Method, device, equipment, medium and product for predicting cattle population structure and genetic progress

Abstract

The application discloses a method and device for predicting the structure and genetic progress of a cattle herd, equipment, a medium and a product, and relates to the technical field of genetic breeding. The method comprises the following steps: acquiring original data of a target dairy farm, a preset mating scheme, a preset frozen semen plan and a target prediction time period; constructing a mature cow chain transfer probability matrix and a replacement heifer chain transfer probability matrix based on historical production data and related production parameters; predicting the structure of the cattle herd based on cattle herd structure data at a prediction starting time, the mature cow chain transfer probability matrix and the replacement heifer chain transfer probability matrix, to obtain cattle herd structure distribution information of each target prediction sub-time period; simulating based on the cattle herd structure distribution information, the preset mating scheme, the preset frozen semen plan and genetic evaluation data, to obtain average breeding values of female calves; and outputting the cattle herd structure distribution information of each target prediction sub-time period and the average breeding values of the female calves. The application can improve the genetic level of the entire dairy herd.

Description

Technical Field

[0001] This application relates to the field of genetic breeding technology, and in particular to a method, apparatus, equipment, medium and product for predicting herd structure and genetic progress in cattle. Background Technology

[0002] In dairy farming, herd structure (including the number of heifers and mature cows, parity distribution, and pregnancy status) and genetic progress (reflecting the long-term upward trend in the herd's genetic level) are two core factors determining farm productivity, reproductive management strategies, and breeding effectiveness. In practice, mating and renewal strategies not only affect herd structure but also further influence the average genetic level of the herd. Therefore, how to jointly predict dairy herd structure and genetic progress to improve the genetic level of dairy herds has become a crucial technical issue in farm management and precision breeding. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium, and product for predicting herd structure and genetic progress in dairy cows, which can improve the genetic level of dairy cow populations.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for predicting cattle herd structure and genetic progression, including: The process involves acquiring raw data from the target dairy farm, a pre-set breeding plan, a pre-set frozen semen plan, and a target prediction time period. The raw data includes historical production data from the target dairy farm, herd structure data at the prediction start time, relevant production parameters, and genetic assessment data. The pre-set breeding plan includes the target dairy farm's pre-set breeding method and breeding ratio. The pre-set frozen semen plan includes the target dairy farm's pre-set frozen semen usage plan. The target prediction time period consists of multiple target prediction sub-time periods. Based on the historical production data and the relevant production parameters, a cow chain transition probability matrix and a reserve cow chain transition probability matrix are constructed. Based on the herd structure data at the predicted start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, herd structure prediction is performed to obtain herd structure distribution information for each target prediction sub-time period. Based on the herd structure distribution information of each target prediction sub-time period, the preset breeding scheme, the preset frozen semen plan, and the genetic evaluation data, the offspring breeding value is simulated to obtain the average breeding value of the female calves corresponding to the target prediction time period. Output the herd structure distribution information and the average breeding value of the female calves for each target prediction sub-time period.

[0005] Optionally, the historical production data includes at least historical calving event data, historical initial pregnancy check event data, historical abortion event data, and historical mortality and culling event data; the historical calving event data includes at least the calf number, parity, age, calving interval, calf number, calf status, calf sex, and calf type; the historical initial pregnancy check event data includes at least the calf number, parity, age, postpartum days, pregnancy check result, and the number of days at the time of the pregnancy check; the historical abortion event data includes at least the calf number, parity, age, postpartum days, and gestation days; the historical mortality and culling event data includes at least the calf number, parity, age, postpartum days, and gestation days. The herd structure data for the predicted start time includes at least the cow number, age, parity, postpartum / lactation days, and gestation / pregnancy days of each cow in the herd at the predicted start time in the target dairy farm. The relevant production parameters include at least the voluntary waiting period, the premature birth rate of adult cows, the age of first mating of heifers, the calf survival rate, and the premature birth rate of heifers. The preset mating plan includes at least a preset mating method and a preset mating ratio; the preset mating method and mating ratio are the mating ratios of sex-controlled frozen semen from dairy cows, conventional frozen semen from dairy cows, embryo transfer from dairy cows, and frozen semen from beef cows. The preset frozen semen plan includes at least the frozen semen number, usage time, frozen semen type, usage quantity, and breeding value of the bulls in the target dairy farm; The genetic assessment data includes at least the cow number, mating method, date of birth, and breeding value of the assessed cows in the target dairy farm.

[0006] Optionally, the construction of the cow herd transition probability matrix and the reserve cow herd transition probability matrix based on the historical production data and the relevant production parameters specifically includes: Based on the relevant production parameters, a cow state space and a heifer state space are constructed; wherein, the cow state space consists of postpartum months, gestation months, and parity, and the heifer state space consists of age and gestation months. Based on the historical production data, determine the production events of each state of the adult cow corresponding to the adult cow state space and the production events of each state of the replacement calf corresponding to the replacement calf state space. Based on the Beta-Bernoulli conjugate model, the production events of the adult cows and the production events of the heifers under each state are calculated to determine the estimated values ​​of the transition probability of the adult cows and the estimated values ​​of the transition probability of the heifers under each state. Based on the state space of the adult cows, the production events in each state of the adult cows, and the estimated transition probability values ​​of the production events in each state, a transition probability matrix of the adult cow chain is constructed. Based on the state space of the reserve cattle, the production events in each state of the reserve cattle, and the estimated transition probability of the reserve cattle in each state, a transition probability matrix of the reserve cattle chain is constructed.

[0007] Optionally, the herd structure prediction based on the herd structure data at the prediction start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, to obtain herd structure distribution information for each target prediction sub-time period, specifically includes: Based on the herd structure data at the predicted start time, the state space of the adult cows, and the state space of the replacement cows, the initial adult cow state of each adult cow in the target dairy farm and the initial replacement cow state of each replacement cow in the replacement cow group are determined. Using a single target prediction sub-time period as the calculation step size, based on the initial adult cow state, the initial replacement cow state, the adult cow chain transition probability matrix, and the replacement cow chain transition probability matrix, state iteration calculations are performed on the adult cow population and the replacement cow population respectively to determine the herd structure distribution information for each target prediction sub-time period.

[0008] Optionally, the step of simulating offspring breeding values ​​based on the herd structure distribution information of each target prediction sub-time period, the preset mating scheme, the preset frozen semen plan, and the genetic evaluation data to obtain the average breeding value of the female calves corresponding to the target prediction time period specifically includes: Based on the herd structure distribution information of each target prediction sub-time period and the preset breeding scheme, the number of female calves under different breeding methods is determined. Based on the pre-set frozen semen plan and the genetic evaluation data, bull herds and cow herds were constructed respectively; Based on the number of female calves under different mating methods, the bull herd, and the cow herd, the calculation operation of the female calf breeding value under different mating methods is repeated to obtain multiple female calf breeding values. The average breeding value of the female calves corresponding to the target prediction time period is determined based on the multiple female calf breeding values.

[0009] Optionally, the calculation operation of the breeding value of the female calf is specifically as follows: Based on the number of female calves under the different mating methods, multiple paternal individuals and multiple maternal individuals are randomly selected from the bull herd and the cow herd, respectively. The paternal breeding value of each sire and the maternal breeding value of each dam are determined based on the bull herd and the cow herd. The paternal breeding value of each male parent and the maternal breeding value of each female parent are input into a pre-constructed maternal calf breeding value calculation model to obtain a maternal calf breeding value output by the maternal calf breeding value calculation model.

[0010] Secondly, this application provides a device for predicting herd structure and genetic progression in cattle, comprising: The acquisition unit is used to acquire raw data, a preset breeding plan, a preset frozen semen plan, and a target prediction time period from the target dairy farm. The raw data includes historical production data of the target dairy farm, herd structure data at the prediction start time, relevant production parameters, and genetic evaluation data. The preset breeding plan includes the preset breeding method and breeding ratio of the target dairy farm. The preset frozen semen plan includes the preset frozen semen usage plan of the target dairy farm. The target prediction time period consists of multiple target prediction sub-time periods. The construction unit is used to construct the cow chain transition probability matrix and the reserve cow chain transition probability matrix based on the historical production data and the relevant production parameters. The prediction unit is used to predict the herd structure based on the herd structure data at the prediction start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, so as to obtain the herd structure distribution information for each target prediction sub-time period. The simulation unit is used to simulate the offspring breeding value based on the herd structure distribution information of each target prediction sub-time period, the preset breeding scheme, the preset frozen semen scheme, and the genetic evaluation data, so as to obtain the average breeding value of female calves and the average breeding value of adult cows in the herd corresponding to the target prediction time period. The output unit is used to output the herd structure distribution information, the average breeding value of the female calves, and the average breeding value of the adult cows in the herd for each target prediction sub-time period.

[0011] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for predicting herd structure and genetic progression of cattle as described above.

[0012] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting herd structure and genetic progression of cattle as described above.

[0013] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for predicting herd structure and genetic progression of cattle as described above.

[0014] In a sixth aspect, this application provides a chip including a processor and a communication interface coupled to the processor. The processor is used to run a program or instructions, and when the processor executes the program or instructions, it implements the steps of the method for predicting herd structure and genetic progression of cattle as described above.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, device, equipment, medium, and product for predicting herd structure and genetic progress. By combining raw data from a target dairy farm, a pre-set breeding program, and a pre-set frozen semen plan, it can simulate the herd structure distribution information and the breeding value distribution of female calves under different breeding methods for each target prediction sub-time period, providing the target dairy farm with a more informed decision-making basis when formulating breeding plans. Managers of the target dairy farm can scientifically plan breeding programs, rationally arrange the selection and renewal of replacement heifers, and selectively choose high-quality breeding cattle for mating and breeding, continuously optimizing the herd breeding model, thereby steadily improving the genetic level of the entire dairy herd and making the breeding and herd management work of the target dairy farm more scientific and efficient. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for predicting cattle herd structure and genetic progression, provided in an embodiment of this application; Figure 2 This application provides a schematic diagram of a dairy cow's state transition according to an embodiment of the present application; Figure 3 A schematic diagram of the functional modules of a bovine herd structure and genetic progression prediction device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting cattle herd structure and genetic progression is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, it includes steps 101 to 105. Wherein: Step 101: Obtain the raw data of the target dairy farm, the preset breeding plan, the preset frozen semen plan, and the target prediction time period.

[0021] In this embodiment, the original data includes historical production data of the target dairy farm, herd structure data at the predicted start time, relevant production parameters, and genetic evaluation data; the preset breeding plan includes the preset breeding method and breeding ratio of the target dairy farm; the preset frozen semen plan includes the preset frozen semen usage plan of the target dairy farm; the target prediction time period consists of multiple target prediction sub-time periods; the duration of one target prediction sub-time period can be one month.

[0022] The historical production data includes at least historical calving event data, historical initial pregnancy checkup event data, historical miscarriage event data, and historical stillbirth event data. The historical calving events shall include at least the calf number, parity of the event, age, calving interval, calf number, calf status, calf sex and calf type; The historical first pregnancy check event shall include at least the cow number, parity of the event, age, number of days postpartum, pregnancy check result, and number of days at the time of the pregnancy check. The historical miscarriage events mentioned above should include at least the cow number, parity, age, number of days postpartum, and number of days of gestation. The historical dead cattle events must include at least the cattle number, parity, age, number of days postpartum, and number of days of gestation. The herd structure data for the predicted start time includes at least the cow number, age, parity, postpartum / lactation days, and gestation / pregnancy days of each cow in the herd at the predicted start time in the target dairy farm. The relevant production parameters include at least the voluntary waiting period, the premature birth rate of adult cows, the age of first mating of heifers, the calf survival rate, and the premature birth rate of heifers. The preset mating plan includes at least a preset mating method and a preset mating ratio; the preset mating method and mating ratio are the mating ratios of sex-controlled frozen semen from dairy cows, conventional frozen semen from dairy cows, embryo transfer from dairy cows, and frozen semen from beef cows. The preset frozen semen plan includes at least the frozen semen number, year of use, type of frozen semen, quantity used, and breeding value of the bulls in the target dairy farm; The genetic assessment data includes at least the cow number, mating method, date of birth, and breeding value of each cow in the target dairy farm.

[0023] In this embodiment of the application, the original data of the target dairy farm can be processed for missing values, outlier removal, time unification and field standardization, and individuals can be mapped to the corresponding production status.

[0024] Step 102: Based on the historical production data and the relevant production parameters, construct the cow chain transition probability matrix and the reserve cow chain transition probability matrix.

[0025] As an optional implementation, step 102, based on the historical production data and the relevant production parameters, can be implemented by constructing the cow chain transition probability matrix and the reserve cow chain transition probability matrix in the following ways: Based on the relevant production parameters, a cow state space and a heifer state space are constructed; wherein, the cow state space consists of postpartum months, gestation months, and parity, and the heifer state space consists of age and gestation months. Based on the historical production data, determine the production events of each state of the adult cow corresponding to the adult cow state space and the production events of each state of the replacement calf corresponding to the replacement calf state space. Based on the Beta-Bernoulli conjugate model, the production events of the adult cows and the production events of the heifers under each state are calculated to determine the estimated values ​​of the transition probability of the adult cows and the estimated values ​​of the transition probability of the heifers under each state. Based on the state space of the adult cows, the production events in each state of the adult cows, and the estimated transition probability values ​​of the production events in each state, a transition probability matrix of the adult cow chain is constructed. Based on the state space of the reserve cattle, the production events in each state of the reserve cattle, and the estimated transition probability of the reserve cattle in each state, a transition probability matrix of the reserve cattle chain is constructed.

[0026] This implementation method, by analyzing various breeding events and using a Beta-Bernoulli conjugate model to calculate transition probabilities, effectively reduces the bias caused by single historical data, making the probability results more objective and reliable. The transition probability matrix constructed in this way can accurately reflect the flow patterns between different states of cattle, laying a solid foundation for accurate prediction of subsequent herd structure, ensuring the accuracy of subsequent genetic simulation results, helping farms optimize breeding strategies, and promoting a steady improvement in the genetic level of dairy cattle populations.

[0027] In this embodiment, the state space s(C) of adult cows is defined as COW(MAC,MIP,PAR); where MAC represents the number of months postpartum; MIP represents the number of months of gestation (where MIP=0 indicates no pregnancy); and PAR represents parity.

[0028] The state space s(R) of the gilt is defined as REP(AIM,MIP); where AIM represents age in months and MIP represents the number of months of gestation.

[0029] For adult cows: When MAC ≤ voluntary waiting period, MIP is set to 0; When MAC ≥ voluntary waiting period + 1, MAC - MIP ≥ 2 must be satisfied. For the pregnancy status, the status with MIP=2 is excluded, and (33-MAC)≥(9-MIP) is further required.

[0030] For reserve cattle: When AIM is less than or equal to the age at first mating, MIP is set to 0; When AIM is greater than or equal to the age at first mating + 1, AIM-MIP must be greater than or equal to 12. For the pregnancy status, the status of MIP=2 is excluded, and (33-AIM)≥(9-MIP) is further required.

[0031] In this embodiment, during the state space construction process, states that are unlikely to occur or have no production significance are eliminated based on the actual laws governing dairy cow reproduction, growth, and production management. Special states related to system outflow and system integration are set to represent processes such as individual culling, calving, and the transfer of heifers to adult cow herds. That is: Individual elimination states: COW(0,0,0) and REP(0,0); Group connectivity states: REP(1,0) and COW(1,0,0).

[0032] For a chain of adult cows in the state space, individuals in different reproductive states have different possible destinations.

[0033] For any state s=COW(m,0,p) of a non-pregnant cow, there are three mutually exclusive destinations for it in the next month: (1) Pregnancy: Transfer to COW(m+1,2,p); (2) Elimination: Transfer to COW(0,0,0); (3) Maintain empty position: transfer to COW(m+1,0,p).

[0034] For any state s=COW(m,i,p) of a pregnant cow, there are five mutually exclusive destinations for her in the next month: (1) Miscarriage: transferred to COW(m+1,0,p); (2) Preterm birth: transferred to COW(1,0,p+1); (3) Elimination: Transfer to COW(0,0,0); (4) Maintain pregnancy: transfer to COW(m+1,i+1,p); (5) Calving: Transfer to COW(1,0,p+1).

[0035] For the reserve cattle chain in the reserve cattle state space, its transfer mechanism is essentially the same as that of adult cows. Only after the conditions for production are met will the individual be transferred from the reserve cattle chain to the adult cow chain, realizing the connection between the two subsystems.

[0036] Please refer to the following: Figure 2 , Figure 2 This is a schematic diagram illustrating the state transition of dairy cows according to an embodiment of this application; it shows the state changes of cattle within two main groups: adult cows and heifers, as well as the subsequent diversion and destination of calves: 1. The adult cow herd includes two core states: pregnant adult cows and non-pregnant adult cows. Pregnant cows can undergo four types of changes: ① transfer within the herd (continued pregnancy); ② death and culling, entering the "death or voluntary elimination" stage; ③ abortion, becoming an empty-pregnant cow; ④ calving, giving birth to a calf; There are three types of changes that can occur in non-pregnant cows: ① Circulation within the herd (continuous non-pregnancy); ② Death and culling, entering the "death or voluntary elimination" stage; ③ Pregnancy, turning into a pregnant cow.

[0037] 2. The heifer herd includes two core states: pregnant heifers and non-pregnant heifers. Pregnant heifers can undergo three types of changes: ① transfer within the herd (continued pregnancy); ② death or culling, entering the "death or voluntary culling" stage; ③ calving, giving birth to calves; There are four types of changes that can occur in non-pregnant heifers: ① Circulation within the herd (continuous non-pregnancy); ② Death and culling, entering the "death or voluntary elimination" stage; ③ Pregnancy, turning into a pregnant heifer; ④ Abortion, turning from a pregnant heifer into a non-pregnant heifer.

[0038] 3. Calves are calves born after adult cows / replacement calves give birth. They will be divided into three categories according to the preset breeding plan: crossbred meat calves, dairy bull calves, and dairy cow female calves.

[0039] Among them, dairy cow calves are kept in the breeding group and then enter the replacement herd to participate in subsequent status transfers; the other two types of calves do not enter the breeding group.

[0040] 4. Elimination Node (Left Side): All cattle in all groups and under all conditions can enter the "death or voluntary elimination" node through the "death and culling" path and leave the herd.

[0041] In this embodiment, the transition probability estimate is based on historical production data. For different events, the corresponding risk set is identified according to the individual's current month's status. Then, combined with the actual number of events occurring in the following month, a beta-Bernoulli conjugate model is used to statistically infer the corresponding transition probability. Its form is as follows: in, This represents the probability of the event occurring in month t-1. and These two parameters represent the prior probability distribution of the event; This represents the number of individuals in the corresponding risk cluster in month t-1. This represents the number of individuals who actually experienced the event from month t-1 to month t, where T is the number of valid months (i.e., the valid range of the MAC or AIM status parameters), which is related to the status settings and exclusion criteria mentioned above.

[0042] Based on the above model, the posterior mean of the event probability is expressed as: in, This represents the estimated transition probability of an event occurring after incorporating observational data; and Let represent the parameters of the posterior distribution.

[0043] Step 103: Based on the herd structure data at the predicted start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, herd structure prediction is performed to obtain herd structure distribution information for each target prediction sub-time period.

[0044] As an optional implementation, step 103, based on the herd structure data at the predicted start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, performs herd structure prediction to obtain herd structure distribution information for each target prediction sub-time period. This can be achieved through methods such as: Based on the herd structure data at the predicted start time, the state space of the adult cows, and the state space of the replacement cows, the initial adult cow state of each adult cow in the target dairy farm and the initial replacement cow state of each replacement cow in the replacement cow group are determined. Using a single target prediction sub-time period as the calculation step size, based on the initial adult cow state, the initial replacement cow state, the adult cow transition probability matrix, and the replacement cow transition probability matrix, state iteration calculations are performed on the adult cow population and the replacement cow population respectively to determine the herd structure distribution information for each target prediction sub-time period.

[0045] This implementation method first clarifies the initial state of all cattle in the farm, then iterative calculations are performed in predicted sub-time periods to fully reconstruct the dynamic flow of the herd. Based on a pre-constructed state space and transition probability matrix, the prediction logic closely aligns with the actual patterns of dairy cow growth, reproduction, and herd transfer, effectively ensuring the accuracy and consistency of the herd structure prediction results. Accurately understanding the herd distribution at different stages provides a reliable basis for subsequent breeding planning and selection, ensuring the effectiveness of genetic progress simulation, and thus assisting farms in continuously optimizing breeding management and steadily improving the overall genetic quality of the dairy herd.

[0046] In this embodiment, based on the herd structure data of the predicted start time, each cow is mapped to a corresponding state label, and an initial adult cow state is constructed for each cow. and initial backup cow state .

[0047] By standardizing and discretizing the original continuous records, actual production data can be transformed into a state representation that the model can recognize. The discretized definitions of postpartum months, maternal age, and gestational months are as follows: in, This indicates rounding up. Through this mapping, continuous records are converted into discrete state variables, providing initial conditions for subsequent monthly iterative predictions.

[0048] During the prediction process, the adult cow chain and the reserve cow chain are iterated monthly based on their respective transition probability matrices; At the same time, through the connecting links such as delivery, production and transfer, the individual transmission between the two subsystems is realized, forming a complete dual-chain joint prediction framework.

[0049] Let t = 0, 1, 2, ..., T represent the number of target prediction sub-time periods (months). For any subsystem, let Let be the state distribution vector at time t, then its basic iterative equation is expressed as: Where n represents the number of iterations (in months); This represents the transition probability matrix.

[0050] Based on the above iterative process, the model can continuously track the dynamic evolution of cattle population size and structure at different stages, and provide basic data for predicting the genetic progress of subsequent breeding methods.

[0051] In each iteration, the model can obtain the number of calves born for different types of cows.

[0052] Based on this, and in conjunction with a pre-set breeding plan, the distribution of calves under different breeding methods is further calculated. The allocation ratio for each path is set by a predetermined plan and can be dynamically adjusted according to time or cow type.

[0053] Step 104: Based on the herd structure distribution information of each target prediction sub-time period, the preset mating scheme, the preset frozen semen plan, and the genetic evaluation data, simulate the offspring breeding value to obtain the average breeding value of the female calves corresponding to the target prediction time period.

[0054] In this embodiment, bull herds are constructed according to the year of use and type of frozen semen, based on a pre-set frozen semen plan. To reflect the growth, development, and first calving time required for dairy cows to transition from the heifer stage to the adult cow herd, heifers are set to enter the adult cow herd two years after birth.

[0055] Specifically, the adult cow herd for a given year consists of the adult cows retained from the previous year and cows born two years prior, and is updated based on established selection rules.

[0056] Based on the preset ratio of different mating methods, newborn female calves are grouped to form corresponding replacement heifer sets for building the next generation of heifer herds.

[0057] First, a pool of cows for subsequent embryo transfer pathways is constructed by random sampling without replacement from the set of newborn calves. After completing the sampling for the embryo transfer pathways, individuals with high Estimated Breeding Value (EBV) are selected from the remaining calves to construct a pool of cows for subsequent sex-controlled frozen semen insemination. Then, from the remaining calves, individuals with intermediate EBV are further selected to construct a pool of cows for subsequent conventional frozen semen insemination.

[0058] As an optional implementation, step 104, based on the herd structure distribution information of each target prediction sub-time period, the preset mating scheme, the preset frozen semen plan, and the genetic evaluation data, simulates the offspring breeding value to obtain the average breeding value of the female calves corresponding to the target prediction time period. This can be achieved by: Based on the herd structure distribution information of each target prediction sub-time period and the preset breeding scheme, the number of female calves under different breeding methods is determined. Based on the pre-set frozen semen plan and the genetic evaluation data, bull herds and cow herds were constructed respectively; Based on the number of female calves under different mating methods, the bull herd, and the cow herd, the calculation operation of the female calf breeding value under different mating methods is repeated to obtain multiple female calf breeding values. The average breeding value of the female calves corresponding to the target prediction time period is determined based on the multiple female calf breeding values.

[0059] This implementation method involves different mating approaches based on herd structure and mating plans, accurately counting the number of female calves, and then establishing breeding bull and cow populations based on a pre-set frozen semen plan and genetic evaluation data. By repeatedly calculating and simulating female calf breeding values, the randomness of the breeding process is effectively accounted for, making the calculation results more closely reflect actual breeding conditions. The accurately obtained average female calf breeding value can directly reflect the genetic improvement effect of the herd, helping farms flexibly adjust mating and selection strategies, continuously select superior individuals, and steadily improve the overall genetic level of the dairy herd.

[0060] Optionally, the specific steps for calculating the breeding value of female calves can be as follows: Based on the number of female calves under the different mating methods, multiple paternal individuals and multiple maternal individuals are randomly selected from the bull herd and the cow herd, respectively. The paternal breeding value of each sire and the maternal breeding value of each dam are determined based on the bull herd and the cow herd. The paternal breeding value of each male parent and the maternal breeding value of each female parent are input into a pre-constructed maternal calf breeding value calculation model to obtain a maternal calf breeding value output by the maternal calf breeding value calculation model.

[0061] This implementation method simulates real-world mating scenarios by randomly selecting parents, fully replicating the actual dairy cow herding and mating process. It uses a pre-set frozen semen plan and existing genetic assessment data to obtain parental breeding values, and then employs a specialized calculation model to derive the female calf breeding value. The calculation logic is standardized, and the results are highly reliable. This calculation process can accurately quantify the genetic performance of offspring, clearly demonstrating the breeding effects of different mating combinations. This facilitates farms in selecting high-quality parental combinations, optimizing mating programs, and continuously promoting the improvement of the genetic quality of dairy cow populations.

[0062] In this embodiment of the application, after obtaining the number of calves for each year and breeding method, the number of dairy calves for each breeding method is further calculated based on the calf production ratio of different breeding methods.

[0063] For example, the calving rate of sex-controlled frozen sperm is set at 0.90, the calving rate of conventional frozen sperm is set at 0.50, and the calving rate of embryo transfer is set at 0.90.

[0064] Based on this, multiple sires and dams were randomly selected from the cow herd and bull herd respectively to simulate the EBV of milk calves produced in that year.

[0065] The EBV calculation model for maternal calves is as follows: , in, Indicates the breeding value of female calves. and Let represent the maternal breeding value of the j-th cow and the paternal breeding value of the i-th bull, respectively (both maternal and paternal breeding values ​​were obtained from genetic evaluation results and a pre-defined frozen semen plan); Z represents a random variable that follows a standard normal distribution. This represents the variance of EBV in a herd of cows; This indicates the reliability of EBV (assuming a fixed value; here, the reliability of the genomic genetic assessment is set to 0.7).

[0066] Subsequently, multiple Monte Carlo simulations were performed on the breeding value of female calves to obtain the expected and fluctuation range of the average breeding value of the offspring population and the average breeding value of adult cows in the herd.

[0067] In this embodiment of the application, the expected and fluctuation range of the average breeding value of adult cows are obtained through the following steps: Based on the herd structure and distribution information, a set of adult cows in the herd is constructed for each predicted sub-time period; By combining the breeding program and the EBV of the parent cattle, the entire process of cattle herd transfer, mating and calving and herd renewal was fully simulated through multiple Monte Carlo simulations. For each simulation result, calculate the average breeding value of adult cows at the corresponding time point; The arithmetic mean of multiple simulation results is used as the expected value of the average breeding value of adult cows. The fluctuation range is determined by calculating the standard deviation or quantile of the simulation results.

[0068] Step 105: Output the herd structure distribution information and the average breeding value of the female calves for each target prediction sub-time period.

[0069] In this embodiment of the application, the expected and fluctuation range of the average breeding value of the herd of adult cows can also be output.

[0070] Implementing steps 101 to 105 above steadily improves the genetic level of the entire dairy herd, making breeding and herd management at the target dairy farm more scientific and efficient. Furthermore, this application can accurately reflect the flow patterns between different states of cattle, laying a solid foundation for accurate prediction of subsequent herd structure and ensuring the accuracy of subsequent genetic simulation results. This helps farms optimize breeding strategies and promotes a steady improvement in the genetic level of the dairy herd. In addition, this application provides a reliable basis for subsequent mating planning and selection work, ensuring the effectiveness of genetic progress simulation, thereby assisting farms in continuously optimizing breeding management and steadily improving the overall genetic quality of the dairy herd. Furthermore, this application can visually reflect the genetic improvement effect of the herd, helping farms flexibly adjust mating and selection strategies, continuously select high-quality individuals, and steadily improve the overall genetic level of the dairy herd. Moreover, this application can clearly demonstrate the breeding effects of different mating combinations, facilitating farms in selecting high-quality parent combinations, optimizing mating programs, and continuously promoting the improvement of the genetic quality of the dairy herd.

[0071] Based on the same inventive concept, this application also provides a device for predicting herd structure and genetic progression to implement the above-described method for predicting herd structure and genetic progression. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the device for predicting herd structure and genetic progression provided below can be found in the limitations of the method for predicting herd structure and genetic progression described above, and will not be repeated here.

[0072] In one exemplary embodiment, such as Figure 3 As shown, a device for predicting cattle herd structure and genetic progression is provided, comprising: The acquisition unit 301 is used to acquire the target dairy farm's raw data, preset breeding plan, preset frozen semen plan, and target prediction time period; wherein, the raw data includes the target dairy farm's historical production data, herd structure data at the prediction start time, relevant production parameters, and genetic evaluation data; the preset breeding plan includes the target dairy farm's preset breeding method and breeding ratio; the preset frozen semen plan includes the target dairy farm's preset frozen semen usage plan; and the target prediction time period consists of multiple target prediction sub-time periods; The historical production data includes at least historical calving event data, historical initial pregnancy check event data, historical abortion event data, and historical mortality and culling event data; the historical calving event data includes at least the calf number, parity, age, calving interval, calf number, calf status, calf sex, and calf type; the historical initial pregnancy check event data includes at least the calf number, parity, age, postpartum days, pregnancy check result, and the number of days at the time of the pregnancy check; the historical abortion event data includes at least the calf number, parity, age, postpartum days, and gestation days; the historical mortality and culling event data includes at least the calf number, parity, age, postpartum days, and gestation days. The herd structure data for the predicted start time includes at least the cow number, age, parity, postpartum / lactation days, and gestation / pregnancy days of each cow in the herd at the predicted start time in the target dairy farm. The relevant production parameters include at least the voluntary waiting period, the premature birth rate of adult cows, the age of first mating of heifers, the calf survival rate, and the premature birth rate of heifers. The preset mating plan includes at least a preset mating method and a preset mating ratio; the preset mating method and mating ratio are the mating ratios of sex-controlled frozen semen from dairy cows, conventional frozen semen from dairy cows, embryo transfer from dairy cows, and frozen semen from beef cows. The preset frozen semen plan includes at least the frozen semen number, usage time, frozen semen type, usage quantity, and breeding value of the bulls in the target dairy farm; The genetic assessment data includes at least the cow number, mating method, date of birth, and breeding value of the assessed cows in the target dairy farm.

[0073] Construction unit 302 is used to construct a cow chain transition probability matrix and a reserve cow chain transition probability matrix based on the historical production data and the relevant production parameters; Prediction unit 303 is used to predict the herd structure based on the herd structure data at the prediction start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, so as to obtain the herd structure distribution information for each target prediction sub-time period. The simulation unit 304 is used to simulate the offspring breeding value based on the herd structure distribution information of each target prediction sub-time period, the preset breeding scheme, the preset frozen semen plan and the genetic evaluation data, so as to obtain the average breeding value of female calves and the average breeding value of adult cows in the herd corresponding to the target prediction time period. The output unit 305 is used to output the herd structure distribution information, the average breeding value of the female calves, and the average breeding value of the adult cows in the herd for each target prediction sub-time period.

[0074] As an optional implementation, the construction unit 302 constructs the cow chain transition probability matrix and the reserve cow chain transition probability matrix based on the historical production data and the relevant production parameters in the following specific ways: Based on the relevant production parameters, a cow state space and a heifer state space are constructed; wherein, the cow state space consists of postpartum months, gestation months, and parity, and the heifer state space consists of age and gestation months. Based on the historical production data, determine the production events of each state of the adult cow corresponding to the adult cow state space and the production events of each state of the replacement calf corresponding to the replacement calf state space. Based on the Beta-Bernoulli conjugate model, the production events of the adult cows and the production events of the heifers under each state are calculated to determine the estimated values ​​of the transition probability of the adult cows and the estimated values ​​of the transition probability of the heifers under each state. Based on the state space of the adult cows, the production events in each state of the adult cows, and the estimated transition probability values ​​of the production events in each state, a transition probability matrix of the adult cow chain is constructed. Based on the state space of the reserve cattle, the production events in each state of the reserve cattle, and the estimated transition probability of the reserve cattle in each state, a transition probability matrix of the reserve cattle chain is constructed.

[0075] This implementation method, by analyzing various historical production events and using a Beta-Bernoulli conjugate model to calculate transition probabilities, effectively reduces the bias caused by single historical data, making the probability results more objective and reliable. The transition probability matrix constructed in this way can accurately reflect the flow patterns between different states of cattle, laying a solid foundation for accurate prediction of subsequent herd structure, ensuring the accuracy of subsequent genetic simulation results, helping farms optimize breeding strategies, and promoting a steady improvement in the genetic level of dairy cattle populations.

[0076] As an optional implementation, the prediction unit 303 predicts the herd structure based on the herd structure data at the prediction start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, and obtains the herd structure distribution information for each target prediction sub-time period in the following specific ways: Based on the herd structure data at the predicted start time, the state space of the adult cows, and the state space of the replacement cows, the initial adult cow state of each adult cow in the target dairy farm and the initial replacement cow state of each replacement cow in the replacement cow group are determined. Using a single target prediction sub-time period as the calculation step size, based on the initial adult cow state, the initial replacement cow state, the adult cow chain transition probability matrix, and the replacement cow chain transition probability matrix, state iteration calculations are performed on the adult cow population and the replacement cow population respectively to determine the herd structure distribution information for each target prediction sub-time period.

[0077] This implementation method first clarifies the initial state of all cattle in the farm, then iterative calculations are performed in predicted sub-time periods to fully reconstruct the dynamic flow of the herd. Based on a pre-constructed state space and transition probability matrix, the prediction logic closely aligns with the actual patterns of dairy cow growth, reproduction, and herd transfer, effectively ensuring the accuracy and consistency of the herd structure prediction results. Accurately understanding the herd distribution at different stages provides a reliable basis for subsequent breeding planning and selection, ensuring the effectiveness of genetic progress simulation, and thus assisting farms in continuously optimizing breeding management and steadily improving the overall genetic quality of the dairy herd.

[0078] As an optional implementation, the simulation unit 304 simulates the offspring breeding values ​​based on the herd structure distribution information of each target prediction sub-time period, the preset mating scheme, the preset frozen semen plan, and the genetic evaluation data. Specifically, the method for obtaining the average breeding value of female calves and the average breeding value of adult cows in the herd corresponding to the target prediction time period can be as follows: Based on the herd structure distribution information of each target prediction sub-time period and the preset breeding scheme, the number of female calves under different breeding methods is determined. Based on the pre-set frozen semen plan and the genetic evaluation data, bull herds and cow herds were constructed respectively; Based on the number of female calves under different mating methods, the bull herd, and the cow herd, the calculation operation of the female calf breeding value under different mating methods is repeated to obtain multiple female calf breeding values. The average breeding value of the female calves corresponding to the target prediction time period is determined based on the multiple female calf breeding values.

[0079] This implementation method involves differentiating breeding methods based on herd structure and mating plans, accurately counting the number of female calves, and then establishing breeding bull and cow populations based on a pre-set frozen semen plan and genetic evaluation data. By repeatedly calculating and simulating female calf breeding values, the randomness of the breeding process is effectively accounted for, making the calculation results more consistent with the actual dairy cow herd selection and mating situation. The accurately obtained average female calf breeding value can intuitively reflect the herd's genetic improvement effect, helping farms flexibly adjust mating and breeding strategies, continuously select high-quality individuals, and steadily improve the overall genetic level of the dairy cow herd.

[0080] As an optional implementation method, the calculation of the breeding value of female calves can be specifically performed as follows: Based on the number of female calves under the different mating methods, multiple paternal individuals and multiple maternal individuals are randomly selected from the bull herd and the cow herd, respectively. The paternal breeding value of each sire and the maternal breeding value of each dam are determined based on the bull herd and the cow herd. The paternal breeding value of each male parent and the maternal breeding value of each female parent are input into a pre-constructed maternal calf breeding value calculation model to obtain a maternal calf breeding value output by the maternal calf breeding value calculation model.

[0081] This implementation method simulates real-world mating scenarios by randomly selecting parents, fully replicating the actual dairy cow herding and mating process. It uses a pre-set frozen semen plan and existing genetic assessment data to obtain parental breeding values, and then employs a specialized calculation model to derive the female calf breeding value. The calculation logic is standardized, and the results are highly reliable. This calculation process can accurately quantify the genetic performance of offspring, clearly demonstrating the breeding effects of different mating combinations. This facilitates farms in selecting high-quality parental combinations, optimizing mating programs, and continuously promoting the improvement of the genetic quality of dairy cow populations.

[0082] Implementing the above-described methods can steadily improve the genetic level of the entire dairy herd, making breeding and herd management at target dairy farms more scientific and efficient. Furthermore, this application can accurately reflect the flow patterns between different states of cattle, laying a solid foundation for accurate prediction of subsequent herd structure and ensuring the accuracy of subsequent genetic simulation results. This helps farms optimize breeding strategies and promotes a steady improvement in the genetic level of the dairy herd. In addition, this application can provide a reliable basis for subsequent mating planning and selection work, ensuring the effectiveness of genetic progress simulation, thereby assisting farms in continuously optimizing breeding management and steadily improving the overall genetic quality of the dairy herd. Furthermore, this application can visually reflect the genetic improvement effect of the herd, helping farms flexibly adjust mating and selection strategies, continuously select high-quality individuals, and steadily improve the overall genetic level of the dairy herd. Moreover, this application can clearly demonstrate the breeding effects of different mating combinations, facilitating farms in selecting high-quality parent combinations, optimizing mating programs, and continuously promoting the improvement of the genetic quality of the dairy herd.

[0083] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores predictive data on cattle herd structure and genetic progression. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for predicting cattle herd structure and genetic progression.

[0084] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0085] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0086] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0087] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0088] In one exemplary embodiment, a chip is provided, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps in the above method embodiments and achieve the same technical effect, and will not be described again here to avoid repetition.

[0089] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0091] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0092] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting cattle herd structure and genetic progression, characterized in that, The methods for predicting herd structure and genetic progression include: The process involves acquiring raw data from the target dairy farm, a pre-set breeding plan, a pre-set frozen semen plan, and a target prediction time period. The raw data includes historical production data from the target dairy farm, herd structure data at the prediction start time, relevant production parameters, and genetic assessment data. The pre-set breeding plan includes the target dairy farm's pre-set breeding method and breeding ratio. The pre-set frozen semen plan includes the target dairy farm's pre-set frozen semen usage plan. The target prediction time period consists of multiple target prediction sub-time periods. Based on the historical production data and the relevant production parameters, a cow chain transition probability matrix and a reserve cow chain transition probability matrix are constructed. Based on the herd structure data at the predicted start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, herd structure prediction is performed to obtain herd structure distribution information for each target prediction sub-time period. Based on the herd structure distribution information of each target prediction sub-time period, the preset breeding scheme, the preset frozen semen plan, and the genetic evaluation data, the offspring breeding value is simulated to obtain the average breeding value of the female calves corresponding to the target prediction time period. Output the herd structure distribution information and the average breeding value of the female calves for each target prediction sub-time period.

2. The method for predicting cattle herd structure and genetic progression according to claim 1, characterized in that, in: The historical production data includes at least historical calving event data, historical initial pregnancy check event data, historical abortion event data, and historical mortality and culling event data; the historical calving event data includes at least the calf number, parity, age, calving interval, calf number, calf status, calf sex, and calf type; the historical initial pregnancy check event data includes at least the calf number, parity, age, postpartum days, pregnancy check result, and the number of days at the time of the pregnancy check; the historical abortion event data includes at least the calf number, parity, age, postpartum days, and gestation days; the historical mortality and culling event data includes at least the calf number, parity, age, postpartum days, and gestation days. The herd structure data for the predicted start time includes at least the cow number, age, parity, postpartum / lactation days, and gestation / pregnancy days of each cow in the herd at the predicted start time in the target dairy farm. The relevant production parameters include at least the voluntary waiting period, the premature birth rate of adult cows, the age of first mating of heifers, the calf survival rate, and the premature birth rate of heifers. The preset mating plan includes at least a preset mating method and a preset mating ratio; the preset mating method and mating ratio are the mating ratios of sex-controlled frozen semen from dairy cows, conventional frozen semen from dairy cows, embryo transfer from dairy cows, and frozen semen from beef cows. The preset frozen semen plan includes at least the frozen semen number, usage time, frozen semen type, usage quantity, and breeding value of the bulls in the target dairy farm; The genetic assessment data includes at least the cow number, mating method, date of birth, and breeding value of the assessed cows in the target dairy farm.

3. The method for predicting bovine herd structure and genetic progression according to claim 1, characterized in that, The construction of the cow breeding chain transition probability matrix and the reserve cow breeding chain transition probability matrix based on the historical production data and the relevant production parameters specifically includes: Based on the relevant production parameters, a cow state space and a heifer state space are constructed; wherein, the cow state space consists of postpartum months, gestation months, and parity, and the heifer state space consists of age and gestation months. Based on the historical production data, determine the production events of each state of the adult cow corresponding to the adult cow state space and the production events of each state of the replacement calf corresponding to the replacement calf state space. Based on the Beta-Bernoulli conjugate model, the production events of the adult cows and the production events of the heifers under each state are calculated to determine the estimated values ​​of the transition probability of the adult cows and the estimated values ​​of the transition probability of the heifers under each state. Based on the state space of the adult cows, the production events in each state of the adult cows, and the estimated transition probability values ​​of the production events in each state, a transition probability matrix of the adult cow chain is constructed. Based on the state space of the reserve cattle, the production events in each state of the reserve cattle, and the estimated transition probability of the reserve cattle in each state, a transition probability matrix of the reserve cattle chain is constructed.

4. The method for predicting bovine herd structure and genetic progression according to claim 3, characterized in that, The herd structure prediction based on the herd structure data at the predicted start time, the adult cow chain transition probability matrix, and the reserve cow chain transition probability matrix yields herd structure distribution information for each target prediction sub-time period, specifically including: Based on the herd structure data at the predicted start time, the state space of the adult cows, and the state space of the replacement cows, the initial adult cow state of each adult cow in the target dairy farm and the initial replacement cow state of each replacement cow in the replacement cow group are determined. Using a single target prediction sub-time period as the calculation step size, based on the initial adult cow state, the initial replacement cow state, the adult cow chain transition probability matrix, and the replacement cow chain transition probability matrix, state iteration calculations are performed on the adult cow population and the replacement cow population respectively to determine the herd structure distribution information for each target prediction sub-time period.

5. The method for predicting cattle herd structure and genetic progression according to claim 2, characterized in that, The method of simulating offspring breeding values ​​based on the herd structure distribution information of each target prediction sub-time period, the preset mating plan, the preset frozen semen plan, and the genetic evaluation data, to obtain the average breeding value of mare calves corresponding to the target prediction time period, specifically includes: Based on the herd structure distribution information of each target prediction sub-time period and the preset breeding scheme, the number of female calves under different breeding methods is determined. Based on the pre-set frozen semen plan and genetic evaluation data, bull herds and cow herds were constructed respectively; Based on the number of female calves under different mating methods, the bull herd, and the cow herd, the calculation operation of the female calf breeding value under different mating methods is repeated to obtain multiple female calf breeding values. The average breeding value of the female calves corresponding to the target prediction time period is determined based on the multiple female calf breeding values.

6. The method for predicting bovine herd structure and genetic progression according to claim 5, characterized in that, The specific steps for calculating the breeding value of the female calf are as follows: Based on the number of female calves under the different mating methods, multiple paternal individuals and multiple maternal individuals are randomly selected from the bull herd and the cow herd, respectively. The paternal breeding value of each sire and the maternal breeding value of each dam are determined based on the bull herd and the cow herd. The paternal breeding value of each male parent and the maternal breeding value of each female parent are input into a pre-constructed maternal calf breeding value calculation model to obtain a maternal calf breeding value output by the maternal calf breeding value calculation model.

7. A device for predicting cattle herd structure and genetic progression, characterized in that, The device for predicting herd structure and genetic progression includes: The acquisition unit is used to acquire raw data, a preset breeding plan, a preset frozen semen plan, and a target prediction time period from the target dairy farm. The raw data includes historical production data of the target dairy farm, herd structure data at the prediction start time, relevant production parameters, and genetic evaluation data. The preset breeding plan includes the preset breeding method and breeding ratio of the target dairy farm. The preset frozen semen plan includes the preset frozen semen usage plan of the target dairy farm. The target prediction time period consists of multiple target prediction sub-time periods. The construction unit is used to construct the cow chain transition probability matrix and the reserve cow chain transition probability matrix based on the historical production data and the relevant production parameters. The prediction unit is used to predict the herd structure based on the herd structure data at the prediction start time, the adult cow chain transition probability matrix, and the backup cow chain transition probability matrix, so as to obtain the herd structure distribution information for each target prediction sub-time period. The simulation unit is used to simulate the offspring breeding value based on the herd structure distribution information of each target prediction sub-time period, the preset breeding scheme, the preset frozen semen plan, and the genetic evaluation data, so as to obtain the average breeding value of female calves and the average breeding value of adult cows in the herd corresponding to the target prediction time period. The output unit is used to output the herd structure distribution information, the average breeding value of the female calves, and the average breeding value of the adult cows in the herd for each target prediction sub-time period.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method for predicting herd structure and genetic progression according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for predicting herd structure and genetic progression according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for predicting herd structure and genetic progression according to any one of claims 1-6.

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