Method and system for screening crab cactus variety

Abstract

The invention discloses a method and a system for screening crab cactus varieties. The method comprises the following steps: receiving respective character data information of crab cactus germplasm resources input by a user; the character data information comprises each character and an attribute value corresponding to each character; based on the deployed evaluation model and the character data information, scoring each character of the germplasm resources of the crab cactus to obtain a scoring result; obtaining a weight corresponding to each character based on each layer weight of a sub-target layer, a constraint layer and a standard layer of the evaluation model; and screening good varieties in the germplasm resources of the crab cactus according to the weight and the scoring result to obtain a target germplasm resource of the crab cactus. According to the method, screening blindness is avoided, and the accuracy of screening excellent germplasm resources of the crab cactus is improved.

Description

Technical Field

[0001] The present invention relates to the field of flower breeding, and in particular to, but not limited to, a method and system for screening Schlumbergera varieties. Background Art

[0002] Schlumbergera is native to Brazil, South America. It is a perennial succulent ornamental plant of the Cactaceae family and Schlumbergera genus. The breeding period of Schlumbergera is relatively long, and the update of new varieties is slow. Compared with other flowers, the germplasm resources of Schlumbergera are relatively scarce. Since it was domesticated and cultivated, there are only more than 240 horticultural varieties, and there are only more than 20 varieties circulating on the market. Germplasm resource evaluation is the key to the selection and breeding of new varieties, especially after the hybridization of Schlumbergera, it takes 8 to 9 months for the seeds to mature, and the seedlings have a long period of infancy after sowing, and it takes three years to bloom. It takes at least 10 years to complete the expansion and promotion of excellent strains to the market. Therefore, the selection of parents and the selection of hybrid offspring are very important, otherwise it will cause a waste of manpower, material resources and time.

[0003] In the related art, horticultural practitioners subjectively select a part of excellent Christmas cacti for breeding based on their own experience. This method lacks a unified, systematic and scientific comprehensive screening and evaluation system, resulting in a waste of manpower, material resources and time.

[0004] Therefore, how to quickly, accurately and efficiently screen out excellent Christmas cactus varieties has become a problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method for screening Schlumbergera varieties, which at least solves the problem that the related technology cannot efficiently and accurately select excellent Schlumbergera varieties.

[0006] According to a first aspect of an embodiment of the present invention, a method for screening Schlumbergera species is provided, comprising:

[0007] Receiving the trait data information of each Schlumbergera germplasm resource input by the user; the trait data information includes each trait and the attribute value corresponding to each trait;

[0008] Scoring each trait of the Schlumbergera germplasm resource based on the deployed evaluation model and the trait data information to obtain a scoring result;

[0009] Obtaining the weight corresponding to each trait based on the weights of the sub-objective layer, the constraint layer and the standard layer of the evaluation model;

[0010] According to the weight and the scoring result, excellent varieties are screened among the Schlumbergera germplasm resources to obtain target Schlumbergera germplasm resources.

[0011] According to a second aspect of an embodiment of the present invention, a system for screening Schlumbergera varieties is provided, which is used for applying the screening of Schlumbergera varieties according to any one of claims 1 to 6, comprising:

[0012] A data receiving module, used to receive the trait data information of each Schlumbergera germplasm resource input by the user; the trait data information includes each trait and the attribute value corresponding to each trait;

[0013] An evaluation module is deployed with an evaluation model, which is used to score each trait of the Schlumbergera germplasm resources based on the trait data information of each Schlumbergera germplasm resource input by the user, obtain a scoring result, and obtain a weight corresponding to each trait based on the weights of each layer of the sub-target layer, the constraint layer and the standard layer;

[0014] A screening module is used to screen excellent varieties among the Schlumbergera germplasm resources according to the weights and the scoring results to obtain target Schlumbergera germplasm resources.

[0015] According to the scheme provided by the embodiment of the present invention, the trait data information of each Schlumbergera germplasm resource input by the user is received, and the trait data information includes each trait and the attribute value corresponding to each trait. Based on the deployed evaluation model and the trait data information, each trait of the Schlumbergera germplasm resource is scored to obtain the scoring result, and the weight corresponding to each trait is obtained based on the weights of each layer of the sub-target layer, constraint layer and standard layer of the evaluation model; the excellent varieties are screened in the Schlumbergera germplasm resources according to the weight and the scoring result to obtain the target Schlumbergera germplasm resources. In this method, each trait of all Schlumbergera germplasm resources input is scored through the evaluation model, and the weight of each trait is determined by the hierarchical analysis method (setting the sub-target layer, constraint layer and standard layer), and the Schlumbergera germplasm resources are screened based on the weight and score of each trait, thereby avoiding the blindness of the screening and improving the accuracy of screening excellent Schlumbergera germplasm resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0017] Figure 1 A schematic diagram of a process for screening Schlumbergera species provided in an embodiment of the present invention;

[0018] Figure 2 A module diagram of a system for screening Schlumbergera species provided in an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0022] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described here can be implemented in an order other than that illustrated or described here.

[0023] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0024] Figure 1 This is a flow chart of a method for screening Schlumbergera varieties provided in an embodiment of the present invention. The method for screening Schlumbergera varieties provided in an embodiment of the present invention can be executed by an electronic device, and the electronic device can be, for example, a computer, a server, etc.

[0025] like Figure 1 As shown, the method for screening Schlumbergera species includes:

[0026] S101. Receive trait data information of each Schlumbergera germplasm resource input by a user; the trait data information includes each trait and an attribute value corresponding to each trait.

[0027] In an embodiment of the present invention, Schlumbergera is native to Brazil in South America. Cactaceae, Schlumbergera is a perennial succulent ornamental plant, which is one of the main indoor potted flowers in winter in my country and one of the main boutique New Year flowers. Schlumbergera germplasm resources refer to the genetic diversity of Schlumbergera, and Schlumbergera germplasm resources include wild species, cultivated varieties and hybrid varieties. Trait data information includes traits and attribute values ​​corresponding to traits, wherein traits can be morphological traits, physiological and chemical traits and ecological traits, such as phyllodes length, phyllodes width, phyllodes length-to-width ratio, cold resistance, growth potential, and petal shape, flower length, flower width and length-to-width ratio of flowers. The attribute value of the trait can be that the phyllodes length is 2 cm, the petal shape of the flower is single petal, etc.

[0028] In an embodiment of the present invention, the user can input the trait data information of each collected Schlumbergera germplasm resource through the terminal.

[0029] Illustratively, as shown in Table 1, Table 1 is a plurality of Schlumbergera germplasm resources provided by an embodiment of the present invention.

[0030] Table 1 Germplasm resources of Schlumbergera

[0031]

[0032] In the above Table 1, 58 collected Schlumbergera germplasm resources are included, including the variety names and sources of the germplasm resources.

[0033] S102. Score each trait of the Schlumbergera germplasm resources based on the deployed evaluation model and trait data information to obtain a scoring result.

[0034] In the embodiment of the present invention, the different trait data information of Schlumbergera germplasm resources leads to different trait states. For example, if the green degree of Schlumbergera germplasm resource A is extremely light, then the score is given according to the “extremely light” as the “green degree”. If the green degree of Schlumbergera germplasm resource B is medium, then the score is given according to the “medium” as the “green degree”, and then the scoring results of each trait of different Schlumbergera germplasm resources are obtained.

[0035] S103, obtaining the weight corresponding to each trait based on the weights of the sub-target layer, constraint layer and standard layer of the evaluation model.

[0036] In the embodiment of the present invention, the overall target layer, sub-target layer, constraint layer and standard layer are set to construct the evaluation model. The overall target layer can be an excellent variety of Schlumbergera cactus, the sub-target layer includes ornamental value and other values, and the constraint layer includes the ornamental value of phyllodes, the ornamental value of flowers, the ornamental value of flower buds, the ornamental value of plants, ecological adaptability and biological characteristics. The standard layer includes 24 traits such as aspect ratio, length, width, green degree, cross-section curvature, edge notch type, flower length, flower width, aspect ratio, petal type, inner perianth piece width, inner perianth piece edge area color, inner perianth piece edge area size, inner perianth piece posture, inner perianth piece tip shape, bud tip color, bud tip color depth, growth habit, plant compactness, cold resistance, heat resistance, third-level phyllodes number, initial flowering period and growth potential. Among them, the weight of each layer is the weight of each attribute information of each layer, such as the sub-target layer needs to calculate the weight of ornamental value and the weight of other values. The standard layer contains 24 traits, and the weight corresponding to each trait is the weight corresponding to each of the 24 traits.

[0037] Exemplarily, the overall target layer is the excellent varieties of Christmas cactus, the sub-target layer is the ornamental value A1 and other values ​​A2, and the constraint layer is the ornamental value of the leaf stems C1, the ornamental value of the flowers C2, the ornamental value of the flower buds C3, the ornamental value of the plants C4, the ecological adaptability C5 and the biological characteristics C6. The standard layer consists of 24 traits including aspect ratio P1, length P2, width P3, green degree P4, cross-section curvature P5, edge notch type P6, flower length P7, flower width P8, aspect ratio P9, petal type P10, width of inner perianth segment P11, color of edge area of ​​inner perianth segment P12, size of edge area of ​​inner perianth segment P13, posture of inner perianth segment P14, shape of tip of inner perianth segment P15, color of bud tip P16, depth of color of bud tip P17, growth habit P18, plant compactness P19, cold resistance P20, heat resistance P21, number of third-level phyllodes P22, beginning of flowering P23 and growth potential P24.

[0038] S104. Screening excellent varieties among Schlumbergera germplasm resources according to the weights and scoring results to obtain target Schlumbergera germplasm resources.

[0039] In an embodiment of the present invention, the target Schlumbergera germplasm resources are used as excellent varieties for subsequent growth and cultivation, and can be screened among the Schlumbergera germplasm resources based on the dual standards of weight and scoring results. Specifically, the screening can be carried out by sorting or grading the Schlumbergera germplasm resources in various ways.

[0040] It can be understood that in the embodiments of the present invention, the trait data information of each Schlumbergera germplasm resource input by the user is received, and each trait of the Schlumbergera germplasm resource is scored based on the deployed evaluation model and the trait data information to obtain the scoring result, and the weight corresponding to each trait is obtained based on the weights of each layer of the sub-target layer, constraint layer and standard layer of the evaluation model; the excellent varieties are screened in the Schlumbergera germplasm resources according to the weight and the scoring result to obtain the target Schlumbergera germplasm resources. In this method, each trait of all Schlumbergera germplasm resources input is scored through the evaluation model, and the weight of each trait is determined by the hierarchical analysis method (setting the sub-target layer, constraint layer and standard layer), and the Schlumbergera germplasm resources are screened based on the weight and score of each trait, thereby avoiding the blindness of the screening and improving the accuracy of screening excellent Schlumbergera germplasm resources.

[0041] In some embodiments of the present invention, S102 can be implemented through S1021 to S1023, which is specifically described by the following steps.

[0042] S1021. Determine a target trait evaluation index corresponding to each trait in the trait evaluation index; the target trait evaluation index includes a plurality of preset matching tags and scores corresponding to the respective matching tags.

[0043] In some embodiments of the present invention, the evaluation model includes a trait evaluation index, and the trait evaluation index includes a plurality of preset matching tags and scores corresponding to the matching tags. The matching tags are multiple states of different traits of Schlumbergera germplasm resources, and the scores are different score values ​​set for the multiple states. There are multiple trait evaluation indicators, and each trait is matched with the trait evaluation index to obtain a target trait evaluation index.

[0044] For example, when the current trait is the aspect ratio, the aspect ratio of P1 is used as the target trait evaluation index among the trait evaluation indexes P1 to P24. As shown in Table 2, Table 2 provides trait evaluation indexes according to an embodiment of the present invention, as follows:

[0045] Table 2 Characteristic evaluation indexes

[0046]

[0047] In the above Table 2, P1 to P24 are each trait, 1 to 9 are scores, extremely short, extremely short to short, short and extremely long in the length of P2 are matching labels, and other traits are similar.

[0048] In the embodiment of the present invention, in calculating the scores of P5 and P6, the matching labels of the two traits of P5 and P6 are inconsistent with the scoring values ​​of the two traits, and need to be converted. The matching labels 1 and 5 corresponding to all Schlumbergera germplasm resources P5 are swapped, 2 and 4 are swapped, and 3 remains unchanged; the matching labels 1 and 3 corresponding to P6 are swapped, and 2 remains unchanged.

[0049] S1022: Match the attribute value corresponding to each trait with the matching label to determine the target label.

[0050] S1023. Use the score corresponding to the target label as the scoring result of each trait.

[0051] In some embodiments of the present invention, as shown in Table 2, when the current attribute is length, the matching labels may be various labels such as very short, very short to short, short, short to medium, medium, and medium to wide. If the attribute value of the current attribute is medium, medium is the target label, and 5 points corresponding to medium is used as the scoring result of the current attribute. The attribute value of the attribute may be input by the user.

[0052] It is understandable that in some embodiments of the present invention, the target trait evaluation index corresponding to each trait is determined in the trait evaluation index; the target trait evaluation index includes a plurality of preset matching tags and scores corresponding to the matching tags, the attribute value corresponding to each trait is matched with the matching tag, the target tag is determined, and the score corresponding to the target tag is used as the scoring result for each trait. This method can determine the importance of different traits to Schlumbergera germplasm resources by customizing matching tags and the scores corresponding to the matching tags, thereby improving the accuracy of screening. Moreover, in the early stage of screening, it is only necessary to compare the different traits of Schlumbergera germplasm resources with the preset trait evaluation index, and the score corresponding to each trait can be directly obtained later, thereby improving the efficiency of screening.

[0053] In some embodiments of the present invention, S1022 also includes S201 and S202, wherein S201 and S202 have no sequence.

[0054] S201. When the current trait is the aspect ratio of the phyllodes, a value of the aspect ratio is obtained through the length of the phyllodes and the width of the phyllodes.

[0055] S202: When the current trait is the aspect ratio of the flower, the attribute value of the aspect ratio of the flower is obtained through the length of the flower and the width of the flower.

[0056] In some embodiments of the present invention, when matching the target trait index with the length-to-width ratio of the phyllox and the length-to-width ratio of the flower, the length-to-width ratio of the phyllox and the length-to-width ratio of the flower are not traits that can be directly obtained by Christmas cactus, so it is necessary to perform division operations by the length and width of the phyllox or the flower respectively to obtain the attribute value of the length-to-width ratio of the phyllox and the attribute value of the length-to-width ratio of the flower.

[0057] It can be understood that in some embodiments of the present invention, the length-to-width ratio of the leaf stem and the length-to-width ratio of the flower are not traits that can be directly obtained from Christmas cactus. By first calculating the length-to-width ratio using the length and width of the leaf stem and the length and width of the flower, and then screening based on the trait of the length-to-width ratio, the accuracy of screening is improved.

[0058] In some embodiments of the present invention, S103 may be implemented through S1031 and S1032, which is specifically described through the following steps.

[0059] S1031. Construct a judgment matrix based on the number of sub-target layers, constraint layers, and standard layers and the information of each layer; each layer of information contains trait data information or multiple value information of Christmas cactus germplasm resources.

[0060] In some embodiments of the present invention, the multiple value information is a comprehensive description of the value of Christmas cactus germplasm resources in different dimensions or aspects. Such as ornamental value and other values, the ornamental value can be the ornamental value of flowers and the ornamental value of flower buds, and other values ​​can be ecological adaptability and biological characteristics. The judgment matrix is ​​a square matrix used to represent the relative importance of each element at the same level. The rows and columns of the matrix correspond to the trait information at the same level, and the elements in the matrix represent the importance of the row elements relative to the column elements.

[0061] For example, as shown in Table 3, Table 3 is a constructed judgment matrix. As follows:

[0062] Table 3 Judgment Matrix

[0063]

[0064] S1032. Determine the weight of each layer of information based on the judgment matrix and the input collected judgment scores; and determine the weight of each trait based on the weight of each layer of information.

[0065] In some embodiments of the present invention, the evaluation score may be a score assigned by an expert, and the evaluation score assigned by the expert is input into the judgment matrix to obtain the weight distribution of each layer, and the consistency verification is performed respectively. The weight values ​​of each hierarchical structure are obtained according to the judgment matrix, and the relative importance weight of each trait is obtained by weighting the weight values.

[0066] For example, as shown in Table 4, Table 4 is the weight of each trait provided by the embodiment of the present invention, as follows:

[0067] Table 4 Weight of each trait

[0068]

[0069] It can be understood that in some embodiments of the present invention, a judgment matrix is ​​constructed based on the number of sub-goal layers, constraint layers, and standard layers and the information in each layer; each layer of information contains trait data information or multiple value information of Schlumbergera germplasm resources, and the weight of each layer of information is determined according to the judgment matrix and the judgment scores collected by input; and the weight of each trait is determined according to the weight of each layer of information. This method obtains the weight of each trait through the hierarchical analysis method (sub-goal layer, constraint layer, and standard layer). The obtained weight has the advantages of systematization, quantification, multi-criteria decision-making, consistency verification, transparency, user-friendliness, and wide applicability, making the screening process more scientific, reasonable, and reliable.

[0070] In some embodiments of the present invention, S104 can be implemented through S1041 to S1042, which is specifically described by the following steps.

[0071] S1041. Multiply the scoring result and the weight to obtain the score of each trait, and determine the first total score corresponding to each Schlumbergera germplasm resource based on the score of each trait.

[0072] S1042. Sort the Schlumbergera germplasm resources according to a set descending sorting condition based on the first total score to obtain a sorting result; and determine the target Schlumbergera germplasm resources according to the sorting result.

[0073] In some embodiments of the present invention, the scoring result of each trait is multiplied by the weight corresponding to each trait to obtain the score of each trait, and the score of each trait is summed to obtain the first total score of each Schlumbergera germplasm resource. A descending sorting condition is set, and each Schlumbergera germplasm resource is sorted according to the first total score of each Schlumbergera germplasm resource to obtain a sorting result, and finally the top-ranked Schlumbergera germplasm resource is used as the target Schlumbergera germplasm resource.

[0074] In some embodiments of the present invention, the user can set the screening conditions in the Christmas cactus variety screening system by himself, such as selecting the sorting basis as descending sorting and the keyword as the first total score on the system, and sorting the Christmas cactus germplasm resources according to the setting to obtain the sorting result. Or the Christmas cactus variety screening system outputs the first total score, the weight of each trait and the corresponding score, and the user can customize the sorting in the form of a table. For example, two columns are established in an EXCEL table, the first column: the name of the Christmas cactus germplasm resource, the second column is the first total score, the heavy data area contains the column name, the custom sorting in the sorting is selected, the main keyword: the first total score, the sorting basis: value, order: descending, and confirm. You can get the first total score of all Christmas cactus germplasm resources from high to low, and select the Christmas cactus germplasm resources with high scores in front as parents or excellent hybrid offspring according to the selection goal.

[0075] It can be understood that in some embodiments of the present invention, the scoring result and the weight are multiplied to obtain the score of each trait, and a first total score is determined based on the score of each trait. The Christmas cactus germplasm resources are sorted according to the set descending sorting conditions based on the first total score to obtain the sorting result; and the target Christmas cactus germplasm resources are determined based on the sorting result. This process screens the target Christmas cactus germplasm resources by obtaining the first total score, thereby improving the accuracy of screening.

[0076] In some embodiments of the present invention, S104 may also be implemented through S104A to S104B, which is specifically described through the following steps.

[0077] S104A. Determine multiple target traits among the traits, and determine a second total score corresponding to each Schlumbergera germplasm resource based on the scoring result and corresponding weight of each target trait.

[0078] S104B. Sort the Schlumbergera germplasm resources according to a set descending sorting condition based on the second total score to obtain a sorting result; and determine the target Schlumbergera germplasm resources according to the sorting result.

[0079] In some embodiments of the present invention, the target trait can be the trait that has the greatest impact on the quality of Schlumbergera germplasm resources. At least one target trait can be determined among the traits, such as the aspect ratio of green flowers and the degree of greenness as target traits, and the scoring results and corresponding weights of each target trait are used to determine the second total score corresponding to each Schlumbergera germplasm resource. The Schlumbergera variety screening system is set to sort in descending order, and the Schlumbergera germplasm resources are sorted by the second total score to determine the target Schlumbergera germplasm resources. Alternatively, the Schlumbergera variety screening system outputs the second total score, and the user can customize the screening settings in the EXCEL table.

[0080] Exemplarily, the traits to be screened can be selected from P1-P24 to select X target traits, 1﹤X﹤24, the scores of the selected target traits are multiplied by the weight values ​​of the target traits, and then summed to obtain the second total score of the corresponding Schlumbergera germplasm resources. Two columns are established in the EXCEL table, the first column: the name of the Schlumbergera germplasm resources, the second column: the second total score, the selected data area contains the column name, select the custom sort in the sort, the main keyword: total score, sort by: value, order: descending, and confirm. You can get the total score of the Schlumbergera germplasm resources in this group of trait comparisons from high to low, and select the Schlumbergera germplasm resources with high scores in the front as parents or excellent hybrid offspring according to the selection target.

[0081] In some embodiments of the present invention, a target trait may also be determined among the traits, and all Schlumbergera germplasm resources may be sorted according to the corresponding score of the target trait. The Schlumbergera variety screening system and users may also perform screening.

[0082] For example, two columns are created, the first column: Schlumbergera germplasm resource name, the second column Pi, i=1, 2, ... 24 (target trait), the selected data area contains the column name, select the custom sort in the sort, the main keyword: Pi, sort by: value, order: descending, and confirm. Then, the sorting of all Schlumbergera germplasm resources Pi from high to low can be obtained, and the Schlumbergera germplasm resources with high scores in the front can be selected as the best parents or excellent hybrid offspring of Pi traits according to the selection target.

[0083] In some embodiments of the present invention, multiple target traits can also be determined in the traits, and the multiple target traits can be divided into main traits and auxiliary traits. Then, the Christmas cactus germplasm resources can be sorted according to the corresponding scores of the main traits and the auxiliary traits to obtain the sorting results, which can also be screened by the Christmas cactus variety screening system and the user.

[0084] For example, randomly select two target traits Pi and Pj (j=1, 2, ... 24), create three columns in the EXCEL table, the first column is the name of Schlumbergera germplasm resources, the second column is Pi, and the third column is Pj. Select the heavy data area containing the column name, select the custom sort in the sorting - data contains the title - main keyword: Pi, sort by: value, order: descending - add conditions - secondary keyword Pj sort by: value, order: descending - OK. You can get the screening results of all Schlumbergera germplasm resources with Pi sorted from high to low as the main and Pj sorted from high to low as the auxiliary, and select the Schlumbergera germplasm resources with high scores in the front as the best parents or excellent hybrid offspring of the two traits Pi and Pj according to the selection target.

[0085] It can be understood that in some embodiments of the present invention, multiple target traits are determined in the traits, the total score of the multiple target traits is determined based on the scoring result and the corresponding weight of each target trait, the Schlumbergera germplasm resources are sorted according to the set descending sorting condition based on the total score of the multiple target traits to obtain the sorting result; and the target Schlumbergera germplasm resources are determined according to the sorting result. This method allows users to screen out target Schlumbergera germplasm resources according to multiple screening methods, which improves the flexibility of screening.

[0086] Figure 2 A module diagram of a Schlumbergera variety screening system provided in an embodiment of the present invention. The Schlumbergera variety screening system provided in an embodiment of the present invention is used to apply the above-mentioned Schlumbergera variety screening method, including:

[0087] The data receiving module 301 is used to receive the trait data information of the Schlumbergera germplasm resources input by the user;

[0088] The evaluation module 302 is deployed with an evaluation model, which is used to score each trait of the Schlumbergera germplasm resources based on the trait data information of the Schlumbergera germplasm resources input by the user, obtain the scoring result, and obtain the weight corresponding to each trait based on the weights of each layer of the sub-goal layer, the constraint layer and the standard layer;

[0089] The screening module 303 is used to screen excellent varieties among the Schlumbergera germplasm resources according to the weights and scoring results to obtain target Schlumbergera germplasm resources.

[0090] In some embodiments of the present invention, the evaluation module 302 is also used to determine the target trait evaluation index corresponding to each trait in the trait evaluation index; the target trait evaluation index includes multiple preset matching tags and scores corresponding to each of the matching tags, and matches the attribute value corresponding to each trait with the matching tag to determine the target tag, and uses the score corresponding to the target tag as the scoring result of each trait.

[0091] In some embodiments of the present invention, the evaluation module 302, before matching the attribute value corresponding to each trait with the matching label and determining the target label, is also used to, when the current trait is the aspect ratio of the leaf-like stem, obtain the attribute value of the aspect ratio by using the length of the leaf-like stem and the width of the leaf-like stem; when the current trait is the aspect ratio of the flower, obtain the attribute value of the aspect ratio of the flower by using the length of the flower and the width of the flower.

[0092] In some embodiments of the present invention, the evaluation module 302 is also used to construct a judgment matrix based on the number of layers of the sub-target layer, the constraint layer and the standard layer and the information of each layer; the information of each layer includes the trait data information or multiple value information; the weight of each layer of information is determined according to the judgment matrix and the judgment scores collected by the input; and the weight of each trait is determined according to the weight of each layer of information.

[0093] In some embodiments of the present invention, the screening module 303 is also used to multiply the scoring result and the weight to obtain the score of each trait, and determine the first total score corresponding to each of the Christmas cactus germplasm resources based on the score of each trait; sort the Christmas cactus germplasm resources according to the set descending sorting condition based on the first total score to obtain the sorting result; and determine the target Christmas cactus germplasm resources according to the sorting result.

[0094] In some embodiments of the present invention, the screening module 303 is also used to determine multiple target traits among the traits, and determine the second total score corresponding to each of the Christmas cactus germplasm resources based on the scoring result and corresponding weight of each target trait; sort the Christmas cactus germplasm resources according to the set descending sorting condition based on the second total score to obtain the sorting result; and determine the target Christmas cactus germplasm resources based on the sorting result.

[0095] In an embodiment of the present invention, when the Christmas cactus germplasm resources are huge, as long as the Christmas cactus variety information is entered into the Christmas cactus variety screening system, the ranking of the Christmas cactus variety evaluation can be obtained, thereby assisting in parent screening and hybrid offspring selection, and can continuously enrich the germplasm resource library, reduce a large amount of repetitive work, avoid the blindness of parent selection, and easily complete the screening of hybrid offspring, which is of great significance to Christmas cactus breeding.

[0096] Reference Figure 3 , shows a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the electronic device.

[0097] like Figure 3 As shown, the electronic device may include: a processor (processor) 502, a communication interface (Communications Interface 504, a memory (memory) 506, and a communication bus 508.

[0098] in:

[0099] The processor 502 , the communication interface 504 , and the memory 506 communicate with each other via a communication bus 508 .

[0100] The communication interface 504 is used to communicate with other electronic devices or servers.

[0101] The processor 502 is used to execute the program 510, and specifically can execute the relevant steps in the above method embodiment.

[0102] Specifically, the program 510 may include program codes, which include computer operation instructions.

[0103] The processor 502 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0104] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0105] The program 510 may be specifically used to enable the processor 502 to execute operations corresponding to the methods described in the above method embodiments.

[0106] The specific implementation of each step in program 510 can refer to the corresponding description of the corresponding steps and units in the above method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above method embodiment, which will not be repeated here.

[0107] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present invention can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.

[0108] The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0109] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present invention.

[0110] The above implementation methods are only used to illustrate the embodiments of the present invention, and are not limitations of the embodiments of the present invention. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present invention. The patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A method for screening Schlumbergera species, characterized in that: include: Receive the trait data information of each Schlumbergera germplasm resource input by the user; The trait data information includes each trait and an attribute value corresponding to each trait; Scoring each trait of the Schlumbergera germplasm resource based on the deployed evaluation model and the trait data information to obtain a scoring result; Obtaining the weight corresponding to each trait based on the weights of the sub-objective layer, the constraint layer and the standard layer of the evaluation model; According to the weight and the scoring result, excellent varieties are screened among the Schlumbergera germplasm resources to obtain target Schlumbergera germplasm resources.

2. The method according to claim 1, characterized in that The evaluation model includes trait evaluation indicators; The evaluation model based on the deployment and the trait data information are used to score each trait of the Schlumbergera germplasm resource to obtain a scoring result, including: Determining a target trait evaluation index corresponding to each trait in the trait evaluation index; the target trait evaluation index includes a plurality of preset matching tags and scores corresponding to each of the matching tags; Matching the attribute value corresponding to each trait with the matching label to determine a target label; The score corresponding to the target label is used as the scoring result of each trait.

3. The method according to claim 2, characterized in that Before matching the attribute value corresponding to each trait with the matching label to determine the target label, the method further includes: When the current property is the aspect ratio of the leaf-like stem, the attribute value of the aspect ratio is obtained through the length of the leaf-like stem and the width of the leaf-like stem; When the current property is the aspect ratio of the flower, the attribute value of the aspect ratio of the flower is obtained through the length of the flower and the width of the flower.

4. The method according to claim 1, characterized in that The step of obtaining the weight corresponding to each trait based on the weights of each layer of the sub-objective layer, the constraint layer and the standard layer of the evaluation model includes: Constructing a judgment matrix based on the number of layers of the sub-target layer, the constraint layer and the standard layer and the information of each layer; the information of each layer includes the property data information or multiple value information; The weight of each layer of information is determined according to the judgment matrix and the input collected judgment scores; and the weight of each trait is determined according to the weight of each layer of information.

5. The method according to claim 1, characterized in that The method of screening excellent varieties from the Schlumbergera germplasm resources according to the weights and the scoring results to obtain target Schlumbergera germplasm resources comprises: Multiplying the scoring result and the weight to obtain a score for each trait, and determining a first total score corresponding to each of the Schlumbergera germplasm resources based on the score for each trait; Based on the first total score, the Schlumbergera germplasm resources are sorted according to a set descending sorting condition to obtain the sorting result; and the target Schlumbergera germplasm resources are determined according to the sorting result.

6. The method according to claim 1, characterized in that The method further comprises: screening excellent varieties from the Schlumbergera germplasm resources according to the weights and the scoring results to obtain target Schlumbergera germplasm resources. Determine a plurality of target traits among the traits, and determine a second total score corresponding to each of the Schlumbergera germplasm resources based on the scoring result and the corresponding weight of each target trait; Based on the second total score, the Schlumbergera germplasm resources are sorted according to a set descending sorting condition to obtain the sorting result; and the target Schlumbergera germplasm resources are determined according to the sorting result.

7. A system for screening Schlumbergera species, characterized in that: The method for screening the Schlumbergera species according to any one of claims 1 to 6 comprises: A data receiving module, used to receive the trait data information of each Schlumbergera germplasm resource input by the user; the trait data information includes each trait and the attribute value corresponding to each trait; An evaluation module is deployed with an evaluation model, which is used to score each trait of the Schlumbergera germplasm resources based on the trait data information of each Schlumbergera germplasm resource input by the user, obtain a scoring result, and obtain a weight corresponding to each trait based on the weights of each layer of the sub-target layer, the constraint layer and the standard layer; A screening module is used to screen excellent varieties among the Schlumbergera germplasm resources according to the weights and the scoring results to obtain target Schlumbergera germplasm resources.

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