Camellia oleifera cultivar selection method and system based on function superposition

Abstract

The application discloses a safflower oil tea fine variety breeding method and system based on function superposition, comprising the following steps: excellent single plant is cloned by high pole grafting technology, and a specific germplasm resource library is established; three types of phenotype data of leaf shape character set, oil yield character set and oil quality character set are measured; six types of application scenarios such as edible oil production raw material forest and specific natural compound production raw material forest are preset, a hierarchical weight distribution method is adopted to construct a comprehensive evaluation model, first-level weights are distributed to the three types of character sets, and second-level weights are distributed to the characters in the subsets; binary comparison method is adopted to standardize the leaf shape characters, and percentage system is adopted to standardize the oil yield characters and the oil quality characters; and target fine varieties are screened according to the comparison result of the comprehensive scores of the varieties to be evaluated and the average scores of existing fine varieties. The application realizes superposition breeding of ornamental, oil production and oil quality functions, and improves the adaptability of fine varieties and industrial scenarios.

Description

Technical Field

[0001] This invention belongs to the field of plant breeding technology, and in particular relates to a method and system for breeding superior varieties of Camellia oleifera based on functional superposition. Background Technology

[0002] Camellia chekiangoleosa is an important woody oilseed and ornamental tree species unique to my country. Compared to the common white flowers of Camellia oleifera, its flowers are more vibrant and diverse, encompassing red, multi-colored, and even rare white hues. Coupled with its large and varied flower shapes, it has high ornamental value. Camellia chekiangoleosa is versatile and adaptable to various planting scenarios. It can be planted in courtyards, as a solitary specimen on lawns, planted in rows as a beautiful street tree, or planted on a large scale in scenic areas to create magnificent flower seas. Some dwarf varieties can also be used for indoor potted cultivation. Furthermore, the tea oil extracted from its seeds is rich in nutrients such as oleic acid and squalene, making it a high-quality edible vegetable oil.

[0003] However, current breeding practices that follow a single-objective orientation often fall into a dilemma: oilseed breeding that pursues high yield and high oil content easily neglects key ornamental traits such as flower color and flowering period; while ornamental breeding that focuses on improving flower characteristics often leads to a decline in economic traits such as fruit yield and oil content. This breeding model that separates ornamental and oil-producing functions makes it difficult to cultivate varieties with comprehensive traits that combine excellent ornamental value and good oil production, and can no longer meet the market's demand for varieties with multiple functions.

[0004] Therefore, future breeding practices need to shift to a new approach of "functional allocation orientation," that is, to purposefully cultivate specialized varieties with different focuses, select ornamental varieties for landscape scenarios such as courtyard greening, select oil-producing varieties for large-scale planting bases, and explore comprehensive varieties that excel in both flowers and fruits for specific scenarios such as leisure farms, so as to maximize the value of Camellia oleifera in different planting scenarios. Summary of the Invention

[0005] To address these issues, this invention provides a method and system for breeding superior varieties of Camellia oleifera based on functional superposition, which solves the above problems.

[0006] In a first aspect, the present invention provides a method for breeding superior varieties of Camellia oleifera based on functional superposition, comprising: We collected superior Camellia oleifera plants and established a unique germplasm resource bank by asexually propagating them using high-stem grafting technology. To determine the phenotypic traits of each clone in the specific germplasm resource bank; After setting different application scenarios, a comprehensive evaluation model is constructed using a hierarchical weight allocation method. The comprehensive evaluation model standardizes the trait data and calculates a comprehensive score. Based on the comparison between the comprehensive score of the variety to be evaluated and the average score of existing improved varieties, target improved varieties matching the preset scenario are selected.

[0007] Secondly, the present invention provides a breeding system for superior varieties of Camellia oleifera based on functional superposition, comprising: The module for adding a unique germplasm resource bank is configured to collect superior single plants of Camellia oleifera and establish a unique germplasm resource bank by asexual propagation using high-stem grafting technology. The phenotypic determination module is configured to determine the phenotypic trait data of each clone in the specific germplasm resource bank. The phenotypic trait data includes mosaic trait set, oil yield trait set and oil quality trait set. The model building and scoring module is configured to build a comprehensive evaluation model and calculate a comprehensive score. The model building and scoring module includes: The scenario preset unit is configured to receive or preset target application scenarios. The application scenarios are set according to function orientation, including edible oil production raw material forests, specific natural compound production raw material forests, ornamental forests, edible oil production-ornamental forests, specific natural compound production-ornamental forests, and balanced improved seed planting scenarios. The hierarchical weight allocation unit is configured to establish a weight system based on the preset application scenario using a hierarchical weight allocation method: first, weight coefficients are allocated to the flower and leaf trait set, oil yield trait set, and oil quality trait set according to functional orientation; then, weight coefficients are allocated to the specific traits within each trait subset according to functional requirements; the strategy for assigning weight coefficients is to assign high weights to the core target traits in the scenario, medium to low weights to the secondary traits that need to be considered, and zero weights to the traits that are not considered in the scenario. The standardization processing unit is configured to perform standardization processing on the phenotypic trait data acquired by the trait determination module; The weighted scoring calculation unit is configured to perform weighted calculation on the trait data processed by the standardized processing unit according to the weight system established by the hierarchical weight allocation unit, so as to obtain the comprehensive score; The improved variety selection module is configured to select target improved varieties that match preset scenarios based on the comparison results of the comprehensive score of the variety to be evaluated with the average score of existing improved varieties.

[0008] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method for breeding superior varieties of Camellia oleifera based on functional superposition according to any embodiment of the present invention.

[0009] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the method for breeding superior varieties of Camellia oleifera based on functional superposition according to any embodiment of the present invention.

[0010] The method and system for breeding superior varieties of Camellia oleifera based on functional superposition proposed in this application have the following specific beneficial effects: 1) The comprehensive evaluation model constructed by this invention transforms subjective experience judgment into objective data decision-making, which can efficiently and accurately screen out excellent strains with balanced development of dual functions from a large number of candidate materials, overcoming the blindness in traditional breeding.

[0011] 2) The improved Camellia oleifera varieties bred through this method can be used for landscaping and courtyard beautification, and can also provide high-quality camellia oil, which enhances the commercial value and market competitiveness of the varieties and strongly promotes the integration of agriculture and tourism and the development of rural industries. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a method for breeding superior varieties of Camellia oleifera based on functional superposition, as provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a safflower oil tea variety breeding system based on functional superposition, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 The diagram shows a flowchart of a method for breeding superior varieties of Camellia oleifera based on functional superposition according to this application.

[0016] In one embodiment, the method of the present invention is demonstrated for breeding a high-edible-and-ornamental red-flowered camellia variety suitable for high-yield camellia oil production bases. This embodiment details how to apply the method of the present invention to breed a high-yield, high-quality red-flowered camellia variety with a certain degree of ornamental value, aiming to provide excellent germplasm for specialized and large-scale camellia oil production bases.

[0017] Perform step S1 to collect superior Camellia oleifera plants and establish a unique germplasm resource bank by asexually propagating them using high-stem grafting technology; Specifically, the collection of superior Camellia oleifera plants and their subsequent asexual propagation using high-stem grafting techniques to establish a unique germplasm resource bank includes: The superior single plant is a single plant with outstanding phenotypic traits, including uncommon flower color, uncommon flower shape, uncommon leaf color, glossy and shiny leaves, and high total fruit yield per unit crown area. The high-stem grafting technique involves grafting spring shoots of the superior single plant, one bud and one shoot, onto old camellia tree stumps that are 10-15 years old and have a stem height of 60-80cm. Each stump is grafted with 3-4 buds.

[0018] In this embodiment, the candidate populations were sourced from, but were not limited to, authoritative national and provincial germplasm resource institutions. The selection criteria were: 100 plants exhibiting one or more prominent phenotypic traits, including: rare deep red or textured flower color (compared to common pink), double-flowered flower type, glossy, waxy leaf surface, and, based on preliminary calculations, significantly higher fruit yield per unit canopy projection area than surrounding plants.

[0019] Healthy spring shoots from these superior individual trees were collected as cuttings. Camellia oleifera seedlings, approximately 12 years old, vigorous, and free from pests and diseases, were selected and cut at about 70 cm above the ground to form stumps, with smooth, even cuts.

[0020] High-stalk grafting was employed. Three buds were grafted onto each stump to ensure survival rate and subsequent tree vigor. After grafting, all grafted stumps underwent meticulous management, including shading, moisture retention, and pest and disease control. Once established, the trees were planted using a completely randomized block design, constructing a unique germplasm resource bank containing 96 clones.

[0021] Specific high-stem grafting techniques shorten the time for superior single-plant clonal traits to manifest. When using bud grafting to obtain clonal lines, the shape of the flowers and leaves in the clonal seedlings of Camellia oleifera can be observed from the time the grafted seedlings survive; however, oil quality traits only become apparent in the fifth year after grafting. In contrast, using high-stem grafting, after grafting scions of superior single plants onto mature Camellia oleifera tree stumps, oil quality traits begin to stabilize in the second year after grafting. This allows for the determination of whether a plant is of superior variety based on measurement data, three years earlier than bud grafting.

[0022] Perform step S2 to determine the phenotypic data of each clone in the specific germplasm resource bank; Specifically, the determination of phenotypic trait data for each clone in the specific germplasm resource bank includes, The number of plants measured for each clone was 15-30; The phenotypic data of the clones include mosaic traits, oil yield traits, and oil quality traits. The set of flower and leaf characteristics reflects the ornamental value of the variety. The set of flower and leaf characteristics includes flower color, flower diameter, flowering period, flower quantity, petal shape, leaf color, leaf shape, and leaf margin characteristics. The set of oil yield traits reflects the efficiency of the variety in producing tea oil, and the set of oil yield traits includes fruit yield per unit canopy area, fresh seed yield, kernel yield, and oil content. The oil quality characteristics set reflects the quality of tea oil produced from the variety, including oleic acid content and the content of specific functional components, wherein the specific functional components include at least one of squalene, sterols, and tocopherols.

[0023] In this step, after the candidate populations have entered their full flowering and fruiting stages, the following trait data will be measured: Characteristics of flowers and leaves: Flower color: The flower color characteristics that the target improved varieties to be bred should possess are used as the direct standard for visual comparison.

[0024] Flower diameter: Measure the diameter of a fully open flower using calipers.

[0025] Flowering period: The number of days of peak flowering for a single plant is recorded when more than 50% of the flowers are open.

[0026] Flower count: The number of flowers within the standard crown width of a single plant.

[0027] Petal shape: recorded as single, semi-double, or double.

[0028] Leaf color: Record the color and special characteristics of the leaves (such as whether they are glossy).

[0029] Leaf shape index: The ratio of leaf length to width.

[0030] Leaf margin characteristics: Record whether there are serrations, etc.

[0031] Oil yield traits set: Fruit yield per unit canopy projection area: Calculated by weighing harvested mature fruits and measuring the canopy projection area (unit: kg / m²). Fresh seed yield: Randomly select 5kg of fresh fruit, remove the seeds, weigh the seeds, and calculate the percentage.

[0032] Kernel yield: After the seeds are dried and shelled, the kernel weight is weighed and the percentage is calculated.

[0033] Oil content: The oil content (%) of the dried kernels was determined by Soxhlet extraction.

[0034] Oil quality characteristics set: Oleic acid content: The relative content (%) of oleic acid in the oil was determined by gas chromatography.

[0035] Content of functional ingredients: The contents (mg / kg) of squalene, sterols and tocopherols were determined by high performance liquid chromatography.

[0036] By incorporating three specific small molecule compounds—squalene, sterol, and tocopherol—into oil trait indicators, a superior breeding trait index for the application of Camellia oleifera fruit pressed oil as a raw material for cosmetics and transdermal care preparations is provided. Generally, Camellia oleifera oil is richer in squalene, sterol, and tocopherol than ordinary Camellia oleifera oil. These small molecule compounds are themselves excellent skin care ingredients, highly compatible with the sebum film, and together with other components in Camellia oleifera fruit pressed oil, form a natural, penetration-enhancing oily medium.

[0037] In step S3, after pre-setting different application scenarios, a comprehensive evaluation model is constructed using a hierarchical weight allocation method. The comprehensive evaluation model standardizes the trait data and calculates a comprehensive score. Specifically, the construction of the comprehensive evaluation model includes: S31. Preset different application scenarios, including m types of functionally oriented forests such as edible oil production raw material forests, specific natural compound production raw material forests, ornamental forests, edible oil production-ornamental forests, specific natural compound production-ornamental forests, and balanced improved seed planting scenarios. S32, The hierarchical weight allocation includes assigning first-level weights to the flower and leaf trait set, the oil yield trait set, and the oil quality trait set; and assigning second-level weights to the specific traits within each trait set; S33. The weighting method is to assign high weights to the core target traits in the scenario, medium to low weights to the secondary traits that need to be taken into account, and zero weights to the traits that are not considered in the scenario. S34. Based on the weighting coefficients, the standardized trait data are weighted and calculated to obtain the comprehensive score.

[0038] Furthermore, the standardization process for the trait data includes:

[0039] For the aforementioned mosaic traits, a binary comparison method based on control varieties is used for standardization: a certain trait of the clone to be tested is compared with the same trait of the control variety, and a value of 100 is assigned when the trait is better than or equal to that of the control variety, and a value of 0 is assigned when the trait is worse than that of the control variety; the control variety is a currently existing improved variety. For the oil yield trait and the oil quality trait, standardization is performed using a percentage system based on existing improved variety control values, as shown in the following formula: ; .

[0040] In this step, step S31 pre-sets different application scenarios, including raw material forests for edible oil production, raw material forests for the production of specific natural compounds, ornamental forests, edible oil production-ornamental forests, specific natural compound production-ornamental forests, and balanced improved seed planting scenarios. In this embodiment, a differentiation algorithm is used for standardization for different trait types: A binary comparison method was used for the mosaic trait.

[0041] Taking flower color standardization as an example: In this embodiment, "bright deep red" is used as the evaluation criterion. When the flower color depth of the clone under test is equal to or deeper than "bright deep red", it is assigned a value of 100; otherwise, it is assigned a value of 0. Other flower and leaf traits are standardized with reference to flower color.

[0042] For the oil yield and oil quality traits, standardization was performed using a percentage system based on existing improved variety control values. The formula is: ; .

[0043] Taking the standardization of oil content as an example: In this embodiment, an oil content of 45% is used as the measured value for improved varieties. When the oil content of the clone "G-15" to be tested is 50%, its standardized value = 50 / 45*100 = 111.1. When the oil content of the clone "G-28" to be tested is 43%, its standardized value = 43-45 = 95.5. Other oil properties are standardized with reference to oil content.

[0044] Next, proceed to step S32 to allocate weights based on the weight coefficient matrix model.

[0045] Select the weight vector for the "high edibility - also ornamental" scenario. In this scenario, the total weight of flower and leaf traits is 0.30, the weight of oil yield traits is 0.48, and the weight of oil quality traits is 0.22.

[0046] The specific weighting coefficients for each trait are assigned as shown in Table 1: Table 1: Specific Weight Coefficient Allocation Table for Each Trait

[0047]

[0048] Finally, step S33 is performed to calculate the overall score.

[0049] According to the formula: , Calculate the score for each candidate clone.

[0050] Step S4 is executed, and the target improved varieties that match the preset scenario are selected based on the comparison results between the comprehensive score of the variety to be evaluated and the average score of the existing improved varieties.

[0051] In this step, we first calculate the historical average score of existing improved varieties in the target scenario. The historical average score in the "high edible and ornamental" scenario is 80.

[0052] Then, the comprehensive score of the varieties to be evaluated is compared with that of the varieties to be evaluated. In this embodiment, the top 10% of the candidate plants with a comprehensive score greater than or equal to the average score of the existing improved varieties are selected as the top 10 clones as the target improved varieties.

[0053] In another embodiment, the method of the present invention is demonstrated for breeding a superior Camellia oleifera variety with "high ornamental value and dual edible value" suitable for scenic area landscaping. This embodiment aims to breed varieties with extremely high ornamental value while also considering basic oil production capacity, and is suitable for scenic areas, parks, and courtyard landscaping. The steps are exactly the same as in the previous embodiment, the difference being that the "high ornamental value and dual edible value" scenario is selected in the weighting coefficient matrix model.

[0054] Weighting strategy adjustment: In this scenario, the total weight of flower and leaf traits is 0.75, the weight of oil yield traits is 0.15, and the weight of oil quality traits is 0.10. Core ornamental traits (such as flower color, flower diameter, and petal shape) are assigned high weights (0.12-0.22), while oil quality traits are given medium to low weights (0.06-0.10) as a secondary consideration.

[0055] Selection criteria adjusted: Candidate plants must rank in the top 10% based on their overall score.

[0056] Results: Several clones were successfully selected, which exhibited excellent ornamental characteristics such as flowering period, flower diameter, flower color, and flower shape, while maintaining an oil content of over 32% in the dried seeds.

[0057] By switching the scenarios in the weight coefficient matrix model, this invention perfectly achieves a method for customized breeding objectives in multiple applications.

[0058] Please see Figure 2 The diagram shows a structural block diagram of a Camellia oleifera breeding system based on functional superposition according to this application.

[0059] like Figure 2 As shown, the modules include: 210 for adding specific germplasm resources to the database, 220 for determining traits, 230 for model construction and scoring, and 240 for selecting superior varieties.

[0060] Among them, the special germplasm resource bank entry module 210 is configured to collect excellent single plants of Camellia oleifera and establish a special germplasm resource bank by asexually propagating them using high-stem grafting technology. The phenotypic determination module 220 is configured to determine the phenotypic trait data of each clone in the specific germplasm resource bank, wherein the phenotypic trait data includes mosaic trait set, oil yield trait set and oil quality trait set; Model building and scoring module 230 is configured to build a comprehensive evaluation model and calculate a comprehensive score. The model building and scoring module includes: The scenario preset unit 221 is configured to receive or preset target application scenarios. The application scenarios are set according to function orientation, including edible oil production raw material forest, specific natural compound production raw material forest, ornamental forest, edible oil production-ornamental, specific natural compound production-ornamental, and balanced improved seed planting scenarios. The hierarchical weight allocation unit 232 is configured to establish a weight system according to the preset application scenario using a hierarchical weight allocation method: first, weight coefficients are allocated to the flower and leaf trait set, oil yield trait set, and oil quality trait set according to the functional orientation; then, weight coefficients are allocated to the specific traits within each trait subset according to the functional requirements; the strategy for assigning weight coefficients is to assign high weights to the core target traits in the scenario, medium to low weights to the secondary traits that need to be taken into account, and zero weights to the traits that are not considered in the scenario. The standardization processing unit 233 is configured to perform standardization processing on the phenotypic trait data acquired by the trait determination module; The weighted scoring calculation unit 234 is configured to perform weighted calculation on the trait data processed by the standardized processing unit according to the weight system established by the hierarchical weight allocation unit to obtain the comprehensive score; The improved variety screening module 240 is configured to screen target improved varieties that match preset scenarios based on the comparison results of the comprehensive score of the variety to be evaluated and the average score of existing improved varieties.

[0061] It should be understood that Figure 2 The modules and references described in the document Figure 1The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0062] In other embodiments, the present invention also provides a computer-readable storage medium storing a computer program thereon, wherein when the program instructions are executed by a processor, the processor performs the method for breeding superior varieties of Camellia oleifera based on functional superposition in any of the above method embodiments: We collected superior Camellia oleifera plants and established a unique germplasm resource bank by asexually propagating them using high-stem grafting technology. To determine the phenotypic traits of each clone in the specific germplasm resource bank; After setting different application scenarios, a comprehensive evaluation model is constructed using a hierarchical weight allocation method. The comprehensive evaluation model standardizes the trait data and calculates a comprehensive score. Based on the comparison between the comprehensive score of the variety to be evaluated and the average score of existing improved varieties, target improved varieties matching the preset scenario are selected.

[0063] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the functionally superimposed Camellia oleifera superior variety breeding system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the functionally superimposed Camellia oleifera superior variety breeding system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0064] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby realizing the above-described method embodiment of the Camellia oleifera superior variety breeding method based on functional superposition. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the Camellia oleifera superior variety breeding system based on functional superposition. The output device 340 may include a display screen or other display device.

[0065] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0066] In one implementation, the above-described electronic device is applied to a functional superposition-based camellia oleifera superior variety breeding system for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: We collected superior Camellia oleifera plants and established a unique germplasm resource bank by asexually propagating them using high-stem grafting technology. To determine the phenotypic traits of each clone in the specific germplasm resource bank; After setting different application scenarios, a comprehensive evaluation model is constructed using a hierarchical weight allocation method. The comprehensive evaluation model standardizes the trait data and calculates a comprehensive score. Based on the comparison between the comprehensive score of the variety to be evaluated and the average score of existing improved varieties, target improved varieties matching the preset scenario are selected.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for breeding superior varieties of Camellia oleifera based on functional superposition, characterized in that, include: We collected superior Camellia oleifera plants and established a unique germplasm resource bank by asexually propagating them using high-stem grafting technology. Phenotypic data of each clone in the specific germplasm resource bank were determined, including mosaic trait set, oil yield trait set, and oil quality trait set; Multiple different application scenarios are pre-set, including raw material forests for edible oil production, raw material forests for the production of specific natural compounds, ornamental forests, edible oil production and ornamental forests, production of specific natural compounds and ornamental forests, and balanced high-quality seed planting scenarios. A comprehensive evaluation model is constructed by a hierarchical weight allocation method. The hierarchical weight allocation includes: assigning first-level weights to the flower and leaf trait set, the oil yield trait set, and the oil quality trait set, and assigning second-level weights to specific traits within each trait set; wherein, the weight allocation assignment strategy is to assign high weights to core target traits in a given application scenario, assign medium to low weights to secondary traits that need to be considered, and assign zero weights to traits that are not considered. The comprehensive evaluation model standardizes the trait data according to the assigned weights and then performs a weighted calculation to obtain a comprehensive score. The standardization of the trait data includes: for the mosaic trait, a binary comparison method based on a control variety is used for standardization: a trait of the clone to be tested is compared with the same trait of the control variety; a value of 100 is assigned when the trait is better than or equal to the control variety, and a value of 0 is assigned when the trait is worse than the control variety; the control variety is a currently existing improved variety. For the oil yield trait and the oil quality trait, a percentage system based on the control values ​​of existing improved varieties is used for standardization, as shown in the following formula: ； ； Based on the comparison between the comprehensive score of the variety to be evaluated and the average score of existing improved varieties, target improved varieties that match the preset scenarios are selected. The average score of existing improved varieties is the historical average score of existing improved varieties in the target application scenarios.

2. The method for breeding superior varieties of Camellia oleifera based on functional superposition according to claim 1, characterized in that, The process of collecting superior Camellia oleifera plants and establishing a unique germplasm resource bank through asexual propagation using high-stem grafting technology includes... The superior single plant is a single plant with outstanding phenotypic traits, including uncommon flower color, uncommon flower shape, uncommon leaf color, glossy and shiny leaves, and high total fruit yield per unit crown area. The high-stem grafting technique involves grafting spring shoots of the superior single plant, one bud and one shoot, onto old camellia tree stumps that are 10-15 years old and have a stem height of 60-80cm. Each stump is grafted with 3-4 buds.

3. The method for breeding superior varieties of Camellia oleifera based on functional superposition according to claim 1, characterized in that, The determination of phenotypic trait data for each clone in the specific germplasm resource bank includes... The number of plants measured for each clone was 15-30; The set of flower and leaf traits reflects the ornamental value of the variety, and the set of flower and leaf traits includes flower color, flower diameter, flowering period, flower quantity, petal shape, leaf color, leaf shape, and leaf margin characteristics; The set of oil yield traits reflects the efficiency of the variety in producing tea oil, and the set of oil yield traits includes fruit yield per unit canopy area, fresh seed yield, kernel yield, and oil content. The oil quality characteristics set reflects the quality of tea oil produced from the variety, including oleic acid content and the content of specific functional components, wherein the specific functional components include at least one of squalene, sterols, and tocopherols.

4. A system for implementing the method for breeding superior varieties of Camellia oleifera as described in any one of claims 1 to 3, characterized in that, include: The module for adding a unique germplasm resource bank is configured to collect superior single plants of Camellia oleifera and establish a unique germplasm resource bank by asexual propagation using high-stem grafting technology. The phenotypic determination module is configured to determine the phenotypic trait data of each clone in the specific germplasm resource bank. The phenotypic trait data includes mosaic trait set, oil yield trait set and oil quality trait set. The model building and scoring module is configured to build a comprehensive evaluation model and calculate a comprehensive score. The model building and scoring module includes: The scenario preset unit is configured to preset multiple different application scenarios. The application scenarios are set according to function orientation, including edible oil production raw material forest, specific natural compound production raw material forest, ornamental forest, edible oil production-ornamental forest, specific natural compound production-ornamental forest, and balanced improved seed planting scenario. The hierarchical weight allocation unit is configured to establish a weight system based on the preset application scenario using a hierarchical weight allocation method: first, weight coefficients are assigned to the flower and leaf trait set, oil yield trait set, and oil quality trait set according to functional orientation; then, weight coefficients are assigned to specific traits within each trait subset according to functional requirements. The weight allocation assignment strategy is that, in a given scenario, high weights are assigned to core target traits, medium to low weights are assigned to secondary traits that need to be considered, and zero weights are assigned to traits that are not considered. The standardization processing unit is configured to perform standardization processing on the phenotypic trait data acquired by the trait determination module; The weighted scoring calculation unit is configured to perform weighted calculation on the trait data processed by the standardized processing unit according to the weight system established by the hierarchical weight allocation unit, so as to obtain the comprehensive score; The improved variety selection module is configured to select target improved varieties that match preset scenarios based on the comparison results of the comprehensive score of the variety to be evaluated with the average score of existing improved varieties.

5. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 3.

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