Selecting and matching method for live pigs

Abstract

The invention discloses a selection and matching method for live pigs, and relates to the technical field of agricultural information, and the method comprises the steps of 1, multi-dimensional breeding data collection, 2, genetic evaluation and selection and matching model, and 3, hybridization implementation and verification model iteration, through multi-dimensional breeding data collection, comprehensive data support is provided for evaluation, the genetic evaluation and selection and matching model is constructed, and the selection and matching of the live pigs is realized. According to the method, accurate sorting of comprehensive breeding indexes and inbreeding risk grading are achieved, inbreeding decline is avoided, excellent character breeding is accelerated, a'matching-verification-optimization 'closed-loop mechanism is formed, the prediction precision and generalization ability of the model are remarkably improved through the closed loop, continuously-optimized forward circulation is also formed, and therefore stable improvement of the overall breeding efficiency is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural information technology, in particular to a mating method for pigs. BACKGROUND

[0002] The traditional mating method for pigs relies on manual experience and combines phenotypes with simple pedigree data. However, it has problems such as insufficient genetic evaluation accuracy, difficulty in improving multiple traits simultaneously, and low data integration efficiency, resulting in long breeding cycles, limited improvement of target traits, and difficulty in precisely controlling the inbreeding recession risk at the genomic level through traditional kinship analysis, which can easily lead to a decrease in the health of offspring. Therefore, it is necessary to study a mating method for pigs to improve breeding efficiency.

[0003] The prior art, such as the invention patent application with publication number CN114358963A, discloses a mating system and method for pigs. The system includes a method that matches by using an inbreeding coefficient threshold to control the inbreeding coefficient within a minimum range, prevent the loss of bloodlines, ensure the diversity of the population, maximize homogeneous optimal mating, effectively avoid pure inbreeding, increase selection intensity, accelerate effective genetic progress, and improve the precision and standardization of the mating process. The prior art, such as the invention patent application with publication number CN119422944A, discloses a mating method and system for a pig core group. The method includes formulating a mating plan that covers all groups of sows to be mated, supporting both homogeneous optimal mating and random mating, and effectively controlling the inbreeding increment of the population while maintaining the diversity of the population to accelerate genetic progress.

[0004] In view of the above solutions, the current mating method for pigs has the following core defects: the data foundation is heavily dependent on traditional phenotypes and pedigree information, and individual whole-genome data is not fully integrated and utilized, resulting in genetic evaluation still being limited to traditional breeding values with limited precision, and unable to obtain earlier and more accurate genomic breeding values. In addition, the inbreeding risk control is only based on a rough pedigree inbreeding coefficient threshold, making it difficult to achieve fine classification evaluation based on allele frequency distance, and thus unable to precisely avoid potential inbreeding recession risks at the genomic level. The existing mating model has mechanism defects and fails to assign economic weights based on the dynamic correlation between performance traits and market prices, resulting in the inability to construct a scientific and quantitative comprehensive selection index, and thus unable to scientifically and quantitatively prioritize individual breeding pigs. Furthermore, the existing solutions lack a key closed-loop verification mechanism, making it impossible to evaluate model accuracy based on the deviation between measured genomic breeding values and model predicted values of offspring, and thus unable to analyze the error of model iteration, limiting the breakthrough in mating efficiency and effectiveness. SUMMARY

[0005] The purpose of the present application is to provide a method for selecting and mating pigs.

[0006] To solve the above technical problems, the present application adopts the following technical solution: The present application provides a method for selecting and mating pigs, step 1. Multi-dimensional breeding data collection: Obtain the genomic data of each breeding sow and each breeding boar, each performance trait characteristic value, and the temperature of the breeding site. The performance traits include body weight, back fat thickness, and feed intake.

[0007] Step 2. Genetic evaluation and mating model: Calculate the individual breeding value of each breeding sow and the individual breeding value of each breeding boar, and calculate the genomic breeding value of each breeding sow and the genomic breeding value of each breeding boar. Analyze the breeding priority order of each breeding sow and each breeding boar, and evaluate the inbreeding risk level of the offspring of each breeding sow and each breeding boar.

[0008] Step 3. Mating implementation and verification model iteration: Group matching according to the breeding priority order of each breeding sow and each breeding boar, select the successful combination of each breeding sow and the matching boar, obtain the embryonic period detection data of the offspring of each breeding sow and the matching boar, analyze the accuracy of the verification model iteration, and upload to the breeding site terminal for analysis to improve the quality of embryonic development and breeding efficiency.

[0009] The present application has the following advantages: (1) The step 1. Multi-dimensional breeding data collection of the present application collects data of each breeding sow and each breeding boar, constructs a multi-dimensional data set covering genetic material basis and important performance traits, and provides more comprehensive data support for subsequent genetic evaluation and model construction, effectively improving the scientificity and accuracy of the selection and mating decision.

[0010] (2) The step 2. Genetic evaluation and mating model of the present application calculates the individual breeding value and genomic breeding value of each breeding sow and each breeding boar, combines market weight to construct a comprehensive index, and evaluates inbreeding risk based on allele frequency distance, significantly improving the selection efficiency and accuracy, effectively avoiding inbreeding recession risk, and accelerating the process of directional breeding of target excellent traits.

[0011] (3) The step 3. Mating implementation and verification model iteration of the present application calculates the genomic breeding value deviation of the actual and predicted offspring, constructs the accuracy evaluation of the verification model iteration to predict the accuracy, and when the accuracy is insufficient, the system will automatically diagnose, locate and update the wrong core model parameters, and then upload the data to the terminal to form a "selection-verification-optimization" closed loop. This closed loop not only significantly improves the prediction accuracy and generalization ability of the model, but also forms a continuous optimization positive loop, thereby ensuring the stable improvement of the overall breeding efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0013] Figure 1 The method module of the present application is shown in the figure. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0015] Referring to Figure 1 As shown in the figure, the present application provides a mating method for pigs, step 1. Multi-dimensional breeding data acquisition: obtaining the genomic data of each breeding sow and each breeding boar in the breeding site, and the characteristic value of each performance trait, the each performance trait including: body weight, back fat thickness, feed intake.

[0016] In one specific embodiment, the method for obtaining the genomic data of each breeding sow and each breeding boar in the breeding site, and the characteristic value of each performance trait is as follows: by collecting the blood samples of each breeding sow and each breeding boar in the breeding site, using a DNA extraction kit to separate genomic DNA, and then obtaining the genomic data, using an intelligent weighing system to measure the body weight regularly, using a veterinary B-ultrasound instrument to scan the back of the pig at the 10th rib to obtain the back fat thickness, and using the RFID ear tag of the automatic feeding station to identify the individual, and combining the weighing sensor to record the daily feed intake.

[0017] The step 1. Multi-dimensional breeding data acquisition of the present application acquires each data of each breeding sow and each breeding boar, constructs a multi-dimensional data set covering the genetic material basis and important performance traits, provides more comprehensive data support for subsequent genetic evaluation and model construction, and effectively improves the scientificity and accuracy of the mating decision.

[0018] Step 2. Genetic evaluation and mating model: calculate individual breeding value of each breeding sow and individual breeding value of each breeding boar, calculate genomic breeding value of each breeding sow and genomic breeding value of each breeding boar, analyze comprehensive selection index of each breeding sow and each breeding boar, calculate allele frequency distance between each breeding sow and each breeding boar, and evaluate inbreeding risk level of offspring of each breeding sow and each breeding boar.

[0019] In a specific embodiment of the present application, the individual breeding value of each breeding sow and the individual breeding value of each breeding boar are calculated as follows: the ideal target value B of each performance trait of each breeding sow is obtained from the local database k and the range radius C of the allowed deviation of each performance trait k , and the characteristic value A of each performance trait of each breeding sow is calculated according to the characteristic value A of each performance trait of each breeding sow xk , wherein x represents the number of each breeding sow, x = 1, 2, …, m, m is a positive integer greater than 2, k represents the number of each performance trait, k = 1, 2, 3, and the individual breeding value of each breeding sow is calculated as The individual breeding value of each breeding boar can be calculated in the same way.

[0020] In a specific embodiment, the ideal target value of each performance trait of each breeding sow and the range radius of the allowed deviation of each performance trait are obtained as follows: the data distribution characteristics of each performance trait of the combination of each breeding sow and each breeding boar that successfully mate in history are set, for example, the ideal backfat thickness of each breeding sow is 15 mm, and the violation radius of the allowed deviation of this performance trait is 3 mm, corresponding to the ideal interval of backfat thickness of 12 to 18 mm.

[0021] It should be noted that the local database is used to store the ideal target value of each performance trait of each breeding sow, the range radius of the allowed deviation of each performance trait, the copy number of each locus carrying alleles in the SNP data of the genomic data of each breeding sow, the copy number of each locus carrying alleles in the SNP data of the genomic data of each breeding boar, the genomic breeding value of the actual breeding boar assigned to each breeding sow, the first allele frequency distance threshold, and the accuracy first threshold and second threshold of model iteration.

[0022] In a specific embodiment of the present application, the genomic breeding value of each breeding sow and the genomic breeding value of each breeding boar are calculated as follows: the genotype value D of each performance trait of each breeding sow at each locus in the SNP data of the genomic data is obtained according to the genomic data of each breeding sow xki and the effect value E corresponding to each performance trait of each breeding sow at each locus genotype value xkiwherein i represents the number of SNP data sites, i = 1, 2, …, p, p is a positive integer greater than 2, and the genomic breeding value of each breeding sow is calculated wherein m is the total number of breeding sows, and the genomic breeding value of each breeding boar can be calculated in the same way wherein y represents the number of each breeding boar, y = 1, 2, …, q, q is a positive integer greater than 2, and q is the total number of breeding boars.

[0023] In one embodiment, the genotype value of each performance trait of each breeding sow at each SNP data site in the genotype data and the corresponding effect value of each performance trait of each breeding sow at the genotype value of the site are obtained, and the specific obtaining method is as follows: based on the genomic data of each breeding sow, the full-genome SNP data sites are detected by means of a high-throughput sequencer to generate a genotype data file, and then the genotype value of each performance trait of each breeding sow at each SNP data site in the genotype data and the corresponding effect value at the genotype value of the site are obtained.

[0024] In the embodiment of the present application, the comprehensive selection index of each breeding sow and each breeding boar is analyzed, and the specific method is as follows: the real-time transaction price F of each sold sow is obtained from local market monitoring z and the characteristic value A of each performance trait zk wherein z represents the number of each sold sow, z = 1, 2, …, n, n is a positive integer greater than 2, the data reliability is ensured by cleaning abnormal data and standardizing the data, and the linear correlation coefficient of each performance trait and the real-time transaction price is calculated wherein is the sow population mean of each performance trait, is the overall mean of the price of all sold sows, and the economic weight of each performance trait is derived according to the correlation coefficient of each performance trait and the transaction price The comprehensive selection index of each breeding sow is analyzed according to the individual breeding value of each breeding sow The comprehensive selection index of each breeding boar can be analyzed in the same way.

[0025] In one embodiment, the sow population mean of each performance trait is calculated according to the characteristic value of each performance trait of each sold sow wherein n is the total number of sold sows, and the boar population mean of each performance trait can be calculated in the same way.

[0026] In one embodiment, the overall mean of the price of all sold sows is calculated according to the real-time transaction price of each sold sow Wherein n is the total number of selling sows, and the total mean of the price of all selling boars can be calculated in the same way.

[0027] In a specific embodiment, the cleaning of abnormal data is specifically performed by: setting a threshold range of each performance trait according to common knowledge, and removing data out of the threshold range, for example, the threshold of body weight is set as ±3 times the standard deviation interval of the normal growth curve, and the extreme value out of the range is determined as abnormal, and when the body weight of a newborn piglet is less than 0.5 kg or greater than 2 kg, the newborn piglet is removed as an extreme value.

[0028] It should be noted that the linear correlation coefficient between the real-time price of each selling sow and each performance trait can indirectly reflect the weight coefficient influence degree of each performance trait on each breeding sow.

[0029] In a specific embodiment of the present application, the calculation of the allele frequency distance between each breeding sow and each breeding boar is specifically performed as follows: obtaining the copy number G of each locus carrying an allele in the SNP data of each breeding sow genomic data from a local database xi and the copy number G of each locus carrying an allele in the SNP data of each breeding boar genomic data yi , calculating the allele frequency of each locus of each breeding sow Similarly, the allele frequency of each locus of each breeding boar can be calculated The allele frequency distance between each breeding sow and each breeding boar is calculated as Wherein m is the total number of breeding sows.

[0030] In a specific embodiment, the copy number of each locus carrying an allele in the SNP data of each breeding sow genomic data is obtained from a local database, and the specific obtaining method is as follows: by extracting the genomic DNA of the breeding sow and using high-throughput sequencing or gene chip for genotyping, after sequence alignment, variation detection and genotype analysis, the copy number of each locus allele is obtained.

[0031] It should be noted that the copy number of each locus carrying an allele in the SNP data of each breeding sow genomic data refers to the number of repetitions of a certain allele in the genome at a specific locus, and each locus is composed of two chromatids, when the genotype is homozygote AA, the copy number of allele A is 2, and a is 0, and in heterozygote Aa, the copy number of A and a is 1 each.

[0032] It should also be noted that in the diploid organism biallelic locus, the value of the allele copy number is 0, 1 or 2.

[0033] In a specific embodiment of the present application, the inbreeding risk level of each breeding sow and each breeding boar offspring is evaluated, and the specific method is as follows: obtaining the first allele frequency distance threshold value from the database, if the allele frequency distance between a certain breeding sow and a certain breeding boar is greater than or equal to the first allele frequency distance threshold value, the inbreeding risk level of the offspring of the breeding sow and the breeding boar is low risk, and if the allele frequency distance between a certain breeding sow and a certain breeding boar is less than the first allele frequency distance threshold value, the inbreeding risk level of the offspring of the breeding sow and the breeding boar is high risk.

[0034] In a specific embodiment, the first allele frequency distance threshold value between each breeding sow and each breeding boar is obtained from the local database, and the specific obtaining method is as follows: according to the correlation analysis of the breed characteristics and the survival rate of the offspring in the historical mating data of the breeding farm, the first allele frequency distance threshold value between each breeding sow and each breeding boar is usually set to 0.125.

[0035] Step 2. Genetic evaluation and mating model of the present application, by calculating the individual breeding value and genomic breeding value of each breeding sow and each breeding boar, combining market weight to build a comprehensive index, and evaluating the inbreeding risk by allele frequency distance, significantly improves the selection efficiency and accuracy, effectively avoids the risk of inbreeding depression, and speeds up the process of directional breeding of target excellent traits.

[0036] Step 3. Mating implementation and verification model iteration: distributing and combining each breeding sow and each breeding boar after sorting, analyzing and verifying the accuracy of the model iteration according to the genomic breeding value of the offspring of each breeding sow and each mating boar after distribution and combination, and uploading the model to the breeding farm terminal for subsequent breeding analysis.

[0037] In a specific embodiment of the present application, the distribution and combination of each breeding sow and each breeding boar after sorting is as follows: obtaining the inbreeding risk level of the offspring of each breeding sow and each breeding boar after sorting according to the comprehensive index.

[0038] Select the first breeding sow and the first breeding boar for matching, if the inbreeding risk level of the offspring of the two is low risk, the matching is successful, if the inbreeding risk level of the offspring of the two is high risk, the matching fails, then the first breeding sow and the second breeding boar are matched, if the inbreeding risk level of the offspring of the two is low risk, the matching is successful, if the inbreeding risk level of the offspring of the two is high risk, and so on for the distribution and combination of each breeding sow and each breeding boar after sorting.

[0039] In specific embodiments of the present application, the inbreeding risk level of each ranked breeding sow and each ranked breeding boar offspring is obtained by: ranking each breeding sow and each breeding boar from high to low according to the comprehensive selection index to obtain ranked each breeding sow and ranked each breeding boar, and mapping the inbreeding risk level of each ranked breeding sow and each ranked breeding boar offspring according to the inbreeding risk level of each breeding sow and each breeding boar offspring.

[0040] In specific embodiments of the present application, the accuracy of the iterative model is analyzed and verified according to the genomic breeding value of each breeding sow and each adapted boar offspring of each assigned combination, and the accuracy of the iterative model is analyzed and verified according to the genomic breeding value of each breeding sow and each adapted boar offspring of each assigned combination. x and the genomic breeding value of each adapted boar, to obtain the genomic breeding value H x of each breeding sow and each assigned breeding boar of each breeding sow, and to obtain the genomic breeding value H x ′ of each breeding sow and each assigned breeding boar of each breeding sow from the local database. x The genomic breeding value deviation of each breeding sow and each assigned breeding boar of each breeding sow is calculated as υ x = |H′ x -H

[0041] In one specific embodiment, the genomic breeding value of each assigned breeding boar of each breeding sow is obtained in the same way as the genomic breeding value of each breeding sow and the genomic breeding value of each breeding boar.

[0042] In specific embodiments of the present application, the model is analyzed for errors and uploaded to the breeding farm terminal for subsequent breeding analysis, and the method is as follows: the first and second threshold values of the accuracy of the iterative model are obtained from the local database, if the accuracy of the iterative model is greater than or equal to the first threshold value, the accuracy of the iterative model is high and the current iteration is continued, if the accuracy of the iterative model is greater than or equal to the second threshold value and less than the first threshold value, the accuracy of the iterative model is lower and needs to be locally optimized, if the accuracy of the iterative model is less than the second threshold value, the accuracy of the iterative model is low and needs to be iterated, when the accuracy of the iterative model needs to be locally optimized or iterated, the system sorts and filters the high-risk assigned combinations with the top 10% deviation value based on the genomic breeding deviation sorting, compares the SNP site genotype values of the parents and offspring in these combinations to determine whether the corresponding effect value is incorrect, updates the effect value of the related SNP site according to the system error, and finally uploads the optimized effect value table to the breeding terminal for subsequent breeding analysis.

[0043] In one specific embodiment, the acquisition model verifies the first index threshold and the second index threshold, and the specific acquisition method is: by comparing the data of the historical breeding model, the model verification first index threshold and the second index threshold are assigned by the breeding experts, and the values of the model verification first index threshold and the second index threshold are between [0, 1], for example: the first index threshold is 0.97, and the second index threshold is 0.93.

[0044] In one specific embodiment, the corresponding effect value is judged to be wrong, and the specific method is: if the genotype of the parent is AA, and the offspring appears aa, the corresponding effect value is wrong, and if the genotype of the parent is AA, and the offspring only appears AA, the corresponding effect value is correct.

[0045] It should be noted that updating the effect value of the related SNP site is to reacquire the effect value corresponding to each performance trait of each breeding sow under each site genotype value.

[0046] Step 3 of the application is to implement and verify the iteration of the mating model. The deviation degree of the actual and predicted offspring genomic breeding value is calculated to construct the accuracy evaluation of the iteration of the verification model to predict the accuracy. When the accuracy is insufficient, the system will automatically diagnose, locate and update the wrong core model parameters, and then upload the data to the terminal to form a "selection-matching-optimization" closed loop. This closed loop not only significantly improves the prediction accuracy and generalization ability of the model, but also forms a continuous optimization positive loop, thereby ensuring the stable improvement of the overall breeding efficiency.

[0047] The above is only an example and description of the concept of the application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the application or exceed the scope defined by the application, and they should belong to the protection scope of the application.

Claims

1. A method for selecting and mating pigs, characterized in that, The application relates to a pig breeding method, which comprises the following steps: Step 1. Multi-dimensional breeding data acquisition: obtaining genomic data of each breeding sow and each breeding boar in a breeding site and characteristic values of each performance trait, wherein the performance traits include body weight, back fat thickness and feed intake; Step 2. Genetic evaluation and mating model: calculating individual breeding values of each breeding sow and each breeding boar, calculating genomic breeding values of each breeding sow and each breeding boar, analyzing comprehensive selection indexes of each breeding sow and each breeding boar, calculating allelic frequency distance between each breeding sow and each breeding boar, and evaluating inbreeding risk levels of offspring of each breeding sow and each breeding boar; Step 3. Mating implementation and verification model iteration: distributing combinations of each breeding sow and each breeding boar after sequencing, analyzing accuracy of verification model iteration according to genomic breeding values of offspring of each breeding sow and each mating boar after distribution of combinations, performing error analysis on the model and uploading the model to a breeding site terminal for subsequent breeding analysis.

2. The method for mating of swine according to claim 1, wherein, The specific method for calculating individual breeding values of each breeding sow and each breeding boar is as follows: B: ideal target value of each performance trait of each breeding sow, which is obtained from a local database k C: range radius of allowable deviation of each performance trait k A: characteristic value of performance trait of each breeding sow xk wherein x represents the number of each breeding sow, x = 1, 2, …, m, m is a positive integer greater than 2, k represents the number of each performance trait, k = 1, 2, 3, and the individual breeding value of each breeding sow is calculated as The individual breeding value of each breeding boar can be calculated in the same way.

3. The method according to claim 2, wherein, The specific method for calculating genomic breeding values of each breeding sow and each breeding boar is as follows: According to the genomic data of each breeding sow, the genotype value D of each performance trait of each breeding sow at each SNP data site in the genomic data is obtained xki and the effect value E corresponding to each performance trait of each breeding sow at each genotype value xki wherein i represents the number of each SNP data site, i=1, 2,..., p, p is a positive integer greater than 2, and the genomic breeding value of each breeding sow is calculated wherein m is the total number of breeding sows, and the genomic breeding value of each breeding boar can be calculated in the same way wherein y represents the number of each breeding boar, y=1, 2,..., q, q is a positive integer greater than 2, and q is the total number of breeding boars.

4. The method according to claim 2, wherein, The specific method for analyzing comprehensive selection indexes of each breeding sow and each breeding boar is as follows: Obtaining the real-time transaction price F of each selling sow from local market monitoring z and the characteristic value A of each performance trait zk , wherein z represents the number of each selling sow, z = 1, 2, …, n, n is a positive integer greater than 2, the data reliability is ensured by cleaning abnormal data and standardizing the data, and the linear correlation coefficient of each performance trait and the real-time transaction price is calculated , wherein is the population mean of each performance trait of sows, is the overall mean of the price of all selling sows, and the economic weight of each performance trait is derived according to the correlation coefficient of each performance trait and the transaction price According to the individual breeding value of each breeding sow, the comprehensive selection index of each breeding sow is analyzed Similarly, the comprehensive selection index of each breeding boar can be analyzed.

5. The method according to claim 3, wherein The specific method for calculating allelic frequency distance between each breeding sow and each breeding boar is as follows: Copy number G of each locus carrying allele in SNP data of each breeding sow genomic data is obtained from the local database xi Copy number G of each locus carrying allele in SNP data of each breeding boar genomic data is obtained from the local database yi Allele frequency of each locus of each breeding sow is calculated Allele frequency of each locus of each breeding boar is calculated Distance of allele frequency between each breeding sow and each breeding boar is calculated as Wherein m is the total number of breeding sows.

6. The method according to claim 5, wherein, The specific method for evaluating inbreeding risk levels of offspring of each breeding sow and each breeding boar is as follows: The first allelic frequency distance threshold is obtained from the database, if the allelic frequency distance between a certain breeding sow and a certain breeding boar is greater than or equal to the first allelic frequency distance threshold, the inbreeding risk level of the offspring of the breeding sow and the breeding boar is low, and if the allelic frequency distance between the certain breeding sow and the certain breeding boar is less than the first allelic frequency distance threshold, the inbreeding risk level of the offspring of the breeding sow and the breeding boar is high.

7. The method according to claim 6, wherein, The specific method for distributing combinations of each breeding sow and each breeding boar after sequencing is as follows: According to the comprehensive indexes of each breeding sow and each breeding boar, the inbreeding risk levels of offspring of each breeding sow and each breeding boar after sequencing are obtained; The first breeding sow and the first breeding boar are matched, if the inbreeding risk level of the offspring of the two is low, the matching is successful, if the inbreeding risk level of the offspring of the two is high, the matching fails, then the first breeding sow and the second breeding boar are matched, if the inbreeding risk level of the offspring of the two is low, the matching is successful, if the inbreeding risk level of the offspring of the two is high, the first breeding sow and the second breeding boar are matched, and so on.

8. The method according to claim 7, wherein, The specific method for obtaining the inbreeding risk levels of offspring of each breeding sow and each breeding boar after sequencing is as follows: The each breeding sow and each breeding boar are ranked from high to low according to the comprehensive selection index, and the ranked each breeding sow and the ranked each breeding boar are obtained, and the inbreeding risk level of the offspring of the ranked each breeding sow and the ranked each breeding boar is obtained according to the inbreeding risk level of the offspring of each breeding sow and each breeding boar.

9. The method according to claim 3, wherein the method is characterized by, The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: According to the distribution combination of each breeding sow and each breeding boar after sorting, the distribution breeding boar of each breeding sow is obtained, and the genomic breeding value H of the distribution breeding boar of each breeding sow is obtained according to the genomic breeding value β of each breeding sow and the genomic breeding value of each adaptive boar x . x The genomic breeding value H of the distribution breeding boar of each breeding sow is obtained from the local database x . The genomic breeding value deviation υ of each breeding sow and the distribution breeding boar of each breeding sow is calculated x = |H′ x -H x |, the accuracy of model iteration is constructed 10. The method for mating of swine according to claim 1, wherein, The model is subjected to error analysis and uploaded to the breeding farm terminal for subsequent breeding analysis, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of the iteration of the genomic breeding value analysis verification model of the offspring of each breeding sow and each adaptive boar in each distribution combination is analyzed, and the specific method is as follows: The accuracy of

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