CN109147871B - Method and device for analyzing variety character difference - Google Patents
Method and device for analyzing variety character difference Download PDFInfo
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- CN109147871B CN109147871B CN201811061180.9A CN201811061180A CN109147871B CN 109147871 B CN109147871 B CN 109147871B CN 201811061180 A CN201811061180 A CN 201811061180A CN 109147871 B CN109147871 B CN 109147871B
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Abstract
The embodiment of the application provides a variety trait difference analysis method, which relates to the technical field of data analysis, and comprises the following steps: obtaining M original sample data of an original seed and N offspring sample data of an offspring variety; obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data; and obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait. The accurate and reasonable quantitative expression of the change degree and the evolution direction of the characters of the filial generation variety cultured from the original plant species is realized through the accurate measurement of the characters of the original sample and the filial generation sample.
Description
Technical Field
The application relates to the technical field of data analysis, in particular to a method and a device for analyzing variety character difference.
Background
At present, most researches aiming at plant floral organ variation are focused on phylogeny and molecular genetics, analysis levels are mostly located at more than one level, and based on phenotype and statistical principles, visual disclosure of floral organ evolution rules of less than one classification unit is rarely reported. Although the results obtained by the phylogenetic and molecular genetics research methods are relatively accurate, the process is complex, the cost is high, and the limitation on the measurement time of a large batch of samples is strong. Although the comparative morphology research method based on phenotype observation has strong operability and high visualization degree, the method has strong subjectivity and seriously influences the accuracy of the plant floral organ variation result. In addition, the above methods can only reveal the evolution direction to a certain extent, and neglect the analysis of the evolution degree.
Content of application
The present application aims to provide a method and an apparatus for analyzing differences in breed traits to solve the above problems. In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a method for analyzing variety trait differences, including: obtaining M original sample data of an original seed and N offspring sample data of an offspring variety; obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data; and obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait.
In some alternative embodiments of the first aspect, said obtaining the degree of change of said progeny variety in each identical trait relative to said original variety based on said original trait value and said progeny trait value of each identical trait comprises: calling a preset calculation model, and obtaining a calculation result of each same character according to the original characteristic value and the offspring characteristic value of each same character; and determining the degree of change of the filial generation variety relative to the original variety on each same trait according to the calculation result of each same trait.
Further, the calling a preset calculation model to obtain a calculation result of each identical trait according to the original feature value and the descendant feature value of each identical trait includes: calling the calculation model, calculating an original expected value and an original standard deviation of each same character, and calculating an expected value, a standard deviation and a range deviation of filial generation of each same character to obtain a variation value of the same character; wherein the eigenvalues include: the expected value of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait.
Further, the calculation result includes a variation value, and the calculation model includes a first calculation formula:
wherein MD is a variation value of the progeny variety in an arbitrary first trait as the same trait as the original variety, and Δ μ ═ μC-μS，Δσ＝σC-σS，μSThe original expectation value, sigma, of the M original sample data of the original species on the first characterSIs the original standard deviation, mu, of M original sample data of the original species on the first characterCThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterCThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
Further, the calculation result further includes a change ratio, and the calculation model further includes a second calculation formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
In a second aspect, an embodiment of the present application provides an apparatus for analyzing differences in traits of varieties, comprising: a first obtaining module, a second obtaining module and a third obtaining module; the first obtaining module is used for obtaining M original sample data of an original seed and N offspring sample data of an offspring variety; the second obtaining module is configured to obtain an original feature value of each identical trait in the a traits according to the M original sample data, and obtain a progeny feature value of each identical trait in the a traits according to the N progeny sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data; and the third obtaining module is used for obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait.
Further, the third obtaining module includes: a calculation unit and a determination unit; the calculation unit is used for calling a preset calculation model and obtaining a calculation result of each same character according to the original characteristic value and the descendant characteristic value of each same character; and the determining unit is used for determining the change degree of the filial generation variety relative to the original variety on each same trait according to the calculation result of each same trait.
Further, the calculation unit is further configured to invoke the calculation model, calculate an original expected value and an original standard deviation of each identical trait, and calculate an expected value, a standard deviation and a range of progeny of each identical trait, so as to obtain a variation value of the identical trait; wherein the eigenvalues include: the expected value of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait.
Further, the calculation result includes a variation value, and the calculation model includes a first calculation formula:
wherein MD is a variation value of a first property which is the same property, and Δ μ ═ μC-μS，Δσ＝σC-σS，μCThe original expectation value, sigma, of the M original sample data of the original species on the first characterCIs the original standard deviation, mu, of M original sample data of the original species on the first characterSThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterSThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
Further, the calculation result further includes a change ratio, and the calculation model further includes a second calculation formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
Compared with the prior art, the method and the device for analyzing the variety trait difference provided by the embodiment of the application have the advantages that:
obtaining M original sample data of an original seed and N offspring sample data of an offspring variety; obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data; and obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait. The accurate measurement of each character of the original sample and the filial generation sample realizes the accurate and reasonable quantification of the character change degree and the evolution direction of the filial generation variety cultivated from the original plant.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram schematically illustrating modules of an embedded device according to a first embodiment of the present application;
FIG. 2 is a flowchart of a method for analyzing differences in traits of a variety according to a second embodiment of the present application;
FIG. 3 is a block diagram of an apparatus for analyzing differences in traits of varieties according to a third embodiment of the present application;
FIG. 4 is a block diagram of a third obtaining module of an apparatus for analyzing differences in breed traits according to a third embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
First embodiment
Referring to fig. 1, a first embodiment of the present application provides an embedded device 10.
The embedded device 10 includes: a memory 11, a memory controller 12, a processor 13, a peripheral interface 14, and a variety trait difference analysis device 100.
The elements of the memory 11, the memory controller 12, the processor 13 and the peripheral interface 14 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The variety trait difference analysis device 100 includes at least one software functional module that can be stored in the memory 11 in the form of software or firmware. The processor 13 is configured to execute an executable module stored in the memory 12, for example, a software function module or a computer program included in the variety trait difference analysis apparatus 100.
The Memory 11 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 11 is configured to store a program, and the processor 13 executes the program after receiving an execution instruction, and the method defined by the flow process disclosed in any embodiment of the present application may be applied to the processor 13, or implemented by the processor 13.
The processor 13 may be an integrated circuit chip having signal processing capabilities. The information digest Processor 13 may be a general-purpose information digest Processor, including a Central Processing Unit (CPU), a Network information digest Processor (NP), and the like; but may also be a digital signal message digest processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. The general purpose message digest processor may be a micro message digest processor or the message digest processor may be any conventional message digest processor or the like.
The peripheral interface 14 couples various input/output devices to the processor 13 and to the memory 11. In some embodiments, peripheral interface 14, processor 13, and memory controller 12 may be implemented in a single chip. In other examples, they may be implemented separately from the individual chips.
Second embodiment
Referring to fig. 2, a second embodiment of the present application provides a method for analyzing variety trait differences, which is applied to an embedded device and may include step S100, step S200, and step S300.
Step S100: m original sample data of the original species and N offspring sample data of the offspring varieties are obtained.
Step S200: obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data. The characteristic value, a measure of the value of an entirety for the entire sample data, is independent of a single sample of the original sample data or the child sample.
Step S300: and obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait.
In step S300, step S310 and step S320 are included.
Step S310: and calling a preset calculation model, and obtaining a calculation result of each same character according to the original characteristic value and the offspring characteristic value of each same character.
Step S320: and determining the degree of change of the filial generation variety relative to the original variety on each same trait according to the calculation result of each same trait.
Specifically, in the execution of step S310, the calculation model is used to calculate an original expected value and an original standard deviation of each identical trait, and calculate an expected value, a standard deviation and a range of progeny of each identical trait, so as to obtain a variation value of the identical trait.
Specifically, in combination with the above example, the example of combining the specific real object in step S310 may be: for example, the number of petals of a begonia is used as a certain trait, expected values and standard deviations of the number of petals of all samples are calculated in original sample data, expected values, standard deviations and pole deviations of the number of petals of all samples are calculated in progeny sample data, and a variation value of the progeny sample in the trait of the number of petals of the progeny sample with respect to the original sample data can be obtained from the data.
Wherein the eigenvalues include: the expected value of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait.
For the calculation, the calculation includes a change value, and the calculation model includes a first calculation formula:
wherein MD is a variation value of the progeny variety in an arbitrary first trait as the same trait as the original variety, and Δ μ ═ μC-μS，Δσ＝σC-σS，μCThe original expectation value, sigma, of the M original sample data of the original species on the first characterCIs the originalOriginal standard deviation (mu) of M original sample data of seed on the first characterSThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterSThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
Wherein, in the present embodiment, R90Optionally, the method is characterized in that 90% by N sample data distributed in the most concentrated manner in the value of the N sample data of the offspring variety have a very poor difference in the first trait.
In addition, the calculation result further includes a change ratio, and the calculation model further includes a second calculation formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
And MD (variation value) and MP (variation proportion) are combined to express the variation degree and the high-low order of the progeny sample relative to the original sample on the same character, so that the precise quantification of the broad concept of the variation degree is realized.
In the present embodiment, it is preferred that,alternatively, a crabapple flower is exemplified as a sample. Investigating and counting the petal number, pistil number and stamen number of ornamental crabapple variety group and variety group (the repetition number is more than 30); the a traits are respectively as follows in this example: petal number, pistil number, stamen number (pistil number + stamen number), petal number (petal number + pistil number + stamen number) 5 characters; based on frequency statistics of 6-10 segments, wherein the numerical values of a plurality of samples on 4 personality characteristics of pistil number, stamen number and petal number conform to normal probability distribution
The numerical values of a plurality of samples on the character of the number of petals conform to a power function distribution (y ═ ax)b)。
For the example that step S310 is combined with the above real object, the following steps may be performed: for example, if the same trait is the number of petals of the begonia, the expected value and the standard deviation of the number of petals of all samples are obtained in the original sample data, the expected value, the standard deviation and the 90% pole difference of the number of petals of all samples are obtained in the progeny sample data, and the change degree (including the change value and the change proportion) of the progeny sample relative to the original sample data on the trait of the number of petals can be calculated according to the characteristic value and the calculation model. By analogy, the change degrees of all other characters (pistil number, stamen number and petal number) can be calculated, and the high and low order of the change degrees of all the characters can be obtained.
The foregoing is merely an example of the embodiments and is not intended to limit the present disclosure in any way.
Third embodiment
Referring to fig. 3 and 4, the present application provides a variety trait difference analysis apparatus 100, and the variety trait difference analysis apparatus 100 includes: a first obtaining module 110, a second obtaining module 120, and a third obtaining module 130;
the first obtaining module 110 is configured to obtain M original sample data of an original seed and N child sample data of a child variety;
the second obtaining module 120 is configured to obtain an original feature value of each identical trait in the a traits according to the M original sample data, and obtain a progeny feature value of each identical trait in the a traits according to the N progeny sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data;
the third obtaining module 130 is configured to obtain a variation degree of the progeny variety on each identical trait relative to the original variety according to the original characteristic value and the progeny characteristic value of each identical trait.
Wherein the third obtaining module 130 includes: a calculation unit 131 and a determination unit 132;
the calculating unit 131 is configured to invoke a preset calculating model, and obtain a calculation result of each identical trait according to the original characteristic value and the offspring characteristic value of each identical trait;
the determining unit 132 is configured to determine, according to the calculation result of each identical trait, a degree of change of the progeny variety in each identical trait relative to the original variety.
In addition, the calculating unit 131 is further configured to invoke the calculation model, calculate an original expected value and an original standard deviation of each identical trait, and calculate an expected value, a standard deviation and a range of progeny of each identical trait, so as to obtain a variation value of the identical trait;
wherein the eigenvalues include: the expected value of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait.
The calculation result comprises a variation value, and the calculation model comprises a first calculation formula:
wherein MD is a variation value of a first property which is the same property, and Δ μ ═ μC-μS，Δσ＝σC-σS，μCThe original expectation value, sigma, of the M original sample data of the original species on the first characterCIs the original standard deviation, mu, of M original sample data of the original species on the first characterSThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterSThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
The calculation result further comprises a change proportion, and the calculation model further comprises a second calculation formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
The determining unit 132 is configured to determine, according to the calculation result of each identical trait, the degree of change of the progeny variety in each identical trait and the order of the degree of change of all traits relative to the original variety.
In summary, the following steps: the embodiment of the application provides a variety trait difference analysis method, which relates to the technical field of data analysis, and comprises the following steps: obtaining M original sample data of an original seed and N offspring sample data of an offspring variety; obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data; and obtaining the variation degree of the filial generation variety relative to the original variety on each same trait according to the original characteristic value and the filial generation characteristic value of each same trait. The accurate and reasonable quantitative expression of the change degree and the evolution direction of the characters of the filial generation variety cultured from the original plant species is realized through the accurate measurement of the characters of the original sample and the filial generation sample.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modifications, equivalents, progeny, etc. that come within the spirit and principle of the application are intended to be included within the scope of this application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (4)
1. A method for analyzing variety trait differences, comprising:
obtaining M original sample data of an original seed and N offspring sample data of an offspring variety;
obtaining an original characteristic numerical value of each same character in a characters according to M original sample data, and obtaining a descendant characteristic numerical value of each same character in the a characters according to the N descendant sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data;
obtaining the variation degree of the filial generation variety relative to the original variety on each same character according to the original characteristic value and the filial generation characteristic value of each same character;
wherein the obtaining the degree of change of the progeny variety relative to the original variety on each identical trait according to the original characteristic value and the progeny characteristic value of each identical trait comprises:
calling a preset calculation model, calculating an original expected value and an original standard deviation of each same character, and calculating an expected value, a standard deviation and a range of descendants of each same character to obtain a variation value of the same character; wherein the eigenvalues include: expected values of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait;
determining the degree of change of the filial generation variety relative to the original variety on each same trait according to the calculation result of each same trait;
the calculation result comprises a variation value, and the calculation model comprises a first calculation formula:
wherein MD is a variation value of the progeny variety in an arbitrary first trait as the same trait as the original variety, and Δ μ ═ μC-μS，Δσ＝σC-σS，μSThe original expectation value, sigma, of the M original sample data of the original species on the first characterSFor M original sample data of said original speciesOriginal standard deviation, μ, over the first traitCThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterCThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
2. The method for variety trait differential analysis of claim 1, wherein the calculation further comprises a change ratio, and the computational model further comprises a second computational formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
3. A variety trait difference analysis device includes: a first obtaining module, a second obtaining module and a third obtaining module;
the first obtaining module is used for obtaining M original sample data of an original seed and N offspring sample data of an offspring variety;
the second obtaining module is configured to obtain an original feature value of each identical trait in the a traits according to the M original sample data, and obtain a progeny feature value of each identical trait in the a traits according to the N progeny sample data; m, N and a are positive integers, and the characteristic numerical value is used for measuring the corresponding same property in a plurality of sample data;
the third obtaining module is configured to obtain, according to the original characteristic value and the offspring characteristic value of each identical trait, a degree of change of the offspring variety with respect to the original variety in each identical trait;
wherein the third obtaining module comprises: a calculation unit and a determination unit, wherein,
the calculation unit is used for calling a preset calculation model, calculating an original expected value and an original standard deviation of each same character, calculating an expected value, a standard deviation and a range of descendants of each same character, and obtaining a change value of the same character; wherein the eigenvalues include: expected values of a plurality of sample data on the same trait, the standard deviation of the plurality of sample data on the same trait, and the range formed by the difference between the upper limit characteristic value and the lower limit characteristic value of the concentrated distribution interval of the plurality of sample data on the same trait;
the determining unit is used for determining the degree of change of the filial generation variety relative to the original variety on each same trait according to the calculation result of each same trait;
the calculation result comprises a variation value, and the calculation model comprises a first calculation formula:
wherein MD is a variation value of a first property which is the same property, and Δ μ ═ μC-μS，Δσ＝σC-σS，μSThe original expectation value, sigma, of the M original sample data of the original species on the first characterSFor M original samples of said original speciesOriginal standard deviation, μ, of the data on the first traitCThe expected value, sigma, of filial generation of the N filial generation sample data of the filial generation variety on the first characterCThe filial generation standard deviation R of the sample data of the N filial generations of the filial generation variety on the first character90For the extreme differences of the first trait of the N sample data of the offspring variety, '+/-' in formula 1 depends on the positivity and negativity of the product of the delta mu and the delta sigma, when the product of the delta mu and the delta sigma is a positive number, '+/-' is taken in formula 1, and when the product of the delta mu and the delta sigma is a negative number, '+/-' is taken in formula 1.
4. The variety trait difference analysis apparatus according to claim 3, wherein the calculation result further includes a change ratio, and the calculation model further includes a second calculation formula:
wherein MP is the change ratio of the filial generation variety to the original variety on the first character, ASThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the original sample data on the coordinate axis and the X axis is ACThe proportion of the area of the non-overlapped part of the graph formed by the distribution curve of the original sample data and the child sample data on the coordinate axis and the X axis to the area of the graph formed by the distribution curve of the child sample data on the coordinate axis and the X axis is shown.
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