelopmental stage of beet to get to each growthdevelopmental stage of beet
Meteorological index data.
Further, the data of the biomass past n based on each growthdevelopmental stage of beet determine each life of beet
Educate period meteorological biomass past n data include:
The data of the biomass past n of each growthdevelopmental stage of beet are generated into biomass sequence data in chronological order；
Using i as sliding step, with the linear slide method of average to the biomass of every i of each growthdevelopmental stage of beet into
Row statistical regression analysis obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual biomass of each growthdevelopmental stage of beet is determined based on j group unary linear regression equation；
The analogue value of annual biomass is determined according to the analogue value of j annual biomass of each growthdevelopmental stage of beet
Average value, and the trend biomass annual as each growthdevelopmental stage of beet；
The annual biomass of each growthdevelopmental stage of beet and trend biomass are subtracted each other as each growthdevelopmental stage of beet
Annual meteorological biomass.
Further, the data of the meteorological index past n based on each growthdevelopmental stage of beet and meteorological biomass
The data of past n determine that meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet includes:
The data of meteorological index past n based on each growthdevelopmental stage of beet and the data of meteorological biomass past n
It determines kernel function, the weight of each kernel function of each meteorological index and meteorological biomass, and is sought according to kernel function determination
The deviation of meteorological biomass；
Kernel function, the weight of each kernel function and deviation based on each meteorological index and meteorological biomass determine
The meteorological index of each growthdevelopmental stage of beet-meteorology biomass prediction model, its calculation formula is:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is beet i-th of growthdevelopmental stage of current year
The kernel function of j-th of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index,
biIt is according to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
Further, the data based on beet economic flow rate past n determine the number of beet Meteorological Output past n
According to including:
The data of beet economic flow rate past n are generated into economic flow rate sequence data in chronological order；
Using i as sliding step, statistical regression point is carried out with the economic flow rate of linear slide method of average i every to beet
Analysis, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual economic flow rate of beet is determined based on j group unary linear regression equation；
The average value of the analogue value of annual economic flow rate is determined according to the analogue value of j annual economic flow rate of beet,
And the trend economic flow rate annual as beet；
The annual economic flow rate of beet and trend economic flow rate are subtracted each other to the Meteorological Output annual as beet.
Further, the data and beet Meteorological Output of the meteorological biomass past n based on each growthdevelopmental stage of beet
The data of past n determine that beet meteorology biomass-Meteorological Output prediction model includes:
The data and beet Meteorological Output of meteorological biomass past n based on each growthdevelopmental stage of beet go over n's
Data determine the meteorological biomass of each growthdevelopmental stage and the kernel function of Meteorological Output, the weight of each kernel function, and according to
Kernel function determines the deviation for seeking Meteorological Output；
Kernel function, the weight of each kernel function of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet,
And deviation determines beet meteorology biomass-Meteorological Output prediction model, its calculation formula is:
In formula, y is the Meteorological Output of beet current year,It is the core letter of beet i-th of growthdevelopmental stage meteorology biomass of current year
Number, ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine the meteorology of beet current year
The deviation of yield.
According to another aspect of the present invention, the present invention provides a kind of system of determining beet Meteorological Output, the system packet
It includes:
Beet meteorological index unit is used for data and the current year of the meteorological index past n based on beet growth is influenced
The data of known time determine the meteorological index of beet current year each growthdevelopmental stage according to the meteorological index prediction model of setting
Data, wherein the meteorological index includes Daily minimum temperature, max. daily temperature, soil moisture and wind speed；
Beet meteorology biomass unit is used for the data of the meteorological index based on beet current year each growthdevelopmental stage, root
According to meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, the gas of beet current year each growthdevelopmental stage is determined
As biomass；
Beet Meteorological Output unit is used for the meteorological biomass based on beet current year each growthdevelopmental stage, according to beet
Meteorological biomass-Meteorological Output prediction model, determines the Meteorological Output of beet current year.
Further, system further include:
Beet breeding time division unit is used for the fertility feature according to beet, if the growth stage of beet is divided into
Dry growthdevelopmental stage；
Data acquisition unit was used to acquire known to the data for influencing the meteorological index past n of beet growth and current year
The data of time, the data of biomass past n of each growthdevelopmental stage, the data of economic flow rate past n and beet are every
The historical data of a growthdevelopmental stage beginning and ending time；
Time breeding time determination unit is used to be worked as according to the determination of the historical data of beet each growthdevelopmental stage beginning and ending time
The beginning and ending time of year each growthdevelopmental stage；
First data cell is used to determine beet based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of the meteorological biomass past n of each growthdevelopmental stage；
First model unit is used for the data and meteorology of the meteorological index past n based on each growthdevelopmental stage of beet
The data of biomass past n determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet；
Second data cell is used to determine that beet Meteorological Output goes over n based on the data that beet economic flow rate goes over n
The data in year；
Second model unit is used for the data and sweet tea of the meteorological biomass past n based on each growthdevelopmental stage of beet
The data of dish Meteorological Output past n determine meteorological biomass-Meteorological Output prediction model of beet.
Further, the beet meteorological index unit includes:
Unknown meteorological index unit is used for the data based on the meteorological index past n for influencing beet growth, according to setting
The meteorological index prediction model set determines the meteorological index data of current year unknown time, wherein the Daily minimum temperature,
The calculation formula of max. daily temperature, soil moisture and forecasting wind speed model is identical as in the method for determining beet Meteorological Output,
Details are not described herein again.
Index determination unit is used for by the meteorological index data of current year known time and by meteorological index prediction model
The meteorological index data of determining current year unknown time were divided to arrive according to the beginning and ending time of each growthdevelopmental stage of beet
The meteorological index data of each growthdevelopmental stage of beet.
Further, first data cell includes:
First ray unit is used for the data of the biomass past n of each growthdevelopmental stage of beet in chronological order
Generate biomass sequence data；
First equation group unit is used for using i as sliding step, with the linear slide method of average to each fertility of beet
The biomass of every i in period carries out statistical regression analysis, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j
≤ i, i, j and n are natural numbers；
First simulation value cell, is used to determine that each growthdevelopmental stage of beet is annual based on j group unary linear regression equation
J biomass the analogue value；
First trend value cell is used to be determined according to the analogue value of j annual biomass of each growthdevelopmental stage of beet
The average value of the analogue value of annual biomass, and the trend biomass annual as each growthdevelopmental stage of beet；
First result unit is used to subtract each other the annual biomass of each growthdevelopmental stage of beet and trend biomass i.e.
For the annual meteorological biomass of each growthdevelopmental stage of beet.
Further, first model unit includes:
First parameters unit is used for the data and meteorology of the meteorological index past n based on each growthdevelopmental stage of beet
The data of biomass past n determine kernel function, the weight of each kernel function of each meteorological index and meteorological biomass, and
The deviation for seeking meteorological biomass is determined according to kernel function；
First formula cells are used for kernel function based on each meteorological index and meteorological biomass, each kernel function
Weight and deviation determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, calculation formula
Are as follows:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is beet i-th of growthdevelopmental stage of current year
The kernel function of j-th of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index,
biIt is according to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
Further, second data cell includes:
Second sequence units, the data for being used to pass by beet economic flow rate n generate economic flow rate in chronological order
Sequence data；
Second equation group unit is used for using i as sliding step, with linear slide method of average i's every to beet
Economic flow rate carries out statistical regression analysis, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n
It is natural number；
Second simulation value cell, is used to determine j annual economic flow rate of beet based on j group unary linear regression equation
The analogue value；
Second trend value cell is used to determine annual economy according to the analogue value of j annual economic flow rate of beet
The average value of the analogue value of yield, and the trend economic flow rate annual as beet；
The annual economic flow rate of beet and trend economic flow rate are subtracted each other the meteorology annual as beet by the second result unit
Yield.
Further, second model unit includes:
Second parameters unit is used for the data and sweet tea of the meteorological biomass past n based on each growthdevelopmental stage of beet
The dish Meteorological Output past data of n determine the meteorological biomass of each growthdevelopmental stage and kernel function, the Mei Gehe of Meteorological Output
The weight of function, and the deviation for seeking Meteorological Output is determined according to kernel function；
Second formula cells are used for the core letter of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet
The weight and deviation of several, each kernel function determine beet meteorology biomass-Meteorological Output prediction model, calculation formula
Are as follows:
In formula, y is the Meteorological Output of beet current year,It is the core letter of beet i-th of growthdevelopmental stage meteorology biomass of current year
Number, ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine the meteorology of beet current year
The deviation of yield.
The method and system for the determination beet Meteorological Output that technical solution of the present invention provides is special according to fertility by beet first
Sign, is divided into several growthdevelopmental stages, and the meteorological index letter of major influence factors in history is combined in different growthdevelopmental stages
Breath, establishes meteorological index-meteorology biomass prediction model with the biomass of identical growthdevelopmental stage in history respectively, and secondly application is gone through
The biomass of identical growthdevelopmental stage and historical Meteorological Output establish meteorological biomass-Meteorological Output prediction model in history；It connects
, it mainly include historical data and current year according to the main meteorological indication information for influencing plant growth when preceding crop region
The data of known time are predicted current year beet meteorological index information, are referred to finally by meteorology by meteorological index prediction model
Mark-meteorology biomass prediction model predicts the meteorological biomass of beet current year each growthdevelopmental stage, passes through meteorological biomass-gas
As Production Forecast Models predict current year beet Meteorological Output.The method and system of determining beet Meteorological Output of the present invention
It has the following beneficial effects:
1, by establishing meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, it can be realized beet
The meteorological biomass of each growthdevelopmental stage is predicted, to increase the accuracy of beet Meteorological Output prediction；
2, can be according to the real-time update of the data such as the weather information of current year beet and meteorological biomass, dynamic adjusts meteorological
The knot of index prediction model, meteorological index-meteorology biomass prediction model and meteorological biomass-Meteorological Output prediction model
Fruit realizes the dynamic release of beet Meteorological Output；
3, can comprehensively, system, in time provide China's beet Meteorological Output wave process, intuitive and accurate beet is provided
Meteorological Output prediction result, to ensure that the beet market supply and demand balance in China provides technical support.
Detailed description of the invention
By reference to the following drawings, exemplary embodiments of the present invention can be more fully understood by:
Fig. 1 is the flow chart according to the method for the determination beet Meteorological Output of the preferred embodiment for the present invention；
Fig. 2 is the structural schematic diagram according to the system of the determination beet Meteorological Output of the preferred embodiment for the present invention.
Specific embodiment
Exemplary embodiments of the present invention are introduced referring now to the drawings, however, the present invention can use many different shapes
Formula is implemented, and is not limited to the embodiment described herein, and to provide these embodiments be at large and fully disclose
The present invention, and the scope of the present invention is sufficiently conveyed to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings
Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements use identical attached
Icon note.
Unless otherwise indicated, term (including scientific and technical terminology) used herein has person of ordinary skill in the field
It is common to understand meaning.Further it will be understood that with the term that usually used dictionary limits, should be understood as and its
The context of related fields has consistent meaning, and is not construed as Utopian or too formal meaning.
Fig. 1 is the flow chart according to the method for the determination beet Meteorological Output of the preferred embodiment for the present invention.Such as Fig. 1 institute
Show, the method 100 of determination beet Meteorological Output is since step 101 according to this preferred embodiment.
In step 101, according to the fertility feature of beet, the growth stage of beet is divided into several growthdevelopmental stages.?
In this preferred embodiment, the growth stage of beet is divided into the insemination and emergence phase, cauline leaf phase in great numbers, stem tuber expand the rise period and
4 growthdevelopmental stages of Sugar content accumulated stage.
In step 102, acquisition influences the data of the meteorological index past n of beet growth and the number of current year known time
When going over the data and each fertility of beet of n according to the data of the biomass past n of, each growthdevelopmental stage, economic flow rate
The historical data of beginning and ending time phase.
In the preferred embodiment, historical data is mainly obtained from the database of major crop monitoring platform, current year
The data of known time are mainly monitored by sensor and are obtained, wherein temperature is monitored by temperature sensor and obtained, and is calculated
Every mean daily temperature, soil moisture are monitored by humidity sensor and are obtained, and wind speed is monitored by air velocity transducer and obtained.Practice
In, the beet biomass refers to the constant weight that beet is reached in the upgrowth of each growthdevelopmental stage with low temperature drying.
In step 103, current year each growthdevelopmental stage is determined according to the historical data of beet each growthdevelopmental stage beginning and ending time
Beginning and ending time.In the preferred embodiment, it takes the time that number is most in beet each growthdevelopmental stage beginning and ending time to be used as to work as
The beginning and ending time of annual growing period.When there are two or more than two date number it is identical when, randomly choose one of them date.
In step 104, when determining each fertility of beet based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of the meteorological biomass past n of phase.
In step 105, the data of the meteorological index past n based on each growthdevelopmental stage of beet and meteorological biomass are gone over
The data of n determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet.
In step 106, the data of beet Meteorological Output past n are determined based on the data of beet economic flow rate past n.
In practice, the beet economic flow rate refers to the dry matter weight of the major product beet harvested according to the cultivation purpose of beet.
In step 107, the data and beet Meteorological Output of the meteorological biomass past n based on each growthdevelopmental stage of beet
The data of past n determine meteorological biomass-Meteorological Output prediction model of beet.
In step 108, the number of data and current year known time based on the meteorological index past n for influencing beet growth
According to determining the data of the meteorological index of beet current year each growthdevelopmental stage according to the meteorological index prediction model of setting, wherein
The meteorological index includes Daily minimum temperature, max. daily temperature, soil moisture and wind speed.
In step 109, the data of the meteorological index based on beet current year each growthdevelopmental stage, when fertility each according to beet
The meteorological index of phase-meteorology biomass prediction model determines the meteorological biomass of beet current year each growthdevelopmental stage.
In step 110, based on the meteorological biomass of beet current year each growthdevelopmental stage, according to beet meteorology biomass-gas
As Production Forecast Models, the Meteorological Output of beet current year is determined.
It is preferably based on the data of the data and current year known time that influence the meteorological index past n of beet growth, root
According to the meteorological index prediction model of setting, determine that the meteorological index data of beet current year each growthdevelopmental stage include:
Based on the data for the meteorological index past n for influencing beet growth, according to the meteorological index prediction model of setting, really
The meteorological index data of settled unknown time in year, in which:
The calculation formula of Daily minimum temperature prediction model are as follows:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain
It is in the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
Tnmin=μmin+σmin×χ
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in mistake
When the Daily minimum temperature standard deviation for going the Daily minimum temperature of n to determine:
In formula, TnminIt is certain day Daily minimum temperature in the current year unknown time, ThmaxIt is certain day in the current year unknown time
Maximum value in the max. daily temperature of past n, μminIt is month where certain day in the current year unknown time in past n
The mean value of Daily minimum temperature, μmaxMonth where certain day in unknown time current year past n max. daily temperature it is equal
Value, σminBe where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxBe current year not
Month where knowing certain day in the time, χ was that the daily standard normal of generation is inclined in the standard deviation of the max. daily temperature of past n
Difference, according to two random number rnd1And rnd2It obtains；
The calculation formula of max. daily temperature prediction model are as follows:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain
It is in the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in mistake
When the Daily minimum temperature standard deviation for going the Daily minimum temperature of n to determine:
Tnmax=μmax+σmax×χ
In formula, TnmaxIt is certain day max. daily temperature in the current year unknown time, ThminIt is certain day in the current year unknown time
Minimum value in the Daily minimum temperature of past n, μminIt is month where certain day in the current year unknown time in past n
The mean value of Daily minimum temperature, μmaxMonth where certain day in unknown time current year past n max. daily temperature it is equal
Value, σminBe where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxBe current year not
Month where knowing certain day in the time, χ was that the daily standard normal of generation is inclined in the standard deviation of the max. daily temperature of past n
Difference, according to two random number rnd1And rnd2It obtains；
The calculation formula of soil moisture prediction model are as follows:
RHUmon=RHmon+(1-RHmon)×exp(RHmon-1)
RHLmon=RHmon×(1-exp(-RHmon))
WhenWhen:
RH=RHLmon+[rnd1×(RHUmon-RHLmon)×(RHmon-RHLmon)]0.5
WhenWhen:
In formula, RHIt is certain day per day relative humidity in the current year unknown time, rnd1It is a random number, RHmonIt is
Average value of the month in the per day relative humidity of past n, R where certain day in the unknown time for the yearHUmonIt is that current year is unknown
Maximum value of the month in the per day relative humidity of past n, R where certain day in timeHLmonIt is in the current year unknown time
Certain day where month minimum value in the per day relative humidity of past n；
The calculation formula of forecasting wind speed model are as follows:
In formula, u is certain day wind speed in the current year unknown time, μuIt is to exist in month where certain day in unknown time current year
Past n day wind speed mean value, σuMonth where certain day in unknown time current year past n day wind speed standard
Difference, ξ be month where certain day in unknown time current year past n day wind speed the coefficient of skewness, χ is the daily mark of generation
Quasi- normal deviate, according to two random number rnd1And rnd2It obtains；
By the meteorological index data of current year known time and the current year unknown time determining by meteorological index prediction model
Meteorological index data divided according to the beginning and ending time of each growthdevelopmental stage of beet to get to each growthdevelopmental stage of beet
Meteorological index data.
Preferably, the data of the biomass past n based on each growthdevelopmental stage of beet determine each fertility of beet
Period meteorological biomass past n data include:
The data of the biomass past n of each growthdevelopmental stage of beet are generated into biomass sequence data in chronological order；
Using i as sliding step, with the linear slide method of average to the biomass of every i of each growthdevelopmental stage of beet into
Row statistical regression analysis obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual biomass of each growthdevelopmental stage of beet is determined based on j group unary linear regression equation；
The analogue value of annual biomass is determined according to the analogue value of j annual biomass of each growthdevelopmental stage of beet
Average value, and the trend biomass annual as each growthdevelopmental stage of beet；
The annual biomass of each growthdevelopmental stage of beet and trend biomass are subtracted each other as each growthdevelopmental stage of beet
Annual meteorological biomass.
Preferably, the data and meteorological biomass mistake of the meteorological index past n based on each growthdevelopmental stage of beet
Meteorological index-meteorology biomass the prediction model for going the data of n to determine each growthdevelopmental stage of beet includes:
The data of meteorological index past n based on each growthdevelopmental stage of beet and the data of meteorological biomass past n
It determines kernel function, the weight of each kernel function of each meteorological index and meteorological biomass, and is sought according to kernel function determination
The deviation of meteorological biomass；
Kernel function, the weight of each kernel function and deviation based on each meteorological index and meteorological biomass determine
The meteorological index of each growthdevelopmental stage of beet-meteorology biomass prediction model, its calculation formula is:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is beet i-th of growthdevelopmental stage of current year
The kernel function of j-th of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index,
biIt is according to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
In the preferred embodiment, the growth stage of beet is divided into the insemination and emergence phase, cauline leaf phase in great numbers, stem tuber expand increasing
Long-term and 4 growthdevelopmental stages of Sugar content accumulated stage.In order to make meteorological index-meteorology biomass prediction model of each growthdevelopmental stage more
To be accurate, the lowest temperature angle value and maximum temperature value that are arranged according to historical experience and rice field day water layer height are all carried out
More specifically interval division, specifically:
The meteorological index of beet insemination and emergence phase-biomass prediction model calculation formula are as follows:
In formula, ybcFor insemination and emergence phase meteorology biomass, BCTDL、Respectively sow out
The kernel function weight of number of days of the Daily minimum temperature less than 5 DEG C, the kernel function of the meteorological index and the meteorological index in seedling stage,
BCTDM、Number of days, the gas of Daily minimum temperature between 5 DEG C -12 DEG C respectively in the insemination and emergence phase
As the kernel function of index and the kernel function weight of the meteorological index, BCTGM、Respectively broadcast
Core letter of the max. daily temperature in 12 DEG C -14 DEG C of number of days, the kernel function of the meteorological index and the meteorological index in the kind seeding stage
Number weight, BCTGH、Respectively in the insemination and emergence phase max. daily temperature greater than 14 DEG C number of days,
The kernel function weight of the kernel function of the meteorological index and the meteorological index, BCSSL、Respectively
In the insemination and emergence phase day number of days of the soil moisture less than 21%, the kernel function of the meteorological index and the kernel function of the meteorological index
Weight, BCSSM、Day of the day soil moisture between 21%-26% respectively in the insemination and emergence phase
The kernel function weight of number, the kernel function of the meteorological index and the meteorological index, BCSSH、Point
Not Wei in the insemination and emergence phase day soil moisture be greater than 26% number of days, the kernel function of the meteorological index and the core of the meteorological index
Function weight, BCFSL、Per day wind speed is less than or equal to 4m/s's respectively in the insemination and emergence phase
The kernel function weight of number of days, the kernel function of the meteorological index and the meteorological index, BCFSH、
Per day wind speed is greater than the number of days of 4m/s, the kernel function of the meteorological index and the meteorological index respectively in the insemination and emergence phase
Kernel function weight, bbcFor deviation.
The meteorological index of beet cauline leaf phase in great numbers-biomass prediction model calculation formula are as follows:
In formula, yjfFor cauline leaf phase meteorology biomass in great numbers, JFTDL、Respectively cauline leaf is in great numbers
The kernel function weight of number of days of the Daily minimum temperature less than 7 DEG C, the kernel function of the meteorological index and the meteorological index in phase,
JFTDM、Number of days, the gas of Daily minimum temperature between 7 DEG C -10 DEG C respectively in the cauline leaf phase in great numbers
As the kernel function of index and the kernel function weight of the meteorological index, JFTGM、Respectively cauline leaf
Kernel function of the max. daily temperature in 10 DEG C -12 DEG C of number of days, the kernel function of the meteorological index and the meteorological index in phase in great numbers
Weight, JFTGH、Number of days of the max. daily temperature greater than 12 DEG C, the gas respectively in the cauline leaf phase in great numbers
As the kernel function of index and the kernel function weight of the meteorological index, JFSSL、Respectively cauline leaf is numerous
In the luxuriant phase day number of days of the soil moisture less than 25%, the kernel function of the meteorological index and the kernel function weight of the meteorological index,
JFSSM、Respectively in the cauline leaf phase in great numbers day number of days of the soil moisture between 25%-27%, should
The kernel function weight of the kernel function of meteorological index and the meteorological index, JFSSH、Respectively stem
The kernel function power of number of days of the day soil moisture greater than 27%, the kernel function of the meteorological index and the meteorological index in the leaf phase in great numbers
Weight, JFFSL、Per day wind speed is less than or equal to the number of days of 4m/s, is somebody's turn to do respectively in the cauline leaf phase in great numbers
The kernel function weight of the kernel function of meteorological index and the meteorological index, JFFSH、Respectively cauline leaf
The kernel function power of number of days of the per day wind speed greater than 4m/s, the kernel function of the meteorological index and the meteorological index in phase in great numbers
Weight, bifFor deviation.
Beet stem tuber expands meteorological index-biomass prediction model calculation formula of rise period are as follows:
In formula, ypzRise period meteorology biomass, PZTD are expanded for stem tuberL、Respectively stem tuber
Expand number of days of the Daily minimum temperature less than 5 DEG C, the kernel function of the meteorological index and the kernel function of the meteorological index in the rise period
Weight, PZTDM、Respectively stem tuber expands in the rise period Daily minimum temperature between 5 DEG C -12 DEG C
Number of days, the kernel function of the meteorological index and the kernel function weight of the meteorological index, PZTGM、Respectively stem tuber expands number of days at 12 DEG C -14 DEG C of max. daily temperature in the rise period, the meteorology
The kernel function weight of the kernel function of index and the meteorological index, PZTGH、Respectively stem tuber is swollen
The kernel function power of number of days of the max. daily temperature greater than 14 DEG C, the kernel function of the meteorological index and the meteorological index in the big rise period
Weight, PZSSL、Respectively stem tuber expand in the rise period day number of days of the soil moisture less than 22%, should
The kernel function weight of the kernel function of meteorological index and the meteorological index, PZSSM、Respectively block
Stem expands number of days of the soil moisture of day in the rise period between 22%-25%, the kernel function of the meteorological index and the meteorology and refers to
Target kernel function weight, PZSSH、 Respectively stem tuber expands in the rise period day soil moisture and is greater than
The kernel function weight of 25% number of days, the kernel function of the meteorological index and the meteorological index, PZFSL、Respectively stem tuber expands number of days of the per day wind speed less than or equal to 4m/s, the meteorology in the rise period
The kernel function weight of the kernel function of index and the meteorological index, PZFSH、Respectively stem tuber is swollen
The kernel function power of number of days of the per day wind speed greater than 4m/s, the kernel function of the meteorological index and the meteorological index in the big rise period
Weight, bpzFor deviation.
The meteorological index of beet Sugar content accumulated stage-biomass prediction model calculation formula are as follows:
In formula, ytjFor Sugar content accumulated stage meteorology biomass, TJTDL、Respectively sucrose accumulation
Daily minimum temperature is less than -5 DEG C of number of days, the kernel function of the meteorological index and the kernel function weight of the meteorological index in phase,
TJTDM、Number of days of the Daily minimum temperature between -5 DEG C -12 DEG C respectively in Sugar content accumulated stage, should
The kernel function weight of the kernel function of meteorological index and the meteorological index, TJTGM、It is respectively sugared
Core letter of the max. daily temperature in 12 DEG C -14 DEG C of number of days, the kernel function of the meteorological index and the meteorological index in point accumulation phase
Number weight, TJTGH、Max. daily temperature is greater than 14 DEG C of number of days, is somebody's turn to do respectively in Sugar content accumulated stage
The kernel function weight of the kernel function of meteorological index and the meteorological index, TJSSL、Respectively sugar
In the accumulation phase day number of days of the soil moisture less than 20%, the meteorological index kernel function and the meteorological index kernel function power
Weight, TJSSM、Day of the day soil moisture between 20%-22% respectively in Sugar content accumulated stage
The kernel function weight of number, the kernel function of the meteorological index and the meteorological index, TJSSH、Respectively
It is greater than 22% number of days, the kernel function of the meteorological index and the core letter of the meteorological index for day soil moisture in Sugar content accumulated stage
Number weight, TJFSL、Per day wind speed is less than or equal to the day of 4m/s respectively in Sugar content accumulated stage
The kernel function weight of number, the kernel function of the meteorological index and the meteorological index, TJFSH、Respectively
It is greater than number of days, the kernel function of the meteorological index and the core letter of the meteorological index of 4m/s for wind speed per day in Sugar content accumulated stage
Number weight, btjFor deviation.
Preferably, the data based on beet economic flow rate past n determine the data of beet Meteorological Output past n
Include:
The data of beet economic flow rate past n are generated into economic flow rate sequence data in chronological order；
Using i as sliding step, statistical regression point is carried out with the economic flow rate of linear slide method of average i every to beet
Analysis, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual economic flow rate of beet is determined based on j group unary linear regression equation；
The average value of the analogue value of annual economic flow rate is determined according to the analogue value of j annual economic flow rate of beet,
And the trend economic flow rate annual as beet；
The annual economic flow rate of beet and trend economic flow rate are subtracted each other to the Meteorological Output annual as beet.
It is preferably based on the data and beet Meteorological Output mistake of the meteorological biomass past n of each growthdevelopmental stage of beet
The data of n are gone to determine that beet meteorology biomass-Meteorological Output prediction model includes:
The data and beet Meteorological Output of meteorological biomass past n based on each growthdevelopmental stage of beet go over n's
Data determine the meteorological biomass of each growthdevelopmental stage and the kernel function of Meteorological Output, the weight of each kernel function, and according to
Kernel function determines the deviation for seeking Meteorological Output；
Kernel function, the weight of each kernel function of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet,
And deviation determines beet meteorology biomass-Meteorological Output prediction model, its calculation formula is:
In formula, y is the Meteorological Output of beet current year,It is the core letter of beet i-th of growthdevelopmental stage meteorology biomass of current year
Number, ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine the meteorology of beet current year
The deviation of yield.
In the preferred embodiment, the growth stage of beet is divided into the insemination and emergence phase, cauline leaf phase in great numbers, stem tuber expand increasing
Long-term and 4 growthdevelopmental stages of Sugar content accumulated stage.It corresponds, the meteorological biomass and meteorology of each growthdevelopmental stage of beet
The calculation formula of the prediction model of yield are as follows:
In formula, z is beet Meteorological Output, ybc、Respectively beet insemination and emergence phase biomass, beet
Insemination and emergence phase biomass kernel function and kernel function weight, yjf、Respectively beet cauline leaf phase biology in great numbers
Amount, beet cauline leaf phase biomass kernel function in great numbers and kernel function weight, ypz、wpzRespectively beet stem tuber expands increasing
Chronobiological amount, beet stem tuber expand rise period biomass kernel function and kernel function weight, ytj、Respectively
Beet Sugar content accumulated stage biomass, beet Sugar content accumulated stage biomass kernel function and kernel function weight, b are deviation.
Fig. 2 is the structural schematic diagram according to the system of the determination beet Meteorological Output of the preferred embodiment for the present invention.Such as Fig. 2
Shown, the system 200 of determination beet Meteorological Output described in this preferred embodiment includes:
Beet breeding time division unit 201 is used for the fertility feature according to beet, the growth stage of beet is divided into
Several growthdevelopmental stages；
Time breeding time determination unit 202 is used for true according to the historical data of beet each growthdevelopmental stage beginning and ending time
The beginning and ending time of settled year each growthdevelopmental stage.
Data acquisition unit 203 has been used to acquire the data for influencing the meteorological index past n of beet growth and current year
Know the data of the data of time, the data of the biomass past n of each growthdevelopmental stage and economic flow rate past n.
First data cell 204 is used to determine sweet tea based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of the meteorological biomass past n of each growthdevelopmental stage of dish.
First model unit 205, the data for being used for the meteorological index past n based on each growthdevelopmental stage of beet are gentle
As the data of biomass past n determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet.
Second data cell 206, the data for being used to go over based on beet economic flow rate n determine beet Meteorological Output mistake
Go the data of n.
Second model unit 207, be used for based on each growthdevelopmental stage of beet meteorological biomass past n data with
The data of beet Meteorological Output past n determine meteorological biomass-Meteorological Output prediction model of beet.
Beet meteorological index unit 208 is used for the data based on the meteorological index past n for influencing beet growth and works as
The data of year known time determine that the meteorology of beet current year each growthdevelopmental stage refers to according to the meteorological index prediction model of setting
Target data, wherein the meteorological index includes Daily minimum temperature, max. daily temperature, soil moisture and wind speed.
Beet meteorology biomass unit 209 is used for the data of the meteorological index based on beet current year each growthdevelopmental stage,
According to the meteorological index of each growthdevelopmental stage of beet-meteorology biomass prediction model, beet current year each growthdevelopmental stage is determined
Meteorological biomass.
Beet Meteorological Output unit 210 is used for the meteorological biomass based on beet current year each growthdevelopmental stage, according to sweet tea
Dish meteorology biomass-Meteorological Output prediction model, determines the Meteorological Output of beet current year.
Preferably, the beet meteorological index unit 208 includes:
Unknown meteorological index unit 281 is used for the data based on the meteorological index past n for influencing beet growth, root
According to the meteorological index prediction model of setting, the meteorological index data of current year unknown time are determined, wherein the Daily minimum temperature,
The calculation formula of max. daily temperature, soil moisture and forecasting wind speed model is identical as in the method for determining beet Meteorological Output,
Details are not described herein again.
Index determination unit 282 is used to predict the meteorological index data of current year known time with by meteorological index
The meteorological index data for the current year unknown time that model determines are divided according to the beginning and ending time of each growthdevelopmental stage of beet, i.e.,
Obtain the meteorological index data of each growthdevelopmental stage of beet.
Preferably, first data cell 204 includes:
First ray unit 241 is used for the data of the biomass past n of each growthdevelopmental stage of beet are temporally suitable
Sequence generates biomass sequence data；
First equation group unit 242 is used for using i as sliding step, each to beet with the linear slide method of average
The biomass of every i of growthdevelopmental stage carries out statistical regression analysis, obtains j group unary linear regression equation, wherein 1≤i≤n,
1≤j≤i, i, j and n are natural numbers；
First simulation value cell 243, is used to determine that each growthdevelopmental stage of beet is every based on j group unary linear regression equation
The analogue value of the j biomass in year；
First trend value cell 244 is used for true according to the analogue value of j annual biomass of each growthdevelopmental stage of beet
The average value of the analogue value of fixed annual biomass, and the trend biomass annual as each growthdevelopmental stage of beet；
First result unit 245 is used for the annual biomass of each growthdevelopmental stage of beet and trend biomass phase
Subtract the annual meteorological biomass as each growthdevelopmental stage of beet.
Preferably, first model unit 205 includes:
First parameters unit 251, the data for being used for the meteorological index past n based on each growthdevelopmental stage of beet are gentle
As biomass past n data determine each meteorological index with meteorology biomass kernel function, the weight of each kernel function, with
And the deviation for seeking meteorological biomass is determined according to kernel function；
First formula cells 252 are used for kernel function, each kernel function based on each meteorological index and meteorological biomass
Weight and deviation determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, calculation formula
Are as follows:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is beet i-th of growthdevelopmental stage of current year
The kernel function of j-th of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index,
biIt is according to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
Preferably, second data cell 206 includes:
Second sequence units 261 are used to generating the data that beet economic flow rate goes over n into economic production in chronological order
Measure sequence data；
Second equation group unit 262 is used for using i as sliding step, with linear slide method of average i every to beet
Economic flow rate carry out statistical regression analysis, obtain j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and
N is natural number；
Second simulation value cell 263 is used to determine j annual economy of beet based on j group unary linear regression equation
The analogue value of yield；
Second trend value cell 264 is used to determine annual warp according to the analogue value of j annual economic flow rate of beet
The average value of the analogue value for yield of helping, and the trend economic flow rate annual as beet；
Second result unit 265 subtracts each other the annual economic flow rate of beet and trend economic flow rate annual as beet
Meteorological Output.
Preferably, second model unit 207 includes:
Second parameters unit 271, be used for based on each growthdevelopmental stage of beet meteorological biomass past n data with
The data of beet Meteorological Output past n determine the meteorological biomass of each growthdevelopmental stage and the kernel function of Meteorological Output, each
The weight of kernel function, and the deviation for seeking Meteorological Output is determined according to kernel function；
Second formula cells 272 are used for the core of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet
Function, the weight of each kernel function and deviation determine beet meteorology biomass-Meteorological Output prediction model, calculate public
Formula are as follows:
In formula, y is the Meteorological Output of beet current year,It is the core letter of beet i-th of growthdevelopmental stage meteorology biomass of current year
Number, ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine the meteorology of beet current year
The deviation of yield.
The present invention is described by reference to a small amount of embodiment.However, it is known in those skilled in the art, as
Defined by subsidiary Patent right requirement, in addition to the present invention other embodiments disclosed above equally fall in it is of the invention
In range.
Normally, all terms used in the claims are all solved according to them in the common meaning of technical field
It releases, unless in addition clearly being defined wherein.All references " one/described/be somebody's turn to do [device, component etc.] " are all opened ground
At least one example being construed in described device, component etc., unless otherwise expressly specified.Any method disclosed herein
Step need not all be run with disclosed accurate sequence, unless explicitly stated otherwise.
Claims (14)
1. a kind of method of determining beet Meteorological Output, which is characterized in that the described method includes:
The data of data and current year known time based on the meteorological index past n for influencing beet growth, according to the gas of setting
As index prediction model, the data of the meteorological index of beet current year each growthdevelopmental stage are determined, wherein the meteorological index includes
Daily minimum temperature, max. daily temperature, soil moisture and wind speed；
The data of meteorological index based on beet current year each growthdevelopmental stage, according to the meteorological index-of each growthdevelopmental stage of beet
Meteorological biomass prediction model determines the meteorological biomass of beet current year each growthdevelopmental stage；
Based on the meteorological biomass of beet current year each growthdevelopmental stage, according to beet meteorology biomass-Meteorological Output prediction model,
Determine the Meteorological Output of beet current year.
2. the method according to claim 1, wherein the method is based on the meteorological index for influencing beet growth
The data of past n and the data of current year known time determine that beet current year is each according to the meteorological index prediction model of setting
Before the data of the meteorological index of growthdevelopmental stage further include:
According to the fertility feature of beet, the growth stage of beet is divided into several growthdevelopmental stages；
Acquisition influences the data of the meteorological index past n of beet growth and data, each growthdevelopmental stage of current year known time
The biomass past data of n, economic flow rate go over the data of n and going through for beet each growthdevelopmental stage beginning and ending time
History data；
The beginning and ending time of current year each growthdevelopmental stage is determined according to the historical data of beet each growthdevelopmental stage beginning and ending time；
The meteorological biomass of each growthdevelopmental stage of beet is determined based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of past n；
The data of data and meteorological biomass past n based on the meteorological index past n of each growthdevelopmental stage of beet determine
The meteorological index of each growthdevelopmental stage of beet-meteorology biomass prediction model；
The data of beet Meteorological Output past n are determined based on the data of beet economic flow rate past n；
The data of meteorological biomass past n based on each growthdevelopmental stage of beet and the data of beet Meteorological Output past n
Determine meteorological biomass-Meteorological Output prediction model of beet.
3. the method according to claim 1, wherein based on the meteorological index past n's for influencing beet growth
The data of data and current year known time determine beet current year each growthdevelopmental stage according to the meteorological index prediction model of setting
Meteorological index data include:
Based on the data for the meteorological index past n for influencing beet growth, according to the meteorological index prediction model of setting, determination is worked as
The meteorological index data of unknown time in year, in which:
The calculation formula of Daily minimum temperature prediction model are as follows:
Existed when the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain day
When the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
Tnmin=μmin+σmin×χ
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in past n
When the Daily minimum temperature standard deviation that the Daily minimum temperature in year determines:
In formula, TnminIt is certain day Daily minimum temperature in the current year unknown time, ThmaxIt is certain day in the current year unknown time in mistake
Remove the maximum value in the max. daily temperature of n, μminMonth where certain day in unknown time current year past n day most
The mean value of low temperature, μmaxBe where certain day in unknown time current year month the max. daily temperature in past n mean value, σmin
Be where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxIt is the current year unknown time
In certain day where month the max. daily temperature of past n standard deviation, χ be generation daily standard normal deviation, according to
Two random number rnd1And rnd2It obtains；
The calculation formula of max. daily temperature prediction model are as follows:
Existed when the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain day
When the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in past n
When the Daily minimum temperature standard deviation that the Daily minimum temperature in year determines:
Tnmax=μmax+σmax×χ
In formula, TnmaxIt is certain day max. daily temperature in the current year unknown time, ThminIt is certain day in the current year unknown time in mistake
Remove the minimum value in the Daily minimum temperature of n, μminMonth where certain day in unknown time current year past n day most
The mean value of low temperature, μmaxBe where certain day in unknown time current year month the max. daily temperature in past n mean value, σmin
Be where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxIt is the current year unknown time
In certain day where month the max. daily temperature of past n standard deviation, χ be generation daily standard normal deviation, according to
Two random number rnd1And rnd2It obtains；
The calculation formula of soil moisture prediction model are as follows:
RHUmon=RHmon+(1-RHmon)×exp(RHmon-1)
RHLmon=RHmon×(1-exp(-RHmon))
WhenWhen:
RH=RHLmon+[rnd1×(RHUmon-RHLmon)×(RHmon-RHLmon)]0.5
WhenWhen:
In formula, RHIt is certain day per day relative humidity in the current year unknown time, rnd1It is a random number, RHmonIt is current year
Average value of the month in the per day relative humidity of past n, R where certain day in the unknown timeHUmonIt is the current year unknown time
In certain day where month maximum value in the per day relative humidity of past n, RHLmonIt is certain in the current year unknown time
Minimum value of the month in the per day relative humidity of past n where it；
The calculation formula of forecasting wind speed model are as follows:
In formula, u is certain day wind speed in the current year unknown time, μuIt is month where certain day in the current year unknown time in past n
Year day wind speed mean value, σuMonth where certain day in unknown time current year past n day wind speed standard deviation, ξ
Month where certain day in unknown time current year past n day wind speed the coefficient of skewness, χ be the daily standard of generation just
State deviation, according to two random number rnd1And rnd2It obtains；
By the gas of the meteorological index data of current year known time and the current year unknown time determined by meteorological index prediction model
As achievement data according to the beginning and ending time of each growthdevelopmental stage of beet divided to get arrive each growthdevelopmental stage of beet meteorology
Achievement data.
4. according to the method described in claim 2, it is characterized in that, the biomass based on each growthdevelopmental stage of beet is gone over
The data of n determine that the data of the meteorological biomass past n of each growthdevelopmental stage of beet include:
The data of the biomass past n of each growthdevelopmental stage of beet are generated into biomass sequence data in chronological order；
Using i as sliding step, unite with biomass of the linear slide method of average to every i of each growthdevelopmental stage of beet
Regression analysis is counted, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual biomass of each growthdevelopmental stage of beet is determined based on j group unary linear regression equation；
Being averaged for the analogue value of annual biomass is determined according to the analogue value of j annual biomass of each growthdevelopmental stage of beet
Value, and the trend biomass annual as each growthdevelopmental stage of beet；
The annual biomass of each growthdevelopmental stage of beet and trend biomass are subtracted each other as the every of each growthdevelopmental stage of beet
The meteorological biomass in year.
5. according to the method described in claim 2, it is characterized in that, the meteorological index mistake based on each growthdevelopmental stage of beet
The data of the data and meteorological biomass past n of removing n determine meteorological index-meteorology biomass of each growthdevelopmental stage of beet
Prediction model includes:
The data of data and meteorological biomass past n based on the meteorological index past n of each growthdevelopmental stage of beet determine
Kernel function, the weight of each kernel function of each meteorological index and meteorological biomass, and meteorology is sought according to kernel function determination
The deviation of biomass；
Kernel function, the weight of each kernel function and deviation based on each meteorological index and meteorological biomass determine beet
The meteorological index of each growthdevelopmental stage-meteorology biomass prediction model, its calculation formula is:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is i-th j-th of growthdevelopmental stage of beet current year
The kernel function of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index, biIt is root
According to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
6. according to the method described in claim 2, it is characterized in that, the data based on beet economic flow rate past n are true
Determine beet Meteorological Output past n data include:
The data of beet economic flow rate past n are generated into economic flow rate sequence data in chronological order；
Using i as sliding step, statistical regression analysis is carried out with the economic flow rate of linear slide method of average i every to beet,
Obtain j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are natural numbers；
The analogue value of j annual economic flow rate of beet is determined based on j group unary linear regression equation；
The average value of the analogue value of annual economic flow rate is determined according to the analogue value of j annual economic flow rate of beet, and will
Its trend economic flow rate annual as beet；
The annual economic flow rate of beet and trend economic flow rate are subtracted each other to the Meteorological Output annual as beet.
7. according to the method described in claim 2, it is characterized in that, the meteorological biomass based on each growthdevelopmental stage of beet is gone over
The data of data and beet Meteorological Output the past n of n determine beet meteorology biomass-Meteorological Output prediction model packet
It includes:
The data of meteorological biomass past n based on each growthdevelopmental stage of beet and the data of beet Meteorological Output past n
Determine the meteorological biomass of each growthdevelopmental stage and the kernel function of Meteorological Output, the weight of each kernel function, and according to core letter
Number determines the deviation for seeking Meteorological Output；
Kernel function, the weight of each kernel function of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet, and
Deviation determines beet meteorology biomass-Meteorological Output prediction model, its calculation formula is:
In formula, y is the Meteorological Output of beet current year,It is the kernel function of beet i-th of growthdevelopmental stage meteorology biomass of current year,
ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine that the meteorological of beet current year produces
The deviation of amount.
8. a kind of system of determining beet Meteorological Output, which is characterized in that the system comprises:
Beet meteorological index unit was used for known to data and current year based on the meteorological index past n for influencing beet growth
The data of time determine the number of the meteorological index of beet current year each growthdevelopmental stage according to the meteorological index prediction model of setting
According to, wherein the meteorological index includes Daily minimum temperature, max. daily temperature, soil moisture and wind speed；
Beet meteorology biomass unit is used for the data of the meteorological index based on beet current year each growthdevelopmental stage, according to sweet tea
The meteorological index of each growthdevelopmental stage of dish-meteorology biomass prediction model determines that the meteorology of beet current year each growthdevelopmental stage is raw
Object amount；
Beet Meteorological Output unit is used for the meteorological biomass based on beet current year each growthdevelopmental stage, according to beet meteorology
Biomass-Meteorological Output prediction model, determines the Meteorological Output of beet current year.
9. system according to claim 8, which is characterized in that system further include:
Beet breeding time division unit, is used for the fertility feature according to beet, and the growth stage of beet is divided into several
Growthdevelopmental stage；
Data acquisition unit is used to acquire the data and current year known time for influencing the meteorological index past n of beet growth
Data, the biomass past data of n of each growthdevelopmental stage, economic flow rate go over the data and each life of beet of n
Educate the historical data of beginning and ending time in period；
Time breeding time determination unit is used to determine that current year is every according to the historical data of beet each growthdevelopmental stage beginning and ending time
The beginning and ending time of a growthdevelopmental stage；
First data cell is used to determine that beet is each based on the data of the biomass past n of each growthdevelopmental stage of beet
The data of the meteorological biomass past n of growthdevelopmental stage；
First model unit is used for the data and meteorology biology of the meteorological index past n based on each growthdevelopmental stage of beet
The data of amount past n determine meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet；
Second data cell is used to determine beet Meteorological Output past n's based on the data that beet economic flow rate goes over n
Data；
Second model unit is used for the data and beet gas of the meteorological biomass past n based on each growthdevelopmental stage of beet
As the data of yield past n determine meteorological biomass-Meteorological Output prediction model of beet.
10. system according to claim 8, which is characterized in that the beet meteorological index unit includes:
Unknown meteorological index unit is used for the data based on the meteorological index past n for influencing beet growth, according to setting
Meteorological index prediction model determines the meteorological index data of current year unknown time, in which:
The calculation formula of Daily minimum temperature prediction model are as follows:
Existed when the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain day
When the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
Tnmin=μmin+σmin×χ
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in past n
When the Daily minimum temperature standard deviation that the Daily minimum temperature in year determines:
In formula, TnminIt is certain day Daily minimum temperature in the current year unknown time, ThmaxIt is certain day in the current year unknown time in mistake
Remove the maximum value in the max. daily temperature of n, μminMonth where certain day in unknown time current year past n day most
The mean value of low temperature, μmaxBe where certain day in unknown time current year month the max. daily temperature in past n mean value, σmin
Be where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxIt is the current year unknown time
In certain day where month the max. daily temperature of past n standard deviation, χ be generation daily standard normal deviation, according to
Two random number rnd1And rnd2It obtains；
The calculation formula of max. daily temperature prediction model are as follows:
Existed when the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is greater than or equal to according to certain day
When the Daily minimum temperature standard deviation that the Daily minimum temperature of past n determines:
When the max. daily temperature standard deviation determined according to certain day max. daily temperature in past n is less than according to certain day in past n
When the Daily minimum temperature standard deviation that the Daily minimum temperature in year determines:
Tnmax=μmax+σmax×χ
In formula, TnmaxIt is certain day max. daily temperature in the current year unknown time, ThminIt is certain day in the current year unknown time in mistake
Remove the minimum value in the Daily minimum temperature of n, μminMonth where certain day in unknown time current year past n day most
The mean value of low temperature, μmaxBe where certain day in unknown time current year month the max. daily temperature in past n mean value, σmin
Be where certain day in unknown time current year month the Daily minimum temperature in past n standard deviation, σmaxIt is the current year unknown time
In certain day where month the max. daily temperature of past n standard deviation, χ be generation daily standard normal deviation, according to
Two random number rnd1And rnd2It obtains；
The calculation formula of soil moisture prediction model are as follows:
RHUmon=RHmon+(1-RHmon)×exp(RHmon-1)
RHLmon=RHmon×(1-exp(-RHmon))
WhenWhen:
RH=RHLmon+[rnd1×(RHUmon-RHLmon)×(RHmon-RHLmon)]0.5
WhenWhen:
In formula, RHIt is certain day per day relative humidity in the current year unknown time, rnd1It is a random number, RHmonIt is current year
Average value of the month in the per day relative humidity of past n, R where certain day in the unknown timeHUmonIt is the current year unknown time
In certain day where month maximum value in the per day relative humidity of past n, RHLmonIt is certain in the current year unknown time
Minimum value of the month in the per day relative humidity of past n where it；
The calculation formula of forecasting wind speed model are as follows:
In formula, u is certain day wind speed in the current year unknown time, μnIt is month where certain day in the current year unknown time in past n
Year day wind speed mean value, σuMonth where certain day in unknown time current year past n day wind speed standard deviation, ξ
Month where certain day in unknown time current year past n day wind speed the coefficient of skewness, χ be the daily standard of generation just
State deviation, according to two random number rnd1And rnd2It obtains；
Index determination unit is used to determine the meteorological index data of current year known time with by meteorological index prediction model
The meteorological index data of current year unknown time divided according to the beginning and ending time of each growthdevelopmental stage of beet to get to beet
The meteorological index data of each growthdevelopmental stage.
11. system according to claim 9, which is characterized in that first data cell includes:
First ray unit is used in chronological order generate the data of the biomass past n of each growthdevelopmental stage of beet
Biomass sequence data；
First equation group unit is used for using i as sliding step, with the linear slide method of average to each growthdevelopmental stage of beet
Every i biomass carry out statistical regression analysis, obtain j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i,
I, j and n is natural number；
First simulation value cell, is used to determine each growthdevelopmental stage of beet annual j based on j group unary linear regression equation
The analogue value of biomass；
First trend value cell is used to be determined according to the analogue value of j annual biomass of each growthdevelopmental stage of beet annual
Biomass the analogue value average value, and the trend biomass annual as each growthdevelopmental stage of beet；
First result unit is used to subtract each other the annual biomass of each growthdevelopmental stage of beet and trend biomass as sweet tea
The annual meteorological biomass of each growthdevelopmental stage of dish.
12. system according to claim 9, which is characterized in that first model unit includes:
First parameters unit is used for the data and meteorology biology of the meteorological index past n based on each growthdevelopmental stage of beet
Amount in the past n data determine each meteorological index with meteorology biomass kernel function, the weight of each kernel function, and according to
Kernel function determines the deviation for seeking meteorological biomass；
First formula cells are used for kernel function, the weight of each kernel function based on each meteorological index and meteorological biomass,
And deviation determines meteorological index-meteorology biomass prediction model of each growthdevelopmental stage of beet, its calculation formula is:
In formula, yiIt is the meteorological biomass of beet i-th of growthdevelopmental stage of current year,It is i-th j-th of growthdevelopmental stage of beet current year
The kernel function of meteorological index, ωijIt is the weight of the kernel function of beet current year i-th of growthdevelopmental stage, j-th of meteorological index, biIt is root
According to kernel functionDetermine the deviation of the meteorological biomass of beet i-th of growthdevelopmental stage of current year.
13. system according to claim 9, which is characterized in that second data cell includes:
Second sequence units, the data for being used to pass by beet economic flow rate n generate economic flow rate sequence in chronological order
Data；
Second equation group unit is used for using i as sliding step, with the economy of linear slide method of average i every to beet
Yield carries out statistical regression analysis, obtains j group unary linear regression equation, wherein 1≤i≤n, 1≤j≤i, i, j and n are
Natural number；
Second simulation value cell, is used to determine the mould of j annual economic flow rate of beet based on j group unary linear regression equation
Analog values；
Second trend value cell is used to determine annual economic flow rate according to the analogue value of j annual economic flow rate of beet
The analogue value average value, and the trend economic flow rate annual as beet；
The annual economic flow rate of beet and trend economic flow rate are subtracted each other the meteorological production annual as beet by the second result unit
Amount.
14. system according to claim 9, which is characterized in that second model unit includes:
Second parameters unit is used for the data and beet gas of the meteorological biomass past n based on each growthdevelopmental stage of beet
As the data of yield past n determine the meteorological biomass of each growthdevelopmental stage and the kernel function of Meteorological Output, each kernel function
Weight, and determined according to kernel function and seek the deviation of Meteorological Output；
Second formula cells are used for the kernel function, every of meteorological biomass and Meteorological Output based on each growthdevelopmental stage of beet
The weight and deviation of a kernel function determine beet meteorology biomass-Meteorological Output prediction model, its calculation formula is:
In formula, y is the Meteorological Output of beet current year,It is the kernel function of beet i-th of growthdevelopmental stage meteorology biomass of current year,
ωiIt is the weight of the kernel function of beet i-th of growthdevelopmental stage of current year, b is according to kernel functionDetermine that the meteorological of beet current year produces
The deviation of amount.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110245444A (en) * | 2019-06-21 | 2019-09-17 | 中国气象科学研究院 | It is a kind of based on crop to the response of environment and the puberty analogy method of adaptation mechanism |
| CN110378521A (en) * | 2019-06-28 | 2019-10-25 | 河南农业大学 | The building and application of Henan northeast Yield Forecast of Winter Wheat model |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170213141A1 (en) * | 2016-01-22 | 2017-07-27 | The Climate Corporation | Forecasting national crop yield during the growing season using weather indices |
| US20170228743A1 (en) * | 2016-02-05 | 2017-08-10 | Weather Analytics, LLC | Crop forecasting with incremental feature selection and spectrum constrained scenario generation |
| 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 |
| CN107423853A (en) * | 2017-07-25 | 2017-12-01 | 中国农业科学院农业信息研究所 | A kind of determination method and system of per unit area yield meteorology variation coefficient |
| CN107950324A (en) * | 2017-12-15 | 2018-04-24 | 上海应用技术大学 | Based on corn irrigation requirement calculates stage by stage irrigation management system and irrigation method |
| 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 |
-
2018
- 2018-12-29 CN CN201811646260.0A patent/CN109615149B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170213141A1 (en) * | 2016-01-22 | 2017-07-27 | The Climate Corporation | Forecasting national crop yield during the growing season using weather indices |
| US20170228743A1 (en) * | 2016-02-05 | 2017-08-10 | Weather Analytics, LLC | Crop forecasting with incremental feature selection and spectrum constrained scenario generation |
| 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 |
| CN107423853A (en) * | 2017-07-25 | 2017-12-01 | 中国农业科学院农业信息研究所 | A kind of determination method and system of per unit area yield meteorology variation coefficient |
| CN107950324A (en) * | 2017-12-15 | 2018-04-24 | 上海应用技术大学 | Based on corn irrigation requirement calculates stage by stage irrigation management system and irrigation method |
| 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 (1)
| Title |
|---|
| 周俊,梁芳芝,陈传轲,伍宏志: "夏大豆产量与气象因素关系的分析" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110245444A (en) * | 2019-06-21 | 2019-09-17 | 中国气象科学研究院 | It is a kind of based on crop to the response of environment and the puberty analogy method of adaptation mechanism |
| CN110378521A (en) * | 2019-06-28 | 2019-10-25 | 河南农业大学 | The building and application of Henan northeast Yield Forecast of Winter Wheat model |
| CN110378521B (en) * | 2019-06-28 | 2022-04-22 | 河南农业大学 | Construction and application of yield prediction model of winter wheat in northeast China of Henan province |
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