Method for improving nutritional value of excrement eggs through precise feeding strategy

Abstract

The invention discloses a method for improving nutritional value of excrement eggs through a precise feeding strategy, which comprises the following steps of: adding micronutrients with high absorptivity into feed in the breeding stage of laying hens, and establishing basic nutritional reserve in the bodies of the laying hens. Before an egg producing period and at the early stage of excrement egg production, feed components are dynamically adjusted by utilizing intestinal microbe data collected in real time, and the intestinal flora structure is optimized. In the fecal egg generation process, absorption and distribution of specific trace elements are optimized through multi-scale modeling and an optimization algorithm, and a staged feed alternation strategy is implemented in the middle and later periods of production. In the mature stage of the fecal eggs, high-energy components and environmental factors in the feed are regulated and controlled through an intelligent algorithm, and concentrated conversion of nutrition reserve is promoted. Nutrient components and structures of excrement eggs are monitored in real time through a multi-spectrum non-invasive detection technology, a feeding strategy is dynamically adjusted, closed-loop feedback is formed, intelligent feeding management is achieved, and the purpose of improving the nutritive value of the excrement eggs is achieved.

Description

Technical Field

[0001] The present invention relates to the field of animal husbandry and breeding technology, and in particular to a method for improving the nutritional value of eggs through a precise breeding strategy. Background Art

[0002] In the field of modern animal husbandry and poultry farming, consumers have increasing requirements for food quality and nutritional value, especially the nutritional content of egg products. The market demand for nutritious and stable quality poultry eggs is growing, but the existing poultry farming technology faces some challenges in the production process of poultry eggs, which leads to large fluctuations in the nutritional content of poultry eggs and makes it difficult to fully meet the expectations of the market and consumers.

[0003] Traditional poultry farming methods mainly rely on fixed feed formulas and empirical management models, which cannot dynamically respond to the growth stage and actual nutritional needs of laying hens. This results in insufficient or excessive nutritional reserves in laying hens during egg production, which directly affects the quality and nutritional content of the eggs. Due to the lack of real-time adjustment of feed ingredients, existing technologies often cannot accurately control the nutritional intake of laying hens, which in turn affects the final nutritional value of eggs.

[0004] The intestinal microbiota plays a key role in the nutrient absorption and metabolism of laying hens. However, existing breeding technologies lack the means to fine-tune the intestinal microbiota. Traditional methods are usually unable to dynamically adjust feed ingredients based on real-time collected data, which affects the balance and function of the intestinal microbiota, thereby adversely affecting the nutritional content of eggs. In addition, the absorption and distribution of trace elements are also important factors in determining the nutritional value of eggs. The existing technology has limited regulatory capabilities in this regard, and it is difficult to achieve uniform deposition and stable distribution of trace elements, resulting in unstable trace element content in eggs and uneven distribution of nutrients.

[0005] Most of the existing feeding and management methods rely on manual operation and experience judgment, and lack intelligent monitoring and adjustment methods. This management model is difficult to cope with the complex and changeable nutritional needs of laying hens during their growth, and it is impossible to dynamically adjust feed and environmental parameters according to actual conditions during the production process. Therefore, it is difficult for the nutritional content and quality of eggs to reach an ideal state. In addition, environmental factors such as temperature, humidity and light have an important influence on the transformation and deposition of nutrients during the mature stage of eggs, but it is often difficult to accurately control these factors through intelligent algorithms in the existing technology, which in turn affects the final nutritional optimization effect of eggs.

[0006] Therefore, how to provide a method for improving the nutritional value of eggs through precise feeding strategies is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose a method for improving the nutritional value of eggs through precise feeding strategies. The present invention uses real-time data collection, intestinal microbiome regulation, multi-scale modeling and optimization algorithms, intelligent feeding management and other technical means to describe in detail the whole process of nutritional optimization from laying hen breeding to egg maturation. The method ensures that the nutritional components of eggs are richer and more stable by dynamically adjusting feed ingredients, optimizing microbiome structure, real-time monitoring and feedback control, and has the advantages of significant nutritional optimization effect, high degree of intelligent management and strong controllability of production process.

[0008] A method for improving the nutritional value of eggs through a precise feeding strategy according to an embodiment of the present invention comprises the following steps:

[0009] S1. During the laying hen rearing stage, micronutrients with high absorption rate are added to the feed to build up the basic nutritional reserve in the laying hen, providing raw materials for the deposition of nutrients in egg production;

[0010] S2. Before laying hens enter the egg-laying period, based on the basic nutritional reserves in the laying hens, adjust the ratio of organic acids and fermentation products in the feed, optimize protein metabolism, and promote the synthesis of functional nutrients in the yolk;

[0011] S3. In the early stage of egg production, the adaptive microbiome regulation algorithm is used to dynamically adjust the feed composition and optimize the intestinal flora structure by collecting intestinal microbiome data in real time;

[0012] S4. During the production of feces and eggs, specific trace elements are regularly added to the feed based on the optimized intestinal microbiome, and multi-scale modeling and optimization algorithms are used to integrate multi-scale physiological data to dynamically optimize the absorption and distribution of specific trace elements, so that they are evenly deposited in the feces and eggs;

[0013] S5. In the middle and late stages of the production of stool eggs, based on the trace element absorption data and the real-time monitoring results of the nutritional components of stool eggs, a phased feed rotation strategy is implemented, alternating between traditional feed and fermented feed, to further optimize the digestion and absorption of nutrients, making the nutritional components of stool eggs richer and more stable;

[0014] S6. During the mature stage of eggs, adjust the proportion of high-energy ingredients in the feed, introduce formulas rich in bioactive substances and specific trace elements, regulate environmental factors through intelligent algorithms, promote the centralized transformation and deposition of nutrient reserves, and optimize the nutrient composition;

[0015] S7. During the production process of the feces egg, the nutritional content, eggshell thickness and yolk texture of the feces egg are monitored in real time using multi-spectral non-invasive detection technology, and the changes in the internal structure and nutritional content of the feces egg are analyzed to ensure that the nutritional value of the feces egg reaches the expected target;

[0016] S8. Based on the detection data and the optimization results of multi-scale modeling and optimization algorithms, dynamically adjust the feeding strategies at each stage, build a closed-loop feedback mechanism, and realize intelligent feeding management.

[0017] Optionally, the S3 specifically includes:

[0018] S31. In the early stage of egg production, intestinal microbiome samples were collected from laying hens, and high-throughput sequencing technology was used to obtain microbiome data in real time to analyze the composition and dynamic changes of the microbial community;

[0019] S32. Based on the real-time collected intestinal microbiome data, identify the key microbial groups K(t) related to nutrient absorption and metabolism, and dynamically evaluate the impact of the microbiome on nutrient absorption efficiency through an adaptive microbiome regulation algorithm;

[0020] S33. According to the dynamic needs of the key microbial community K(t), the feed ingredients F(t) are adjusted in real time to optimize the proportion of specific nutrients in the feed to make it more in line with the metabolic needs of the microbial community:

[0021]

[0022] Among them, F(t) represents the feed ingredient adjustment at time t, f0 is the basic feed ingredient, α j For key microorganisms j (t) Corresponding feed composition adjustment coefficient, k j (t) is the relative abundance of the jth key microorganism at time t, γ is the adjustment coefficient of the microbial abundance change rate, Represents microorganism k j (t) is the rate of change at time t, λ is the regulation coefficient related to microbial metabolism, and ∈ is a small positive number to avoid the denominator being zero;

[0023] S34. Implement the real-time adjusted feed plan F(t), continuously monitor and feedback the changes in the microbiome structure, and ensure that the intestinal flora is always in an optimized state during the egg production process to maximize the absorption and conversion efficiency of nutrients.

[0024] Optionally, the S4 specifically includes:

[0025] S41. During the egg production process, based on the optimization of the intestinal microbiome K(t), multi-scale physiological data of laying hens are regularly collected, including the concentration of trace elements in the blood C i (t), intestinal absorption efficiency η(t), and liver metabolic rate μ(t);

[0026] S42, using multi-scale modeling and optimization algorithms, integrating the collected multi-scale physiological data to establish a dynamic absorption and distribution model M i(t):

[0027]

[0028] Among them, M i (t) represents the absorption and distribution model of the i-th trace element at time t, C i (τ) represents the concentration of the i-th trace element at time τ, η(τ) is the intestinal absorption efficiency at time τ, μ(τ) is the liver metabolism rate at time τ, ω j is the jth key microorganism k j (t) related weight coefficient;

[0029] S43, based on model M i (t) results, dynamically adjust the proportion of specific trace elements added to the feed E i (t):

[0030]

[0031] Among them, E i (t) represents the amount of trace element added to the feed at time t, E0 is the basic trace element addition amount, γ i is the adjustment coefficient related to the i-th trace element, β i is the adjustment coefficient for smoothing the absorption rate of trace elements, represents the rate of change of the absorption and distribution model of the i-th trace element;

[0032] S44. Implement the adjusted feed plan E i (t), and further optimize the model M by real-time monitoring of the deposition of trace elements in the stool and combining feedback data i (t) and feed formula to ensure uniform deposition of trace elements in the eggs, thereby improving the overall nutritional value of the eggs.

[0033] Optionally, the S5 specifically includes:

[0034] S51, in the middle and late stages of egg production, based on the dynamic absorption and distribution model M i (t) and real-time monitoring of egg nutritional data N i (t) Regularly evaluate the absorption of trace elements by laying hens and the distribution of nutrients in eggs;

[0035] S52. Based on the evaluation results, an adaptive genetic algorithm is used to optimize the phased feed rotation strategy and establish a feed rotation model R(t):

[0036] R(t)=α·F trad (t)+β·F fer (t)+δ·θ(t);

[0037] Where R(t) represents the feed rotation strategy at time t, F trad (t) is the proportion of traditional feed used, F fer (t) is the use ratio of fermented feed, α and β are adjustment coefficients related to trace element absorption and nutrient stability, δ is the environmental condition adjustment coefficient, and θ(t) is the environmental adjustment function at time t;

[0038] S53, dynamically adjust the values ​​of α, β and δ to make the feed rotation model R(t) reach the optimal state:

[0039]

[0040] in, represents the rate of change of the nutritional content of eggs at time t, It represents the rate of change of the trace element absorption and distribution model, γ is the adjustment coefficient, which is used to balance the relationship between the change of nutrients and the absorption of trace elements, and ξ is the adjustment coefficient of the influence of environmental conditions on the change of nutrients;

[0041] S54. Implement the optimized feed rotation strategy R(t), continuously monitor and provide feedback, and further optimize the feed rotation model based on real-time data to maintain the optimal nutritional value of eggs.

[0042] Optionally, the S6 specifically includes:

[0043] S61. In the mature stage of feces and eggs, based on the feed rotation model R(t) and the feces and eggs nutritional data N i (t), assess the current nutritional requirements of laying hens and adjust the proportion of high-energy ingredients in the feed;

[0044] S62. While adjusting the high-energy ingredients, introduce a formula rich in bioactive substances and specific trace elements:

[0045]

[0046] Where B(t) represents the formula adjustment amount of bioactive substances and trace elements at time t, B0 is the basic formula amount, λ is the adjustment coefficient, μ j is the weight coefficient associated with the jth trace element, K j (t) represents the content of key trace elements at time t, ∈ is a small positive number to avoid the denominator being zero;

[0047] S63. Optimize the absorption and deposition of nutrients by regulating environmental factors θ(t), including light, temperature, humidity, etc., through intelligent algorithms:

[0048]

[0049] Among them, θ(t) is the environmental control function at time t, θ0 is the basic environmental control parameter, ζ k is the kth environmental factor ψ k (t) The relevant weight coefficient, ψ k (t) represents the environmental factor value at time t;

[0050] S64. Implement adjusted feed formula and environmental control plan, continuously monitor the centralized transformation and deposition of nutrient reserves in laying hens, and further optimize H(t), B(t) and θ(t) based on feedback data to achieve the best nutritional content of eggs.

[0051] Optionally, the S7 specifically includes:

[0052] S71. In the process of producing the feces egg, a multi-spectral non-invasive detection technology is used based on the environmental control parameter θ(t) and the feces egg nutritional data N i (t), real-time collection of the nutritional content of the eggs N i (t), eggshell thickness T s (t) and egg yolk texture Y d (t) data, the multi-spectral detection technology combines near-infrared spectroscopy, Raman spectroscopy and ultrasonic imaging technology to achieve high-precision capture of the external and internal multi-dimensional information of the feces;

[0053] S72, input the collected multi-spectral data into the multi-modal data fusion algorithm, and combine the environmental control parameter θ(t) and the feed formula F(t) to conduct a comprehensive analysis of the changes in the internal structure and nutritional components of the eggs:

[0054]

[0055] Among them, S(t) represents the comprehensive quality index of eggs laid at time t, T s (t) is the eggshell thickness at time t, Y d (t) is the yolk texture at time t, N i (t) is the content of the ith nutrient at time t, α, β, γ i are the weight coefficients of the corresponding indicators, δ is the fusion adjustment coefficient, and f(θ(t), F(t)) is the fusion function of environmental control parameters and feed formula;

[0056] S73. Based on the comprehensive analysis result S(t), dynamically adjust the detection frequency and parameter settings of the multi-spectrum detection technology, combine the real-time updated environmental control parameters θ(t) and feed formula F(t), and keep the changes in the internal structure and nutritional components of the eggs within the expected range through the feedback mechanism;

[0057] S74. Combine the test data and analysis results to optimize the parameter configuration of the multi-spectrum non-invasive detection technology so that the nutritional content and structure of the stool eggs reach the expected goals.

[0058] Optionally, the S8 specifically includes:

[0059] S81, based on the stool data S(t) collected using multi-spectral non-invasive detection technology, combined with multi-scale modeling and optimization algorithm M i (t) and the optimization results of the feed rotation model R(t), and the key feeding parameters of laying hens at different physiological stages are obtained in real time;

[0060] S82. Using the deep belief network optimization algorithm, key feeding parameters are processed to dynamically generate an optimized feeding strategy C(t):

[0061] C(t)=κ1·Mi(t)+κ2·R(t)+κ3·S(t)+ζ·D(t);

[0062] Among them, C(t) represents the feeding strategy at time t, M i (t) is the output of the multiscale modeling and optimization algorithm, R(t) is the output of the feed rotation model, S(t) is the comprehensive quality index, D(t) is the feeding strategy adjustment value calculated by the deep belief network optimization algorithm, κ1, κ2, κ3 and ζ are the corresponding weight coefficients;

[0063] S83, based on the generated optimized feeding strategy C(t), dynamically adjust the feed composition, feeding amount, environmental control parameters and key feeding conditions at each stage, optimize the nutritional intake and environmental adaptability of laying hens in real time, and optimize the nutritional composition and quality of eggs;

[0064] S84. Build a closed-loop feedback mechanism to further optimize the feeding strategies at each stage and realize intelligent feeding management through continuous monitoring of real-time detection data and dynamic adjustment of feeding strategy C(t) combined with adaptive learning of deep belief network optimization algorithm.

[0065] The beneficial effects of the present invention are:

[0066] The present invention introduces an adaptive microbiome regulation algorithm, collects and analyzes the intestinal microbiome data of laying hens in real time, and dynamically adjusts the feed ingredients to keep the intestinal flora in an optimized state. This innovation effectively improves the nutrient absorption efficiency of laying hens, ensures the optimal synthesis and deposition of key nutrients in eggs, and thus significantly improves the overall nutritional value of eggs. Multiscale modeling and optimization algorithms are used to integrate multiscale physiological data and dynamically optimize the absorption and distribution of specific trace elements in eggs. By regularly adding trace elements and dynamically adjusting their proportions, the uniform deposition of these nutrients in eggs is ensured, and the nutritional uniformity and stability of eggs are enhanced, which is difficult to achieve in the prior art.

[0067] The present invention successfully solves the problem of nutritional imbalance caused by fixed feed ingredients by using an adaptive genetic algorithm to optimize the phased feed rotation strategy. Traditional feed and fermented feed are used alternately, and the feed rotation model is adjusted based on real-time data, so that the eggs can obtain the most suitable nutrients at different production stages, thereby improving the nutritional richness and stability of the eggs. In the mature stage of the eggs, intelligent algorithms are used to regulate environmental factors such as temperature, humidity and light, which promotes the centralized transformation and deposition of nutrient reserves. This intelligent environmental control ensures the ultimate optimization of the nutritional components of the eggs, so that the nutritional value of the eggs reaches the optimal state.

[0068] The present invention introduces multi-spectral non-invasive detection technology, combined with a multimodal data fusion algorithm, to perform real-time, high-precision monitoring and analysis of the nutritional content, eggshell thickness and yolk texture of the eggs. The application of this technology enables us to adjust the feeding strategy in a timely manner to ensure that the nutritional content of the eggs remains within the expected range during the production process, further improving the overall quality of the eggs. Through the deep belief network optimization algorithm, an intelligent closed-loop feedback control system was constructed to achieve dynamic adjustment of the feeding strategy at each stage. This innovation greatly improves the system's ability to adapt to various variables in the production process, ensures the stability and consistency of the nutritional content and quality of the eggs, and significantly improves the level of intelligent management of the entire production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0070] Figure 1 This is a flow chart of a method proposed by the present invention for improving the nutritional value of eggs through a precise feeding strategy;

[0071] Figure 2 A schematic diagram of dynamically optimizing the absorption and distribution of trace elements through multi-scale modeling and optimization algorithms in the present invention;

[0072] Figure 3 The present invention is a flow chart for real-time monitoring of the nutritional composition and structure of stool eggs using multi-spectral non-invasive detection technology. DETAILED DESCRIPTION

[0073] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0074] refer to Figure 1-3 , a method for improving the nutritional value of eggs through precise feeding strategy, comprising the following steps:

[0075] S1. During the laying hen rearing stage, micronutrients with high absorption rate are added to the feed to build up the basic nutritional reserve in the laying hen, providing raw materials for the deposition of nutrients in egg production;

[0076] S2. Before laying hens enter the egg-laying period, based on the basic nutritional reserves in the laying hens, adjust the ratio of organic acids and fermentation products in the feed, optimize protein metabolism, and promote the synthesis of functional nutrients in the yolk;

[0077] S3. In the early stage of egg production, the adaptive microbiome regulation algorithm is used to dynamically adjust the feed composition and optimize the intestinal flora structure by collecting intestinal microbiome data in real time;

[0078] S4. During the production of feces and eggs, specific trace elements are regularly added to the feed based on the optimized intestinal microbiome, and multi-scale modeling and optimization algorithms are used to integrate multi-scale physiological data to dynamically optimize the absorption and distribution of specific trace elements, so that they are evenly deposited in the feces and eggs;

[0079] S5. In the middle and late stages of the production of stool eggs, based on the trace element absorption data and the real-time monitoring results of the nutritional components of stool eggs, a phased feed rotation strategy is implemented, alternating between traditional feed and fermented feed, to further optimize the digestion and absorption of nutrients, making the nutritional components of stool eggs richer and more stable;

[0080] S6. During the mature stage of eggs, adjust the proportion of high-energy ingredients in the feed, introduce formulas rich in bioactive substances and specific trace elements, regulate environmental factors through intelligent algorithms, promote the centralized transformation and deposition of nutrient reserves, and optimize the nutrient composition;

[0081] S7. During the production process of the feces egg, the nutritional content, eggshell thickness and yolk texture of the feces egg are monitored in real time using multi-spectral non-invasive detection technology, and the changes in the internal structure and nutritional content of the feces egg are analyzed to ensure that the nutritional value of the feces egg reaches the expected target;

[0082] S8. Based on the detection data and the optimization results of multi-scale modeling and optimization algorithms, dynamically adjust the feeding strategies at each stage, build a closed-loop feedback mechanism, and realize intelligent feeding management.

[0083] In this implementation, S3 specifically includes:

[0084] S31. In the early stage of egg production, intestinal microbiome samples were collected from laying hens, and high-throughput sequencing technology was used to obtain microbiome data in real time to analyze the composition and dynamic changes of the microbial community;

[0085] S32. Based on the real-time collected intestinal microbiome data, identify the key microbial groups K(t) related to nutrient absorption and metabolism, and dynamically evaluate the impact of the microbiome on nutrient absorption efficiency through an adaptive microbiome regulation algorithm;

[0086] S33. According to the dynamic needs of the key microbial community K(t), adjust the feed ingredients F(t) in real time to optimize the proportion of specific nutrients in the feed to make it more in line with the metabolic needs of the microbial community:

[0087]

[0088] Among them, F(t) represents the feed ingredient adjustment at time t, f0 is the basic feed ingredient, α j For key microorganisms j (t) Corresponding feed composition adjustment coefficient, k j (t) is the relative abundance of the jth key microorganism at time t, γ is the adjustment coefficient of the microbial abundance change rate, Represents microorganism k j (t) is the rate of change at time t, λ is the regulation coefficient related to microbial metabolism, and ω is a small positive number to avoid the denominator being zero;

[0089] S34. Implement the real-time adjusted feed plan F(t), continuously monitor and feedback the changes in the microbiome structure, and ensure that the intestinal flora is always in an optimized state during the egg production process to maximize the absorption and conversion efficiency of nutrients.

[0090] In this implementation, S4 specifically includes:

[0091] S41. During the egg production process, based on the optimization of the intestinal microbiome K(t), multi-scale physiological data of laying hens are regularly collected, including the concentration of trace elements in the blood C i (t), intestinal absorption efficiency η(t), and liver metabolic rate μ(t);

[0092] S42, using multi-scale modeling and optimization algorithms, integrating the collected multi-scale physiological data to establish a dynamic absorption and distribution model Mi (t):

[0093]

[0094] Among them, M i (t) represents the absorption and distribution model of the i-th trace element at time t, C i (τ) represents the concentration of the i-th trace element at time τ, η(τ) is the intestinal absorption efficiency at time τ, μ(τ) is the liver metabolism rate at time τ, ω j is the jth key microorganism k j (t) related weight coefficient;

[0095] S43, based on model M i (t) results, dynamically adjust the proportion of specific trace elements added to the feed E i (t):

[0096]

[0097] Among them, E i (t) represents the amount of trace element added to the feed at time t, E0 is the basic trace element addition amount, γ i is the adjustment coefficient related to the i-th trace element, β i is the adjustment coefficient for smoothing the absorption rate of trace elements, represents the rate of change of the absorption and distribution model of the i-th trace element;

[0098] S44. Implement the adjusted feed plan E i (t), and further optimize the model M by real-time monitoring of the deposition of trace elements in the stool and combining feedback data i (t) and feed formula to ensure uniform deposition of trace elements in the eggs, thereby improving the overall nutritional value of the eggs.

[0099] In this implementation manner, S5 specifically includes:

[0100] S51, in the middle and late stages of egg production, based on the dynamic absorption and distribution model M i (t) and real-time monitoring of egg nutritional data N i (t) Regularly evaluate the absorption of trace elements by laying hens and the distribution of nutrients in eggs;

[0101] S52. Based on the evaluation results, an adaptive genetic algorithm is used to optimize the phased feed rotation strategy and establish a feed rotation model R(t):

[0102] R(t)=α·F trad (t)+β·F fer(t)+δ·θ(t);

[0103] Where R(t) represents the feed rotation strategy at time t, F trad (t) is the proportion of traditional feed used, F fer (t) is the use ratio of fermented feed, α and β are adjustment coefficients related to trace element absorption and nutrient stability, δ is the environmental condition adjustment coefficient, and θ(t) is the environmental adjustment function at time t;

[0104] S53, dynamically adjust the values ​​of α, β and δ to make the feed rotation model R(t) reach the optimal state:

[0105]

[0106] in, represents the rate of change of the nutritional content of eggs at time t, It represents the rate of change of the trace element absorption and distribution model, γ is the adjustment coefficient, which is used to balance the relationship between the change of nutrients and the absorption of trace elements, and ξ is the adjustment coefficient of the influence of environmental conditions on the change of nutrients;

[0107] S54. Implement the optimized feed rotation strategy R(t), continuously monitor and provide feedback, and further optimize the feed rotation model based on real-time data to maintain the optimal nutritional value of eggs.

[0108] In this implementation manner, S6 specifically includes:

[0109] S61. In the mature stage of feces and eggs, based on the feed rotation model R(t) and the feces and eggs nutritional data N i (t), assess the current nutritional requirements of laying hens and adjust the proportion of high-energy ingredients in the feed;

[0110] S62. While adjusting the high-energy ingredients, introduce a formula rich in bioactive substances and specific trace elements:

[0111]

[0112] Where B(t) represents the formula adjustment amount of bioactive substances and trace elements at time t, B0 is the basic formula amount, λ is the adjustment coefficient, μ j is the weight coefficient associated with the jth trace element, K j (t) represents the content of key trace elements at time t, and ω is a small positive number to avoid the denominator being zero;

[0113] S63. Optimize the absorption and deposition of nutrients by regulating environmental factors θ(t), including light, temperature, humidity, etc., through intelligent algorithms:

[0114]

[0115] Among them, θ(t) is the environmental control function at time t, θ0 is the basic environmental control parameter, ζ k is the kth environmental factor ψ k (t) The relevant weight coefficient, ψ k (t) represents the environmental factor value at time t;

[0116] S64. Implement adjusted feed formula and environmental control plan, continuously monitor the centralized transformation and deposition of nutrient reserves in laying hens, and further optimize H(t), B(t) and θ(t) based on feedback data to achieve the best nutritional content of eggs.

[0117] In this implementation manner, the S7 specifically includes:

[0118] S71. In the process of producing the feces egg, a multi-spectral non-invasive detection technology is used based on the environmental control parameter θ(t) and the feces egg nutritional data N i (t), real-time collection of the nutritional content of the eggs N i (t), eggshell thickness T s (t) and egg yolk texture Y d (t) data, the multi-spectral detection technology combines near-infrared spectroscopy, Raman spectroscopy and ultrasonic imaging technology to achieve high-precision capture of the external and internal multi-dimensional information of the feces;

[0119] S72, input the collected multi-spectral data into the multi-modal data fusion algorithm, and combine the environmental control parameter θ(t) and the feed formula F(t) to conduct a comprehensive analysis of the changes in the internal structure and nutritional components of the eggs:

[0120]

[0121] Among them, S(t) represents the comprehensive quality index of eggs laid at time t, T s (t) is the eggshell thickness at time t, Y d (t) is the yolk texture at time t, N i (t) is the content of the ith nutrient at time t, α, β, γ i are the weight coefficients of the corresponding indicators, δ is the fusion adjustment coefficient, and f(θ(t), F(t)) is the fusion function of environmental control parameters and feed formula;

[0122] S73. Based on the comprehensive analysis result S(t), dynamically adjust the detection frequency and parameter settings of the multi-spectrum detection technology, combine the real-time updated environmental control parameters θ(t) and feed formula F(t), and keep the changes in the internal structure and nutritional components of the eggs within the expected range through the feedback mechanism;

[0123] S74. Combine the test data and analysis results to optimize the parameter configuration of the multi-spectrum non-invasive detection technology so that the nutritional content and structure of the stool eggs reach the expected goals.

[0124] In this implementation manner, S8 specifically includes:

[0125] S81, based on the stool data S(t) collected using multi-spectral non-invasive detection technology, combined with multi-scale modeling and optimization algorithm M i (t) and the optimization results of the feed rotation model R(t), and the key feeding parameters of laying hens at different physiological stages are obtained in real time;

[0126] S82. Using the deep belief network optimization algorithm, key feeding parameters are processed to dynamically generate an optimized feeding strategy C(t):

[0127] C(t)=κ1·Mi(t)+κ2·R(t)+κ3·S(t)+ζ·D(t);

[0128] Among them, C(t) represents the feeding strategy at time t, M i (t) is the output of the multiscale modeling and optimization algorithm, R(t) is the output of the feed rotation model, S(t) is the comprehensive quality index, D(t) is the feeding strategy adjustment value calculated by the deep belief network optimization algorithm, κ1, κ2, κ3 and ζ are the corresponding weight coefficients;

[0129] S83, based on the generated optimized feeding strategy C(t), dynamically adjust the feed composition, feeding amount, environmental control parameters and key feeding conditions at each stage, optimize the nutritional intake and environmental adaptability of laying hens in real time, and optimize the nutritional composition and quality of eggs;

[0130] S84. Build a closed-loop feedback mechanism to further optimize the feeding strategies at each stage and realize intelligent feeding management through continuous monitoring of real-time detection data and dynamic adjustment of feeding strategy C(t) combined with adaptive learning of deep belief network optimization algorithm.

[0131] Embodiment 1:

[0132] In order to verify the feasibility of the present invention in practice, we applied the present invention to a large poultry farm and conducted an experiment to improve the nutritional value of eggs through precision feeding strategies. The farm has more than 10,000 laying hens and plans to improve the nutritional content and market competitiveness of eggs through optimized feeding management. The main goal of the experiment is to achieve a significant improvement in the nutritional value of eggs by dynamically adjusting feed ingredients, optimizing microbiome structure, rationally controlling the absorption and distribution of trace elements, and using intelligent management technology.

[0133] First, during the laying hen rearing stage, the farm introduced micronutrients with high absorption rates as additives in the feed. This strategy aims to establish a sufficient basic nutritional reserve for laying hens to ensure the deposition of nutrients in subsequent egg production. The specific operation is to add supplements rich in trace elements such as calcium, magnesium, and phosphorus to the feed in proportion, and adjust different proportions at different growth stages of laying hens. During the 60-day rearing period, it was observed that the overall health of laying hens improved significantly, their weight gained evenly, and their basal metabolic rate increased by about 15%, laying a good foundation for subsequent nutritional reserves.

[0134] As laying hens enter the egg-laying period, the experimental team begins to apply an adaptive microbiome regulation algorithm to dynamically adjust feed ingredients by collecting intestinal microbiome data of laying hens in real time to optimize the intestinal flora structure. Through high-throughput sequencing technology, data is collected from laying hens' intestinal samples every week, and algorithms are used to analyze the impact of key microbial communities on nutrient absorption efficiency. During the 30-day initial egg-laying stage, it was found that the proportion of key beneficial bacteria in the intestinal flora, such as Lactobacillus and Bifidobacterium, increased significantly, the feed conversion rate increased by more than 10%, and the protein absorption efficiency of laying hens was significantly improved, with the protein content in eggs increasing by an average of 8%.

[0135] During the production of eggs, specific trace elements are regularly added to the feed based on the optimized intestinal microbiota, and the multi-scale physiological data of laying hens are integrated through multi-scale modeling and optimization algorithms to dynamically optimize the absorption and distribution of these trace elements. The farm collects blood samples from laying hens every two weeks to analyze indicators such as trace element concentration, intestinal absorption efficiency, and liver metabolic rate, and optimizes them through modeling algorithms. Experimental data show that after adding optimized trace elements such as zinc, iron, and selenium, the content of these elements in eggs is more uniform, and the eggshell thickness and yolk color have been significantly improved. The eggshell thickness has increased by an average of 7%, and the vitamin D content in the yolk has increased by 12%.

[0136] In the middle and late stages of the production of the eggs, the experimental team implemented a phased feed rotation strategy, alternating between traditional feed and fermented feed to further optimize the digestion and absorption of nutrients. Based on the real-time monitoring of trace element absorption data and the analysis results of the nutritional composition of the eggs, the feed formula was adjusted every 10 days to make the nutritional composition of the eggs richer and more stable. At this stage, the nutritional composition of the eggs was further improved, especially the protein and fat content increased by 9% and 6% respectively, and the eggshell breakage rate decreased by about 5%.

[0137] In the egg maturation stage, intelligent algorithms are introduced to precisely control environmental factors such as light, temperature and humidity to promote the centralized transformation and deposition of nutrient reserves in laying hens. The specific operation is to install an environmental monitoring system in the laying hen house, and automatically adjust the lighting time, temperature and humidity through intelligent algorithms. Data shows that after intelligent environmental control, the egg production rate of laying hens has increased by about 12%, and the overall nutritional content of eggs has reached the expected target.

[0138] To further ensure the high quality of the eggs, the test team used multi-spectral non-invasive detection technology to monitor the nutritional content, eggshell thickness and yolk texture of the eggs in real time. Through near-infrared spectroscopy and Raman spectroscopy technology, combined with multimodal data fusion algorithms, a comprehensive analysis of the internal structure and nutritional changes of the eggs was conducted. During the 60-day test period, the eggshell thickness of the eggs was stabilized at about 0.38 mm, the texture uniformity of the yolk increased by about 15%, and the key nutrients in the yolk, such as Omega-3 fatty acids, increased by 10%. Through the deep belief network optimization algorithm, the test team built a closed-loop feedback control system to dynamically adjust the feeding strategy at each stage. The combination of real-time detection data and feeding strategies enables farms to continuously optimize the nutritional content and quality of eggs during the production process. Data show that after adopting this system, the overall feeding efficiency of the farm has increased by about 20%, and the market price of eggs has also increased by more than 15%.

[0139] Table 1 Summary of key data at each stage of egg production

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[0141]

[0142] It can be seen from the above key data summary table that the health and nutritional status of laying hens have been significantly improved after the implementation of the precision feeding strategy. The weight gain rate of laying hens reached 12%, and the basal metabolic rate increased by 15%. These data show that the overall health of laying hens has been significantly improved, laying a solid foundation for the subsequent optimization of the nutritional components of eggs. At the same time, the optimization effect of intestinal microbiota is also very significant. The proportion of lactobacilli increased by 20% and the proportion of bifidobacteria increased by 17%, which directly promoted the improvement of feed conversion rate and increased the protein content of eggs by 8%. The nutritional components and structure of eggs have also been optimized. With the implementation of optimization measures, the thickness of eggshells increased by 7%, the content of vitamin D in egg yolks increased by 12%, and the content of Omega-3 fatty acids increased by 10%. These data show that the nutritional value of eggs has been significantly improved by optimizing the absorption and distribution of trace elements. Intelligent management also has a positive impact on production efficiency. Through the intelligent algorithm to regulate environmental factors, the egg production rate increased by 12%, the feeding efficiency increased by 20%, and the uniformity of egg yolk texture increased by 15%. These results show that the optimization effect of intelligent management on the production process is very significant. The improvement in economic benefits is also obvious. The optimized feeding strategy has increased the market price of feces eggs by 15%, while reducing production costs. This not only improves the market competitiveness of feces eggs, but also significantly improves the overall economic benefits of the farm. Through this series of optimization measures, the present invention achieves the improvement of production efficiency and market competitiveness while improving the nutritional value of feces eggs, achieving the expected beneficial effects.

[0143] In summary, the present invention proposes a method for improving the nutritional value of eggs through precise feeding strategies, and innovates and improves the deficiencies of existing poultry farming technology in terms of nutrition control and intelligent management. By establishing basic nutritional reserves in the laying hen rearing stage, dynamically adjusting feed ingredients, optimizing the intestinal microbiome structure, and combining multi-scale modeling and optimization algorithms, intelligent environmental regulation and deep belief network optimization algorithms, the refined management of the entire process of egg production is achieved. The method introduces innovative technologies in multiple links, such as adaptive microbiome regulation algorithms, multi-spectrum non-invasive detection technologies and closed-loop feedback control systems, which not only improves the nutritional content and uniformity of eggs, but also significantly improves production efficiency and the market competitiveness of products. By implementing these precise feeding strategies, the present invention effectively solves the problems of inaccurate nutrition control and unintelligent feeding management in the prior art, greatly improves the overall quality of eggs, enhances economic benefits, and promotes the sustainable development of the poultry farming industry.

[0144] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for improving the nutritional value of eggs through precise feeding strategy, characterized in that: The steps include: S1. During the laying hen rearing stage, micronutrients with high absorption rate are added to the feed to build up the basic nutritional reserve in the laying hen, providing raw materials for the deposition of nutrients in egg production; S2. Before laying hens enter the egg-laying period, based on the basic nutritional reserves in the laying hens, adjust the ratio of organic acids and fermentation products in the feed, optimize protein metabolism, and promote the synthesis of functional nutrients in the yolk; S3. In the early stage of egg production, the adaptive microbiome regulation algorithm is used to dynamically adjust the feed composition and optimize the intestinal flora structure by collecting intestinal microbiome data in real time; S4. During the production of feces and eggs, specific trace elements are regularly added to the feed based on the optimized intestinal microbiome, and multi-scale modeling and optimization algorithms are used to integrate multi-scale physiological data to dynamically optimize the absorption and distribution of specific trace elements, so that they are evenly deposited in the feces and eggs; S5. In the middle and late stages of egg production, based on the trace element absorption data and real-time monitoring results of egg nutrients, a phased feed rotation strategy is implemented, alternating between traditional feed and fermented feed to further optimize nutrient digestion and absorption; S6. During the mature stage of eggs, adjust the proportion of high-energy ingredients in the feed, introduce formulas rich in bioactive substances and specific trace elements, regulate environmental factors through intelligent algorithms, promote the centralized transformation and deposition of nutrient reserves, and optimize the nutrient composition; S7. During the production process of the fecal egg, the nutritional content, eggshell thickness and yolk texture of the fecal egg are monitored in real time using multi-spectral non-invasive detection technology, and the changes in the internal structure and nutritional content of the fecal egg are analyzed; S8. Based on the detection data and the optimization results of multi-scale modeling and optimization algorithms, dynamically adjust the feeding strategies at each stage, build a closed-loop feedback mechanism, and realize intelligent feeding management.

2. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S3 specifically includes: S31. In the early stage of egg production, intestinal microbiome samples were collected from laying hens, and high-throughput sequencing technology was used to obtain microbiome data in real time to analyze the composition and dynamic changes of the microbial community; S32. Based on the real-time collected intestinal microbiome data, identify the key microbial groups K(t) related to nutrient absorption and metabolism, and dynamically evaluate the impact of the microbiome on nutrient absorption efficiency through an adaptive microbiome regulation algorithm; S33. According to the dynamic needs of the key microbial community K(t), the feed ingredients F(t) are adjusted in real time to optimize the proportion of specific nutrients in the feed to make it more in line with the metabolic needs of the microbial community: Among them, F(t) represents the feed ingredient adjustment at time t, f0 is the basic feed ingredient, α j For key microorganisms j (t) Corresponding feed composition adjustment coefficient, k j (t) is the relative abundance of the jth key microorganism at time t, γ is the adjustment coefficient of the microbial abundance change rate, Represents microorganism k j (t) is the rate of change at time t, λ is the regulation coefficient related to microbial metabolism, and ∈ is a small positive number to avoid the denominator being zero; S34. Implement the real-time adjusted feed plan F(t), continuously monitor and provide feedback on changes in the microbiome structure, so that the intestinal flora is always in an optimized state during the egg production process.

3. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S4 specifically includes: S41. During the egg production process, based on the optimization of the intestinal microbiome K(t), multi-scale physiological data of laying hens are regularly collected, including the concentration of trace elements in the blood C i (t), intestinal absorption efficiency η(t), and liver metabolic rate μ(t); S42, using multi-scale modeling and optimization algorithms, integrating the collected multi-scale physiological data to establish a dynamic absorption and distribution model M i (t): Among them, M i (t) represents the absorption and distribution model of the i-th trace element at time t, C i (τ) represents the concentration of the i-th trace element at time τ, η(τ) is the intestinal absorption efficiency at time τ, μ(τ) is the liver metabolism rate at time τ, ω j is the jth key microorganism k j (t) related weight coefficient; S43, based on model M i (t) results, dynamically adjust the proportion of specific trace elements added to the feed E i (t): Among them, E i (t) represents the amount of trace element added to the feed at time t, E0 is the basic trace element addition amount, γ i is the adjustment coefficient related to the i-th trace element, β i is the adjustment coefficient for smoothing the absorption rate of trace elements, represents the rate of change of the absorption and distribution model of the i-th trace element; S44. Implement the adjusted feed plan E i (t), and further optimize the model M by real-time monitoring of the deposition of trace elements in the stool and combining feedback data i (t) and feed formulation to ensure uniform deposition of trace elements in the eggs.

4. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S5 specifically includes: S51, in the middle and late stages of egg production, based on the dynamic absorption and distribution model M i (t) and real-time monitoring of egg nutritional data N i (t) Regularly evaluate the absorption of trace elements by laying hens and the distribution of nutrients in eggs; S52. Based on the evaluation results, an adaptive genetic algorithm is used to optimize the phased feed rotation strategy and establish a feed rotation model R(t): R(t)=α·F trad (t)+β·F fer (t)+δ·θ(t); Where R(t) represents the feed rotation strategy at time t, F trad (t) is the proportion of traditional feed used, F fer (t) is the use ratio of fermented feed, α and β are adjustment coefficients related to trace element absorption and nutrient stability, δ is the environmental condition adjustment coefficient, and θ(t) is the environmental adjustment function at time t; S53, dynamically adjust the values ​​of α, β and δ to make the feed rotation model R(t) reach the optimal state: in, represents the rate of change of the nutritional content of eggs at time t, represents the rate of change of the trace element absorption and distribution model, γ is the adjustment coefficient, and ξ is the adjustment coefficient of the effect of environmental conditions on the change of nutrient components; S54. Implement the optimized feed rotation strategy R(t), continuously monitor and provide feedback, and further optimize the feed rotation model based on real-time data.

5. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S6 specifically includes: S61. In the mature stage of feces and eggs, based on the feed rotation model R(t) and the feces and eggs nutritional data N i (t), assess the current nutritional requirements of laying hens and adjust the proportion of high-energy ingredients in the feed; S62. While adjusting the high-energy ingredients, introduce a formula rich in bioactive substances and specific trace elements: Where B(t) represents the formula adjustment amount of bioactive substances and trace elements at time t, B0 is the basic formula amount, λ is the adjustment coefficient, μ j is the weight coefficient associated with the jth trace element, K j (t) represents the content of key trace elements at time t, ∈ is a small positive number to avoid the denominator being zero; S63. Regulate environmental factors θ(t), including light, temperature, and humidity, through intelligent algorithms to optimize the absorption and deposition of nutrients: Among them, θ(t) is the environmental control function at time t, θ0 is the basic environmental control parameter, ζ k is the kth environmental factor ψ k (t) The relevant weight coefficient, ψ k (t) represents the environmental factor value at time t; S64. Implement adjusted feed formula and environmental control plan, continuously monitor the centralized transformation and deposition of nutrient reserves in laying hens, and further optimize H(t), B(t) and θ(t) based on feedback data to achieve the best nutritional content of eggs.

6. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S7 specifically includes: S71. In the process of producing the feces egg, a multi-spectral non-invasive detection technology is used based on the environmental control parameter θ(t) and the feces egg nutritional data N i (t), real-time collection of the nutritional content of the eggs N i (t), eggshell thickness T s (t) and egg yolk texture Y d (t) data, the multi-spectral detection technology combines near-infrared spectroscopy, Raman spectroscopy and ultrasonic imaging technology to achieve high-precision capture of the external and internal multi-dimensional information of the feces; S72, input the collected multi-spectral data into the multi-modal data fusion algorithm, and combine the environmental control parameter θ(t) and the feed formula F(t) to conduct a comprehensive analysis of the changes in the internal structure and nutritional components of the eggs: Among them, S(t) represents the comprehensive quality index of eggs laid at time t, T s (t) is the eggshell thickness at time t, Y d (t) is the yolk texture at time t, N i (t) is the content of the ith nutrient at time t, α, β, γ i are the weight coefficients of the corresponding indicators, δ is the fusion adjustment coefficient, and f(θ(t), F(t)) is the fusion function of environmental control parameters and feed formula; S73. Based on the comprehensive analysis result S(t), dynamically adjust the detection frequency and parameter settings of the multi-spectrum detection technology, combine the real-time updated environmental control parameters θ(t) and feed formula F(t), and keep the changes in the internal structure and nutritional components of the eggs within the expected range through the feedback mechanism; S74. Combine the test data and analysis results to optimize the parameter configuration of the multi-spectrum non-invasive detection technology so that the nutritional content and structure of the stool eggs reach the expected goals.

7. The method for improving the nutritional value of eggs through precise feeding strategy according to claim 1, characterized in that: The S8 specifically includes: S81, based on the stool data S(t) collected using multi-spectral non-invasive detection technology, combined with multi-scale modeling and optimization algorithm M i (t) and the optimization results of the feed rotation model R(t), and the key feeding parameters of laying hens at different physiological stages are obtained in real time; S82. Using the deep belief network optimization algorithm, key feeding parameters are processed to dynamically generate an optimized feeding strategy C(t): C(t)=κ1·M i (t)+κ2·R(t)+κ3·S(t)+ζ·D(t); Among them, C(t) represents the feeding strategy at time t, M i (t) is the output of the multiscale modeling and optimization algorithm, R(t) is the output of the feed rotation model, S(t) is the comprehensive quality index, D(t) is the feeding strategy adjustment value calculated by the deep belief network optimization algorithm, κ1, κ2, κ3 and ζ are the corresponding weight coefficients; S83, based on the generated optimized feeding strategy C(t), dynamically adjust the feed composition, feeding amount, environmental control parameters and key feeding conditions at each stage, optimize the nutritional intake and environmental adaptability of laying hens in real time, and optimize the nutritional composition and quality of eggs; S84. Build a closed-loop feedback mechanism to optimize the feeding strategies at each stage and realize intelligent feeding management through continuous monitoring of real-time detection data and dynamic adjustment of feeding strategy C(t) combined with adaptive learning of deep belief network optimization algorithm.

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