Egg laying rate simulation model and application thereof

Abstract

The invention relates to the technical field of animal production, and discloses a laying rate simulation model and application thereof. Comprising the following steps: according to the to-be-simulated time t of a target laying hen group, the total time m required from the time when a first individual sees eggs to the time when all individuals sees eggs for the first time, the estimated value g (i) of the proportion of a sub-group composed of the individuals seeing eggs at i in the target laying hen group, and the estimated value f (ti) of the current laying rate of the sub-group composed of the individuals seeing eggs at i, calculating the current laying rate of the target laying hen group; and through the laying rate simulation model, obtaining the whole group laying rate of the target laying hen group at the to-be-simulated time t. According to the method, the egg laying record data with high relevance to the egg laying performance is introduced into the egg laying rate simulation model, so that the group egg laying rate in the early egg laying period can be simulated more accurately. The laying rate simulation model provided by the invention fills the blank in the field of animal production, and meanwhile, the invention provides a simulation method which is more reasonable for the laying rate of a group.

Description

Technical Field

[0001] The invention relates to the technical field of animal production, and in particular to an egg production rate simulation model and application thereof. Background Art

[0002] The simulation and prediction of the egg-laying process have important guiding significance for laying hen production. Since the change of laying rate of laying hens itself is a nonlinear process, the simulation of the entire egg-laying process usually requires nonlinear fitting. Some nonlinear models have been introduced into the field of laying hen production to simulate the egg-laying rate, such as the Wood model, which simulates the production performance of laying hens through the gamma distribution function. The gamma distribution function has three parameters. Under certain values ​​of these three parameters, the function curve is asymmetric, which can represent a trend of rapid increase and then slow decrease, which is consistent with the change of egg-laying rate. However, due to the nature of the function itself, the gamma distribution function cannot accurately simulate the egg-laying rate of the group in the early stage of egg-laying. Therefore, the Wood model has not been widely used in poultry production.

[0003] Therefore, a new egg production rate simulation model is urgently needed to accurately simulate the egg-laying process of laying hens. Summary of the invention

[0004] The invention provides an egg-laying rate simulation model and application thereof, which are used to solve the defect that the prior art cannot accurately simulate the egg-laying rate of a group in the early stage of egg-laying.

[0005] The present invention provides an egg production rate simulation model, the expression of which is:

[0006]

[0007] In the formula, En t represents the egg-laying rate of the whole flock at time t, t represents a certain time after the whole flock starts laying eggs, and the t value is accumulated from the time when the whole flock first sees eggs. The t value is recorded as 1 when the whole flock first sees eggs, and then the t value increases by 1 for each preset time unit. m represents the total time required from the first egg-seeing of the first individual in the whole flock to the time when all individuals in the whole flock see eggs. i represents the first egg-seeing time of a subgroup in the whole flock. g(i) represents the proportion of the subgroup composed of individuals that see eggs at time i in the whole flock. t i represents the cumulative egg-laying time of the subgroup composed of individuals that lay eggs at time i to time t, that is, the difference between a certain time t and i, t i =ti,f(t i ) represents the egg-laying rate of the subgroup composed of individuals laying eggs at time i at time t, where t, m, and i are positive integers. When t and i are equal, f(t i ) is 1, and for the sub-group that has not seen eggs at time t, its egg-laying rate f(ti ) is 0.

[0008] The present invention provides an egg production rate simulation method, comprising:

[0009] Obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg;

[0010] According to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i );

[0011] According to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0012] The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0013] According to the time t to be simulated of the target laying hen population, the total time m required from the first individual to see eggs for the first time to all individuals seeing eggs, the estimated value g(i) of the proportion of the sub-population composed of individuals who see eggs at time i in the target laying hen population, and the estimated value f(t i ), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0014] In one embodiment, after obtaining the egg laying record data of the target laying hen group, the time t to be simulated, and the total time m required from the first individual seeing the eggs for the first time to all individuals seeing the eggs, the method includes:

[0015] According to the egg-laying record data of the target egg-laying hen group, the distribution frequency of each individual in the target egg-laying hen group in the preset egg-laying cycle is obtained;

[0016] The distribution frequency of each individual in the target laying hen group in the preset laying cycle is divided by the total number of individuals in the target laying hen group to obtain the distribution frequency of each individual in the target laying hen group;

[0017] Correcting the egg-laying record data of the target egg-laying hen group to obtain corrected egg-laying record data, wherein the correction processing includes data length cutting and / or missing value filling;

[0018] Among them, the egg-laying record data of the target egg-laying chicken group is the egg-laying age data or the egg-laying age data. When the egg-laying record data of the target egg-laying chicken group is the egg-laying age data, the preset egg-laying cycle is day; when the egg-laying record data of the target egg-laying chicken group is the egg-laying age data, the preset egg-laying cycle is week.

[0019] In one embodiment, according to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i ),include:

[0020] According to the distribution frequency of each individual in the target laying hen population, the observed value G(i) of the proportion of the sub-population composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0021] According to the corrected egg-laying record data, the observed value of the current egg-laying rate F(t i ).

[0022] In one embodiment, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained based on the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, comprising:

[0023] When the egg-laying record data of the target laying hen group is the egg-laying age data, according to the observed value G(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group, the estimated value g(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group is obtained through the probability density function of the normal distribution, where the expression of the probability density function of the normal distribution is:

[0024]

[0025] In the formula, g(i) represents the estimated value of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, i represents the age of each subgroup when laying eggs, μ and σ represent the equation parameters of the probability density function of the normal distribution, μ and σ are based on the observed value G(i) of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, and are obtained using the nonlinear least squares method, π represents the pi constant, and e represents the natural constant;

[0026] When the egg production record data of the target layer population is the age at first egg data, according to the observed value G(i) of the proportion of the sub-population composed of individuals that started laying eggs at time i in the target layer population, the estimated value g(i) of the proportion of the sub-population composed of individuals that started laying eggs at time i in the target layer population is obtained through the probability density function of the binomial distribution. The expression of the probability density function of the binomial distribution is as follows:

[0027]

[0028] In the formula, i represents the age at first egg of each sub-population, n and p represent the equation parameters of the probability density function of the binomial distribution, and n and p are obtained by using the non-linear least squares method based on the observed value G(i) of the proportion of the sub-population composed of individuals that started laying eggs at time i in the target layer population.

[0029] In one embodiment, the obtaining of the estimated value f(t i ) of the current egg production rate of the sub-population composed of individuals that started laying eggs at time i based on the observed value F(t i ) includes:

[0030] When i < t or the sub-population composed of individuals that started laying eggs at time i has not started laying eggs at time t, f(t i ) is recorded as 0;

[0031] In the case where the sub-population composed of individuals that started laying eggs at time i has started laying eggs at time t, according to the observed value F(t i ) of the current egg production rate of the sub-population composed of individuals that started laying eggs at time i, the estimated value f(t i ) of the current egg production rate of the sub-population composed of individuals that started laying eggs at time i is obtained by using the expression of f(t i ). The expression of f(t i ) is as follows:

[0032]

[0033] In the formula, f(t i ) represents the estimated value of the current egg production rate of the sub-population composed of individuals that started laying eggs at time i, e represents the natural constant, t i represents the cumulative egg production time from the sub-population composed of individuals that started laying eggs at time i to time t, and a, b, and c represent the equation parameters of the expression of f(t i ), and a, b, and c are obtained by using the non-linear least squares method based on the observed value F(t i ) of the current egg production rate of the sub-population composed of individuals that started laying eggs at time i.

[0034] In one embodiment, the nonlinear least squares method is solved using a Gauss-Newton iteration method.

[0035] The present invention also provides an egg production rate simulation system, comprising:

[0036] The data acquisition module is used to obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual seeing the egg for the first time to all individuals seeing the egg;

[0037] The processing module is used to obtain, based on the egg-laying record data of the target egg-laying hen group, an observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and an observed value F(t i );

[0038] The first estimation module is used to obtain an estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population according to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population;

[0039] The second estimation module is used to: according to the observed value F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0040] The simulation module is used to: calculate the target egg-laying hen population according to the time t to be simulated, the total time m required from the first individual to see eggs until all individuals see eggs, the estimated value g(i) of the proportion of the sub-group composed of individuals who see eggs at time i in the target egg-laying hen population, and the estimated value f(t i ), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0041] The present invention also provides an electronic device, comprising a processor and a memory storing a computer program, wherein the processor implements any of the above-mentioned egg production rate simulation methods when executing the computer program.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements any of the egg production rate simulation methods described above.

[0043] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned egg production rate simulation methods.

[0044] The present invention provides an egg-laying rate simulation model and its application, which incorporates egg-laying record data highly correlated with egg-laying performance into the egg-laying rate simulation model, wherein the egg-laying record data includes egg-laying age data and egg-laying age data, which are important figures used to describe egg-laying performance. The egg-laying rate simulation model incorporating the egg-laying record data can more accurately simulate the egg-laying rate of a group in the early stage of egg-laying. The egg-laying rate simulation model proposed by the present invention fills a gap in the field of animal production, and at the same time provides a more reasonable simulation method for the egg-laying rate of a group. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 The present invention provides a schematic flow chart of an egg production rate simulation method.

[0047] Figure 2 The present invention provides a schematic structural diagram of an egg production rate simulation system.

[0048] Figure 3 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] Combine the following Figure 1-Figure 3The egg production rate simulation model and its application provided by the present invention are described. It should be noted that the execution subject of the egg production rate simulation method provided by the present invention can be any network side device / terminal side device that meets the technical requirements, such as an egg production rate simulation device.

[0051] Figure 1 Schematic diagram of the process of simulating egg production rate provided by the present invention. Figure 1 The present invention provides an egg production rate simulation method, which may include:

[0052] Step S110, obtaining the egg-laying record data of the target laying hen group, the time t to be simulated, and the total time m required from the first time the first individual sees the egg to the time when all individuals see the egg.

[0053] Specifically, when the model parameters are unknown, it is necessary to record the number of eggs laid by each individual in the target laying hen group at each day or week of age in units of days or weeks until a certain time point T (T ≥ t) to obtain the egg laying record data of the target laying hen group. In this embodiment, the time point T should be no earlier than the 10th week after the peak of egg laying. When the model parameters are known, only the egg laying rate of the group can be recorded, and this record should be no earlier than the time point when the egg laying rate of the group reaches 50%.

[0054] Step S120: pre-processing the egg laying record data of the target laying hen group. Specifically, step S120 may include:

[0055] According to the egg-laying record data of the target egg-laying hen group, the distribution frequency of each individual in the target egg-laying hen group in the preset egg-laying cycle is obtained, and the distribution frequency represents the size of the subgroup composed of each egg-laying individual in the preset egg-laying cycle;

[0056] The distribution frequency of each individual in the target laying hen group in the preset laying cycle is divided by the total number of individuals in the target laying hen group to obtain the distribution frequency of each individual in the target laying hen group;

[0057] Correcting the egg-laying record data of the target egg-laying hen group to obtain corrected egg-laying record data, wherein the correction processing includes data length cutting and / or missing value filling;

[0058] Among them, the egg-laying record data of the target egg-laying chicken group includes egg-laying age data and / or egg-laying age data. When the egg-laying record data of the target egg-laying chicken group is egg-laying age data, the preset egg-laying cycle is day; when the egg-laying record data of the target egg-laying chicken group is egg-laying age data, the preset egg-laying cycle is week.

[0059] Step S130: According to the preprocessed egg-laying record data, obtain the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen group and the observed value F(t i ).

[0060] The observed value is a real-time value, expressed as a percentage, %. That is, the size of the subgroup composed of individuals that lay eggs at time i accounts for the size of the target group. G(i) and F(t i ) Similarly. In one embodiment, step S130 may include:

[0061] According to the distribution frequency of each individual in the target laying hen population, the observed value G(i) of the proportion of the sub-population composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0062] According to the corrected egg-laying record data, the observed value of the current egg-laying rate F(t i ).

[0063] Step S140, according to the observed value G(i) of the proportion of the sub-population composed of individuals that laid eggs at time i in the target laying hen population, obtain an estimated value g(i) of the proportion of the sub-population composed of individuals that laid eggs at time i in the target laying hen population.

[0064] In one embodiment, step S140 may include:

[0065] When the egg-laying record data of the target laying hen group is the egg-laying age data, according to the observed value G(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group, the estimated value g(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group is obtained through the probability density function of the normal distribution, where the expression of the probability density function of the normal distribution is:

[0066]

[0067] In the formula, g(i) represents the estimated value of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, i represents the age of each subgroup when laying eggs, μ and σ represent the equation parameters of the probability density function of the normal distribution, which are based on the observed value G(i) of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, and are obtained using the nonlinear least squares method, π represents the pi constant, and e represents the natural constant;

[0068] When the egg production record data of the target laying hen population is the age at first egg data, according to the observed value G(i) of the proportion of the sub-population composed of individuals that first lay eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the sub-population composed of individuals that first lay eggs at time i in the target laying hen population is obtained through the probability density function of the binomial distribution. The expression of the probability density function of the binomial distribution is as follows:

[0069]

[0070] In the formula, i represents the age at first egg of each sub-population, and n and p represent the equation parameters of the probability density function of the binomial distribution. They are obtained by using the non-linear least squares method based on the observed value G(i) of the proportion of the sub-population composed of individuals that first lay eggs at time i in the target laying hen population.

[0071] Step S150: According to the observed value F(t i ) of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i, the estimated value f(t i ) of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i is obtained.

[0072] In one implementation, step S150 may include:

[0073] When i < t or the sub-population composed of individuals that first lay eggs at time i has not laid eggs yet at time t, record f(t i ) as 0;

[0074] In the case where the sub-population composed of individuals that first lay eggs at time i has laid eggs at time t, according to the observed value F(t i ) of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i, using the expression of f(t i ), the estimated value f(t i ) of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i is obtained. The expression of f(t i ) is as follows:

[0075]

[0076] In the formula, f(t i ) represents the estimated value of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i, e represents the natural constant, t i represents the cumulative egg production time of the sub-population composed of individuals that first lay eggs at time i at time t, and a, b, and c represent the equation parameters of the expression of f(t i ). They are obtained by using the non-linear least squares method based on the observed value F(t i ) of the current egg production rate of the sub-population composed of individuals that first lay eggs at time i.

[0077] This embodiment uses the Gauss-Newton iteration method to solve the nonlinear least squares method used in each step.

[0078] Step S160, according to the time t to be simulated of the target laying hen group, the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg, the estimated value g(i) of the proportion of the sub-group composed of individuals who see the egg at time i in the target laying hen group, the estimated value f(t i ), through the egg production rate simulation model, the egg production rate of the target laying hen group at the time t to be simulated is obtained.

[0079] In one embodiment, the expression of the egg production rate simulation model is:

[0080]

[0081] In the formula, En t represents the egg-laying rate of the whole flock at time t, t represents a certain time after the whole flock starts laying eggs, and the t value is accumulated from the time when the whole flock first sees eggs. The t value is recorded as 1 when the whole flock first sees eggs, and then the t value increases by 1 for each preset time unit. m represents the total time required from the first egg-seeing of the first individual in the whole flock to the time when all individuals in the whole flock see eggs. i represents the first egg-seeing time of a subgroup in the whole flock. g(i) represents the proportion of the subgroup composed of individuals that see eggs at time i in the whole flock. t i represents the cumulative egg-laying time of the subgroup composed of individuals that lay eggs at time i to time t, that is, the difference between a certain time t and i, t i =ti,f(t i ) represents the egg-laying rate of the subgroup composed of individuals laying eggs at time i at time t, where t, m, and i are positive integers. When t and i are equal, f(t i ) is 1, and for the sub-group that has not seen eggs at time t, its egg-laying rate f(t i ) is 0.

[0082] Through steps S110-160, the egg production rate of the target egg-laying hen group can be simulated or the egg production rate of other similar groups can be predicted. After obtaining the time t to be simulated of the target egg-laying hen group, the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg, the estimated value g(i) of the proportion of the subgroup composed of individuals who saw the egg at time i in the target egg-laying hen group, the estimated value f(t i ), they can be substituted into the expression of the egg production rate simulation model to obtain the estimated value En of the egg production rate of the target egg-laying chicken group at the time t to be simulated. tThe time t to be simulated can also be a number of consecutive positive integers from 1 to t. In this way, the predicted value of the egg production rate of the whole flock from the beginning of laying to a specific time point can be obtained.

[0083] This embodiment uses mean square error and mean absolute percentage error to evaluate the simulation or prediction effect of the egg production rate simulation model. Mean square error is used to represent the mean of the sum of squares of the errors of the corresponding points of the simulated data and the real data. The mean absolute percentage error represents the degree of closeness between the simulated data and the real data in percentage. When the mean absolute percentage error is 0%, it means that the model can completely simulate the real data, and when the value is greater than 100%, it means that the simulation effect is very poor.

[0084] The expression of mean square error is:

[0085]

[0086] In the formula, MSE represents mean square error, n represents the sample size to be verified, and y i represents the true value of the i-th sample, Represents the simulated value of the ith sample.

[0087] The expression for mean absolute percentage error is:

[0088]

[0089] In the formula, MAPE represents the mean absolute percentage error, n represents the sample size to be verified, and y i represents the true value of the i-th sample, Represents the simulated value of the ith sample.

[0090] After evaluation, it is believed that the egg production rate simulation model provided by the present invention has a good simulation or prediction effect and is better than the Wood model.

[0091] Example 1

[0092] This embodiment uses an egg production rate simulation model and its application provided by the present invention to simulate the egg production rate of the entire flock of 6 pure line chickens (target laying hen group). The egg production record data used is derived from the pure line chicken production performance record provided by Zhongnong Bangyang Laying Chicken Co., Ltd. The egg production record data is divided into daily records and weekly records, that is, the egg production record data includes egg-laying day age data or egg-laying week age data.

[0093] 1. Data collation

[0094] 1) Arrangement of egg-laying day / week age

[0095] The egg-laying days / weeks of the target egg-laying chickens were recorded and grouped using the table() function in the base package. The distribution frequency of all individuals on each egg-laying day / week was obtained, and each frequency was divided by the number of individuals in the entire group to obtain the distribution frequency of each individual in the preset egg-laying cycle.

[0096] 2) Correction of egg laying records

[0097] The egg-laying records of individuals generally start from a fixed age and end when they are eliminated. In the daily records, the number of eggs laid by egg-laying individuals is recorded, and those that do not lay eggs are recorded as 0. The weekly records are obtained by summing the daily records according to the age in weeks. To correct the egg-laying records, it is necessary to cut the egg-laying record data of each individual according to the recorded egg-laying day / age in weeks. For each individual's egg-laying record, only the egg-laying records from the first egg-laying to a fixed length of time are retained. Since the egg-laying age of different individuals may be different, the length of the effective record after cutting will be different. For individuals whose effective record length is less than the cut-off length, in order to fill the data length, the missing data after the record cut-off is uniformly recorded as missing value NA.

[0098] 2. Parameter Estimation

[0099] 1) Estimate the parameters in g(i)

[0100] If daily records are used, the probability density function of the normal distribution needs to be used for simulation. The implementation of nonlinear least squares requires the use of the nls() function in the stats package. The formula used in the function is the probability density function of the normal distribution or the function dnorm() in the stats package. The initial parameters of the function are generally set to μ=20 and σ=5.

[0101] If weekly records are used, the probability density function of the binomial distribution needs to be used for simulation. The formula used in the function is the probability density function of the binomial distribution or the function dbinorm() in the stats package. The initial parameters of the function are generally set to n=m, p=0.5, and m is the length of the observed egg age distribution.

[0102] 2) f(t i ) to estimate the parameters in

[0103] Use the corrected data to calculate the egg production rate of the group at each time point. When calculating the egg production rate of each day / week, all missing values ​​need to be eliminated. The calculated results will be used to simulate the parameters of the gamma cumulative distribution function. The simulation process requires the use of nonlinear least squares method, and the formula used is the formula of the gamma cumulative distribution function. The initial values ​​of the three parameters of the function are generally set to a=1, b=0.08, c=0.002 (daily records) or a=6, b=0.08, c=0.002 (weekly records).

[0104] 3. Calculation of simulated or predicted egg production rate

[0105] For a simulation with a time length of T (T ≥ t), it is necessary to have T data from 1 to T and the time t to be simulated, the total time m from the first individual to the time when all individuals see eggs, the estimated value g(i) of the proportion of the subgroup composed of individuals who see eggs at time i in the target laying hen population, the estimated value f(t i ) are substituted into formula (1). Thus, the egg production rate of the whole flock corresponding to T time points is obtained. For the sub-population with an egg-seeking time of 1, the simulated value of its egg production rate is the result obtained by substituting T integers from 1 to T into formula (4). That is, the simulated value of T egg production rates. For the sub-population with an egg-seeking time of i, the simulated value of its egg production rate needs to add i-1 zeros before the above result. Because these individuals did not start laying eggs before time i. The proportion of all sub-populations needs to be calculated by substituting i into g(i). The formula of g(i) is determined by the data type. If it is in days, formula (2) can be used. If it is in weeks or months, formula (3) can be used. The parameters required for the calculation are obtained by the previous parameter estimation step.

[0106] If the length of the observed data used is greater than or equal to the T time points, and the parameters used in the formula are estimated from the observed data used, then this embodiment refers to this series (T) of egg production rates as simulated egg production rates. If existing parameters are used to simulate egg production time points that have not been observed or groups whose egg production rates have not been observed, then the egg production rate simulated here is called the predicted egg production rate.

[0107] 4. Simulation results

[0108] According to the application method of a simulated egg production rate model constructed by the present invention, the daily egg production record data and weekly egg production record data of 6 pure lines are used for simulation, and the error results are shown in Table 1. As can be seen from the table, the average absolute percentage error of the simulated egg production rate model constructed by the present invention is within a reasonable range (10%) under different pure line conditions.

[0109] Table 1 Statistics of errors in a model for simulating egg production rate

[0110]

[0111] The present invention provides an egg-laying rate simulation model and its application, which incorporates egg-laying record data highly correlated with egg-laying performance into the egg-laying rate simulation model, wherein the egg-laying record data includes egg-laying age data and egg-laying age data, which are important figures used to describe egg-laying performance. The egg-laying rate simulation model incorporating the egg-laying record data can more accurately simulate the egg-laying rate of a group in the early stage of egg-laying. The egg-laying rate simulation model proposed by the present invention fills a gap in the field of animal production, and at the same time provides a more reasonable simulation method for the egg-laying rate of a group.

[0112] The egg production rate simulation system provided by the present invention is described below. The egg production rate simulation system described below and the egg production rate simulation method described above can be referenced to each other.

[0113] Reference Figure 2 The present invention provides an egg production rate simulation system, which may include:

[0114] The data acquisition module is used to obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual seeing the egg for the first time to all individuals seeing the egg;

[0115] The processing module is used to obtain, based on the egg-laying record data of the target egg-laying hen group, an observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and an observed value F(t i );

[0116] The first estimation module is used to obtain an estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population according to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population;

[0117] The second estimation module is used to: according to the observed value F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0118] The simulation module is used to: calculate the target egg-laying hen population according to the time t to be simulated, the total time m required from the first individual to see eggs until all individuals see eggs, the estimated value g(i) of the proportion of the sub-group composed of individuals who see eggs at time i in the target egg-laying hen population, and the estimated value f(t i ), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0119] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communications interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the egg production rate simulation method, which includes:

[0120] Obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg;

[0121] According to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i );

[0122] According to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0123] The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0124] According to the time t to be simulated of the target laying hen population, the total time m required from the first individual to see eggs for the first time to all individuals seeing eggs, the estimated value g(i) of the proportion of the sub-population composed of individuals who see eggs at time i in the target laying hen population, and the estimated value f(t i ), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0125] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0126] On the other hand, the present invention also provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the egg production rate simulation method provided by the above methods, the method comprising:

[0127] Obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg;

[0128] According to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i );

[0129] According to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0130] The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0131] According to the time t to be simulated of the target laying hen population, the total time m required from the first individual to see eggs for the first time to all individuals seeing eggs, the estimated value g(i) of the proportion of the sub-population composed of individuals who see eggs at time i in the target laying hen population, and the estimated value f(t i), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0132] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the egg production rate simulation method provided by the above methods, the method comprising:

[0133] Obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg;

[0134] According to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i );

[0135] According to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained;

[0136] The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i );

[0137] According to the time t to be simulated of the target laying hen population, the total time m required from the first individual to see eggs for the first time to all individuals seeing eggs, the estimated value g(i) of the proportion of the sub-population composed of individuals who see eggs at time i in the target laying hen population, and the estimated value f(t i ), through any of the egg production rate simulation models described above, the egg production rate of the entire group of target laying hens at the time t to be simulated is obtained.

[0138] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0139] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An egg production rate simulation model, characterized in that: The expression of the egg production rate simulation model is: In the formula, En t represents the egg-laying rate of the whole flock at time t, t represents a certain time after the whole flock starts laying eggs, and the t value is accumulated from the time when the whole flock first sees eggs. The t value is recorded as 1 when the whole flock first sees eggs, and then the t value increases by 1 for each preset time unit. m represents the total time required from the first egg-seeing of the first individual in the whole flock to the time when all individuals in the whole flock see eggs. i represents the first egg-seeing time of a subgroup in the whole flock. g(i) represents the proportion of the subgroup composed of individuals that see eggs at time i in the whole flock. t i represents the cumulative egg-laying time of the subgroup composed of individuals that lay eggs at time i to time t, that is, the difference between a certain time t and i, t i =ti,f(t i ) represents the egg-laying rate of the subgroup composed of individuals laying eggs at time i at time t, where t, m, and i are positive integers. When t and i are equal, f(t i ) is 1, and for the sub-group that has not seen eggs at time t, its egg-laying rate f(t i ) is 0.

2. A method for simulating egg production rate, characterized in that: include: Obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual to see the egg for the first time to all individuals seeing the egg; According to the egg-laying record data of the target egg-laying hen group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and the observed value F(t i ); According to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population is obtained; The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i ); According to the time t to be simulated of the target laying hen population, the total time m required from the first individual to see eggs for the first time to all individuals seeing eggs, the estimated value g(i) of the proportion of the sub-population composed of individuals who see eggs at time i in the target laying hen population, and the estimated value f(t i ), through the egg production rate simulation model described in claim 1, the egg production rate of the entire flock of target laying hens at the time t to be simulated is obtained.

3. The egg production rate simulation method according to claim 2, characterized in that: After obtaining the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual seeing the eggs for the first time to all individuals seeing the eggs, the method includes: According to the egg-laying record data of the target egg-laying hen group, the distribution frequency of each individual in the target egg-laying hen group in the preset egg-laying cycle is obtained; The distribution frequency of each individual in the target laying hen group in the preset laying cycle is divided by the total number of individuals in the target laying hen group to obtain the distribution frequency of each individual in the target laying hen group; Correcting the egg-laying record data of the target egg-laying hen group to obtain corrected egg-laying record data, wherein the correction processing includes data length cutting and / or missing value filling; Among them, the egg-laying record data of the target egg-laying chicken group is the egg-laying age data or the egg-laying age data. When the egg-laying record data of the target egg-laying chicken group is the egg-laying age data, the preset egg-laying cycle is day; when the egg-laying record data of the target egg-laying chicken group is the egg-laying age data, the preset egg-laying cycle is week.

4. The egg production rate simulation method according to claim 3, characterized in that: According to the egg-laying record data of the target egg-laying chicken group, the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying chicken group and the observed value F(t i ),include: According to the distribution frequency of each individual in the target laying hen population, the observed value G(i) of the proportion of the sub-population composed of individuals that laid eggs at time i in the target laying hen population is obtained; According to the corrected egg-laying record data, the observed value of the current egg-laying rate F(t i ).

5. The egg production rate simulation method according to claim 4, characterized in that: The method of obtaining an estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population based on the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population comprises: When the egg-laying record data of the target laying hen group is the egg-laying age data, according to the observed value G(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group, the estimated value g(i) of the proportion of the subgroup composed of individuals laying eggs at time i in the target laying hen group is obtained through the probability density function of the normal distribution, where the expression of the probability density function of the normal distribution is: Where g(i) is the estimated value of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, i is the age of each subgroup at the time of laying eggs, μ and σ are the equation parameters of the probability density function of the normal distribution, μ and σ are based on the observed value G(i) of the proportion of the subgroup composed of individuals that lay eggs at time i in the target laying hen population, and are obtained using the nonlinear least squares method, π is the pi constant, and e is a natural constant.

6. The egg production rate simulation method according to claim 5, characterized in that: The method of obtaining an estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population based on the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population comprises: When the egg-laying record data of the target laying hen group is the egg-laying age data, according to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen group, the estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen group is obtained through the probability density function of the binomial distribution, where the expression of the probability density function of the binomial distribution is: Where i represents the age of each sub-population when laying eggs, n and p represent the equation parameters of the probability density function of the binomial distribution, and n and p are obtained based on the observed value G(i) of the proportion of the sub-population composed of individuals laying eggs at time i in the target laying hen population using the nonlinear least squares method.

7. The egg production rate simulation method according to claim 6, characterized in that: The observed value of the current egg-laying rate F(t i ), and obtain the estimated value of the current egg-laying rate f(t i ),include: If the sub-population consisting of individuals who saw eggs when i < t or at i had not seen eggs at time t, then f(t i ) is recorded as 0; In the case that the sub-population composed of individuals that laid eggs at time i has already laid eggs at time t, according to the observed value of the current egg-laying rate F(t i ), using f(t i ) expression, and obtain the estimated value of the current egg-laying rate f(t i ), where f(t i ) is: In the formula, f(t i ) represents the estimated value of the current egg-laying rate of the subpopulation composed of individuals laying eggs at time i, e represents a natural constant, t i represents the cumulative egg-laying time of the subgroup composed of individuals laying eggs at time i to time t, a, b, c represent f(t i ) is an expression of the equation parameters, a, b, c based on the observed value of the current egg production rate F(t i ), obtained using the nonlinear least squares method.

8. An egg production rate simulation system, characterized in that: include: The data acquisition module is used to obtain the egg-laying record data of the target egg-laying hen group, the time t to be simulated, and the total time m required from the first individual seeing the egg for the first time to all individuals seeing the egg; The processing module is used to obtain, based on the egg-laying record data of the target egg-laying hen group, an observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target egg-laying hen group and an observed value F(t i ); The first estimation module is used to obtain an estimated value g(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population according to the observed value G(i) of the proportion of the subgroup composed of individuals that laid eggs at time i in the target laying hen population; The second estimation module is used to: according to the observed value F(t i ), and obtain the estimated value of the current egg-laying rate f(t i ); The simulation module is used to: calculate the target egg-laying hen population according to the time t to be simulated, the total time m required from the first individual to see eggs until all individuals see eggs, the estimated value g(i) of the proportion of the sub-group composed of individuals who see eggs at time i in the target egg-laying hen population, and the estimated value f(t i ), through the egg production rate simulation model described in claim 1, the egg production rate of the entire flock of target laying hens at the time t to be simulated is obtained.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the egg production rate simulation method as described in any one of claims 2 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the egg production rate simulation method according to any one of claims 2 to 7 is implemented.

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