A method and system for tracing the provenance of elite tree species

Abstract

This invention relates to the field of seedling management technology, specifically disclosing a method and system for tracing and managing the superior varieties of precious tree species. The method includes: acquiring information on the collection of superior seed resources and breeding environment information, wherein the information on the collection of superior seed resources includes post-planting growth information and harvested seed information; acquiring the mother tree code of the superior variety planted in the superior seed base, and generating registration number association information based on the mother tree code and post-planting growth data; and acquiring a seed quality index based on the harvested seed information. This invention achieves precise tracing of precious tree species from germplasm resources to afforestation results by constructing a multi-dimensional, dynamic evaluation system and full-chain traceability. At the germplasm evaluation level, based on continuous years of growth data from mother trees, the single-tree growth potential coefficient and the population growth consistency index are calculated to construct a trait expression coefficient, and a seed stability factor is introduced for cross-generational correction, solving the problems of separation between individuals and populations, and disconnect between mother trees and offspring in traditional evaluation methods.

Description

Technical Field

[0001] This invention relates to the field of seedling management technology, and in particular to a method and system for tracing and managing the superior varieties of precious tree species. Background Technology

[0002] Precious tree species refer to those with high-quality timber, wide applications, high value, and significant ecological and landscape functions. Their products possess artistic, collectible, and cultural heritage value, making them invaluable national natural and strategic resources. With socio-economic development and rising living standards, market demand for precious timber is increasing. However, for a long time, my country has overexploited its precious tree species resources, resulting in a serious disconnect between protection and cultivation efforts, leading to insufficient total resources and low quality.

[0003] Current methods for tracing and managing precious tree species primarily rely on paper-based records and physical tags, which have the following drawbacks: Existing methods typically only record single growth data of the mother tree or focus solely on population statistics, lacking a comprehensive consideration of individual tree growth vigor and population growth consistency, and failing to fully evaluate the trait expression level of germplasm resources. Furthermore, there is a lack of correlation analysis between the growth performance of the mother tree and the quality of its offspring seeds, resulting in incomplete decision-making for superior varieties. Moreover, focusing only on single indicators such as thousand-seed weight and germination rate, or relying solely on data from a single year, fails to provide a comprehensive evaluation from three dimensions: physical quality, vigor performance, and genetic stability. It also makes it difficult to distinguish between accidental performance and stable inheritance, affecting the accurate selection of superior seed-collecting mother trees. Furthermore, the lack of a systematic correlation mechanism between germplasm resource registration numbers, mother tree codes, seed batch numbers, and seedling batch codes leads to a break in information flow from germplasm source to seedling production. After seedlings leave the nursery, their provenance and cultivation process cannot be effectively traced, and afforestation effectiveness data cannot be fed back into provenance evaluation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for tracing and managing the superior varieties of precious tree species, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for tracing and managing the provenance of valuable tree species includes: Acquire information on the collection of superior seed resources and information on the breeding environment, wherein the information on the collection of superior seed resources includes post-planting growth information and information on harvested seeds; Obtain the mother tree code of the improved variety planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data; The seed quality index is obtained based on the harvested seed information, and breeding association information between the improved varieties and the mother tree codes is generated based on the seed quality index. The seedling technology compliance is obtained based on the breeding environment information, and seed source association information is generated based on the seedling technology compliance and mother tree code. The seedling production batch is generated based on the registration number association information, and the seedling traceability information is generated based on the seedling production batch, breeding association information, and seed source association information.

[0006] Preferably, the steps of obtaining the mother tree code of the improved variety planted in the improved variety base, and generating registration number association information based on the mother tree code and post-planting growth data, include: The continuous annual growth data of the corresponding mother tree are obtained according to the mother tree code, and the single-plant growth potential coefficient reflecting the growth rate of a single plant is generated according to the continuous annual growth data. Obtain the individual growth potential coefficients of all mother trees under the same germplasm resource registration number, and generate a growth consistency index that characterizes the synchronicity of population growth based on the individual growth potential coefficients. The phenotypic expression coefficient of this germplasm resource is constructed by weighted combination of the single plant growth potential coefficient and the growth uniformity index. Vigor detection data of seeds harvested over the years under the same germplasm resource registration number are obtained. Seed stability factors are generated based on the fluctuation of the vigor detection data, and the seed stability factors are used to correct the trait expression coefficients. The corrected trait expression coefficients are re-bound with the corresponding germplasm resource registration numbers to generate updated registration number association information.

[0007] Preferably, the step of obtaining a seed quality index based on the harvested seed information and generating breeding association information between improved varieties and mother tree codes based on the seed quality index includes: Based on the mother tree code, obtain the thousand-grain weight and plumpness detection data of the seeds harvested from the corresponding mother tree over the years, and generate the grain plumpness index based on the thousand-grain weight and plumpness detection data. Based on the mother tree code, obtain the germination rate and germination potential detection data of the seeds harvested from the corresponding mother tree over the years, and generate a germination potential coefficient based on the germination rate and germination potential detection data; The genetic transmission stability factor is obtained based on the germination potential coefficient; The germplasm heterogeneity coefficient is generated based on the grain plumpness index and germination potential coefficient of seeds harvested from different mother trees under the same germplasm resource registration number. The genetic transmission stability factor is then modified based on the germplasm heterogeneity coefficient to obtain the modified genetic transmission stability factor. A comprehensive quality index for harvested seeds is generated based on the seed plumpness index, germination potential coefficient, and modified genetic transmission stability factor. The comprehensive quality index is then associated with the corresponding mother tree code to generate breeding association information that includes seed quality dimensions.

[0008] Preferably, the step of obtaining the thousand-grain weight and plumpness detection data of seeds harvested from the corresponding mother tree over the years based on the mother tree code, and generating a grain plumpness index based on the thousand-grain weight and plumpness detection data, includes: The thousand-grain weight detection value of each batch of seeds harvested from the corresponding mother tree in consecutive harvesting years is obtained according to the mother tree code, and a grain weight deviation coefficient is generated based on the thousand-grain weight detection value and the average thousand-grain weight detection value of the same batch of seeds. The degree of variation of the thousand-grain weight of seeds in a given year is obtained by measuring the grain weight deviation coefficient of different harvest batches within the same harvest year, and an annual stable value of the grain weight for that year is generated based on the degree of variation. The plumpness detection value of seeds from each harvest batch of the corresponding mother tree in the same harvest year is obtained according to the mother tree code. The plumpness matching coefficient of the seeds is generated according to the degree of closeness between the plumpness detection value and the standard plumpness benchmark value of the same tree species. Based on the discrete distribution of the plumpness matching coefficient of all harvested batches in the same year, a plumpness annual homogeneity index reflecting the consistency of seed appearance quality in that year is generated, and the annual stable value of grain weight is corrected based on the plumpness annual homogeneity index to obtain the grain weight-plumpness composite coefficient. The grain plumpness index of the mother tree's seeds is generated based on the grain weight-plumpness composite coefficient from multiple consecutive harvest years.

[0009] Preferably, the step of obtaining the seedling technology compliance degree based on the breeding environment information, and generating seed source association information based on the seedling technology compliance degree and the mother tree code, includes: The substrate ratio adjustment record of the seeds produced by the corresponding mother tree during the seedling stage is obtained according to the mother tree code, and the substrate ratio deviation is generated according to the substrate ratio adjustment record. Based on the mother tree code, obtain the slow-release fertilizer application record of the seeds produced by the corresponding mother tree during the seedling stage, and generate the slow-release fertilizer loading matching coefficient based on the slow-release fertilizer application record; Based on the mother tree code, obtain the mycorrhizal inoculation record of the seeds produced by the corresponding mother tree during the seedling stage, and generate the mycorrhizal infection synchronization index based on the mycorrhizal inoculation record; Based on the mother tree code, obtain the container specification record value of the seeds produced by the corresponding mother tree during the seedling stage, and generate the container volume fit degree based on the container specification record value. The comprehensive seedling cultivation technology compliance is generated based on the substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume suitability. The compliance of the integrated seedling cultivation technology is associated with the mother tree code to generate seed source association information.

[0010] Preferably, the steps of generating seedling production batches based on the registration number association information, and generating seedling traceability information based on the seedling production batches, breeding association information, and seed source association information include: Based on the registration number association information, obtain the germplasm resource registration number and mother tree code corresponding to the seedling cultivation, and generate the seedling production batch based on the germplasm resource registration number and mother tree code; The corresponding comprehensive quality index is retrieved from the breeding-related information based on the seedling production batch, and the seed quality grade to which the seed belongs is generated based on the comprehensive quality index. The corresponding comprehensive seedling technology compliance is obtained from the seed source association information based on the mother tree code, and the seedling technology level to which the seedling belongs is generated based on the comprehensive seedling technology compliance. The expected seedling quality index for each batch of seedlings is generated based on the seed quality grade and the seedling technology grade. The data on seedling production batches, seed quality grades, and seedling technology grades are integrated to generate seedling traceability information.

[0011] Preferably, the step of integrating the seedling production batch, seed quality grade, and seedling technology grade to generate seedling traceability information includes: Based on the seedling production batch, obtain the seedling start time and seedling site environmental monitoring data, and generate the seedling environmental feature code for that batch based on the seedling start time and seedling site environmental monitoring data; Based on the seedling production batches, obtain substrate ratio adjustment records and slow-release fertilizer application records, and generate seedling process dynamic curves based on the substrate ratio adjustment records and slow-release fertilizer application records; First historical data of the trait expression coefficient of the germplasm resource registration number are obtained based on the seed quality grade, and genetic potential coefficient is generated based on the first historical data of the trait expression coefficient. The second historical data of the comprehensive seedling technology compliance is obtained based on the seedling technology level, and the cultivation quality coefficient is generated based on the second historical data of the comprehensive seedling technology compliance. The genetic potential coefficient and the cultivation quality coefficient are used to obtain the source seed dual-factor quality prediction value of the seedling batch, and the expected nursery grade is generated based on the source seed dual-factor quality prediction value. Based on the seedling environment feature code, seedling process dynamic curve and expected nursery grade, a composite traceability identifier for the seedling batch is generated, and the composite traceability identifier is bound to the seedling production batch code to generate seedling traceability information.

[0012] This invention also discloses a traceability management system for precious tree species, comprising: The improved seed information acquisition module is used to acquire improved seed resource collection information and breeding environment information, wherein the improved seed resource collection information includes post-planting growth information and harvested seed information; The registration number acquisition module is used to obtain the mother tree code of the improved varieties planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data. The breeding association information acquisition module is used to acquire the seed quality index based on the harvested seed information, and generate breeding association information between the improved variety and the mother tree code based on the seed quality index. The seed source association information acquisition module is used to acquire the seedling technology compliance degree based on the breeding environment information, and generate seed source association information based on the seedling technology compliance degree and the mother tree code; The traceability information acquisition module is used to generate seedling production batches based on the registration number association information, and to generate seedling traceability information based on the seedling production batches, breeding association information, and seed source association information.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for tracing and managing the superior varieties of precious tree species.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for tracing and managing the superior varieties of precious tree species.

[0015] The beneficial effects of this application are as follows: This invention achieves precise traceability of precious tree species from germplasm resources to afforestation results by constructing a multi-dimensional, dynamic evaluation system and full-chain traceability. At the germplasm evaluation level, based on continuous years of growth data from mother trees, the single-tree growth potential coefficient and population growth consistency index are calculated to construct trait expression coefficients. Seed stability factors are introduced for cross-generational correction, solving the problems of separation between individuals and populations, and between mother trees and offspring, in traditional evaluations. At the seed quality level, the seed plumpness index, germination potential coefficient, and genetic transmission stability factor are integrated to comprehensively evaluate from three dimensions: physical quality, vigor performance, and genetic stability, overcoming the limitations of single-indicator or single-year evaluations. At the seedling technology level, operational parameters such as substrate ratio, slow-release fertilizer loading, mycorrhizal inoculation, and container specifications are quantified into technical compliance, making the standardization of the seedling process calculable and comparable. Through a four-level coding progressive association system, the germplasm resource registration number, mother tree code, seed batch number, and seedling batch code are linked together to form a unique seedling traceability file. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0019] Figure 4 This is a dynamic curve of the seedling cultivation process according to an embodiment of this application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] like Figure 1 As shown, this application provides a method for tracing and managing the provenance of superior varieties of precious tree species, including: S1. Obtain information on the collection of improved seed resources and information on the breeding environment, wherein the information on the collection of improved seed resources includes post-planting growth information and information on harvested seeds; For example, improved seed resource collection information refers to the set of raw data related to germplasm resources obtained through manual surveys and sampling from selected superior trees in the wild or from existing improved seed bases. Post-planting growth information includes growth indicators such as tree height, diameter at breast height (DBH), and crown width recorded from regular observations of each mother tree after the collected germplasm is planted in the improved seed base. These data reflect the growth performance and adaptability characteristics of the germplasm resources under specific environmental conditions. Seed harvesting information includes physical property data such as thousand-seed weight, plumpness, and purity recorded when seeds produced by the mother trees are collected during the seed maturity season, as well as vigor indicators such as germination rate and germination potential obtained from subsequent laboratory tests. Breeding environment information refers to environmental condition data of the germplasm resources from collection and preservation to propagation, including site factor data such as altitude, slope, aspect, and soil type of the improved seed base, as well as environmental monitoring data such as temperature, humidity, and light during the nursery seedling stage.

[0023] S2. Obtain the mother tree code of the improved variety planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data; For example, the mother tree code refers to a unique identification code assigned to each mother tree planted in the improved variety base. This code typically includes information elements such as tree species code, base code, planting year, and serial number, used to accurately identify and locate specific individual mother trees in all subsequent management stages. Post-planting growth data refers to the measurements of growth indicators such as tree height, diameter at breast height (DBH), and crown width collected annually starting from the year the mother tree was planted. These data constitute a time-series dataset reflecting the growth dynamics of the mother trees. Registration number association information refers to the information set generated by associating and binding the growth performance data of individual mother trees with the germplasm resource registration number. This information reflects the differences in trait expression of specific germplasm resources in different mother trees within the improved variety base, as well as the growth change patterns of the same mother tree in different years, providing basic data support for subsequent germplasm resource evaluation and improved variety breeding.

[0024] S3. Obtain the seed quality index based on the harvested seed information, and generate breeding association information between the improved variety and the mother tree code based on the seed quality index; For example, the seed quality index is a quantitative value calculated based on the physical traits and vigor indicators of harvested seeds, used to evaluate the overall quality level of seeds produced by a specific mother tree. Physical traits include indicators such as thousand-seed weight and plumpness, reflecting the fullness and appearance quality of the seeds; vigor indicators include germination rate and germination potential, reflecting the vitality and emergence potential of the seeds. By weighted integration of these indicators, a quantitative index that can comprehensively characterize seed quality can be obtained. Breeding association information refers to the information set generated by associating the seed quality index with the corresponding mother tree code. This information reveals the genetic differences in seed quality among different mother trees, as well as the stability of seed quality from the same mother tree in different years, providing a scientific basis for subsequent selection of superior seed-producing mother trees and prediction of seedling quality.

[0025] S4. Obtain the seedling technology compliance degree based on the breeding environment information, and generate seed source association information based on the seedling technology compliance degree and mother tree code; For example, seedling technology compliance refers to the degree to which the implementation of various technical measures in the actual seedling cultivation process matches the standard technical requirements. Seedling technology measures include key technical parameters such as substrate ratio, slow-release fertilizer loading, mycorrhizal inoculation, and container size selection. The values ​​of these parameters directly affect the growth, development, and quality formation of seedlings. By comparing the actual recorded values ​​of various technical parameters with the standard technical parameter thresholds for the corresponding tree species, calculating the degree of deviation, and then comprehensively analyzing the deviations of each technical indicator, a quantitative compliance degree reflecting the overall technical level of the batch of seedlings can be obtained. Seed source association information refers to the information set generated after associating seedling technology compliance with the corresponding mother tree code. This information records the technical measures adopted and their implementation quality during the seedling stage for seeds from a specific seed source, providing data support for subsequent analysis of the impact of seedling technology on seedling quality and optimization of seedling cultivation processes.

[0026] S5. Generate a seedling production batch based on the registration number association information, and generate seedling traceability information based on the seedling production batch, breeding association information, and seed source association information; For example, a seedling production batch refers to a unique production batch code assigned to a group of seedlings cultivated from seeds originating from the same mother tree or the same group of mother trees, within the same time period, at the same nursery site, and using the same seedling cultivation techniques. This code typically includes information elements such as the germplasm resource registration number, mother tree code, seedling start date, and nursery site code, used to accurately trace the source and cultivation process of seedlings in subsequent stages such as nursery release, sales, and afforestation. Seedling traceability information refers to the information set generated by integrating the seedling production batch code with corresponding breeding association information and germplasm association information. This information comprehensively records the entire chain of data from germplasm source, mother tree traits, seed quality to seedling cultivation techniques, forming a comprehensive seedling file that includes germplasm identity, cultivation process, and quality attributes, providing a traceable data foundation for subsequent afforestation effectiveness analysis and germplasm performance evaluation.

[0027] As described in steps S1-S5 above, this application provides basic data support for subsequent traceability management by acquiring information on the collection of superior seed resources and information on the breeding environment. The information on the collection of superior seed resources includes post-planting growth information and harvested seed information, which respectively record the growth performance of the mother trees in the superior seed base and the quality characteristics of the seeds produced; the information on the breeding environment covers environmental condition data from germplasm preservation to seedling cultivation.

[0028] Based on the obtained mother tree codes, registration number association information was generated according to the mother tree codes and post-planting growth data. This achieved the association between the growth performance of individual mother trees and the germplasm resource registration number, providing a basis for evaluating the differences in trait expression of germplasm resources across different mother trees. Seed quality indices were obtained based on harvested seed information, and breeding association information between superior varieties and mother tree codes was generated. This achieved the association between seed quality characteristics and individual mother trees, laying the foundation for selecting superior seed-collecting mother trees and predicting seedling quality. Seedling technology compliance was obtained based on breeding environment information, and germplasm association information was generated. This achieved the association between the quality of seedling technology implementation and mother tree codes, providing data support for analyzing the impact of seedling technology on seedling quality.

[0029] Finally, seedling production batches are generated based on the registration number and associated information. These batches are then integrated with breeding and germplasm information to generate seedling traceability information. This information comprehensively records the entire chain of data, from germplasm origin, mother tree traits, seed quality to seedling cultivation techniques, forming a seedling file with a unique identifier and complete quality attributes. This method enables precise traceability of precious tree species throughout their entire lifecycle, from germplasm resources to seedling production, solving prominent problems in existing technologies such as broken germplasm information chains, untraceable seedling cultivation processes, and lack of feedback on germplasm performance.

[0030] In one embodiment, the step of obtaining the mother tree code of the improved variety planted in the improved variety base, and generating registration number association information based on the mother tree code and post-planting growth data includes: S201. Obtain the continuous annual growth data of the corresponding mother tree according to the mother tree code, and generate a single-plant growth potential coefficient reflecting the growth rate of a single plant according to the continuous annual growth data. For example, the single-tree growth potential coefficient is a quantitative indicator calculated based on the growth data of each individual mother tree in the improved variety base over many years, used to characterize the growth rate of the mother tree under specific environmental conditions. The continuous annual growth data includes measurements of growth indicators such as tree height, diameter at breast height (DBH), and crown width, taken regularly each year starting from the year the mother tree was planted. These data constitute a time-series dataset reflecting the growth dynamics of the mother trees.

[0031] First, the annual growth data is extracted from the continuous annual growth data. For each mother tree, the tree height measured in the current year is subtracted from the tree height measured in the previous year to obtain the tree height growth for that year; the diameter at breast height (DBH) measured in the current year is subtracted from the DBH measured in the previous year to obtain the DBH growth for that year; and the crown width measured in the current year is subtracted from the crown width measured in the previous year to obtain the crown width growth for that year. Data from the planting year, lacking the previous year's measurements, is stored only as a baseline and is not included in the growth calculation.

[0032] Secondly, considering the differences in growth patterns among different tree species and at different age stages, it is necessary to standardize the annual growth figures and convert them into relative growth rates. Benchmark values ​​for expected growth at the same age stage for each tree species are obtained from a tree species characteristic parameter database, including expected tree height growth, expected diameter at breast height (DBH) growth, and expected crown width growth. These expected values ​​are derived from the standard growth model or historical statistical data for that tree species. The relative growth rate of tree height is obtained by dividing the actual tree height growth of the year by the expected tree height growth; the relative growth rate of DBH is obtained by dividing the actual DBH growth of the year by the expected DBH growth; and the relative growth rate of crown width is obtained by dividing the actual crown width growth of the year by the expected crown width growth.

[0033] Then, the relative growth rates of the three indicators—tree height, diameter at breast height (DBH), and crown width—are weighted and combined to obtain the overall relative growth rate for that year. In the weighted combination, tree height and DBH best reflect the tree's growth rate and are therefore given higher weights, generally 40% each; crown width, as a secondary indicator, is given a 20% weight. For different tree species, the weighting can be adjusted according to their growth characteristics; for example, for species with rapid crown expansion, the weight of crown width can be appropriately increased.

[0034] Next, the relative growth rates over multiple years are weighted and averaged over time to obtain the individual tree growth potential coefficient. Considering that recent growth data better reflects the current growth status of the mother tree, more recent years are assigned higher weights, and earlier years are assigned lower weights. The weighting is done in an arithmetic progression, with the earliest year having the lowest weight, the most recent year having the highest weight, and the weights of intermediate years increasing linearly in chronological order. The individual tree growth potential coefficient is obtained by multiplying the relative growth rates of all years by their corresponding weights and summing the results.

[0035] The final growth potential coefficient for a single tree is a dimensionless value. If the coefficient is greater than one, it indicates that the mother tree is growing faster than the average growth rate of trees of the same species and age; if the coefficient is less than one, it indicates that the growth rate is slower than the average. The larger the coefficient, the more vigorous the growth, and the greater the growth potential under the same conditions.

[0036] S202. Obtain the individual growth potential coefficients of all mother trees under the same germplasm resource registration number, and generate a growth consistency index that characterizes the synchronicity of population growth based on the individual growth potential coefficients. For example, the growth uniformity index is a statistic calculated based on the individual growth potential coefficients of all mother trees within the same germplasm resource (i.e., those with the same germplasm resource registration number). It measures the uniformity and synchronicity of growth performance within the population. To calculate this index, the individual growth potential coefficients of all mother trees under that registration number are first aggregated to form a dataset. Then, the dispersion of this dataset is analyzed, using commonly used dispersion indicators such as standard deviation and coefficient of variation. A small dispersion indicates that the growth potential of each mother tree is similar, and the population's growth performance is consistent, reflecting relatively stable genetic traits of the germplasm resource. A large dispersion indicates significant differences between individuals, potentially indicating genetic differentiation or heterogeneity in environmental response. The growth uniformity index is usually calculated using the reciprocal of the coefficient of variation or after normalization, so that a larger index value indicates better population uniformity.

[0037] S203. Construct the phenotypic expression coefficient of the germplasm resource based on the weighted combination of the single plant growth potential coefficient and the growth uniformity index. For example, the trait expression coefficient is a quantitative indicator that comprehensively evaluates the overall performance of a specific germplasm resource in terms of growth traits. It is obtained by weighting and combining the individual plant growth potential coefficient and the growth uniformity index. The purpose of the weighted combination is to balance the relationship between individual growth level and population uniformity. For example, in some breeding objectives, growth rate may be more important, in which case the weight of the individual plant growth potential coefficient is higher; while in other cases, such as cultivating timber forests that require uniform forest stand, the weight of the growth uniformity index is increased accordingly. The formula for calculating the trait expression coefficient is: ;in, Indicates the trait expression coefficient. This indicates the weight of the growth potential coefficient of a single plant. This represents the growth potential coefficient of a single plant. Indicates the weight of the growth consistency index. This represents the growth consistency index.

[0038] S204. Obtain vigor detection data of seeds harvested in previous years under the same germplasm resource registration number, generate a seed stability factor based on the fluctuation of the vigor detection data, and use the seed stability factor to correct the trait expression coefficient. For example, the vigor test data of seeds harvested over the years refers to the vigor index data such as germination rate and germination potential obtained after conducting germination tests on seeds produced by all mother trees under the same germplasm resource registration number in different years. These data constitute a time series dataset reflecting the annual changes in seed quality of the germplasm resource. The seed stability factor is a quantitative value calculated based on the fluctuation of the aforementioned vigor test data over the years. (This involves obtaining seed vigor test data from all mother trees under the same germplasm resource registration number across multiple consecutive harvest years. The vigor test data includes germination rate or germination potential indicators. For each mother tree, its vigor test values ​​from different years are compiled into a time series dataset. Next, the mean of the mother tree's vigor test data over the years is calculated. The sum of the vigor test values ​​from all years is divided by the total number of years to obtain a benchmark value reflecting the average level of seed vigor of the mother tree. Then, the standard deviation of the mother tree's vigor test data over the years is calculated. The sum of the squares of the differences between the vigor test values ​​of each year and the mean is divided by the total number of years, and then the square root is taken to obtain the absolute fluctuation reflecting the dispersion of the vigor test values ​​of each year relative to the mean. Finally, the standard deviation is divided by the mean to obtain the coefficient of variation, which characterizes the relative fluctuation of the vigor test data over the years. A larger coefficient of variation indicates more drastic fluctuations in seed vigor between different years and poorer stability; a smaller coefficient of variation indicates more stable seed vigor performance between years.) This factor is used to characterize the stability and consistency of seed quality produced by the germplasm resource across different years. Specifically, the standard deviation or coefficient of variation of germination rate or germination potential data over the years can be calculated. The smaller the fluctuation, the more stable the seed quality, and the larger the seed stability factor value; the larger the fluctuation, the greater the annual variation in seed quality, and the smaller the seed stability factor value. This stability factor can be used to correct the trait expression coefficients, for example, by using multiplicative or additive correction methods, so that the trait expression coefficients of germplasm resources with unstable seed quality are appropriately reduced, while the trait expression coefficients of germplasm resources with stable seed quality are maintained or increased.

[0039] S205. Rebind the corrected trait expression coefficients with the corresponding germplasm resource registration numbers to generate updated registration number association information; For example, the corrected trait expression coefficient refers to the final trait evaluation index obtained after adjustment by the seed stability factor. This index comprehensively reflects the overall level of the germplasm resource in both growth performance and seed quality. Rebinding the corrected trait expression coefficient with the corresponding germplasm resource registration number means updating the trait expression information associated with that registration number in the germplasm resource file, replacing the original record with the latest evaluation result. The updated registration number association information includes the latest trait expression coefficient value of the germplasm resource and the seed stability factor data on which this correction was based, forming a dynamically updated evaluation file for the germplasm resource, providing a basis for subsequent breeding of superior varieties, selection of seed mother trees, and allocation of germplasm resources.

[0040] As described in steps S201-S205 above, this application obtains continuous annual growth data of the corresponding mother tree based on the mother tree code, and generates a single-tree growth potential coefficient reflecting the growth rate of a single tree based on these data. This coefficient is obtained by comprehensively calculating growth indicators such as tree height and diameter at breast height over the years, and is used to quantify the growth vitality and relative growth rate of each individual mother tree.

[0041] Subsequently, the growth potential coefficients of individual mother trees under the same germplasm resource registration number were obtained. The dispersion of these coefficients was statistically analyzed to generate a growth consistency index characterizing the synchronicity of population growth. This index reflects the genetic stability and performance consistency of the germplasm resource at the population level; the smaller the degree of variation, the higher the consistency, indicating that the growth performance of the germplasm resource tends to be more uniform within the improved seed base.

[0042] After obtaining the individual plant growth potential coefficient and the population growth uniformity index, a weighted combination of these two coefficients with different weights is constructed to generate the trait expression coefficient of the germplasm resource. This coefficient integrates individual and population performance into a comprehensive evaluation index. The weight allocation can be adjusted according to different breeding objectives, making the evaluation results more targeted and practical.

[0043] To further verify the stability of germplasm resources, vigor testing data of seeds harvested over the years under the same germplasm resource registration number were obtained. A seed stability factor was generated based on the annual fluctuations of these data. This factor reflects the stability of seed quality produced by the germplasm resource across different years; the smaller the fluctuation, the higher the stability.

[0044] Finally, the corrected trait expression coefficients are re-linked with the corresponding germplasm resource registration numbers to generate updated registration number association information. This information is a dynamically updated germplasm resource evaluation file, recording the latest trait expression level of the germplasm resource and the basis for its correction, providing traceable dynamic evaluation data for subsequent breeding decisions, seed mother tree selection, and germplasm resource allocation.

[0045] In one embodiment, the step of obtaining a seed quality index based on the harvested seed information and generating breeding association information between improved varieties and mother tree codes based on the seed quality index includes: S301. Obtain the thousand-grain weight and plumpness detection data of the seeds harvested from the corresponding mother tree in previous years according to the mother tree code, and generate the grain plumpness index according to the thousand-grain weight and plumpness detection data. For example, the thousand-grain weight and plumpness test data of seeds harvested over the years refer to the physical trait data obtained after sampling and testing seeds produced by a mother tree with a specific mother tree code in different harvest years. Thousand-grain weight refers to the weight of one thousand randomly selected seeds, expressed in grams, reflecting the seed's fullness and nutrient accumulation level; plumpness refers to the fullness of the seed's appearance, usually assessed visually or by specific gravity, reflecting the degree of seed development. The grain plumpness index is a quantitative value calculated based on the above-mentioned thousand-grain weight and plumpness test data, used to characterize the overall level of physical quality of seeds produced by a specific mother tree. Specifically, the thousand-grain weight test values ​​of each year can be compared with the benchmark thousand-grain weight value for the same tree species to obtain a relative thousand-grain weight coefficient; the plumpness test values ​​of each year can be compared with the plumpness grading standard to obtain a plumpness score; and then, by weighted integration of the data from previous years, a grain plumpness index that comprehensively reflects the physical quality of the seeds from that mother tree can be generated.

[0046] S302. Obtain the germination rate and germination potential detection data of the seeds harvested from the corresponding mother tree in previous years according to the mother tree code, and generate a germination potential coefficient according to the germination rate and germination potential detection data. For example, the germination rate and germination potential test data of seeds harvested over the years refer to the vigor index data obtained after conducting germination tests on seeds produced by individual mother trees with specific mother tree codes in different harvest years. Germination rate refers to the percentage of normally germinating seeds out of the total number of tested seeds under specified conditions and time, reflecting the maximum germination capacity of the seeds; germination potential refers to the percentage of germinating seeds out of the total number of tested seeds under specified conditions and within a short period, reflecting the germination speed and uniformity of the seeds. The germination potential coefficient is a quantitative value calculated based on the above germination rate and germination potential test data, used to characterize the overall level of vigor performance of seeds produced by a specific mother tree. Specifically, the germination rate test values ​​of each year can be compared with the germination rate standard of the same tree species to obtain a relative germination rate coefficient; the germination potential test values ​​of each year can be compared with the germination potential benchmark value to obtain a germination speed coefficient; and then, by weighting and integrating the data from previous years, a germination potential coefficient that comprehensively reflects the germination capacity of the seeds of that mother tree can be generated.

[0047] S303. Obtain the genetic transmission stability factor based on the germination potential coefficient; For example, the genetic transmission stability factor refers to a quantitative value derived from further analysis of the germination potential coefficient, used to characterize the genetic ability of a specific mother tree to stably pass on its superior vigor traits to its offspring. The germination potential coefficient reflects the actual vigor level of the seeds produced by the mother tree, but the degree of fluctuation of this coefficient between different years reflects the stability of the expression of the mother tree's genetic traits. Specifically, the germination potential coefficients of the mother tree in multiple consecutive harvest years can be obtained, and the degree of variation of these coefficients can be calculated. The smaller the degree of variation, the more stable the seed vigor of the mother tree is under different environmental conditions, the stronger its genetic transmission ability, and the larger the value of the genetic transmission stability factor; the larger the degree of variation, the greater the influence of annual environmental changes on seed vigor, and the smaller the value of the genetic transmission stability factor.

[0048] S304. Genetic heterogeneity coefficient is generated based on the grain plumpness index and germination potential coefficient of seeds harvested from different mother trees under the same germplasm resource registration number, and the genetic transmission stability factor is modified based on the genetic heterogeneity coefficient to obtain the modified genetic transmission stability factor. For example, different mother trees under the same germplasm resource registration number refer to multiple mother tree individuals originating from the same germplasm resource, which together constitute the propagation population of that germplasm resource. The progeny heterogeneity coefficient is a quantitative value calculated based on the grain plumpness index and germination potential coefficient of all mother trees within the population. It is used to characterize the degree of difference and heterogeneity in seed quality among different mother trees within the germplasm resource. Specifically, the degree of variation in the grain plumpness index and germination potential coefficient of all mother trees within the population can be calculated, and the two can be combined to obtain the progeny heterogeneity coefficient. The larger the heterogeneity coefficient, the greater the difference in seed quality among different mother trees within the germplasm resource, and the higher the degree of progeny heterogeneity. This heterogeneity coefficient is used to correct the genetic transmission stability factor, so that the genetic transmission stability factor of a population with a high degree of progeny heterogeneity is appropriately reduced to reflect the impact of insufficient internal consistency on genetic transmission stability; while the genetic transmission stability factor of a population with a low degree of progeny heterogeneity is maintained or increased.

[0049] S305. Generate a comprehensive quality index for harvested seeds based on the grain plumpness index, germination potential coefficient, and modified genetic transmission stability factor, and associate the comprehensive quality index with the corresponding mother tree code to generate breeding association information containing seed quality dimensions. For example, the comprehensive quality index is a quantitative value obtained by integrating the grain plumpness index, germination potential coefficient, and modified genetic transmission stability factor. It is used to comprehensively evaluate the overall quality level of seeds produced by a specific mother tree from three dimensions: physical quality, vigor performance, and genetic stability. The grain plumpness index reflects the physical fullness of the seed, the germination potential coefficient reflects the seed vigor level, and the modified genetic transmission stability factor reflects the mother tree's ability to stably pass on superior traits to offspring. When constructing the comprehensive quality index, different weights can be assigned to the three components according to actual needs. The formula for calculating the comprehensive quality index is: ;in, Indicates the overall quality index. Indicates the first quality weight. This indicates the grain plumpness index. Indicates the second quality weight. Indicates the germination potential coefficient. Indicates the third quality weight. This represents the modified genetic transmission stability factor. For example, for seedling production with a primary goal of seedling emergence rate, a higher weight can be assigned to the germination potential coefficient; for germplasm preservation with a goal of long-term genetic improvement, a higher weight can be assigned to the modified genetic transmission stability factor. The calculated comprehensive quality index is associated with the corresponding mother tree code to generate breeding association information that includes seed quality dimensions. This information records the multi-dimensional quality evaluation results of the seeds produced by each mother tree, forming a complete archive of the mother tree in terms of seed quality.

[0050] As described in steps S301-S305 above, this invention obtains the thousand-grain weight and plumpness test data of seeds harvested from the corresponding mother tree over the years based on the mother tree code. By comprehensively calculating these physical trait test data, a grain plumpness index is generated. This index reflects the overall level of physical plumpness of seeds produced by a specific mother tree, providing a basic physical quality indicator for subsequent seed quality evaluation.

[0051] Subsequently, germination rate and germination potential data of seeds harvested from the same mother tree over the years were obtained based on the same mother tree code. A germination potential coefficient was generated by comprehensively calculating these vigor index data. This coefficient reflects the overall level of germination ability and vigor performance of seeds produced by a specific mother tree, providing a vigor dimension indicator for seed quality evaluation.

[0052] Based on the obtained germination potential coefficient, the fluctuation degree of the germination potential coefficient of the mother tree in different years was further analyzed to generate a genetic transmission stability factor. This factor reflects the genetic ability of the mother tree to stably pass on its excellent vigor traits to its offspring, and is an in-depth evaluation of the genetic quality of the mother tree, providing a basis for distinguishing between accidental expression and stable inheritance.

[0053] To consider the consistency within germplasm resources, the seed plumpness index and germination potential coefficient of different mother trees under the same germplasm resource registration number were obtained. The dispersion of these coefficients was analyzed to generate a progeny heterogeneity coefficient. This coefficient reflects the degree of difference in seed quality among different mother trees within the germplasm resource. This heterogeneity coefficient was used to correct the genetic transmission stability factor, resulting in a corrected genetic transmission stability factor that considers not only the genetic stability of individual mother trees but also the influence of heterogeneity at the population level in the evaluation results.

[0054] Finally, the grain plumpness index, germination potential coefficient, and modified genetic transmission stability factor were integrated to generate a comprehensive quality index for the harvested seeds. This comprehensive quality index was then associated with the corresponding mother tree code to generate breeding association information that includes seed quality dimensions. This information comprehensively evaluates the seed quality of each mother tree from three dimensions: physical quality, vigor performance, and genetic stability, forming a complete file of each mother tree in terms of propagation material quality.

[0055] In one embodiment, the step of obtaining the thousand-grain weight and plumpness detection data of seeds harvested from the corresponding mother tree over the years based on the mother tree code, and generating a grain plumpness index based on the thousand-grain weight and plumpness detection data, includes: S3011. Obtain the thousand-grain weight detection value of each batch of seeds harvested from the corresponding mother tree in consecutive harvesting years according to the mother tree code, and generate a grain weight deviation coefficient based on the thousand-grain weight detection value and the average thousand-grain weight detection value of the same batch of seeds. For example, the thousand-grain weight of each harvested batch within a consecutive harvesting year refers to the measured thousand-grain weight data obtained by sampling and testing seeds produced by an individual mother tree with a specific mother tree code in different harvesting years and different harvesting batches. The average thousand-grain weight of seeds in the same batch refers to the average thousand-grain weight calculated after testing multiple samples in the same harvesting batch, representing the overall grain weight level of that batch of seeds. The grain weight deviation coefficient refers to the relative degree of deviation obtained by comparing the thousand-grain weight of a single batch with the average thousand-grain weight of seeds in the same batch. Specifically, the difference between the thousand-grain weight of this batch and the average of the same batch can be calculated, and the ratio of the difference to the average is used as the deviation coefficient.

[0056] S3012. Obtain the degree of variation of the thousand-grain weight of seeds in the same harvest year based on the grain weight deviation coefficient of different harvest batches in the same harvest year, and generate the annual stable value of grain weight in the same year based on the degree of variation. For example, different harvest batches within the same harvest year refer to seeds collected from the same mother tree in batches within the same harvest year. These batches may differ due to variations in harvest time and harvested parts. The variability of the thousand-seed weight in that year refers to a statistical measure calculated based on the seed weight deviation coefficient of all harvest batches within that year. It is used to characterize the fluctuation and stability level of the thousand-seed weight produced by the mother tree within the same year. Specifically, the standard deviation or range of the seed weight deviation coefficient of all batches within that year can be calculated. The smaller the variability, the more uniform and stable the seed weight among different batches of the mother tree within the same year; the larger the variability, the more significant the difference in seed weight between different batches. The annual stable value of seed weight refers to a quantitative indicator that maps the above variability to stability. The smaller the variability, the larger the stable value; the larger the variability, the smaller the stable value.

[0057] S3013. Obtain the plumpness detection value of seeds of each harvest batch of the corresponding mother tree in the same harvest year according to the mother tree code, and generate the plumpness matching coefficient of seeds according to the degree of closeness between the plumpness detection value and the standard plumpness benchmark value of the same tree species. For example, the plumpness test value for each batch of seeds harvested within the same harvest year refers to the test data obtained after assessing the plumpness of seeds from each batch produced by a mother tree individual with a specific mother tree code within a specific harvest year. Plumpness is usually graded based on the fullness of the seed's appearance, reflecting the degree of seed development completeness. The standard plumpness benchmark value for the same tree species refers to the plumpness evaluation reference standard established for that precious tree species, usually determined based on the normal developmental characteristics of the seeds of that tree species. The plumpness matching coefficient is a quantitative value obtained by comparing the plumpness test value with the standard plumpness benchmark value, used to characterize how close the plumpness of that batch of seeds is to the standard level. Specifically, the ratio of the test value to the benchmark value can be calculated, or the matching coefficient can be determined based on the position of the test value in the plumpness grading. The higher the matching coefficient, the closer the plumpness of that batch of seeds is to or reaches the standard level; the lower the matching coefficient, the more the seed plumpness deviates from the standard requirements.

[0058] S3014. Generate a fullness annual homogeneity index that reflects the consistency of seed appearance quality in the same year based on the discrete distribution of the fullness matching coefficient of all harvested batches in the same year, and correct the annual stable value of grain weight based on the fullness annual homogeneity index to obtain the grain weight-fullness composite coefficient. For example, the discrete distribution of the plumpness matching coefficients of all harvested batches within the same year refers to the distribution characteristics obtained after statistical analysis of the plumpness matching coefficients of each batch within that year, including the central tendency and dispersion of these coefficients. The annual plumpness homogeneity index is a quantitative value calculated based on the above discrete distribution, used to characterize the consistency and uniformity of the appearance quality of seeds produced by the mother tree in the same year. Specifically, the coefficient of variation of the plumpness matching coefficients of each batch can be calculated. The smaller the coefficient of variation, the higher the homogeneity index, indicating that the plumpness of different batches of seeds within that year is more consistent; the larger the coefficient of variation, the lower the homogeneity index, indicating that the differences in plumpness between different batches are more obvious.

[0059] S3015. Generate the grain plumpness index of the mother tree's seeds based on the grain weight-plumpness composite coefficient of multiple consecutive harvest years. For example, multiple consecutive harvest years refer to the range of harvest years covered by continuous tracking and observation of the same mother tree over many years, typically including observation data for three, five, or longer periods. The grain weight-fullness composite coefficient is a comprehensive value obtained after correction for each year, constituting the time series data of the seed quality performance of the mother tree in different years. The grain fullness index is the final quantitative value obtained after comprehensive calculation based on the grain weight-fullness composite coefficient of multiple consecutive harvest years, used to comprehensively evaluate the overall physical quality level of the seeds produced by the mother tree from a time perspective. Specifically, the weighted average of the grain weight-fullness composite coefficient for each year can be calculated, with the weight of earlier years appropriately reduced and the weight of more recent years appropriately increased to reflect the stability of seed quality changes after the mother tree matures; alternatively, the mean and variability of the coefficients for each year can be considered comprehensively, and mother trees with high mean levels and small inter-year variability are assessed as having a high grain fullness index. The formula for calculating the grain fullness index is: ;in, This indicates the grain plumpness index. Indicates the number of consecutive harvesting years. Indicates the first The mother tree in the first Annual grain weight-saturation composite coefficient Indicates the first The annual weighting coefficient.

[0060] As described in steps S3011-S3015 above, the thousand-grain weight of seeds from each harvested batch of seeds from the corresponding mother tree within consecutive harvesting years is obtained based on the mother tree code. A grain weight deviation coefficient is generated by comparing the thousand-grain weight of a single batch with the average value of the same batch. This coefficient reflects the degree of deviation of each batch of seeds in terms of grain weight from the average level of the same batch, providing basic data for subsequent analysis of grain weight variation between different harvested batches from the same mother tree.

[0061] Subsequently, for different harvest batches within the same harvest year, the degree of variation in the thousand-seed weight of seeds for that year was calculated based on the grain weight deviation coefficient of each batch, and the degree of variation was converted into an annual stable value for grain weight. This stable value characterizes the uniformity and stability of the grain weight of seeds produced by the mother tree in the same year; the smaller the degree of variation, the higher the stable value, indicating that the grain weight of seeds among different batches within that year is more consistent.

[0062] Based on grain weight analysis, the plumpness test values ​​of each harvested batch of seeds within the same harvest year were obtained according to the mother tree code. A plumpness matching coefficient was generated by comparing these values ​​with the standard plumpness benchmark value for the same tree species. This coefficient reflects the degree to which the plumpness of each batch of seeds closely approximates the standard level, providing a quantitative indicator for subsequent analysis of seed appearance quality.

[0063] By analyzing the discrete distribution of the plumpness matching coefficient of all harvested batches within the same year, a plumpness annual homogeneity index reflecting the consistency of seed appearance quality for that year is generated. This homogeneity index is then used to correct the aforementioned annual stable value of grain weight, resulting in a grain weight-plumpness composite coefficient. This composite coefficient integrates information from both grain weight stability and plumpness consistency, providing a comprehensive assessment of the physical quality of seeds from a single year.

[0064] Finally, based on the grain weight-fullness composite coefficient from multiple consecutive harvest years, a grain fullness index for the mother tree's seeds was generated through weighted integration or comprehensive analysis. This index comprehensively reflects the overall level and stability of the physical quality of seeds produced by the mother tree over many years from a temporal perspective.

[0065] In one embodiment, the step of obtaining seedling technology compliance based on the breeding environment information and generating seed source association information based on the seedling technology compliance and the mother tree code includes: S401. Obtain the substrate ratio adjustment record of the seeds produced by the corresponding mother tree during the seedling stage according to the mother tree code, and generate the substrate ratio deviation according to the substrate ratio adjustment record; For example, a substrate ratio adjustment record refers to the record of changes in the actual substrate composition and its proportions used during the seedling cultivation process for seedlings grown from seeds coded from a specific mother tree. The substrate is typically composed of peat, rice husks, bark powder, and yellow clay mixed in a certain volume ratio to provide a suitable rhizosphere environment for seedling growth. Substrate ratio deviation refers to the quantitative deviation value obtained by comparing the actual substrate ratio used with the standard substrate ratio established for that tree species. Specifically, the reference proportions of each component in the standard substrate ratio and the actual proportions of each component in the actual record can be obtained. The absolute value or sum of squares of the differences in the proportions of each component can be calculated, and these differences can then be combined into a deviation index. The smaller the deviation, the closer the actual ratio is to the standard requirement; the larger the deviation, the greater the difference between the actual ratio and the standard requirement, which may affect the normal growth and development of the seedlings. The formula for calculating the substrate ratio deviation is: ;in, Indicates the deviation of the matrix ratio. This indicates the number of substrate components (the total number of substrate components actually used in this seedling cultivation (e.g., peat, rice husks, bark powder, yellow soil, etc.)). Indicates the first The actual volume ratio of each component (the volume ratio of the matrix components used in the actual preparation). Indicates the first The standard volume ratio of each component (i.e., the first component taken from the standard substrate ratio scheme of this tree species) (Standard volume ratio of the components).

[0066] S402. Obtain the slow-release fertilizer application record of the seeds produced by the corresponding mother tree during the seedling stage according to the mother tree code, and generate the slow-release fertilizer loading matching coefficient according to the slow-release fertilizer application record. For example, a slow-release fertilizer application record refers to the record of slow-release fertilizer application for seedlings from a specific mother tree source during the seedling cultivation process, including parameters such as fertilization time, fertilizer amount, and N / P ratio. The slow-release fertilizer loading conformity coefficient refers to the quantitative degree of conformity obtained by comparing the actual fertilization parameters with a standard fertilization plan developed for that tree species. Specifically, the recommended slow-release fertilizer loading range and N / P ratio requirements in the standard plan can be obtained. The loading amount value in the actual fertilization record is compared with the median of the standard range to calculate the loading amount conformity; the actual N / P ratio is compared with the standard N / P ratio to calculate the ratio conformity; and then a weighted sum is used to obtain the loading conformity coefficient. The higher the coefficient, the more the actual fertilization operation conforms to the technical standards, and the more precise the nutrient supply; the lower the coefficient, the further the fertilization operation deviates from the standard, which may affect the nutrient absorption and dry matter accumulation of the seedlings.

[0067] S403. Obtain the mycorrhizal inoculation record of the seeds produced by the corresponding mother tree during the seedling stage according to the mother tree code, and generate the mycorrhizal infection synchronization index according to the mycorrhizal inoculation record. For example, a mycorrhizal inoculation record refers to the record of mycorrhizal fungal inoculation operations performed on seedlings from a specific mother tree during the seedling cultivation process, including information such as whether inoculation was performed, the type of inoculated fungicide, the inoculation time, and the inoculation method. For certain ectomycorrhizal tree species, such as *Quercus variabilis*, inoculation with mycorrhizal fungi can significantly promote seedling growth and root development. The mycorrhizal infection synchronization index is a quantitative value reflecting the synchronization between the mycorrhizal symbiosis effect and the expected degree of synchronization, calculated based on the mycorrhizal inoculation record and subsequent mycorrhizal infection rate data. Specifically, the average mycorrhizal infection rate of the same batch of seedlings can be obtained, and this infection rate can be compared with the standard infection rate target value to obtain the infection level coefficient; at the same time, the time required for the infection rate to reach a stable state can be obtained and compared with the standard synchronization time to obtain the synchronization time coefficient; the two coefficients are combined to obtain the mycorrhizal infection synchronization index. The higher the index, the better the mycorrhizal inoculation effect, and the faster and more the mycorrhizal symbiosis is established as expected.

[0068] S404. Obtain the container specification record value of the seeds produced by the corresponding mother tree during the seedling stage according to the mother tree code, and generate the container volume fit degree according to the container specification record value. For example, the container specification record value refers to the dimensional parameters of the seedling containers used for seedlings from a specific mother tree source during the seedling cultivation process. This typically includes the container's height and diameter, or directly records the container's volume. Container volume fit refers to the quantitative degree of fit obtained by comparing the actual container specifications with the recommended standard container specifications for that tree species and seedling cultivation goals (e.g., 1-year-old seedlings, 2-year-old large-sized seedlings). Specifically, the recommended volume range of the standard container specifications can be obtained, and the ratio of the actual container volume to the median of the recommended volume can be calculated. The closer the ratio is to 1, the higher the fit. Simultaneously, the influence of container shape (e.g., cylindrical, square) on root growth can be considered, and the fit can be fine-tuned. This fit reflects the suitability of the container space for seedling root development. A higher fit is more conducive to full root extension and nutrient absorption, while avoiding substrate waste and increased transportation costs caused by excessive volume.

[0069] S405. Generate a comprehensive seedling technology compliance degree based on the substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index and container volume suitability. For example, the comprehensive seedling technology compliance rate refers to a comprehensive evaluation index obtained by integrating multiple single-dimensional technical indicators such as substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume suitability. It is used to quantify the degree of compliance of the overall technical implementation of seeds from a specific mother tree during the seedling stage. The formula for calculating the comprehensive seedling technology compliance rate is as follows: ;in, Indicates the overall quality index. Indicates the weight of the first degree of conformity. Indicates the deviation of the matrix ratio. This indicates the second degree of conformity weight. Indicates the coefficient of fit for slow-release fertilizer loading. This indicates the third degree of conformity weight. Indicates the mycorrhizal infection synchronization index. This indicates the fourth degree of conformity weight. This indicates the container volume fit. The substrate ratio deviation is an inverse indicator (the smaller the better), and needs to be converted to a positive value first during integration, such as by taking its reciprocal or a negative value. The slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume fit are all positive indicators (the larger the better). When generating the overall consistency, different weights can be assigned according to the importance of each technical step to seedling quality. For example, for container seedling cultivation, substrate ratio and slow-release fertilizer loading usually have higher weights, while mycorrhizal inoculation and container specifications have their weights adjusted according to the tree species characteristics.

[0070] S406. Associate the compliance of the integrated seedling cultivation technology with the mother tree code to generate seed source association information; For example, seed source association information refers to the information record generated by binding the comprehensive seedling cultivation technology compliance degree with the corresponding mother tree code. This information reflects the level of technical treatment experienced by seeds from a specific mother tree during the seedling stage. This information not only includes the numerical value of the comprehensive seedling cultivation technology compliance degree but can also include detailed data for each sub-indicator, such as substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume suitability, forming a complete seedling cultivation technology file. After associating this information with the mother tree code, subsequent stages such as seedling delivery, afforestation acceptance, and effectiveness monitoring can trace the technical implementation status of this batch of seedlings during the seedling stage through the mother tree code. This provides a basis for analyzing the impact of different seedling cultivation technologies on seedling quality and afforestation effectiveness, and also accumulates data for optimizing seedling cultivation processes and selecting suitable technology combinations.

[0071] As described in steps S401-S406 above, this invention obtains the substrate ratio adjustment record of seeds produced by the corresponding mother tree during the seedling stage based on the mother tree code. By comparing the actual substrate ratio with the standard ratio, a substrate ratio deviation is generated. This deviation reflects the degree of deviation between the seedling substrate preparation process and the standard requirements, providing a quantitative indicator for evaluating the technical execution quality of the substrate process.

[0072] Secondly, slow-release fertilizer application records were obtained based on the same mother tree code. By comparing the actual fertilization parameters with the standard fertilization plan, a slow-release fertilizer loading conformity coefficient was generated. This coefficient reflects the degree of conformity between the fertilization process and the standard requirements, providing a basis for evaluating the accuracy of nutrient supply.

[0073] Next, mycorrhizal inoculation records were obtained based on the mother tree code, and combined with subsequent mycorrhizal infection detection data to generate a mycorrhizal infection synchronization index. This index reflects the degree of synchronization between the mycorrhizal inoculation effect and the expected goal, providing a quantitative indicator for evaluating the quality of mycorrhizal symbiosis establishment.

[0074] Subsequently, the container specifications used during the seedling stage were recorded based on the mother tree code. By comparing the actual container specifications with standard container specifications, a container volume fit was generated. This fit reflects the suitability of the container space for seedling root development, providing a basis for evaluating the rationality of container selection.

[0075] After obtaining the above four individual technical indicators, the substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume suitability were integrated to generate a comprehensive seedling cultivation technology compliance rate. This comprehensive value comprehensively evaluates the overall implementation level of the seedling cultivation technology of this batch from four dimensions: substrate, fertilization, mycorrhizae, and container, providing a unified standard for comparing the seedling cultivation quality among different mother tree seed sources.

[0076] Finally, the overall seedling technology compliance is linked with the corresponding mother tree code to generate seed source association information. This information records the technical processing data of seeds from each mother tree during the seedling stage, forming a link between seed source and seedling technology.

[0077] In one embodiment, the steps include generating a seedling production batch based on the registration number association information, and generating seedling traceability information based on the seedling production batch, breeding association information, and seed source association information; S501. Based on the seedling production batch, retrieve the corresponding comprehensive quality index from the breeding association information, and generate the seed quality grade to which the seed belongs based on the comprehensive quality index. For example, a seedling production batch refers to a unique production batch code assigned to a group of seedlings cultivated from seeds originating from the same mother tree or the same group of mother trees, within the same time period, at the same nursery site, and using the same nursery technology. This code is the core identifier for subsequent traceability of the seedling's origin and cultivation process. Breeding association information refers to the information set generated by associating the mother tree code with the comprehensive quality index. The comprehensive quality index is a quantitative value calculated based on the grain plumpness index, germination potential coefficient, and modified genetic transmission stability factor, used to evaluate the overall quality level of seeds produced by a specific mother tree from three dimensions: physical quality, vigor performance, and genetic stability. The corresponding mother tree code is obtained by parsing the seedling production batch, and then the comprehensive quality index associated with that mother tree code is retrieved from the breeding association information. Seed quality grade refers to the qualitative or semi-quantitative classification result obtained by mapping the numerical range of the comprehensive quality index to a preset grade division. Specifically, multiple quality grade thresholds can be preset, for example, dividing the comprehensive quality index into first-level, second-level, and third-level grades, each corresponding to a certain index range. The comprehensive quality index ranges from zero to one. Based on the distribution characteristics of historical data for this tree species and expert experience, the seed quality grading thresholds are set as follows: Seeds with a comprehensive quality index greater than or equal to 0.8 are classified as Grade 1, indicating excellent comprehensive quality, high genetic potential, and strong vigor, suitable for cultivating high-quality seedlings or preserving as core germplasm resources; seeds with a comprehensive quality index between 0.6 and 0.8 are classified as Grade 2, indicating good comprehensive quality, suitable for normal seedling production; seeds with a comprehensive quality index below 0.6 are classified as Grade 3, indicating average comprehensive quality, requiring cautious use or strengthened management during seedling cultivation. These thresholds can be adjusted according to the biological characteristics and practical application needs of different tree species and stored in a tree species characteristic parameter database. When new historical data accumulates, the thresholds can be dynamically optimized using statistical analysis methods such as percentile methods. For example, the top 30% of historical data can be classified as Grade 1, the middle 40% as Grade 2, and the bottom 30% as Grade 3, making the grading more closely reflect the actual distribution.

[0078] S502. Obtain the corresponding comprehensive seedling technology compliance degree from the seed source association information according to the mother tree code, and generate the seedling technology level to which the seedling belongs based on the comprehensive seedling technology compliance degree. For example, seed source association information refers to the information record generated by associating the comprehensive seedling technology compliance degree with the corresponding mother tree code, used to reflect the level of technical treatment experienced by seeds from a specific mother tree during the seedling stage. The comprehensive seedling technology compliance degree is a comprehensive evaluation index obtained by integrating multiple single-dimensional technical indicators such as substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume fit, used to quantify the degree of compliance of the overall technical execution of seeds from a specific mother tree during the seedling stage. The corresponding mother tree code is obtained by parsing the seedling production batch, and then the comprehensive seedling technology compliance degree associated with that mother tree code is obtained from the seed source association information. The seedling technology level refers to the qualitative or semi-quantitative classification result obtained by mapping the numerical range of the comprehensive seedling technology compliance degree to a preset level division. Specifically, seedling batches with a comprehensive seedling technology compliance rate of 0.9 or higher are classified as Grade A, indicating that the seedling technology was executed very well and all indicators were close to optimal; batches with a comprehensive seedling technology compliance rate between 0.7 and 0.9 are classified as Grade B, indicating that the seedling technology was executed well and the main indicators met the standards; batches with a comprehensive seedling technology compliance rate between 0.5 and 0.7 are classified as Grade C, indicating that the seedling technology was executed moderately with some deviations; and batches with a comprehensive seedling technology compliance rate below 0.5 are classified as Grade D, indicating that the seedling technology was executed poorly and needed improvement. Similarly, these thresholds can be dynamically adjusted according to tree species characteristics and seedling objectives and stored in a tree species-seedling process parameter database. The obtained comprehensive seedling technology compliance rate is compared with these thresholds to determine its grade, thereby generating the seedling technology grade for that seedling production batch.

[0079] S503. Generate the expected seedling quality index for the seedling batch based on the seed quality grade and seedling technology grade. For example, the expected seedling quality index is a quantitative value obtained by weighted summation of seed quality grade and seedling cultivation technology grade, used to predict the final seedling quality level that a particular batch of seedlings can produce. Seed quality grade reflects the intrinsic genetic potential and basic vigor of the seed source, while seedling cultivation technology grade reflects the quality level of human technical intervention during the seedling cultivation process; both together determine the final quality performance of the seedlings. When generating the expected seedling quality index, different weights can be assigned to seed quality grade and seedling cultivation technology grade based on the correlation strength between historical seedling cultivation data and the actual pass rate of seedlings leaving the nursery. For example, if historical data shows that seed quality has a greater impact on seedling quality, then seed quality grade is given a higher weight; if the impact of seedling cultivation technology is more significant, then seedling cultivation technology grade is given a higher weight. Through weighted summation or table lookup mapping, seed quality grade and seedling cultivation technology grade are transformed into a continuous expected seedling quality index. The higher the index, the higher the expected quality of the seedlings produced in this batch; the lower the index, the higher the potential risk to seedling quality.

[0080] S504. Integrate the data of the seedling production batch, seed quality grade, and seedling technology grade to generate seedling traceability information; For example, seedling traceability information refers to a set of information generated by integrating the seedling production batch code with the corresponding seed quality grade, seedling technology grade, and expected seedling quality index. This information comprehensively records a seedling file from seed source quality and seedling technology to expected quality. During data integration, the seedling production batch code is first used as the primary key, and the seed quality grade, seedling technology grade, and expected seedling quality index are stored as attribute values. Furthermore, basic information about the batch can be added, such as the seedling start date, seedling site, mother tree code of the seeds used, and substrate ratio records, forming a multi-dimensional traceability data record. This seedling traceability information is unique and traceable. Subsequently, in the stages of seedling delivery, sales, afforestation, and effectiveness monitoring, the complete traceability file of the batch of seedlings can be quickly retrieved by scanning or querying the seedling production batch code to understand its seed source quality, cultivation process, and expected quality.

[0081] As described in steps S501-S504 above, this application retrieves the corresponding comprehensive quality index from the breeding-related information based on the seedling production batch, and generates a seed quality grade by comparing this index with a preset grade threshold. This grade directly reflects the intrinsic quality level of the seeds used in that batch, serving as a fundamental guarantee of seedling quality.

[0082] Secondly, based on the mother tree code corresponding to the seedling production batch, the comprehensive seedling technology compliance rate is obtained from the seed source association information. This compliance rate is then compared with a preset grade threshold to generate a seedling technology grade. This grade reflects the quality level of technology execution during the seedling production process for that batch, serving as a process guarantee for seedling quality.

[0083] After obtaining the seed quality grade and seedling cultivation technology grade, the two are combined to generate the expected seedling quality index for the seedling batch. This index quantitatively predicts the final seedling quality by considering the weighting of the influence of seed quality and seedling cultivation technology on the actual quality of seedlings in historical data, providing a scientific basis for quality control and graded management.

[0084] Finally, the data on seedling production batches, seed quality grades, seedling technology grades, and expected seedling quality indices are integrated to generate seedling traceability information. This information links seed source quality, cultivation process, and quality prediction, forming a comprehensive seedling file with a unique identifier.

[0085] In one embodiment, the step of integrating the seedling production batch, seed quality grade, and seedling technology grade to generate seedling traceability information includes: S5041. Obtain the seedling start time and seedling site environmental monitoring data according to the seedling production batch, and generate the seedling environmental feature code for the batch according to the seedling start time and seedling site environmental monitoring data. For example, the seedling start time refers to the specific date on which the seedling production batch officially begins sowing or transplanting, recording the starting point of the seedling cultivation process. Seedling site environmental monitoring data refers to the data set obtained through continuous monitoring of environmental factors within the seedling site during the seedling cultivation process, including daily or weekly records of indicators such as temperature, humidity, light intensity, and carbon dioxide concentration. The seedling environment feature code is a unique code or feature vector generated after integrating and processing the seedling start time and seedling site environmental monitoring data, used to characterize the environmental conditions of this batch of seedlings. Specifically, the seedling start time can be converted into year and week codes, and key statistical features, such as average temperature, accumulated temperature, average humidity, and cumulative light intensity throughout the entire seedling cycle, can be extracted from the environmental monitoring data. These feature values ​​are then normalized and quantified to form a set of feature codes that reflect the characteristics of the seedling environment. This feature code can be used for subsequent analysis of differences in seedling growth under different environmental conditions and the impact of environmental factors on seedling quality.

[0086] S5042. Obtain substrate ratio adjustment records and slow-release fertilizer application records based on the seedling production batches, and generate a dynamic curve of the seedling process based on the substrate ratio adjustment records and slow-release fertilizer application records. For example, a substrate ratio adjustment record refers to the actual operational record of adjusting the substrate component ratio according to the needs of seedlings at different growth stages during the seedling cultivation process. This includes information such as the time of each adjustment, the ratio of each component before adjustment, and the ratio of each component after adjustment. A slow-release fertilizer application record refers to the record of multiple fertilization operations performed during the seedling cultivation process, including information such as the time of each fertilization, the amount of fertilizer applied, the N / P ratio of the fertilizer, and the type of fertilizer.

[0087] The seedling cultivation process dynamic curve is a curve generated by integrating substrate ratio adjustment records and slow-release fertilizer application records according to a time series. It is used to visually demonstrate the dynamic changes of key process parameters over time during seedling cultivation. The specific representation of this curve is as follows: The horizontal axis represents the seedling cultivation period, starting from the seedling initiation date and recorded in days, showing the complete seedling cultivation cycle from sowing or transplanting to seedling delivery. The vertical axis contains two dimensions: the left vertical axis represents the proportion of key components in the substrate, usually selecting the component with the greatest impact on seedling growth as a representative, such as the volume percentage of peat in the substrate; the right vertical axis represents the cumulative application of slow-release fertilizer, measured in grams per seedling or kilograms per cubic meter, reflecting the total amount of fertilizer applied from the start of seedling cultivation to the current time point.

[0088] The curves are generated as follows: First, the adjustment events in the substrate ratio adjustment record are arranged chronologically. During the time interval between two adjacent adjustments, the substrate ratio is considered a constant value, thus appearing as a horizontal line segment on the curve. At the adjustment time point, the ratio changes abruptly, appearing as a vertical rise or fall on the curve. Connecting these horizontal line segments yields a stepped line representing the change in substrate ratio over time. For the cumulative loading of slow-release fertilizer, the fertilizer application amounts corresponding to each fertilization event are summed to obtain the cumulative loading amount at each time point. Connecting these discrete points with a line yields an upward curve showing the cumulative loading amount of slow-release fertilizer over time.

[0089] like Figure 4 As shown, the following key characteristic parameters can be extracted from the dynamic curve of the seedling raising process: First adjustment time: The number of days from the start of seedling cultivation to the first adjustment of the substrate ratio (seedling time), reflecting the response speed of the seedlings to the substrate in the early stage of seedling cultivation; Number of adjustments: The total number of times the substrate ratio is adjusted throughout the entire seedling cycle, reflecting the degree of refinement in the process; Peak peat percentage: The maximum value of peat percentage in the substrate during the entire seedling period, reflecting the seedlings' need for a high-organic-matter environment; Peak occurrence time: The number of days from the start of seedling cultivation to the peak peat content; First fertilization time: The number of days from the start of seedling cultivation to the first fertilization, reflecting the start time of seedlings' nutrient demand; Number of fertilizations: The total number of fertilizations during the entire seedling cycle; Final cumulative load: The total cumulative load of slow-release fertilizer at the time of delivery; Peak fertilization period: The period during which the amount of fertilizer applied per unit of time is the largest, reflecting the rapid growth period of seedlings; Start time of stabilization period: The time point when both substrate ratio and fertilization frequency tend to stabilize, reflecting the time when seedlings enter the slow growth or lignification stage.

[0090] These characteristic parameters are stored in the form of digital codes for subsequent seedling process analysis and quality traceability. For example, four key parameters—first adjustment time, peak peat ratio, final cumulative loading, and stabilization period start time—are extracted and combined into a four-dimensional feature vector, which serves as a digital representation of the dynamic curve of the seedling process for this batch.

[0091] The generation of dynamic curves for seedling cultivation processes transforms previously discrete, text-based operation records into visualized, quantifiable dynamic information, comprehensively documenting the technical intervention processes experienced by this batch of seedlings. These curves and their characteristic parameters can be used for subsequent analysis of the impact of different dynamic process modes on seedling quality. For example, comparing the process curves of different batches can identify which process adjustments led to significant changes in seedling growth, thus providing data support for optimizing seedling cultivation processes. Simultaneously, these curves are also a crucial component of seedling traceability information, enabling subsequent monitoring of seedling delivery and afforestation effectiveness by querying the dynamic process curves to understand the details of the technical interventions experienced by this batch of seedlings during cultivation. S5043. Obtain the first historical data of the trait expression coefficient of the germplasm resource registration number based on the seed quality grade, and generate the genetic potential coefficient based on the first historical data of the trait expression coefficient. For example, the seed quality grade is generated based on a comprehensive quality index retrieved from breeding association information, which is a set of information that associates the mother tree code with the comprehensive quality index. Therefore, the seed quality grade can be used to trace back to the specific mother tree code, and then to the germplasm resource registration number to which the mother tree belongs.

[0092] The current seedling production batch code is associated with the mother tree code used. This mother tree code corresponds to a comprehensive quality index in the breeding association information, and the level corresponding to this comprehensive quality index is the current seed quality level. Therefore, starting from the seed quality level, a specific mother tree code can be located, and then its germplasm resource registration number can be obtained from the mother tree file.

[0093] The method for obtaining the first historical data is as follows: The mother tree code of the seeds used is obtained by parsing the seedling production batch code. The comprehensive quality index associated with this mother tree code is retrieved from the breeding association information, and its corresponding seed quality grade is determined. Simultaneously, the germplasm resource registration number to which the mother tree belongs is obtained from its archive. Then, the time-series data of trait expression coefficients accumulated over many years for all mother trees under this registration number are retrieved from the germplasm resource database. These data constitute the first historical data. The trait expression coefficient is a weighted combination of the individual plant growth potential coefficient and the growth uniformity index, and is a quantified value obtained after correction by the seed stability factor.

[0094] The genetic potential coefficient is used to characterize the genetic growth potential and quality performance probability of a germplasm resource. Its calculation is based on first-historical data: First, the mean of the trait expression coefficients across all historical data is calculated as the baseline potential level of the germplasm resource. Second, the annual trends of the historical data are analyzed. By performing linear regression on the annual mean, it is determined whether the trait expression coefficient is increasing or decreasing year by year, and the strength of the trend. An increasing trend indicates that the germplasm resource is continuously improving, and the potential should be appropriately increased; a decreasing trend indicates possible degeneration, and the potential should be appropriately decreased. Simultaneously, the coefficient of variation (COP) of the historical data is calculated, i.e., the standard deviation divided by the mean, to measure the annual fluctuation of the trait expression coefficient. The smaller the COP, the more stable the performance of the germplasm resource, and the more reliable its genetic potential; the larger the COP, the more drastic the fluctuation, and the genetic potential needs to be appropriately reduced.

[0095] Using the mean as a baseline, adjustments are made for positive or negative values ​​based on the trend term, and then stability reduction is applied based on the coefficient of variation to finally obtain the genetic potential coefficient.

[0096] S5044. Obtain the second historical data of the comprehensive seedling technology compliance degree according to the seedling technology level, and generate the cultivation quality coefficient according to the second historical data of the comprehensive seedling technology compliance degree. For example, the comprehensive seedling technology compliance rate refers to the comprehensive seedling technology compliance rate corresponding to the seedling technology level. This compliance rate is generated by integrating the substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume fit. The second historical data refers to the historical data set of comprehensive seedling technology compliance rates recorded from multiple past applications of the seedling technology scheme represented by this seedling technology level. The cultivation quality coefficient is a quantitative value calculated based on the second historical data, used to characterize the stability and reliability of the seedling technology scheme in cultivating high-quality seedlings historically. Specifically, the mean of the second historical data can be calculated as a benchmark coefficient, and its correlation with the final qualified rate of seedlings is analyzed. If historical data shows that the technology scheme consistently produces high-quality seedlings, the cultivation quality coefficient is high; if historical data shows that the implementation effect of the technology scheme fluctuates greatly, the cultivation quality coefficient is low.

[0097] S5045. Obtain the predicted quality value of the source seed dual factors for the seedling batch based on the genetic potential coefficient and the cultivation quality coefficient, and generate the expected seedling grade based on the predicted quality value of the source seed dual factors. For example, the source seed dual-factor quality prediction value refers to a quantitative prediction value obtained by combining the genetic potential coefficient and the cultivation quality coefficient. It is used to jointly estimate the final seedling quality level that the seedling batch may achieve from two dimensions: the genetic potential of the seed source and the quality of cultivation techniques. The genetic potential coefficient reflects the upper limit of the intrinsic quality of the seed source, while the cultivation quality coefficient reflects the degree of quality assurance achievable through technical implementation. The combination of the two constitutes a comprehensive prediction of seedling quality. When generating the source seed dual-factor quality prediction value, a weighted combination can be performed based on the influence weights of genetic potential and cultivation quality on the actual nursery quality in historical data, for example, using a multiplicative or additive model. The expected nursery grade refers to the qualitative classification result obtained by mapping the numerical range of the source seed dual-factor quality prediction value to a preset nursery grade division, such as special grade, first grade, second grade, etc. This grade intuitively predicts the quality level that the batch of seedlings may achieve upon nursery exit.

[0098] S5046. Generate a composite traceability identifier for the seedling batch based on the seedling environment feature code, seedling process dynamic curve, and expected nursery grade, and bind the composite traceability identifier with the seedling production batch code to generate seedling traceability information. For example, a composite traceability identifier is a unique identifier generated by integrating and encoding the seedling environment feature code, the seedling process dynamic curve, and the expected delivery grade. It is used to condense and characterize the complete traceability information of the seedling batch. The seedling environment feature code reflects the environmental conditions of seedling cultivation, the seedling process dynamic curve records the details of technical interventions during the seedling process, and the expected delivery grade provides a prediction of seedling quality. Together, these three elements constitute a multi-dimensional description of the seedling process and quality of this batch. When generating the composite traceability identifier, the key feature parameters of the seedling environment feature code and the seedling process dynamic curve, as well as the expected delivery grade, can be digitally encoded to form a string of characters or a QR code. This composite traceability identifier is then bound to the seedling production batch code, allowing the identifier to be retrieved through the batch code. The resulting seedling traceability information includes the batch code, the composite traceability identifier, and all associated detailed data, forming a complete, multi-dimensional, and traceable seedling archive.

[0099] As described in steps S501-S505 above, this invention obtains seedling start-up time and seedling site environmental monitoring data based on the seedling production batch. By integrating these data, a seedling environment feature code is generated, which encapsulates the environmental conditions of the seedling batch. Secondly, based on the same seedling production batch, substrate ratio adjustment records and slow-release fertilizer application records are obtained, and a dynamic curve of the seedling process is generated through time series integration. This curve fully records the changes in key process parameters during the seedling process.

[0100] Next, based on the seed quality grade, the first historical data of trait expression coefficients corresponding to the germplasm resource registration number were obtained. By analyzing the level and trend of the historical data, a genetic potential coefficient was generated, which quantifies the intrinsic genetic quality potential of the seed source. Simultaneously, based on the seedling technology grade, the second historical data of comprehensive seedling technology compliance was obtained. By analyzing the stability and effectiveness of the historical data, a cultivation quality coefficient was generated, which quantifies the reliability and maturity of the seedling technology scheme itself.

[0101] After obtaining the genetic potential coefficient and the cultivation quality coefficient, the two are combined to obtain the source seed dual-factor quality prediction value, which is then mapped to the expected nursery grade. This grade intuitively estimates the quality level that the batch of seedlings may reach upon leaving the nursery. Finally, the seedling environment feature code, the seedling process dynamic curve, and the expected nursery grade are integrated and encoded to generate a composite traceability identifier, which is then bound to the seedling production batch code to generate the final seedling traceability information.

[0102] like Figure 2 As shown, the present invention also provides a traceability management system for precious tree species, comprising: The improved seed information acquisition module is used to acquire improved seed resource collection information and breeding environment information, wherein the improved seed resource collection information includes post-planting growth information and harvested seed information; The registration number acquisition module is used to obtain the mother tree code of the improved varieties planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data. The breeding association information acquisition module is used to acquire the seed quality index based on the harvested seed information, and generate breeding association information between the improved variety and the mother tree code based on the seed quality index. The seed source association information acquisition module is used to acquire the seedling technology compliance degree based on the breeding environment information, and generate seed source association information based on the seedling technology compliance degree and the mother tree code; The traceability information acquisition module is used to generate seedling production batches based on the registration number association information, and to generate seedling traceability information based on the seedling production batches, breeding association information, and seed source association information.

[0103] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the process of tracing and managing the superior varieties of precious tree species. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the tracing and management method for superior varieties of precious tree species.

[0104] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0105] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0106] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for tracing and managing the provenance of precious tree species, characterized in that, include: Acquire information on the collection of superior seed resources and information on the breeding environment, wherein the information on the collection of superior seed resources includes post-planting growth information and information on harvested seeds; Obtain the mother tree code of the improved variety planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data; The seed quality index is obtained based on the harvested seed information, and breeding association information between the improved varieties and the mother tree codes is generated based on the seed quality index. The seedling technology compliance is obtained based on the breeding environment information, and seed source association information is generated based on the seedling technology compliance and mother tree code. The seedling production batch is generated based on the registration number association information, and the seedling traceability information is generated based on the seedling production batch, breeding association information, and seed source association information.

2. The method for tracing and managing the provenance of precious tree species according to claim 1, characterized in that, The steps for obtaining the mother tree codes of improved varieties planted in the improved variety base, and generating registration number association information based on the mother tree codes and post-planting growth data, include: The continuous annual growth data of the corresponding mother tree are obtained according to the mother tree code, and the single-plant growth potential coefficient reflecting the growth rate of a single plant is generated according to the continuous annual growth data. Obtain the individual growth potential coefficients of all mother trees under the same germplasm resource registration number, and generate a growth consistency index that characterizes the synchronicity of population growth based on the individual growth potential coefficients. The phenotypic expression coefficient of this germplasm resource is constructed by weighted combination of the single plant growth potential coefficient and the growth uniformity index. Vigor detection data of seeds harvested over the years under the same germplasm resource registration number are obtained. Seed stability factors are generated based on the fluctuation of the vigor detection data, and the seed stability factors are used to correct the trait expression coefficients. The corrected trait expression coefficients are re-bound with the corresponding germplasm resource registration numbers to generate updated registration number association information.

3. The method for tracing and managing the provenance of precious tree species according to claim 1, characterized in that, The steps of obtaining a seed quality index based on the harvested seed information and generating breeding association information between improved varieties and mother tree codes based on the seed quality index include: Based on the mother tree code, obtain the thousand-grain weight and plumpness detection data of the seeds harvested from the corresponding mother tree over the years, and generate the grain plumpness index based on the thousand-grain weight and plumpness detection data. Based on the mother tree code, obtain the germination rate and germination potential detection data of the seeds harvested from the corresponding mother tree over the years, and generate a germination potential coefficient based on the germination rate and germination potential detection data; The genetic transmission stability factor is obtained based on the germination potential coefficient; The germplasm heterogeneity coefficient is generated based on the grain plumpness index and germination potential coefficient of seeds harvested from different mother trees under the same germplasm resource registration number. The genetic transmission stability factor is then modified based on the germplasm heterogeneity coefficient to obtain the modified genetic transmission stability factor. A comprehensive quality index for harvested seeds is generated based on the seed plumpness index, germination potential coefficient, and modified genetic transmission stability factor. The comprehensive quality index is then associated with the corresponding mother tree code to generate breeding association information that includes seed quality dimensions.

4. The method for tracing and managing the superior varieties of precious tree species according to claim 3, characterized in that, The steps of obtaining the thousand-grain weight and plumpness detection data of seeds harvested from the corresponding mother tree over the years based on the mother tree code, and generating a grain plumpness index based on the thousand-grain weight and plumpness detection data, include: The thousand-grain weight detection value of each batch of seeds harvested from the corresponding mother tree in consecutive harvesting years is obtained according to the mother tree code, and a grain weight deviation coefficient is generated based on the thousand-grain weight detection value and the average thousand-grain weight detection value of the same batch of seeds. The degree of variation of the thousand-grain weight of seeds in a given year is obtained by measuring the grain weight deviation coefficient of different harvest batches within the same harvest year, and an annual stable value of the grain weight for that year is generated based on the degree of variation. The plumpness detection value of seeds from each harvest batch of the corresponding mother tree in the same harvest year is obtained according to the mother tree code. The plumpness matching coefficient of the seeds is generated according to the degree of closeness between the plumpness detection value and the standard plumpness benchmark value of the same tree species. Based on the discrete distribution of the plumpness matching coefficient of all harvested batches in the same year, a plumpness annual homogeneity index reflecting the consistency of seed appearance quality in that year is generated, and the annual stable value of grain weight is corrected based on the plumpness annual homogeneity index to obtain the grain weight-plumpness composite coefficient. The grain plumpness index of the mother tree's seeds is generated based on the grain weight-plumpness composite coefficient from multiple consecutive harvest years.

5. The method for tracing and managing the superior varieties of precious tree species according to claim 1, characterized in that, The steps of obtaining seedling technology compliance based on the breeding environment information and generating provenance association information based on the seedling technology compliance and mother tree coding include: The substrate ratio adjustment record of the seeds produced by the corresponding mother tree during the seedling stage is obtained according to the mother tree code, and the substrate ratio deviation is generated according to the substrate ratio adjustment record. Based on the mother tree code, obtain the slow-release fertilizer application record of the seeds produced by the corresponding mother tree during the seedling stage, and generate the slow-release fertilizer loading matching coefficient based on the slow-release fertilizer application record; The mycorrhizal infection synchronization index of the seeds produced by the corresponding mother tree during the seedling stage is obtained based on the mother tree code. Based on the mother tree code, obtain the container specification record value of the seeds produced by the corresponding mother tree during the seedling stage, and generate the container volume fit degree based on the container specification record value. The comprehensive seedling cultivation technology compliance is generated based on the substrate ratio deviation, slow-release fertilizer loading consistency coefficient, mycorrhizal infection synchronization index, and container volume suitability. The compliance of the integrated seedling cultivation technology is associated with the mother tree code to generate seed source association information.

6. The method for tracing and managing the superior varieties of precious tree species according to claim 1, characterized in that, The steps of generating seedling production batches based on the registration number association information, and generating seedling traceability information based on the seedling production batches, breeding association information, and seed source association information include: Based on the registration number association information, obtain the germplasm resource registration number and mother tree code corresponding to the seedling cultivation, and generate the seedling production batch based on the germplasm resource registration number and mother tree code; The corresponding comprehensive quality index is retrieved from the breeding-related information based on the seedling production batch, and the seed quality grade to which the seed belongs is generated based on the comprehensive quality index. The corresponding comprehensive seedling technology compliance degree is obtained from the seed source association information based on the mother tree code, and the seedling technology level to which the seedling belongs is generated based on the comprehensive seedling technology compliance degree. The expected seedling quality index for each batch of seedlings is generated based on the seed quality grade and the seedling technology grade. The data on seedling production batches, seed quality grades, and seedling technology grades are integrated to generate seedling traceability information.

7. The method for tracing and managing the superior varieties of precious tree species according to claim 6, characterized in that, The steps of integrating the seedling production batches, seed quality grades, and seedling technology grades to generate seedling traceability information include: Based on the seedling production batch, obtain the seedling start time and seedling site environmental monitoring data, and generate the seedling environmental feature code for that batch based on the seedling start time and seedling site environmental monitoring data; Based on the seedling production batches, obtain substrate ratio adjustment records and slow-release fertilizer application records, and generate seedling process dynamic curves based on the substrate ratio adjustment records and slow-release fertilizer application records; First historical data of the trait expression coefficient of the germplasm resource registration number are obtained based on the seed quality grade, and genetic potential coefficient is generated based on the first historical data of the trait expression coefficient. The second historical data of the comprehensive seedling technology compliance is obtained based on the seedling technology level, and the cultivation quality coefficient is generated based on the second historical data of the comprehensive seedling technology compliance. The genetic potential coefficient and the cultivation quality coefficient are used to obtain the source seed dual-factor quality prediction value of the seedling batch, and the expected nursery grade is generated based on the source seed dual-factor quality prediction value. Based on the seedling environment feature code, seedling process dynamic curve and expected nursery grade, a composite traceability identifier for the seedling batch is generated, and the composite traceability identifier is bound to the seedling production batch code to generate seedling traceability information.

8. A traceability management system for precious tree species, characterized in that, include: The improved seed information acquisition module is used to acquire improved seed resource collection information and breeding environment information, wherein the improved seed resource collection information includes post-planting growth information and harvested seed information; The registration number acquisition module is used to obtain the mother tree code of the improved varieties planted in the improved variety base, and generate registration number association information based on the mother tree code and post-planting growth data. The breeding association information acquisition module is used to acquire the seed quality index based on the harvested seed information, and generate breeding association information between the improved variety and the mother tree code based on the seed quality index. The seed source association information acquisition module is used to acquire the seedling technology compliance degree based on the breeding environment information, and generate seed source association information based on the seedling technology compliance degree and the mother tree code; The traceability information acquisition module is used to generate seedling production batches based on the registration number association information, and to generate seedling traceability information based on the seedling production batches, breeding association information, and seed source association information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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