Goat selective breeding method and system based on genetic algorithm

Abstract

The invention relates to the technical field of bioinformatics, in particular to a goat selective breeding method and system based on a genetic algorithm, and the method comprises the following steps: based on goat population genetic initialization data, extracting a character characteristic index and a genetic relationship parameter of each pairing combination in a population, analyzing genetic diversity, character matching degree and genetic relationship, and calculating the genetic diversity, character matching degree and genetic relationship; and screening matched individuals and pairing hereditary characteristics, and rearranging and recombining to obtain primary pairing data of goat breeding. According to the method, genetic diversity and character matching degree are analyzed, genetic diversity and excellent character inheritance are balanced, genetic resource utilization efficiency is improved, local optimal combinations are accurately screened, genetic drift risks are reduced, character stability is enhanced, a two-point crossing rule and global character dynamic analysis are combined, and global breeding layout is optimized; according to the method, short-term improvement and long-term stability are both considered, the genetic structure is optimized through Pareto rule screening and global state updating, and the sustainability and accuracy of a breeding scheme are improved.

Description

Technical Field

[0001] The invention relates to the technical field of bioinformatics, and in particular to a goat selection breeding method and system based on a genetic algorithm. Background Art

[0002] The field of bioinformatics technology includes research and application technologies based on the intersection of biology, mathematics, statistics and computer science. The core content of this technical field is to process and analyze the sequence data of biological macromolecules such as DNA, RNA and protein through algorithms and computing tools to reveal the genetic information and biological characteristics of organisms. It widely carries out genome sequence analysis, protein structure prediction, genetic variation research and other work, including genetic marker positioning, biological network construction, molecular systematics and other applications. The field of bioinformatics technology also covers the analysis of genetic characteristics of animal and plant populations. By establishing genetic models and association analysis methods, it provides theoretical basis and technical means for animal and plant variety optimization, disease prevention and treatment.

[0003] Among them, goat selection and breeding methods refer to technical methods for screening and breeding strategies for genetically superior individuals in goat populations, including the application of means based on genome selection, genetic evaluation, breeding value prediction, etc. In this patent subject, by using specific genetic markers and molecular detection technologies, the genome data of individual goats are compared and analyzed to identify gene loci that are highly associated with excellent traits, and based on this, candidate breeding sheep with genetic advantages are determined, and then combined with genotype data and genetic algorithms, the optimal breeding and pairing plan for breeding sheep is designed to achieve the goal of genetic improvement.

[0004] In genetic marker positioning and genome analysis, existing technologies lack a full balance between population diversity and global genetic characteristics, and only rely on genetic markers and molecular detection technology to identify excellent gene sites, which can easily lead to a decrease in population genetic diversity and increase the risk of inbreeding depression. Genotype data analysis is mostly limited to static trait matching, and lacks prediction of dynamic trait evolution and its impact on the long-term adaptability of the population, resulting in an imbalance between short-term benefits and long-term genetic stability in practical applications of breeding programs. Existing technologies have failed to fully integrate individual traits, kinship and mutation analysis in breeding value prediction and genetic algorithm application, resulting in the potential adaptability and genotype diversity between individuals in the design of breeding strategies not being fully utilized, and the optimization efficiency of breeding programs being low. This limitation leads to a lack of adaptive expansion of breeding combinations in applications, and the inability to fully meet the diverse needs of complex breeding goals, reducing the sustainability and productivity of the overall breeding strategy. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a goat selection breeding method and system based on genetic algorithm.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a goat selection breeding method based on genetic algorithm, comprising the following steps:

[0007] S1: Based on the genetic initialization data of goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the population are extracted, the genetic diversity, trait matching degree and kinship are analyzed, the matching individuals and pairing genetic characteristics are screened, and the rearranged combinations are rearranged to obtain the first generation pairing data of goat breeding;

[0008] S2: Based on the first generation pairing data of goats, extract the individual gene mutation type, set the gene mutation range, generate a local candidate pairing characteristic data set, analyze the neighborhood combination trait matching change value and the kinship change amount, screen the matching change value combination, and obtain the local optimal pairing combination;

[0009] S3: Based on the local optimal pairing combination, extract the pairing trait characteristic index and kinship parameter, analyze the matching increment and kinship change value according to the double-point crossover rule, obtain the change data of the kinship of the paired individuals, analyze the global matching distribution of the population in combination with the neighborhood matching interval, screen the global matching pairing data, update the genetic information, and obtain the optimization of the reproductive characteristics layout result;

[0010] S4: Based on the optimized reproduction characteristic layout results, the population reproduction combination is screened according to the Pareto frontier rule, and the matching degree distribution of the reproduction combination is combined with the global trait dynamic characteristic value to update the global population trait distribution state and obtain the optimized goat reproduction matching plan.

[0011] As a further solution of the present invention, the step of obtaining the primary generation pairing data of goats is specifically as follows:

[0012] S111: Based on the genetic initialization data of the goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the goat population are extracted, and the trait data of each pair of individuals are analyzed, including three categories: weight, production performance, and disease resistance, to form a preliminary pairing combination characteristic data set;

[0013] S112: Analyze the preliminary paired combination feature data set, analyze each pair of paired traits, and perform pair matching evaluation based on the correlation of trait indicators, using the formula:

[0014]

[0015] Screen the best pairing and calculate the trait matching value;

[0016] Among them, S match Representative trait matching value, w i represents the weight of the ith trait, f irepresents the value of the ith trait, α represents the weight adjustment coefficient of kinship, r ij represents the kinship coefficient between individuals i and j, n represents the number of traits, i is the trait item index, and j is the paired individual index;

[0017] S113: Based on the trait matching value, the trait matching degree and kinship coefficient of the optimal pairing are called, and the individuals that meet the matching standard in the population are rearranged to obtain the first generation pairing data of goat breeding.

[0018] As a further solution of the present invention, the step of acquiring the local candidate pairing feature data set is specifically:

[0019] S211: Based on the goat breeding first generation pairing data, extract the paired individual gene information, mark the gene sequence according to the chromosome position segment, extract the mutation types of base substitution, insertion and deletion and duplication fragments, classify the mutation data according to the mutation frequency and occurrence area in the chromosome segment, and establish a gene mutation type data table;

[0020] S212: Based on the gene mutation type data table, screening the pairing combinations in the mutation feature data, extracting and annotating the starting site, range length and associated base features of the combined mutation region one by one, setting the mutation parameter range and classifying the parameter data, and constructing a candidate gene pairing parameter set;

[0021] S213: Based on the candidate gene pairing parameter set, a multi-parameter cross-matching of mutation site and range data is performed for pairing combinations that meet the set parameter range, common data of the mutation region is extracted, the result data is called to adjust the regional matching degree, and a local candidate pairing feature data set is constructed.

[0022] As a further solution of the present invention, the step of obtaining the local optimal pairing combination is specifically:

[0023] S221: Analyze the local candidate pairing characteristic data set, extract the matching degree change value and the kinship change amount of each candidate pairing combination, select the combination with a matching degree change value higher than a threshold, and obtain a matching degree change data set;

[0024] S222: Based on the matching degree change data set, analyzing the corresponding relationship between the matching degree change value and the kinship change amount in the differentiated combination, calculating the weighted average of the matching degree change value and the kinship change amount, screening the weighted combination, and obtaining the feature matching preferred group;

[0025] S223: Based on the feature matching preferred group, local optimization adjustment is performed to adjust the influencing factor of kinship, using the formula:

[0026]

[0027] Calculate the scores of candidate pairing combinations to obtain the local optimal pairing combination;

[0028] Among them, R final Represents the score of the candidate pairing combination, M k represents the change in matching degree of the kth candidate pair, P k represents the change in kinship of the kth candidate pair, E1 and E2 are the weighted coefficients of matching degree and kinship, S k represents the weight coefficient of the combination, and m is the total number of candidate pairing combinations.

[0029] As a further solution of the present invention, the step of obtaining the change data of the kinship relationship of the paired individuals is specifically as follows:

[0030] S311: Based on the local optimal pairing combination, extract the trait characteristic indicators of the paired individuals, summarize the classification mark characteristic indicators by dimension, organize the classification results and data range, and generate a pairing trait and kinship characteristic table;

[0031] S312: Based on the paired trait and kinship feature table, the trait matching increments in the paired combinations are analyzed item by item according to the double-point crossover rule, the paired trait index change interval and the kinship change difference are identified, and a paired trait matching increment table is generated;

[0032] S313: Based on the table of matching degree of paired traits and kinship increments, extract key data of trait characteristics and kinship increments in the changed paired combinations, perform grouping analysis and matching adjustment between data, optimize the paired combinations according to the changed characteristics, and obtain the kinship change data of paired individuals.

[0033] As a further solution of the present invention, the steps for obtaining the optimized reproduction characteristic layout result are specifically as follows:

[0034] S321: Based on the change data of the kinship of the paired individuals, calculate the kinship change value of the paired individuals, perform numerical normalization processing on the kinship change value, sort the processed values ​​and assign neighborhood ranges, and generate a result of the kinship change of the paired individuals;

[0035] S322: Combined with the result of the change in the affinity of the pairing, the matching degree distribution of the global population is analyzed, and the pairings with low matching degrees are screened and eliminated in combination with the threshold, using the formula:

[0036]

[0037] Get the global matching result;

[0038] Among them, G matchRepresents the global matching value, F h represents the weight of each pair, p h represents the kinship change value, T h represents the matching threshold, z h represents the adjustment parameter, N represents the total number of pairs, and h represents the sequence number of the pairs;

[0039] S323: Based on the global matching result, the optimal pairing data is screened, and the genetic information is adjusted in combination with the screened data. Through local correction and global update of the genetic information, an optimized reproduction characteristic layout result is generated.

[0040] As a further solution of the present invention, the steps for obtaining the optimized goat breeding pairing scheme are specifically as follows:

[0041] S411: Based on the optimized reproduction characteristic layout result, retrieve the individual affinity data from the genetic database, analyze the gene homogeneity and difference between each pair of individuals, analyze through the Pareto front rule, and perform reproduction screening to generate a reproduction combination after Pareto front screening;

[0042] S412: For the breeding combination after the Pareto frontier screening, combined with the global population trait dynamic characteristic value, the matching degree distribution is optimized by weight adjustment, using the formula:

[0043]

[0044] Generate optimized matching degree distribution results;

[0045] Among them, P adj Represents the adjusted matching degree distribution value, M pair represents the kinship matching value of the breeding combination, W dyn Represents the weight of the dynamic characteristic value of the population trait, S div Represents the trait diversity index of the breeding combination, T norm is the standardized coefficient of matching;

[0046] S413: Based on the results of the optimized matching degree distribution, the selected breeding combinations are evaluated, the trait balance between the combinations is adjusted through dynamic trait factors, and an optimized goat breeding matching plan is generated.

[0047] A goat selection breeding system based on a genetic algorithm, the goat selection breeding system based on a genetic algorithm is used to execute the goat selection breeding method based on a genetic algorithm, the system comprising:

[0048] The population genetic parameter analysis module extracts the trait characteristic indicators and kinship parameters of each pairing combination based on the genetic initialization data of the goat population, calculates the weighted value and weight value, normalizes each pair and analyzes the indicator distribution, sorts and selects the combination according to the genetic diversity value, and generates the first generation breeding pairing data;

[0049] The local genetic property optimization module extracts individual gene mutation types and trait characteristic indicators based on the primary generation breeding pairing data, identifies the value range of mutation types and associates trait indicators, analyzes neighborhood trait change trends, and screens combinations of matching degree change values ​​to obtain local genetic optimization pairing data;

[0050] The global reproduction characteristic tuning module analyzes the variation range of cross traits based on the local genetic optimization pairing data, analyzes the global matching increment in combination with the variation of kinship, classifies the increment value according to the global distribution partition, screens the combination that meets the distribution, and obtains the global optimization reproduction pairing data;

[0051] The breeding combination optimization and distribution update module analyzes the matching degree distribution range and dynamic trait characteristic values ​​of the breeding combination based on the global optimized breeding pairing data, reclassifies the matching degree range and analyzes the dynamic trait changes of the classification results, updates the global population genetic characteristics in combination with the trait distribution status, and generates an optimized goat breeding pairing plan.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] In the present invention, by extracting the trait characteristic indicators and kinship parameters of the paired individuals of the population, combined with the analysis of genetic diversity and trait matching, the matched individuals and their genetic characteristics are screened and recombined to ensure that the initial pairing can effectively balance the genetic diversity and the inheritance of excellent traits, avoid the genetic bottleneck problem, and improve the utilization efficiency of the population genetic resources. On the basis of gene mutation type extraction and range setting, the neighborhood combination is used to analyze the matching degree changes and kinship changes to form a local candidate pairing characteristic data set, so as to achieve a more accurate local optimization strategy, reduce the risk of unnecessary genetic drift, and enhance the cumulative stability of excellent traits. Through the two-point crossover rule and global trait dynamic analysis, the local optimization is organically combined with the global layout to ensure that the population pairing plan can take into account the needs of short-term trait improvement and long-term genetic stability, avoid the risk of population degeneration, screen the breeding combination based on the Pareto frontier rule, update the population state in combination with the matching degree distribution and the global trait dynamic characteristics, improve the balance of the population genetic structure, make the breeding plan more sustainable and forward-looking, and enhance the optimization potential of population adaptability and production performance. The innovative process ensures more accurate updating of genetic information through analysis of the global matching degree distribution and optimization of the reproductive characteristics layout, greatly improving the efficiency and accuracy of goat breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0055] Figure 2 It is a flow chart of the first generation pairing data of goat breeding in the present invention;

[0056] Figure 3 It is a flowchart of the local candidate pairing feature data set in the present invention;

[0057] Figure 4 It is a flowchart of the local optimal pairing combination in the present invention;

[0058] Figure 5 A flow chart showing the change data of the kinship relationship of paired individuals in the present invention;

[0059] Figure 6 A flow chart showing the results of optimizing the reproductive characteristics layout in the present invention;

[0060] Figure 7 The present invention is a flowchart of the optimized goat breeding and matching program. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] Embodiment 1

[0064] See also Figure 1 The present invention provides a technical solution: a goat selection breeding method based on a genetic algorithm, comprising the following steps:

[0065] S1: Based on the genetic initialization data of goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the population are extracted, the genetic diversity, trait matching degree and kinship are analyzed, the matching individuals and pairing genetic characteristics are screened, and the rearranged combinations are rearranged to obtain the first generation pairing data of goat breeding;

[0066] S2: Based on the first generation pairing data of goats, extract the individual gene mutation type, set the gene mutation range, generate a local candidate pairing characteristic data set, analyze the neighborhood combination trait matching change value and kinship change, screen the matching change value combination, and obtain the local optimal pairing combination;

[0067] S3: Based on the local optimal pairing combination, extract the pairing trait characteristic index and kinship parameters, analyze the matching increment and kinship change value according to the double-point crossover rule, obtain the change data of the kinship of the paired individuals, analyze the global matching distribution of the population in combination with the neighborhood matching interval, screen the global matching pairing data, update the genetic information, and obtain the optimization of the reproductive characteristics layout results;

[0068] S4: Based on the results of optimizing the reproductive characteristics layout, the population reproductive combination is screened according to the Pareto frontier rule. The matching degree distribution of the reproductive combination is combined with the global trait dynamic characteristic value to update the global population trait distribution state and obtain the optimized goat breeding grouping plan.

[0069] The first-generation pairing data of goat breeding include trait characteristic indicators, kinship parameters, genetic diversity data, and pairing genetic characteristics. The local candidate pairing characteristic data set includes gene mutation type, gene mutation range, neighborhood combination trait matching change value, and kinship change amount. The local optimal pairing combination includes trait characteristic indicators, matching change value combination, and kinship parameters. The change data of paired individual kinship include matching increment, kinship change value, and global matching distribution data. The results of optimized breeding characteristic layout include global matching pairing data, genetic information update status, and global trait distribution status. The adjusted goat breeding pairing plan includes Pareto frontier breeding combination, global trait dynamic characteristic value, and global trait distribution result.

[0070] See also Figure 2 The specific steps for obtaining the first generation pairing data of goat breeding are as follows:

[0071] S111: Based on the genetic initialization data of the goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the goat population are extracted, and the trait data of each pair of individuals are analyzed, including three categories: weight, production performance, and disease resistance, to form a preliminary pairing combination characteristic data set;

[0072] The trait characteristic data of individuals in each pairing combination are gradually disassembled, and the trait characteristic index data are divided into three categories: weight, production performance and disease resistance. Specific indicator parameter values ​​are obtained from the experimental monitoring data. The experimental monitoring data include weight record data in the individual growth cycle, production performance data in continuous production records, and disease resistance parameters in pathological analysis data. After the parameters are classified and archived, the relationship coefficient between individuals is calculated based on the existing data on the relationship between paired individuals. This coefficient is calculated by calling the genetic similarity between individuals in the population genetic pedigree tree. The fragment matching degree of the gene sequence is accumulated according to the fragment weight and then its normalized value is taken. The trait characteristic index and the relationship coefficient are associated and integrated into a set of pairing combination data, and finally a preliminary pairing combination feature data set is generated.

[0073] S112: Analyze the preliminary paired combination feature data set, analyze each pair of paired traits, and perform pair matching evaluation based on the correlation of trait indicators, using the formula:

[0074]

[0075] Screen the best pairing and calculate the trait matching value;

[0076] Among them, S match Representative trait matching value, w i represents the weight of the ith trait, f i represents the value of the ith trait, α represents the weight adjustment coefficient of kinship, r ij represents the kinship coefficient between individuals i and j, n represents the number of traits, i is the trait item index, and j is the paired individual index;

[0077] The benefit of the formula is that, by introducing the weighted calculation of trait characteristics and the kinship adjustment factor, the matching degree between traits and kinship is comprehensively evaluated, so that the selected pairs can meet the requirements of trait matching and genetic diversity at the same time;

[0078] First, obtain w by monitoring data i and f i The specific value of w i is the trait weight, which is dynamically adjusted according to the contribution of the trait to the breeding goal. The importance of the influence of different trait characteristics is ranked by expert scoring method, and the weight value is obtained by normalization, such as w1 = 0.4, w2 = 0.35, w3 = 0.25, f i is the standardized value of each trait index, through the formula Calculated, where x iis the original trait value. Taking weight, production performance and disease resistance data as examples, f1=0.8, f2=0.6, and f3=0.9 are calculated respectively. The kinship adjustment factor α is set according to the dynamic weight of genetic diversity and is calculated through historical population genetic data, such as α=0.5. The kinship coefficient r ij Through the analysis of family tree data, it is found that ij =0.125;

[0079] Substitute the above data into the formula for calculation, first calculate the weighted matching degree:

[0080]

[0081] Then calculate the affinity adjustment:

[0082] Finally, calculate the trait matching value: S match =0.733 0.941 = 0.689;

[0083] The results showed that the trait matching value was 0.689, indicating that the pairing combination had a high matching degree in trait characteristics and met the requirements of genetic diversity after kinship adjustment, and could be used as a preferred pairing.

[0084] S113: Based on the trait matching value, the trait matching degree and kinship coefficient of the optimal pair are called, and the individuals that meet the matching standard in the population are rearranged to obtain the first generation pairing data of goat breeding;

[0085] First, the matching results are sorted, and then the combinations are arranged in order from high to low according to the trait matching. The pairings with matching degrees below a certain threshold are eliminated, and the combinations with high matching degrees are screened. The trait matching degrees and kinship coefficients are recalculated based on the screening results. The weights of the combinations with too high kinship coefficients are adjusted, and the pairing feature data of the individuals are called. The combinations are rearranged according to the optimization rules of the kinship between individuals, and finally the rearranged breeding combinations are generated and reanalyzed to ensure that the first-generation breeding standards are met. Finally, the first-generation pairing data of goat breeding is obtained.

[0086] See also Figure 3 , the steps for obtaining the local candidate pairing feature dataset are as follows:

[0087] S211: Based on the first generation goat breeding pairing data, extract the individual gene information of the pairing, mark the gene sequence according to the chromosome position segment, extract the mutation types of base substitution, insertion and deletion and duplication fragments, classify the mutation data according to the mutation frequency and occurrence area in the chromosome segment, and establish a gene mutation type data table;

[0088] By numbering each chromosome segment to form a continuous identification, each chromosome segment is further divided into several sub-intervals. For each sub-interval, the mutation types of base substitutions, insertions and deletions, and repeated fragments are extracted one by one, the occurrence location and distribution pattern of the mutation event are clarified, and the number of each type of mutation event is counted in sub-intervals, and the frequency proportion of different mutation types is calculated respectively. Within the chromosome segment, according to the set mutation event classification standard, the chromosome segment with a mutation frequency greater than a certain benchmark value is marked as a high-frequency area, and its starting and ending positions are extracted. At the same time, it is analyzed whether there is a repetitive pattern of base distribution or specific structural characteristics in the region. By summarizing and integrating the mutation information of each chromosome segment, a gene mutation type data table containing mutation type, chromosome location, occurrence area characteristics and mutation frequency is generated. The data table further adds functional annotation information of each mutation type to provide support for subsequent analysis.

[0089] S212: Based on the gene mutation type data table, screening the pairing combinations in the mutation feature data, extracting and annotating the starting site, range length and associated base features of the combined mutation region one by one, setting the mutation parameter range and classifying the parameter data, and constructing a candidate gene pairing parameter set;

[0090] The starting site and range length of the mutation region of each combination are extracted, and the associated base features in the region are extracted and annotated item by item using the segmented statistical method, such as the periodicity of base repetition, the distribution density of rare bases and other information. For the extracted mutation features, a parameter range including region length, base type distribution, mutation type density, etc. is set, and the mutation data that meets the range is collected. The data of the associated mutation regions in each combination are clustered and divided into different candidate region groups. The mutation parameter characteristic values ​​of each group are counted to construct a candidate gene pairing parameter set. To ensure the comprehensiveness of candidate gene pairing, the screening and annotation of boundary mutation regions are increased, and regions with mutation features that are too discrete or irrelevant to the target mutation type are eliminated, laying the foundation for subsequent pairing optimization.

[0091] S213: Based on the candidate gene pairing parameter set, a multi-parameter cross-comparison of the mutation site and range data is performed for the pairing combination that meets the set parameter range, the common data of the mutation region is extracted, the result data is called to adjust the regional matching degree, and a local candidate pairing characteristic data set is constructed;

[0092] By decomposing the mutation parameters of each pairing combination into detailed indicators such as starting site, range length and base density, two-way matching is performed item by item to clarify the specific range and common characteristics of the intersection area, especially focusing on hot spots with high mutation frequencies and target areas with specific associated base characteristics. In the cross-matching, the common data of each group of regions are extracted, and the coverage ratio of each common data in different pairing combinations is counted to quantify the matching degree. In the matching process, for low-matching areas, the matching parameters of the region are dynamically adjusted in combination with the mutation data extracted through multiple iterations to improve the matching accuracy. After completing multi-parameter matching and adjustment, the local candidate pairing characteristic data set is further screened to provide more accurate genetic information support for subsequent breeding strategies.

[0093] See also Figure 4 , the steps to obtain the local optimal pairing combination are as follows:

[0094] S221: Analyze the local candidate pairing feature data set, extract the matching degree change value and the kinship change amount of each candidate pairing combination, select the combination with a matching degree change value higher than a threshold, and obtain a matching degree change data set;

[0095] Through the analysis of the local candidate pairing feature data set, the matching degree change value of each candidate pairing combination is obtained by calculating the absolute value of the difference between each feature attribute and summing it up. It is necessary to extract the feature attributes of the candidate pairing combination, calculate the difference of the attribute value and take the absolute value respectively, and then sum the absolute value of each feature attribute as the basis of the matching degree change value. The kinship change amount is obtained by calculating the shared attribute ratio of the candidate pairing combination. The shared attribute ratio is based on the ratio of the number of the same attributes to the total number of attributes in each combination. Finally, the matching degree change value and the kinship change amount are used as the screening basis respectively. The combination with a matching degree change value higher than the threshold and a kinship change amount higher than a certain standard is screened. In the screening process, the dynamic threshold adjustment method is used to gradually reduce the matching degree change value screening threshold and recalculate the candidate combination list. Finally, it is terminated when the threshold converges or the candidate list no longer changes, and the matching degree change data set is obtained.

[0096] S222: Based on the matching degree change data set, analyzing the corresponding relationship between the matching degree change value and the kinship change amount in the differentiated combination, calculating the weighted average of the matching degree change value and the kinship change amount, screening the weighted combination, and obtaining the feature matching preferred group;

[0097] The weighted result is obtained by calculating the weighted average of the matching degree change value and the kinship change amount of each group of candidate pairings. The weighted average is calculated as the sum of the matching degree change value multiplied by the weight coefficient plus the kinship change amount multiplied by the weight coefficient divided by the total weight coefficient. The weight coefficient is set based on the importance ranking of the matching degree change value and the kinship change amount, and different weight ratios are assigned to each of them. When screening candidate combinations with higher weight results, the weighted average is calculated for all candidate combinations and sorted in descending order according to the weighted results to obtain the preferred group for feature matching.

[0098] S223: Based on the feature matching optimization group, local optimization adjustment is performed to adjust the influencing factor of kinship, using the formula:

[0099]

[0100] Calculate the scores of candidate pairing combinations to obtain the local optimal pairing combination;

[0101] Among them, R final Represents the score of the candidate pairing combination, M k represents the change in matching degree of the kth candidate pair, P k represents the change in kinship of the kth candidate pair, E1 and E2 are the weighted coefficients of matching degree and kinship, S k represents the weight coefficient of the combination, and m is the total number of candidate pairing combinations;

[0102] The benefit of the formula is that it improves the accuracy and adaptability of combined screening by comprehensively considering the relative weights of the change in matching degree and the change in kinship and optimizing it with the final weighted coefficient;

[0103] M k represents the change in matching degree of the kth candidate pair, obtained by summing up the absolute values ​​of the differences in step 1, P k represents the change in kinship of the kth candidate pair, which is calculated by the proportion of shared attributes in step 1. E1 and E2 are the weighted coefficients of matching degree and kinship, respectively, which are set based on the relative importance of the two in the result analysis. S k is the final weighting coefficient, which is set by the relative weights of matching degree and kinship, and m is the total number of candidate combinations;

[0104] Assume M1 = 0.75, M2 = 0.65, M3 = 0.85, corresponding to P1 = 0.8, P2 = 0.7, P3 = 0.9, weight coefficients E1 = 0.6, E2 = 0.4, weight coefficients S1 = 0.9, S2 = 0.8, S3 = 1.0;

[0105] Substitute the values ​​into the formula to calculate R final :

[0106] R final =(0.6 0.75+0.4 0.8) 0.9+(0.6 0.65+0.4 0.7) 0.8+(0.6 0.85+0.4 0.9) 1.0;

[0107] R final =(0.45+0.32)·0.9+(0.39+0.28)·0.8+(0.51+0.36)·1.0;

[0108] R final =0.693+0.536+0.87=2.099;

[0109] The results show that the score of the candidate pairing combination calculated by the formula is 2.099, which is significantly improved compared with the benchmark screening value of 2.0, indicating that the candidate combination has high adaptability and superiority in terms of matching degree and change in kinship, and directly constitutes the screening basis for the local optimal pairing combination.

[0110] See also Figure 5 , the specific steps for obtaining the change data of the kinship relationship of paired individuals are:

[0111] S311: Based on the local optimal pairing combination, the trait characteristic indicators of the paired individuals are extracted, the classification mark characteristic indicators are summarized by dimension, the classification results and data ranges are sorted, and a pairing trait and kinship characteristic table is generated;

[0112] The trait characteristic indicators of paired individuals, including multidimensional trait data such as body shape, coat color, milk production, disease resistance, etc., are extracted, and each trait indicator is normalized through standardized methods to make different types of indicators comparable. According to the biological significance and heritability of each trait characteristic, the indicators are divided into two categories: major genetic traits and secondary traits, and a weight parameter is assigned to each indicator. The weight is determined according to the genetic stability and the degree of influence on the breeding goal. On the basis of classification marking, the indicators are summarized according to the trait characteristic dimensions, and the average value, fluctuation range and degree of variation of similar indicators are statistically analyzed. The variation range of key genetic traits is extracted, and the sorted results are combined with the kinship data of the paired individuals to generate a paired trait and kinship characteristic table, in which each paired combination is marked with the classification label of the trait indicator and the proportion of its genetic contribution in the paired combination, providing data support for subsequent cross-analysis.

[0113] S312: Based on the paired trait and kinship feature table, the trait matching increments in the paired combination are analyzed item by item according to the double-point crossover rule, the paired trait index change interval and the kinship change difference are identified, and a paired trait matching increment table is generated;

[0114] The value range of common trait indicators in each pair of paired individuals is extracted, the genetic crossover prediction of each indicator is statistically analyzed, and the probability of the offspring traits being within the target range after crossover is calculated. According to the incremental value of trait matching, the changes in trait indicators are divided into three types: gain change, stability change and deterioration change, and the proportion of each type of change is grouped and analyzed. By analyzing the difference in kinship changes for each pair of paired combinations, the impact of kinship changes on the trait matching increment is identified, with a focus on the risk of adverse trait expression caused by high kinship. After collating the analysis results, a table of paired trait matching and kinship increment is generated, which marks the growth of trait matching in each paired combination and its corresponding kinship difference, providing support for evaluating the genetic stability of the paired combination.

[0115] S313: Based on the table of matching degree of paired traits and kinship increment, extract key data of trait characteristics and kinship increment in the changed paired combination, perform grouping analysis and matching adjustment between data, optimize the paired combination according to the change characteristics, and obtain the kinship change data of paired individuals;

[0116] All pairing combinations are grouped according to trait increments and kinship differences, and each group of data is further compared internally to screen out the pairing combinations with the greatest reproductive potential. For the trait characteristic change interval in each group of data, the matching ratio of each indicator within the target genetic range is calculated. At the same time, the genetic risk of the pairing combination is judged in combination with the kinship increment data. Combinations with low risk and high trait matching are prioritized to ensure the achievement of breeding goals. After completing the data grouping analysis, the pairing combinations are arranged and optimized according to the changing characteristics of the pairing combinations. At the same time, high-risk combinations are marked as secondary choices to avoid potential adverse genetic transmission. Finally, the kinship change data of the paired individuals are obtained to provide accurate data support for subsequent breeding decisions.

[0117] See also Figure 6 , the specific steps for obtaining the results of optimizing the reproduction characteristic layout are:

[0118] S321: based on the change data of the kinship of the paired individuals, calculating the kinship change value of the paired individuals, performing numerical normalization processing on the kinship change value, sorting the processed values ​​and assigning neighborhood ranges, and generating a result of the kinship change of the paired individuals;

[0119] First, based on the actual genetic data set collected, the genetic similarities and differences between each pair of individuals are calculated through genotype comparison analysis. This is done by analyzing the matching degree of homologous genes in the DNA sequences of the two individuals, where the similarity scores of each gene locus are accumulated to form a total score. The score is further converted into a kinship change value between 0 and 1 through standardization. The normalization process can eliminate the impact of sample size differences and ensure data comparability. According to the change value, individuals can be grouped and thresholds can be set to determine which individuals have a high enough match to enter the next step of pairing consideration. For example, setting a threshold of 0.5 means that individuals with a kinship change value higher than this number will be regarded as potential excellent pairs. This process is automatically performed by the program without human intervention, and the pairing kinship change results of each pair of individuals are obtained for subsequent breeding decisions.

[0120] S322: Combine the results of the change in pairing affinity, analyze the matching degree distribution of the global population, and filter and eliminate pairs with low matching degrees based on the threshold, using the formula:

[0121]

[0122] Get the global matching result;

[0123] Among them, G match Represents the global matching value, F h represents the weight of each pair, p h represents the kinship change value, T h represents the matching threshold, z h represents the adjustment parameter, N represents the total number of pairs, and h represents the sequence number of the pairs;

[0124] The benefit of the formula is that by introducing the weight F h and the tuning parameter z h , the kinship change value p of each pair can be flexibly adjusted h With threshold T h The influence of differences between them can enhance the adaptability and accuracy of the model;

[0125] There are three pairings, where N = 3, weights F1 = 0.5, F2 = 0.3, F3 = 0.2, kinship change values ​​p1 = 0.8, p2 = 0.6, p3 = 0.4, thresholds T1 = 0.3, T2 = 0.4, T3 = 0.5, and adjustment parameters z1 = 2, z2 = 2, z3 = 2;

[0126] Calculate the global matching degree G match as follows:

[0127]

[0128] Gmatch =(0.25) 0.5 +(0.06) 0.5 +(-0.02) 0.5 ≈0.5+0.245+0=0.745;

[0129] The results showed that according to the given pairing data and model parameters, the global matching value reached 0.745, indicating that the overall pairing relationship showed good matching after adjustment, which is beneficial to the genetic diversity management of the population.

[0130] S323: based on the global matching result, the optimal pairing data is screened, the genetic information is adjusted in combination with the screened data, and the optimized reproduction characteristic layout result is generated through local correction and global update of the genetic information;

[0131] First, the global matching results are compared with the preset breeding matching standards, and the pairs that are higher than the standard value (the standard value is set to 0.5) are selected. The screening process is executed by an automated system, and the pairs that meet the breeding standards are automatically marked according to the global matching results. Combined with the screening data, local adjustments to the genetic information are made, such as adjusting genetic markers through artificial selection or gene editing technology to enhance the expression of dominant genes. Through optimization and adjustment, new population layout characteristics are obtained, which will directly affect the genetic diversity and breeding success rate of the population, and generate optimized breeding characteristic layout results.

[0132] See also Figure 7 The specific steps for obtaining the optimized goat breeding and matching plan are as follows:

[0133] S411: Based on the results of optimizing the layout of reproductive characteristics, retrieve the affinity data of individuals from the genetic database, analyze the genetic similarity and difference between each pair of individuals, analyze them using the Pareto frontier rule, and perform reproductive screening to generate a reproductive combination after Pareto frontier screening;

[0134] By comparing and analyzing individual genotypes, the trait coverage and diversity indicators between each pair of individuals are calculated. This is done by gradually comparing whether the alleles of each gene locus are consistent. The coverage is obtained by the proportion of the same alleles, and the diversity index is obtained by calculating the variation values ​​of the statistical gene loci. The candidate combinations are screened using the Pareto front rule. After the coverage and diversity are visualized in two-dimensional graphics, an effective screening threshold is set to eliminate reproductive combinations with disadvantaged distribution positions, and further corrections are made based on the coverage distribution of the population to ensure that the screening results are more scientific and applicable. Finally, the combinations are sorted and output to obtain the reproductive combinations after Pareto front screening.

[0135] S412: For the breeding combination after Pareto frontier screening, combined with the global population trait dynamic characteristic value, the matching degree distribution is optimized through weight adjustment, using the formula:

[0136]

[0137] Generate optimized matching degree distribution results;

[0138] Among them, P adj Represents the adjusted matching degree distribution value, M pair represents the kinship matching value of the breeding combination, W dyn Represents the weight of the dynamic characteristic value of the population trait, S div Represents the trait diversity index of the breeding combination, T norm is the standardized coefficient of matching;

[0139] The benefit of the formula is that by adjusting the weight W dyn and the standardized coefficient T norm , the sensitivity of the matching degree distribution can be precisely controlled to ensure that the selected breeding combinations both meet the genetic diversity requirements of the population and have high affinity;

[0140] First, set the specific parameter values ​​and set the M pair , the kinship matching value is 0.75, which indicates that the average kinship is high;

[0141] W dyn is the weight of population dynamic traits, which is set to 1.2 based on the changes in population historical data;

[0142] S div is the trait diversity index, which is obtained by counting the frequency of occurrence of different traits. If its value is set to 16, its square root is 4;

[0143] T norm is the standardized adjustment coefficient, which is adjusted according to the average matching degree of the population and is set to 2;

[0144] Substitute the values ​​into the formula to calculate:

[0145] This value indicates the adjusted matching distribution value, which optimizes the kinship and trait diversity of the population. The result shows that the optimized breeding combination maintains genetic diversity while also enhancing the overall matching of the population, thus providing a scientific basis for achieving a healthier breeding strategy. The result shows that the adjusted pairing strategy significantly improves the matching degree and provides an optimization tool for genetic management.

[0146] S413: Based on the optimized matching degree distribution results, the selected breeding combinations are evaluated, the trait balance between the combinations is adjusted through dynamic trait factors, and an optimized goat breeding matching plan is generated;

[0147] First, the dynamic trait data in the combination is retrieved, and its trait factors are differentially analyzed. The balance of the trait factors in each combination is calculated by statistically analyzing the mean, variance and distribution range of the trait factors. Trait adjustment factors are then introduced to correct the balance between combinations to ensure that the genetic characteristics of the overall population are evenly distributed and have higher genetic health. After the adjustment, the trait diversity value of each combination is recalculated, and combinations with low diversity are further eliminated to generate an optimized goat breeding and matching plan.

[0148] The goat selection breeding system based on genetic algorithm is used to implement the goat selection breeding method based on genetic algorithm, and the system includes:

[0149] The population genetic parameter analysis module extracts the trait characteristic indicators and kinship parameters of each pairing combination based on the genetic initialization data of the goat population, calculates the weighted value and weight value, normalizes each pair and analyzes the indicator distribution, sorts and selects the combination according to the genetic diversity value, and generates the first generation breeding pairing data;

[0150] The local genetic characteristic optimization module extracts individual gene mutation types and trait characteristic indicators based on the primary breeding pairing data, identifies the value range of mutation types and associates trait indicators, analyzes the trend of neighborhood trait changes, and screens the combination of matching degree change values ​​to obtain local genetic optimization pairing data;

[0151] The global reproductive characteristics tuning module analyzes the variation range of cross traits based on local genetic optimization pairing data, analyzes the global matching increment in combination with the variation of kinship, classifies the increment value according to the global distribution partition, selects the combination that meets the distribution, and obtains the global optimization reproductive pairing data;

[0152] The breeding combination optimization and distribution update module is based on the global optimization breeding pairing data, analyzes the matching distribution range and dynamic trait characteristic values ​​of the breeding combination, reclassifies the matching range and analyzes the dynamic trait changes of the classification results, updates the global population genetic characteristics in combination with the trait distribution status, and generates an optimized goat breeding pairing plan.

[0153] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for selecting and breeding goats based on a genetic algorithm, characterized in that: The following steps are involved: S1: Based on the genetic initialization data of goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the population are extracted, the genetic diversity, trait matching degree and kinship are analyzed, the matching individuals and pairing genetic characteristics are screened, and the rearranged combinations are rearranged to obtain the first generation pairing data of goat breeding; S2: Based on the first generation pairing data of goats, extract the individual gene mutation type, set the gene mutation range, generate a local candidate pairing characteristic data set, analyze the neighborhood combination trait matching change value and the kinship change amount, screen the matching change value combination, and obtain the local optimal pairing combination; S3: Based on the local optimal pairing combination, extract the pairing trait characteristic index and kinship parameter, analyze the matching increment and kinship change value according to the double-point crossover rule, obtain the change data of the kinship of the paired individuals, analyze the global matching distribution of the population in combination with the neighborhood matching interval, screen the global matching pairing data, update the genetic information, and obtain the optimization of the reproductive characteristics layout result; S4: Based on the optimized reproduction characteristic layout results, the population reproduction combination is screened according to the Pareto frontier rule, and the matching degree distribution of the reproduction combination is combined with the global trait dynamic characteristic value to update the global population trait distribution state and obtain the optimized goat reproduction matching plan.

2. The goat selection breeding method based on genetic algorithm according to claim 1, characterized in that: The steps for obtaining the first generation goat breeding pairing data are specifically as follows: S111: Based on the genetic initialization data of the goat population, the trait characteristic indicators and kinship parameters of each pairing combination in the goat population are extracted, and the trait data of each pair of individuals are analyzed, including three categories: weight, production performance, and disease resistance, to form a preliminary pairing combination characteristic data set; S112: Analyze the preliminary paired combination feature data set, analyze each pair of paired traits, and perform pair matching evaluation based on the correlation of trait indicators, using the formula: Screen the best pairing and calculate the trait matching value; Among them, S match Representative trait matching value, w i represents the weight of the ith trait, f i represents the value of the ith trait, α represents the weight adjustment coefficient of kinship, r ij represents the kinship coefficient between individuals i and j, n represents the number of traits, i is the trait item index, and j is the paired individual index; S113: Based on the trait matching value, the trait matching degree and kinship coefficient of the optimal pairing are called, and the individuals that meet the matching standard in the population are rearranged to obtain the first generation pairing data of goat breeding.

3. The goat selection breeding method based on genetic algorithm according to claim 2, characterized in that: The steps for obtaining the local candidate pairing feature data set are specifically as follows: S211: Based on the goat breeding first generation pairing data, extract the paired individual gene information, mark the gene sequence according to the chromosome position segment, extract the mutation types of base substitution, insertion and deletion and duplication fragments, classify the mutation data according to the mutation frequency and occurrence area in the chromosome segment, and establish a gene mutation type data table; S212: Based on the gene mutation type data table, screening the pairing combinations in the mutation feature data, extracting and annotating the starting site, range length and associated base features of the combined mutation region one by one, setting the mutation parameter range and classifying the parameter data, and constructing a candidate gene pairing parameter set; S213: Based on the candidate gene pairing parameter set, a multi-parameter cross-matching of mutation site and range data is performed for pairing combinations that meet the set parameter range, common data of the mutation region is extracted, the result data is called to adjust the regional matching degree, and a local candidate pairing feature data set is constructed.

4. The goat selection breeding method based on genetic algorithm according to claim 3, characterized in that: The steps for obtaining the local optimal pairing combination are specifically as follows: S221: Analyze the local candidate pairing characteristic data set, extract the matching degree change value and the kinship change amount of each candidate pairing combination, select the combination with a matching degree change value higher than a threshold, and obtain a matching degree change data set; S222: Based on the matching degree change data set, analyzing the corresponding relationship between the matching degree change value and the kinship change amount in the differentiated combination, calculating the weighted average of the matching degree change value and the kinship change amount, screening the weighted combination, and obtaining the feature matching preferred group; S223: Based on the feature matching preferred group, local optimization adjustment is performed to adjust the influencing factor of kinship, using the formula: Calculate the scores of candidate pairing combinations to obtain the local optimal pairing combination; Among them, R final represents the score of the candidate pairing combination, M k represents the change in matching degree of the kth candidate pair, P k represents the change in kinship of the kth candidate pair, E1 and E2 are the weighted coefficients of matching degree and kinship, S k represents the weight coefficient of the combination, and m is the total number of candidate pairing combinations.

5. The goat selection breeding method based on genetic algorithm according to claim 4, characterized in that: The steps for obtaining the change data of the kinship relationship of the paired individuals are specifically as follows: S311: Based on the local optimal pairing combination, extract the trait characteristic indicators of the paired individuals, summarize the classification mark characteristic indicators by dimension, organize the classification results and data range, and generate a pairing trait and kinship characteristic table; S312: Based on the paired trait and kinship feature table, the trait matching increments in the paired combinations are analyzed item by item according to the double-point crossover rule, the paired trait index change interval and the kinship change difference are identified, and a paired trait matching increment table is generated; S313: Based on the table of matching degree of paired traits and kinship increments, extract key data of trait characteristics and kinship increments in the changed paired combinations, perform grouping analysis and matching adjustment between data, optimize the paired combinations according to the changed characteristics, and obtain the kinship change data of paired individuals.

6. The goat selection breeding method based on genetic algorithm according to claim 5, characterized in that: The steps for obtaining the optimized reproduction characteristic layout result are specifically as follows: S321: Based on the change data of the kinship of the paired individuals, calculate the kinship change value of the paired individuals, perform numerical normalization processing on the kinship change value, sort the processed values ​​and assign neighborhood ranges, and generate a result of the kinship change of the paired individuals; S322: Combined with the result of the change in the affinity of the pairing, the matching degree distribution of the global population is analyzed, and the pairings with low matching degrees are screened and eliminated in combination with the threshold, using the formula: Get the global matching result; Among them, G match Represents the global matching value, F h represents the weight of each pair, p h represents the kinship change value, T h represents the matching threshold, z h represents the adjustment parameter, N represents the total number of pairs, and h represents the sequence number of the pairs; S323: Based on the global matching result, the optimal pairing data is screened, and the genetic information is adjusted in combination with the screened data. Through local correction and global update of the genetic information, an optimized reproduction characteristic layout result is generated.

7. The goat selection breeding method based on genetic algorithm according to claim 6, characterized in that: The steps for obtaining the optimized goat breeding and matching scheme are specifically as follows: S411: Based on the optimized reproduction characteristic layout result, retrieve the individual affinity data from the genetic database, analyze the gene homogeneity and difference between each pair of individuals, analyze through the Pareto front rule, and perform reproduction screening to generate a reproduction combination after Pareto front screening; S412: For the breeding combination after the Pareto frontier screening, combined with the global population trait dynamic characteristic value, the matching degree distribution is optimized by weight adjustment, using the formula: Generate optimized matching degree distribution results; Among them, P adj Represents the adjusted matching degree distribution value, M pair represents the kinship matching value of the breeding combination, W dyn Represents the weight of the dynamic characteristic value of the population trait, S div Represents the trait diversity index of the breeding combination, T norm is the standardized coefficient of matching; S413: Based on the results of the optimized matching degree distribution, the selected breeding combinations are evaluated, the trait balance between the combinations is adjusted through dynamic trait factors, and an optimized goat breeding matching plan is generated.

8. A goat selection breeding system based on genetic algorithm, characterized in that: According to any one of claims 1 to 7, the method for selective breeding of goats based on genetic algorithms comprises: The population genetic parameter analysis module extracts the trait characteristic indicators and kinship parameters of each pairing combination based on the genetic initialization data of the goat population, calculates the weighted value and weight value, normalizes each pair and analyzes the indicator distribution, sorts and selects the combination according to the genetic diversity value, and generates the first generation breeding pairing data; The local genetic property optimization module extracts individual gene mutation types and trait characteristic indicators based on the primary generation breeding pairing data, identifies the value range of mutation types and associates trait indicators, analyzes neighborhood trait change trends, and screens combinations of matching degree change values ​​to obtain local genetic optimization pairing data; The global reproduction characteristic tuning module analyzes the variation range of cross traits based on the local genetic optimization pairing data, analyzes the global matching increment in combination with the variation of kinship, classifies the increment value according to the global distribution partition, screens the combination that meets the distribution, and obtains the global optimization reproduction pairing data; The breeding combination optimization and distribution update module analyzes the matching degree distribution range and dynamic trait characteristic values ​​of the breeding combination based on the global optimized breeding pairing data, reclassifies the matching degree range and analyzes the dynamic trait changes of the classification results, updates the global population genetic characteristics in combination with the trait distribution status, and generates an optimized goat breeding pairing plan.

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