A method for breeding high-yield laying hens

Abstract

The application discloses a kind of high-yield laying hen's breeding method, belong to chicken breeding technical field.The method includes: S1, environmental monitoring and comfort degree calculation;S2, individual feeding amount calculation and feeding;S3, flock behavior monitoring and welfare optimization;S4, flavor and taste control;S5, health and safety monitoring;S6, digital twin closed-loop optimization;S7, according to the breeding strategy generated by digital twin system to carry out actual breeding.The application carries out dynamic prediction and regulation to environment, feeding, behavior, welfare, flavor and health through digital twin flock model, realizes the closed-loop optimization of whole process of breeding, so that flock is always in close to optimal physiological and environmental state, thereby significantly improves egg production rate, reduces the phenomenon of egg production decline caused by environmental fluctuation, nutritional deficiency or stress response.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chicken breeding, more particularly, to a high-yield laying hen breeding method. BACKGROUND

[0002] Laying hen breeding is an important part of modern animal husbandry. Eggs are a high-quality source of animal protein, and the demand is growing. The existing laying hen breeding method mainly relies on artificial experience and traditional feeding and management methods, usually through fixed formula feed feeding and conventional environmental regulation to maintain egg production performance. However, this method has the following disadvantages: most farms only use artificial adjustment of temperature, humidity and light to improve the environment, lack real-time monitoring and dynamic regulation of harmful gases such as ammonia and carbon dioxide, which can easily cause environmental fluctuations and cause stress reactions in the flock, thereby reducing egg production and egg quality. The traditional feeding method usually formulates a fixed feeding amount according to the age of the chicken, lacks correction of recent feeding data and individual differences, and causes a mismatch between nutrient supply and actual demand of the flock, thereby affecting the health of laying hens and the stability of egg production. In large-scale farms, the behavior needs and welfare level of laying hens are often ignored, such as insufficient exercise space and unreasonable light cycle, which can easily cause stress and pecking behavior problems in the flock, thereby affecting egg production performance and breeding sustainability. SUMMARY

[0003] The purpose of the present application is to provide a high-yield laying hen breeding method to solve the problems raised in the background art: most farms only use artificial adjustment of temperature, humidity and light to improve the environment, lack real-time monitoring and dynamic regulation of harmful gases such as ammonia and carbon dioxide, which can easily cause environmental fluctuations and cause stress reactions in the flock, thereby reducing egg production and egg quality. The traditional feeding method usually formulates a fixed feeding amount according to the age of the chicken, lacks correction of recent feeding data and individual differences, and causes a mismatch between nutrient supply and actual demand of the flock, thereby affecting the health of laying hens and the stability of egg production. In large-scale farms, the behavior needs and welfare level of laying hens are often ignored, such as insufficient exercise space and unreasonable light cycle, which can easily cause stress and pecking behavior problems in the flock, thereby affecting egg production performance and breeding sustainability.

[0004] Technical solution: A high-yield laying hen breeding method, comprising the following steps:

[0005] S1, environmental monitoring and comfort calculation:

[0006] Temperature, humidity, light and gas sensors are arranged in the henhouse to collect environmental parameters in real time: temperature , humidity , light intensity , ammonia concentration , carbon dioxide concentration ;

[0007] Computing the environmental comfort:

[0008] ;

[0009] wherein, represents the overall environmental comfort of the chicken house, with a value range of 0~1, and the closer to 1 indicates that the environment is more suitable for the growth and egg production of the chicken population; is a function of standardizing the actual environmental parameters to 0~1, which is obtained by fitting experimental data; is a weight coefficient, which is determined by statistical analysis to determine the importance of different factors to the comfort;

[0010] According to the deviation from the preset optimal value , the environmental control system is dynamically adjusted, including temperature and humidity adjustment, light regulation and air circulation, to realize closed-loop control;

[0011] S2, individual feeding amount calculation and feeding:

[0012] According to the age of the chicken and the historical actual feeding data, today's feeding amount is calculated;

[0013] Combine the baseline amount and the historical amount for weighted fusion:

[0014] ;

[0015] wherein, is the target feeding amount of each egg-laying hen today, is the historical correction weight coefficient, used to adjust the balance between the baseline recommendation and the recent actual; is the baseline feeding amount function, is the historical smoothed feeding amount in the past days.

[0016] S3, chicken behavior monitoring and welfare optimization:

[0017] Through the camera and behavior recognition algorithm, chicken behavior data is extracted, including the number of individuals in free activity, rest and stress behavior ;

[0018] Calculate the welfare index:

[0019] ;

[0020] wherein, represents the welfare index of the chicken population, with a value range of 0~1, and the higher the value, the better the welfare; is the number of individuals in free activity, is the number of individuals in normal rest, is the number of individuals in stress behavior, is the total number of the chicken population; The stress behavior penalty coefficient reflects the negative impact of stress behavior on welfare;

[0021] According to The deviation from the target value Adjust the chicken house environment or feeding strategy, such as increasing the activity space, adjusting the light or feeding rhythm, reducing stress behavior, and improving chicken welfare.

[0022] S4, flavor and taste control:

[0023] Calculate the comprehensive flavor score , Specifically:

[0024] ;

[0025] Among them, Represents the measured concentration of the metabolite; Represents the target or ideal concentration; The metabolite contribution coefficient determined by sensory evaluation experiment (reflecting the importance of the material to the overall flavor); The nutrition-flavor correlation weight (obtained by modeling the metabolic pathway, reflecting the sensitivity of metabolites to feed nutrients).

[0026] When , it means that the sample flavor is consistent with the target flavor; when or , it means that the flavor is insufficient or deviates from the target, and the nutritional components in the feed are adjusted according to

[0027] S5, health and safety monitoring:

[0028] Based on the immune state of the chicken flock, disease monitoring and drug residue, calculate the health risk index:

[0029] ;

[0030] Among them, Represents the health risk index of the chicken flock, the higher the value, the greater the risk; The disease incidence is obtained by statistical data; The drug residue risk is calculated according to the feeding records and detection data; The weight coefficient reflects the contribution of disease and drug residue to the overall health risk;

[0031] If , trigger intervention measures, including vaccine adjustment, isolation of high-risk individuals or optimization of drug use plan, to ensure the health of the chicken flock and the safety of the egg products.

[0032] ​S6, digital twin closed-loop optimization:

[0033] The indicators of steps S1-S5 are input into the digital twin chicken group model to establish a virtual chicken group state prediction.

[0034] When the actual chicken group data deviates from the predicted value by more than a threshold value, the system generates an automatic control strategy that acts on environmental control, feeding scheme, welfare improvement, flavor optimization, and health management.

[0035] The system continuously self-learns and optimizes weights and parameters to achieve dynamic optimization of egg production, egg quality, welfare level, and health and safety of the chicken group.

[0036] Preferably, in S2, the method for obtaining the baseline feeding amount is:

[0037] According to the age of the target laying hen Query the preset baseline feeding amount function , the formed by big data statistics, reflecting the average recommended feeding amount corresponding to the age;

[0038] Calculation of historical average feeding amount:

[0039] Record the actual daily feeding amount of laying hens in the past days ;

[0040] Calculate the historical average feeding amount:

[0041] ;

[0042] where, represents the historical smoothed feeding amount, reflecting the recent actual feeding level, is the smoothing days;

[0043] Also includes safety boundary constraints:

[0044] The is subjected to boundary constraints:

[0045] ;

[0046] where, and are the minimum and maximum allowed feeding amounts, which can be set as a floating range (e.g. ±20%) above and below the baseline feeding amount to avoid excessive or insufficient feeding.

[0047] Preferably, in S4, the comprehensive flavor score The collected egg and chicken samples are used for calculation, and the concentration of key flavor metabolites, including at least nucleotides (such as inosinic acid), amino acids (such as glutamic acid), fatty acids (such as linoleic acid, DHA), and volatile organic compounds, are detected by gas chromatography-mass spectrometry (GC-MS) or high performance liquid chromatography (HPLC).

[0048] Preferably, in S4, the method for adjusting the nutritional components in the feed according to The method for adjusting the nutritional components in the feed is:

[0049] According to the deviation , the corresponding nutritional components in the feed are adjusted:

[0050] If the nucleotide metabolites are insufficient ( ), high-protein raw materials or yeast extract are added;

[0051] If the amino acid metabolites are insufficient, amino acid additives containing glutamic acid or soybean meal protein are supplemented;

[0052] If the fatty acid metabolites are low, raw materials containing unsaturated fatty acids (such as flaxseed oil, fish oil, and algae powder) are added;

[0053] If the metabolites are excessive ( ), the proportion of related feed components is reduced to avoid excessive flavor or fishy smell.

[0054] The adjusted feed formula is re-input into breeding, and the flavor score is detected again in the next cycle to form a closed-loop correction mechanism.

[0055] Preferably, S1 further includes a calculation of ventilation rate:

[0056] ;

[0057] wherein, is the ventilation rate of the fan;

[0058] are the optimal temperature and humidity values, respectively;

[0059] is the adjustment gain coefficient;

[0060] According to the calculation results, the fan is automatically adjusted to realize closed-loop control of temperature and humidity.

[0061] Preferably, the chicken welfare index in S3 further includes an activity uniformity index:

[0062] ;

[0063] wherein, is the standard deviation of the behavior characteristics, For the mean value; The closer to 1 indicates the higher uniformity of the chicken activity;

[0064] If Trigger environmental or feeding strategy adjustment to improve the uniform activity level of the chicken flock.

[0065] Preferably, the health risk index The disease probability Calculated by logistic regression:

[0066] ;

[0067] Wherein, Environmental stress, immune status and pathogen load factors;

[0068] The regression coefficient;

[0069] Used for early intervention in disease risk, to achieve chicken health management.

[0070] Compared with the prior art, the advantages of the present application are:

[0071] (1) The present application realizes the dynamic prediction and control of environment, feeding, behavior, welfare, flavor and health through the digital twin chicken flock model, realizes the closed-loop optimization of the whole process of breeding, makes the chicken flock always in the optimal physiological and environmental state, thereby significantly improves the egg production rate, reduces the phenomenon of egg production decline caused by environmental fluctuations, nutritional deficiency or stress reaction.

[0072] (2) The present application can adjust the feed formula in real time through the detection of key metabolites (such as inosinic acid, glutamic acid, fatty acid) and the introduction of flavor score model, so that the nutritional value, flavor substance content and taste quality of chicken eggs are significantly improved, thereby producing more high-quality and differentiated chicken eggs to meet the needs of high-end consumer market.

[0073] (3) The present application can timely alarm when the chicken flock appears potential health risk through the establishment of health risk index model, combined with the concentration of harmful gases such as ammonia and carbon dioxide and behavior abnormality detection, and intervene through measures such as feeding probiotics, Chinese herbal medicine additives, etc., to guarantee the safety of eggs and chicken from the source and reduce the dependence on antibiotics.

[0074] (4) The present application uses behavior monitoring (activity, feather condition, group distribution characteristics, etc.) and welfare score model to dynamically optimize light, exercise space and environmental stress source control, improve the welfare level of laying hens, reduce stress reaction, and prolong the high-yield period, so as to realize welfare-friendly breeding. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 A schematic diagram of the overall process of a high-yield laying hen breeding method. DETAILED DESCRIPTION

[0076] Embodiments, please refer to Figure 1 A high-yield laying hen breeding method, comprising the following steps:

[0077] S1, environmental monitoring and comfort calculation:

[0078] Temperature, humidity, light and gas sensors are arranged in the henhouse to collect environmental parameters in real time: temperature , humidity , light intensity , ammonia concentration , carbon dioxide concentration ;

[0079] Calculate the environmental comfort:

[0080] ;

[0081] Wherein, represents the overall environmental comfort of the henhouse, the value range is 0~1, the closer to 1 indicates that the environment is more suitable for the growth and egg production of the chicken flock; is a function of standardizing the actual environmental parameters to 0~1, which is obtained by fitting experimental data;

[0082] In the high-yield laying hen breeding method of the present application, in order to realize the quantitative management of environmental control and intelligent breeding, the collected environmental parameters (including temperature , humidity , air quality indicators and light intensity ) are standardized to dimensionless indicators between 0 and 1, so as to be uniformly weighted and comprehensively evaluated. The standardization steps are as follows:

[0083] Temperature standardization function : Set the target suitable temperature range as , wherein is the optimal temperature point.

[0084] When , the standardized value is calculated by the following formula:

[0085] ;

[0086] When out of range, the value is 0, and the function reflects that the closer the temperature is to the optimal point, the higher the standardized score.

[0087] Humidity standardization function : Set the target humidity range as , and the optimal humidity point as .

[0088] When :

[0089] ;

[0090] When out of range, take value 0. This function guarantees a higher score when the humidity is within the appropriate range.

[0091] Air quality standardization function : Set the safety threshold for ammonia and carbon dioxide concentrations as .

[0092] Its joint function is defined as:

[0093] ;

[0094] When a concentration exceeds the safety threshold, the corresponding item takes value 0. This function reflects the overall air quality, and the lower the pollutant concentration, the higher the score.

[0095] Illumination standardization function : Set the target illumination range as , and the optimal illumination intensity as .

[0096] When :

[0097] ;

[0098] When out of range, take value 0.

[0099] Through the above steps, each environmental parameter is standardized to the interval.

[0100] The closer the standardized value is to 1, the closer the parameter is to the optimal breeding environment conditions.

[0101] The standardized results of each parameter can be further used as input for comprehensive environmental scoring to guide the adjustment of environmental control equipment (such as temperature control, ventilation, humidity adjustment, and light regulation), thereby maintaining the optimal growth and egg production environment for high-yield laying hens.

[0102] is the weight coefficient, and the importance of different factors on comfort is determined through statistical analysis;

[0103] According to and the pre-set optimal value, the optimal environmental comfort The bias of the deviation, dynamically adjusting the environmental control system, including temperature and humidity adjustment, light regulation and air circulation, realizing closed-loop control;

[0104] S2, individual feeding amount calculation and feeding:

[0105] According to the age of the chicken and the historical actual feeding data to calculate today's feeding amount;

[0106] Combined with the baseline amount and the historical amount for weighted fusion:

[0107] ;

[0108] Among them, is the target feeding amount of each hen today, is the historical correction weight coefficient, used to adjust the balance between the baseline recommendation and the recent actual; is the baseline feeding amount function, is the historical smooth feeding amount in the past days.

[0109] S3, chicken behavior monitoring and welfare optimization:

[0110] Through the camera and behavior recognition algorithm to extract chicken behavior data, including the number of free activity, rest and stress behavior individuals ;

[0111] Calculate the welfare index:

[0112] ;

[0113] Among them, represents the welfare index of the chicken group, the value range is 0~1, the higher the value, the better the welfare; is the number of free activity individuals, is the number of normal rest individuals, is the number of stress behavior individuals, is the total number of chicken groups; is the stress behavior penalty coefficient, reflecting the negative impact of stress behavior on welfare;

[0114] According to the deviation of and the target value Adjust the chicken house environment or feeding strategy, such as increasing the activity space, adjusting the light or feeding rhythm, reducing the stress behavior, and improving the welfare of the chicken group.

[0115] S4, flavor and taste regulation:

[0116] Calculate the comprehensive flavor score , specifically:

[0117] ;

[0118] wherein, measured concentration of the metabolite; target or ideal concentration; metabolite contribution factor determined by sensory evaluation experiment (reflecting the importance of the substance to the overall flavor); nutrient-flavor correlation weight (obtained by metabolic pathway modeling, reflecting the sensitivity of the metabolite to the nutritional components of the feed).

[0119] When , it indicates that the sample flavor is consistent with the target flavor; when or , it indicates that the flavor is insufficient or deviates from the target, and the nutritional components in the feed are adjusted according to

[0120] S5, health and safety monitoring:

[0121] Based on the immune status of the chicken flock, disease monitoring and drug residue, calculate the health risk index:

[0122] ;

[0123] wherein, indicates the health risk index of the chicken flock, the higher the value, the greater the risk; is the disease occurrence probability, obtained by statistical data; is the drug residue risk, calculated according to the feeding records and detection data; is the weight coefficient, reflecting the contribution of disease and drug residue to the overall health risk;

[0124] If , trigger intervention measures, including vaccine adjustment, isolation of high-risk individuals or optimization of drug use plan, to ensure the health of the chicken flock and the safety of the egg product.

[0125] S6, digital twin closed-loop optimization:

[0126] In the present application, based on the multi-dimensional data obtained in steps S1-S5 (including individual signs and behavior data, feeding amount and intake data, environmental monitoring data, welfare state data, flavor metabolite detection data and health index data), a digital twin chicken flock model is constructed, which is used for dynamic prediction and closed-loop optimization control of virtual chicken flock. The specific steps are as follows:

[0127] Data input and virtual modeling

[0128] All indicators obtained in steps S1-S5 are used as input variables, including body weight, intake, environmental standardization indicators , welfare score ​, flavor score and health risk index .

[0129] Using historical breeding big data and experimental calibration results, a multi-input, multi-output dynamic prediction model is established on a computing platform to form a digital twin chicken flock. The model can output prediction results for a certain period of future, including: daily average feed intake prediction value, egg production prediction value, egg quality prediction value, welfare level prediction value and health risk prediction value.

[0130] Deviation detection and threshold determination

[0131] Compare the data collected from the actual chicken flock with the prediction results of the digital twin model.

[0132] If the deviation of a certain index exceeds the preset threshold , for example:

[0133] ;

[0134] The system determines that the current breeding state deviates from the optimal interval and needs to be intervened.

[0135] Automatic control strategy generation

[0136] For different types of deviations, the system automatically generates corresponding control strategies:

[0137] Environmental control: if or is low, automatically adjust the temperature control or humidification / dehumidification system; if , increase the ventilation frequency.

[0138] Feeding plan: if the feed intake is lower than the predicted value, adjust the daily feeding amount or change the energy / protein ratio; if the flavor score is low, supplement functional feed additives (such as specific amino acids or fatty acids).

[0139] Welfare improvement: if the welfare score is low, increase the exercise space, optimize the light cycle or reduce stressors.

[0140] Flavor optimization: when the flavor score deviates greatly from the target, adjust the proportion of protein, fat and functional additives in the feed to make the metabolite concentration closer to the target interval.

[0141] Health management: if the health risk index is high, the system prompts for additional detection or automatically dispenses probiotics / Chinese herbal medicine extract.

[0142] Self-learning and parameter updating

[0143] The system uses reinforcement learning algorithm to feedback learn the effect of regulation strategy.

[0144] When the regulation measure is executed, if the actual data improves and approaches the predicted value, the system automatically increases the weight of the strategy; if the effect is not good, the weight is reduced and an alternative solution is tried.

[0145] Through continuous learning iteration, the prediction accuracy of the digital twin model gradually improves, realizing dynamic optimal adjustment of parameters and weights.

[0146] Dynamic optimization

[0147] Through the above closed-loop control, the system ultimately realizes dynamic optimization of egg production, egg quality, welfare level and health and safety attributes of the chicken flock.

[0148] This optimization process is running in real time, and can quickly make prediction and regulation response when the breeding environment fluctuates or the chicken flock state is abnormal, so as to ensure the efficiency, stability and intelligentization of breeding.

[0149] In S2, the method for obtaining the baseline feeding amount is:

[0150] According to the age of the target laying hen Query the preset baseline feeding amount function , the formed by big data statistics, reflecting the average recommended feeding amount corresponding to the age;

[0151] Calculation of historical average feeding amount:

[0152] Record the actual daily feeding amount of the laying hen in the past days ;

[0153] Calculate the historical average feeding amount:

[0154] ;

[0155] Among them, represents the historical smooth feeding amount, reflecting the recent actual feeding level, is the smoothing days;

[0156] It also includes safety boundary constraints:

[0157] Boundary constraints are imposed on the :

[0158] ;

[0159] Among them, and The minimum and maximum allowed feeding amounts, respectively, can be set as a floating range (e.g. ±20%) above and below the baseline feeding amount to avoid over- or under-feeding.

[0160] The comprehensive flavor score in S4 The collected egg and chicken samples are used for calculation, and the concentrations of key flavor metabolites, including at least nucleotides (such as inosinic acid), amino acids (such as glutamic acid), fatty acids (such as linoleic acid, DHA), and volatile organic compounds, are detected by gas chromatography-mass spectrometry (GC-MS) or high performance liquid chromatography (HPLC).

[0161] The S4 is based on The method for adjusting the nutritional components in the feed is:

[0162] According to the deviation , the corresponding nutritional components in the feed are adjusted:

[0163] If the nucleotide metabolites are insufficient ( ), high-protein raw materials or yeast extract are added;

[0164] If the amino acid metabolites are insufficient, amino acid additives containing glutamic acid or soybean meal protein are supplemented;

[0165] If the fatty acid metabolites are low, raw materials containing unsaturated fatty acids (such as flaxseed oil, fish oil, and algae powder) are added;

[0166] If the metabolites are excessive ( ), the proportion of related feed components is reduced to avoid excessive flavor or fishy smell.

[0167] The adjusted feed formula is re-input into breeding, and the flavor score is detected again in the next cycle to form a closed-loop correction mechanism.

[0168] The S1 also includes ventilation rate calculation:

[0169] ;

[0170] wherein, is the fan ventilation rate;

[0171] are the optimal temperature and humidity values, respectively;

[0172] is the adjustment gain coefficient;

[0173] According to the calculation results, the fan is automatically adjusted to realize closed-loop control of temperature and humidity.

[0174] The chicken welfare index in S3 further includes the activity uniformity index:

[0175] ;

[0176] wherein, is the standard deviation of the behavior feature, is the mean value; The closer to 1 indicates the higher uniformity of the flock activity;

[0177] If , the environmental or feeding strategy is adjusted to improve the uniform activity level of the flock.

[0178] wherein the health risk index is the disease probability calculated by logistic regression:

[0179] ;

[0180] wherein, is the environmental stress, immune status and pathogen load factor;

[0181] is the regression coefficient;

[0182] for early intervention in disease risk, to achieve the health management of the flock.

[0183] The above shows and describes the basic principles, main features and advantages of the present application; those skilled in the art should understand that the present application is not limited to the above examples, the above examples and descriptions in the specification are only preferred examples of the present application, and are not intended to limit the present application, various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application; the scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of rearing high-yielding laying hens, characterized by, Comprising the following steps: S1, environmental monitoring and comfort calculation: Temperature, humidity, light and gas sensors are arranged in the henhouse to collect environmental parameters in real time: temperature , humidity , light intensity , ammonia concentration , carbon dioxide concentration ; Calculate environmental comfort: ; wherein, represents the overall environmental comfort of the henhouse; is a function to normalize the actual environmental parameters to 0-1, obtained by fitting experimental data; is a weight coefficient; According to deviation from the preset optimal value , dynamically adjust the environmental control system, including temperature and humidity regulation, light regulation and air circulation, to achieve closed-loop control; S2, individual feeding amount calculation and feeding: Calculate today's feeding amount according to chicken age and historical actual feeding data; Weighted fusion of baseline amount and historical amount: ; wherein, is the target feed ration for each bird today, is a history correction weight factor to adjust the balance between the baseline recommendation and the recent actual; is the baseline feed ration function, is the past smoothed feed ration over the last 7 days. S3, flock behavior monitoring and welfare optimization: Extracting chicken behavior data including free activity, rest, and stress behavior individual number through camera and behavior recognition algorithm ; Calculate welfare index: ; wherein, represents the welfare index of the chicken group, the value range is 0~1, the higher the value, the better the welfare; is the number of free individuals, is the number of normal resting individuals, is the number of individuals with stress behavior, is the total number of the chicken group; is the stress behavior penalty coefficient, which reflects the negative impact of stress behavior on welfare; According to adjust the deviation of the target value of the henhouse environment or feeding strategy, reduce stress behavior, and improve chicken welfare; S4, flavor and taste regulation: Computing an overall flavor score , in particular: ; wherein, represents the measured concentration of the metabolite; represents the ideal concentration; is the metabolite contribution factor determined for the sensory evaluation experiment; is the nutrition-flavor association weight; When , the sample flavor is consistent with the target flavor; when or , the flavor is insufficient or deviates from the target, and the nutritional components in the feed are adjusted according to ; S5, health and safety monitoring: Based on the immune state of the flock, disease monitoring and drug residue, calculate the health risk index: ; wherein, represents the flock health risk index; is the disease occurrence probability; is the drug residue risk; is the weight coefficient; If , interventions are triggered, including vaccine adjustments, isolation of high-risk individuals, and optimization of drug use plans, to ensure flock health and egg safety; S6, digital twin closed-loop optimization: Input each index of steps S1-S5 into the digital twin flock model to establish a virtual flock state prediction; When the actual flock data deviates from the predicted value by more than the threshold value, the digital twin system generates an automatic control strategy to act on environmental regulation, feeding scheme, welfare improvement, flavor optimization and health management; The digital twin system continuously self-learns and optimizes weights and parameters to achieve dynamic optimization of flock egg production, egg quality, welfare level and health safety; S7, actual breeding according to the breeding strategy generated by the digital twin system.

2. The method for raising high-yielding laying hens according to claim 1, characterized in that, In the S2, Method for obtaining baseline feeding amount: According to the age of the target laying hen Query the preset baseline feeding amount function , the Formed by big data statistics, reflecting the average recommended feeding amount corresponding to the age Calculation of historical average feeding amount: Recorded the actual daily feed intake of laying hens over the past 24 hours ; Calculate the historical average feeding amount: ； wherein, represents a history smoothed feeding amount, reflecting a recent actual feeding level, is a smoothing day number.

3. The method for raising high-yielding laying hens according to claim 2, characterized in that, In the S2, it also includes safety boundary constraints: applying boundary constraints: applying boundary constraints: ； wherein, and are the minimum and maximum allowed feeding amounts, respectively.

4. The method for raising high-yielding laying hens according to claim 1, characterized in that, The integrated flavor score in S4 Chicken egg and chicken meat samples were collected and analyzed for key flavor metabolite concentrations, including at least nucleotides, amino acids, fatty acids, and volatile organic compounds, using gas chromatography-mass spectrometry or high-performance liquid chromatography.

5. The method for breeding high egg production chicken according to claim 1, characterized in that, The S4 according to The method for adjusting the nutrient components in the feed is: According to the deviation , the corresponding nutrient component in the feed is adjusted: If nucleotide metabolites are insufficient ( ), increase the high protein raw material or yeast extract; If the amino acid metabolite is insufficient, supplement amino acid additives containing glutamic acid or soybean meal protein; If the fatty acid metabolite is low, increase the raw material containing unsaturated fatty acids; If the metabolites are excessive ( ), the proportion of the relevant feed components is reduced to avoid excessive flavour or fishy smell.

6. The method for breeding high egg production chicken according to claim 5, characterized in that, Put the adjusted feed formula back into breeding, and detect the flavor score again in the next cycle to form a closed-loop correction mechanism.

7. The method for breeding high egg production chicken as claimed in claim 1, wherein, The S1 also includes ventilation rate calculation: ; wherein, is the fan ventilation rate; are the optimal temperature and humidity values, respectively; is the adjustment gain coefficient; According to the calculation result, automatically adjust the fan to realize closed-loop control of temperature and humidity.

8. The method for breeding high egg production chicken as claimed in claim 1, wherein, The flock welfare index in S3 further includes activity uniformity index: ； wherein, is the standard deviation of the behavioral characteristic, is the mean; The closer to 1 indicates the higher uniformity of the flock activity.

9. The method of claim 8, wherein the high egg production chicken is a chicken of a breed selected from the group consisting of Leghorn, Rhode Island Red, Barred Plymouth Rock, and Golden Comet. 8 If the activity uniformity index , trigger environmental or feeding strategy adjustments to improve the uniform activity level of the flock.

10. The method for breeding high egg production chicken as claimed in claim 1, wherein, where the health risk index disease probability calculated by logistic regression: ； wherein, is an environmental stress, immune status, and pathogenic load factor; is a regression coefficient; for early intervention in disease risk, enabling flock health management.

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