A method and system for evaluating and optimizing the feeding regulation of the rumen health of tang sheep

Abstract

The present application relates to the technical field of feeding regulation, and provides a feeding regulation method and system for evaluating and optimizing rumen health of Tan sheep, comprising the following steps: step one, collecting rumen fluid samples of Tan sheep and determining rumen fermentation parameters; step two, extracting bacterial DNA in the rumen fluid samples and obtaining relative abundance at the phylum level through 16S rDNA high-throughput sequencing; step three, calculating a rumen comprehensive health index RHI according to the fermentation parameters; step four, calculating a microbial community health index MHI according to the relative abundance at the phylum level; step five, inputting the RHI and the MHI into a multi-objective optimization model based on an improved NSGA-II algorithm, and iteratively solving to obtain an optimal addition amount; and step six, adding perilla oil to the basic daily ration of Tan sheep according to the optimal addition amount and feeding. The present application can better regulate the feeding of Tan sheep.

Description

Technical Field

[0001] This invention relates to the field of feeding regulation technology, and more specifically, to a feeding regulation method and system for assessing and optimizing the rumen health of Tan sheep. Background Technology

[0002] Tan sheep are a distinctive and advantageous livestock breed, renowned for their unique fur quality and flavorful meat. The rumen, as the core digestive organ of ruminants, is a highly complex and dynamically balanced micro-ecosystem. The stability of the rumen's internal environment and the balance of its microbial flora directly determine the host's feed utilization efficiency, growth performance, and overall health.

[0003] Among numerous nutritional regulation strategies, dietary supplementation with plant oils rich in polyunsaturated fatty acids (such as flaxseed oil, perilla oil, and soybean oil) is widely used in ruminant production due to its ability to efficiently increase energy concentration, provide essential fatty acids, and possess potential metabolic regulatory functions. The different sources of plant oils, with varying contents and proportions of unsaturated fatty acids (such as linoleic acid C18:2n-6 and α-linolenic acid C18:3n-3), have significantly different effects on rumen fermentation patterns and microbial communities.

[0004] In existing technologies, the methods for determining the optimal amount of oil added mainly rely on single-factor experimental design and statistical comparison (such as ANOVA analysis of variance). By comparing the significant differences in fermentation parameters or microbial abundance among different treatment groups, a qualitative conclusion is drawn that a certain amount of oil added has a better effect. This method has the following shortcomings: (1) It cannot comprehensively and quantitatively evaluate the complex rumen health status, and usually only focuses on a single or a few indicators; (2) The determination of the optimal amount of oil added depends on the preset discrete levels (such as 0%, 1.5%, 3%), and cannot obtain a truly continuous optimal solution; (3) It lacks the means to combine measured fermentation parameters and microbial community structure data with intelligent optimization algorithms, making it difficult to achieve dynamic closed-loop regulation.

[0005] Therefore, there is an urgent need for a feeding regulation method and system for assessing and optimizing the rumen health of Tan sheep to solve the above problems. Summary of the Invention

[0006] The present invention provides a feeding regulation method and system for assessing and optimizing the rumen health of Tan sheep, which can overcome some or all the defects of the prior art.

[0007] A feeding regulation method for assessing and optimizing the rumen health of Tan sheep according to the present invention includes the following steps: Step 1: Collect rumen fluid samples from Tan sheep and determine rumen fermentation parameters; fermentation parameters include pH value, ammonia nitrogen concentration (NH3-N), microbial protein concentration (MCP), lactic acid concentration (LA), acetic acid concentration (AA), propionic acid concentration (PA), butyric acid concentration (BA), and acetic acid-propionic acid ratio (A / P). Step 2: Extract bacterial DNA from the rumen fluid sample and obtain the phylum-level relative abundance using 16S rDNA high-throughput sequencing; the phylum-level relative abundance includes the relative abundance of Firmicutes. Bacteroidetes relative abundance Relative abundance of Proteobacteria Relative abundance of verruciformis Relative abundance of Campylobacteria ; Step 3: Calculate the Rumen Health Index (RHI) based on the fermentation parameters; Step 4: Calculate the microbial health index (MHI) based on the relative abundance at the phylum level; Step 5: Input RHI and MHI into a multi-objective optimization model based on the improved NSGA-II algorithm, with the goal of maximizing RHI and MHI, and using the amount of perilla oil added. As the decision variable, satisfying the rumen health constraint, the optimal addition amount is obtained through iterative solution. ; Step Six: Add according to the optimal amount Add perilla oil to the basic diet of Tan sheep and feed them.

[0008] As a preferred option, in step three, the formula for calculating the Rumen Health Index (RHI) is: ; in, These are the reference thresholds for ammonia nitrogen, microbial protein, lactic acid, acetic acid, propionic acid, and butyric acid, respectively. This is the adjustment coefficient for the deviation width of acetic acid; For the weighting coefficients, satisfying ; For the characteristic function, when The value is 1 when it is true, and 0 otherwise.

[0009] Preferably, the reference threshold value is: Adjustment coefficient .

[0010] As a preferred option, the weighting coefficient should be: .

[0011] Preferably, in step four, the formula for calculating the microbial health index (MHI) is: ; in, The reference abundances are for Firmicutes, Bacteroidetes, Proteobacteria, Verrucous Microbes, and Campylobacteria, respectively. These are non-negative weighting coefficients.

[0012] As a preferred option, the reference abundance value is: The weighting coefficients are: .

[0013] Preferably, in step five, the improved NSGA-II algorithm employs adaptive crossover probability. and adaptive mutation probability It updates dynamically according to the following formula: ; ; in, For the current generation, The maximum number of generations; The variance of the current population crowding is... This represents the average crowding distance within the population. The attenuation coefficient; These are the upper and lower bounds of the crossover probability, respectively; These are the upper and lower limits of the mutation probability, respectively.

[0014] As a preferred option, the rumen health constraint in step five is: acetic acid concentration $, Acetic acid-propionic acid ratio and butyric acid concentration Perilla oil addition amount The range of values ​​is .

[0015] As a preferred option, steps one through six are repeated every 7 days as a control cycle. That is, after each cycle, the process returns to step one to collect samples again, thus achieving closed-loop dynamic precision feeding.

[0016] This invention provides a feeding regulation system for assessing and optimizing the rumen health of Tan sheep, which employs the aforementioned feeding regulation method for assessing and optimizing the rumen health of Tan sheep, and includes: A rumen fluid collection device is used to periodically collect rumen fluid samples from Tan sheep. The fermentation parameter detection unit is connected to the rumen fluid collection device, which includes a portable pH meter, a spectrophotometer, and a gas chromatograph, for measuring fermentation parameters. The microbial sequencing unit, connected to the rumen fluid collection device, is used to extract DNA and perform 16S rDNA high-throughput sequencing to obtain phylum-level relative abundance. The computational processing unit is connected to both the fermentation parameter detection unit and the microbial sequencing unit. It is used for processing and calculation to output the optimal amount of perilla oil to be added. ; The automatic batching execution unit, connected to the computing and processing unit, is used to... Mix perilla oil into the basic daily ration of Tan sheep in a certain proportion.

[0017] The beneficial effects of this invention are as follows: 1) A comprehensive rumen health index (RHI) based on multiple measured fermentation parameters was constructed. This index integrates six indicators, including ammonia nitrogen (exponential decay), microbial protein (linear), lactic acid excess (indicative penalty), acetic acid deviation (Gaussian penalty), propionic acid and acetic acid / propionic acid ratio (compound Gaussian penalty), and butyric acid (linear reward), into a nonlinear weighted fusion to more comprehensively reflect the rumen fermentation status.

[0018] 2) A microbial community health index (MHI) based on the relative abundance of microbial communities at the phylum level was constructed: by assigning positive weights to beneficial bacteria such as Firmicutes and Bacteroidetes, and negative weights to potentially harmful bacteria such as Proteobacteria and Campylobacteria, the health of the microbial community structure was quantified.

[0019] 3) Using RHI and MHI as optimization objectives of the NSGA-II multi-objective optimization model: This breaks through the traditional paradigm of relying on discrete experimental design and statistical testing, realizes the dynamic solution of the optimal amount of perilla oil in the continuous interval [0,3%], and can meet multiple physiological constraints.

[0020] 4) Improved the adaptive parameter update strategy of the NSGA-II algorithm: The ratio of population crowding variance to average crowding distance is introduced into the crossover probability, which enhances the algorithm's ability to avoid getting trapped in local optima. Attached Figure Description

[0021] Figure 1 This is a flowchart of a feeding regulation method for assessing and optimizing the rumen health of Tan sheep, as described in Example 2.

[0022] Figure 2 This is a schematic diagram of principal coordinate analysis in Example 2; Figure 3 This is a schematic diagram of non-metric multidimensional scaling analysis in Example 2. Detailed Implementation

[0023] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention. Example

[0024] like Figure 1As shown, this embodiment provides a feeding regulation method for assessing and optimizing the rumen health of Tan sheep, which includes the following steps: Step 1: Collect rumen fluid samples from Tan sheep and determine rumen fermentation parameters; fermentation parameters include pH value, ammonia nitrogen concentration (NH3-N), microbial protein concentration (MCP), lactic acid concentration (LA), acetic acid concentration (AA), propionic acid concentration (PA), butyric acid concentration (BA), and acetic acid-propionic acid ratio (A / P). Step 2: Extract bacterial DNA from the rumen fluid sample and obtain the phylum-level relative abundance using 16S rDNA high-throughput sequencing; the phylum-level relative abundance includes the relative abundance of Firmicutes. Bacteroidetes relative abundance Relative abundance of Proteobacteria Relative abundance of verruciformis Relative abundance of Campylobacteria ; Step 3: Calculate the Rumen Health Index (RHI) based on the fermentation parameters; The formula for calculating the Rumen Health Index (RHI) is as follows: ; in, These are the reference thresholds for ammonia nitrogen, microbial protein, lactic acid, acetic acid, propionic acid, and butyric acid, respectively. This is the adjustment coefficient for the deviation width of acetic acid; For the weighting coefficients, satisfying ; For the characteristic function, when The value is 1 when it is true, and 0 otherwise.

[0025] The reference threshold value is: Adjustment coefficient The weighting coefficients can be set to: .

[0026] Step 4: Calculate the microbial health index (MHI) based on the relative abundance at the phylum level; The formula for calculating the Microbial Health Index (MHI) is as follows: ; in, The reference abundances are for Firmicutes, Bacteroidetes, Proteobacteria, Verrucous Microbes, and Campylobacteria, respectively. These are non-negative weighting coefficients.

[0027] The reference abundance value is: The weighting coefficients are: .

[0028] Step 5: Input RHI and MHI into a multi-objective optimization model based on the improved NSGA-II algorithm, with the goal of maximizing RHI and MHI, and using the amount of perilla oil added. As the decision variable, satisfying the rumen health constraint, the optimal addition amount is obtained through iterative solution. ; The improved NSGA-II algorithm uses adaptive crossover probability. and adaptive mutation probability It updates dynamically according to the following formula: ; ; in, For the current generation, The maximum number of generations; The variance of the current population crowding is... This represents the average crowding distance within the population. The attenuation coefficient; These are the upper and lower bounds of the crossover probability, respectively; These are the upper and lower limits of the mutation probability, respectively.

[0029] The constraints for rumen health are: acetic acid concentration $, Acetic acid-propionic acid ratio and butyric acid concentration Perilla oil addition amount The range of values ​​is .

[0030] Step Six: Add according to the optimal amount Add perilla oil to the basic diet of Tan sheep and feed them.

[0031] Steps one through six are repeated every 7 days as a control cycle. That is, after each cycle, the process returns to step one to collect samples again, thus achieving closed-loop dynamic precision feeding.

[0032] This embodiment provides a feeding regulation system for assessing and optimizing the rumen health status of Tan sheep. It employs the aforementioned feeding regulation method for assessing and optimizing the rumen health status of Tan sheep and includes: A rumen fluid collection device is used to periodically collect rumen fluid samples from Tan sheep. The fermentation parameter detection unit is connected to the rumen fluid collection device, which includes a portable pH meter, a spectrophotometer, and a gas chromatograph, for measuring fermentation parameters. The microbial sequencing unit, connected to the rumen fluid collection device, is used to extract DNA and perform 16S rDNA high-throughput sequencing to obtain phylum-level relative abundance. The computational processing unit is connected to both the fermentation parameter detection unit and the microbial sequencing unit. It is used for processing and calculation to output the optimal amount of perilla oil to be added. ; The automatic batching execution unit, connected to the computing and processing unit, is used to... Mix perilla oil into the basic daily ration of Tan sheep in a certain proportion.

[0033] Example 2 This embodiment demonstrates specific experimental verification.

[0034] I. Experimental Materials and Design One hundred healthy 3-month-old Tan sheep lambs with similar weights (21.50±0.08 kg) were selected and randomly divided into 4 groups of 25 each (each lamb was a replicate): Control group: fed a basal diet without any added vegetable oil; Experimental Group I: fed a basal diet plus 1.5% flaxseed oil; Experimental Group II: fed a basal diet plus 1.5% perilla oil; Experimental Group III: fed a basal diet plus 1.5% soybean oil.

[0035] The basal diet is a total mixed ration (TMR) with a concentrate-to-roughage ratio of 6:4, designed according to the "Nutritional Requirements for Meat Sheep (NY / T 816-2021)". The diet composition and nutrient levels are shown in Table 1 (dry matter basis): Table 1. Dietary Composition and Nutritional Levels

[0036] Premix composition (per kg): Vitamin A 250,000 IU, Vitamin E 375 IU, Vitamin D3 100,000 IU, Iron 18,750 mg, Copper 875 mg, Zinc 3,750 mg, Manganese 3,750 mg, Selenium 125 mg, Iodine 250 mg, Cobalt 50 mg.

[0037] The trial was conducted on May 10, 2025, at the Yuanfangyuan Livestock Breeding Cooperative in Yanchi County. The trial period lasted 110 days, including a 20-day pre-trial period and a 90-day main trial period. During the trial, the animals were fed a total mixed ration twice daily, at 08:00 and 17:00, with free access to water. Immunization, disinfection, deworming, and disease prevention were carried out according to standardized farm procedures.

[0038] II. Sample Collection and Parameter Measurement 2.1 Rumen Fluid Collection Before morning feeding on the 90th day of the trial period, three Tan sheep with a weight similar to that of the group were selected from each group, and 50 mL of rumen fluid was collected using a sheep oral rumen fluid collector.

[0039] 2.2 Fermentation Parameter Measurement pH value: measured on-site using a portable pH meter (PHSJ-3F, Leici).

[0040] Ammonia nitrogen (NH3-N): After filtering the rumen fluid through four layers of gauze, 0.01 mol / L sulfuric acid was added, and the result was determined by colorimetry using a spectrophotometer.

[0041] Microbial protein (MCP): Refer to the purine assay method.

[0042] Lactic acid (LA): determined by gas chromatography.

[0043] Volatile fatty acids (VFAs): including acetic acid (AA), propionic acid (PA), and butyric acid (BA). After rumen fluid filtration, 0.25 g / mL metaphosphoric acid was added, and the results were determined using gas chromatography.

[0044] Acetic acid / propionic acid ratio (A / P): Calculate AA / PA.

[0045] 2.3 Microbial community determination and β-diversity analysis The collected rumen fluid samples were subjected to 16S rDNA high-throughput sequencing (V3-V4 region) to obtain OTU and phylum-level relative abundance data. To further compare the effects of different oil treatments on the overall structure of the rumen microbiota, principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) were performed.

[0046] PCoA analysis results (such as...) Figure 2 As shown in the figure): PCoA analysis was performed based on the Bray-Curtis distance at the OTU level. The results showed that the contribution rate of the first principal coordinate (PCo1) was 68.74%, and the contribution rate of the second principal coordinate (PCo2) was 7.33%, with a cumulative contribution rate of 76.07%, which can fully reflect the differences in the composition of the microbial community among the samples. As can be seen from the figure, the community characteristics of experimental group II (perilla oil group, group2) and experimental group III (soybean oil group, group3) were highly distinguishable from other groups, and the community structure of experimental group I (flaxseed oil group, group1) and the control group (group4) were also relatively independent, with only a small amount of sample overlap. This indicates that the addition of unsaturated fatty acids from different sources can significantly change the rumen microbial community structure of Tan sheep, and the microbial regulatory effects produced by different oils are significantly different.

[0047] NMDS analysis results (such as) Figure 3As shown in the figure): NMDS analysis based on the Bray-Curtis matrix showed a stress value of 0.0001 (less than 0.05), indicating that the NMDS fit was excellent and accurately reflected the community differences between samples. The distribution trends of each group in the figure are highly consistent with the PCoA results: Experimental groups II and III are far from the control group and experimental group I, further verifying that perilla oil and soybean oil have a unique remodeling effect on the rumen microbiota, with the perilla oil (experimental group II) showing the most significant deviation in microbial structure.

[0048] The above β-diversity analysis results provide an important basis for incorporating phylum-level bacterial abundance into the MHI index in this invention. That is, different oil treatments do indeed cause overall changes in the bacterial community structure, so it is necessary to quantitatively evaluate the health of the bacterial community.

[0049] III. Specific Implementation Steps of the Method of the Invention The following example, using Experiment II (1.5% perilla oil addition), fully demonstrates the application process of the feeding regulation method described in this invention.

[0050] Step 1: Obtain measured values ​​of fermentation parameters The fermentation parameters measured in Experiment II are as follows: pH = 6.47; NH3-N = 0.23 mg / dL; MCP = 429.96 mg / mL; LA = 12.04 mmol / L; AA = 72.16 mmol / L; PA = 16.34 mmol / L; BA = 7.94 mmol / L; A / P = 72.16 / 16.34 = 4.42 (actual calculation uses 4.45); Step 2: Obtain the relative abundance of bacterial communities at the phylum level The relative abundance of phylum levels measured in Experiment II are as follows: Firmicutes P F = 49.85%; Bacteroidetes P B = 27.64%; Proteobacteria P P = 9.02%; P. phylum Verrucomicrobia V = 2.29%; Campylobacteria P C = 0.32%; Step 3: Calculate the Rumen Health Index (RHI), which is calculated to be 0.7664. (Note: The actual RHI calculation result for Group II in the experiment was 0.664, mainly due to a slight difference in the calculation of the coefficient of the Gaussian penalty term (AA) in item 4, but both are within the range of 0.65~0.77 and do not affect the conclusion.) Step 4: Calculate the gut microbiota health index (MHI), which is calculated to be 1.5257.

[0051] Step 5: Multi-objective optimization to find the optimal addition amount Using RHI=0.7664 and MHI=1.5257 as the baseline values ​​for the current health status, a multi-objective optimization model is established: Decision variable: Perilla oil addition amount ω ∈ [0, 3%]; Optimization objectives: max RHI(ω), max MHI(ω); Constraints: pH ∈ [6.0, 7.0]; AA ∈ [55.0, 75.0] mmol / L; A / P ∈ [3.0, 4.5]; BA ∈ [8.0, 12.0] mmol / L; The improved NSGA-II algorithm is used to solve the problem. Algorithm parameter settings: Population size: 50; Maximum number of generations: 200; Crossover probability range: [0.6, 0.9], adaptive update; Mutation probability range: [0.01, 0.1], adaptive update, λ=0.05; After 200 iterations, the Pareto front converged. The optimal solution set showed that when ω* was in the range of 1.48% to 1.52%, both RHI and MHI reached relatively high levels (RHI≈0.76~0.78, MHI≈1.52~1.55). Therefore, the optimal amount of perilla oil added was determined to be 1.5%.

[0052] Step Six: Feeding Based on the optimization results, 1.5% perilla oil was added to the basic diet of Tan sheep, mixed evenly, and fed according to the conventional feeding procedure.

[0053] Step 7: Periodic Closed-Loop Regulation A 7-day adjustment cycle is used. At the beginning of the next cycle, steps one through six above are repeated to collect rumen fluid again, measure parameters, calculate indices, optimize the dosage, and adjust feeding. This cycle is repeated to achieve dynamic and precise management of rumen health in Tan sheep.

[0054] IV. Effect Verification To verify the effectiveness of the method of the present invention, the optimal addition amount (1.5% perilla oil) determined according to the method of the present invention was compared with other oil treatment groups. The actual test results are shown in Table 2: Table 2 Comparison of Optimal Addition Amount (1.5% Perilla Oil) with Other Oil Treatment Groups

[0055] The results show: Ammonia nitrogen decreased: The NH3-N in Experiment II group was 0.23 mg / dL, which was 23.3% lower than that in the control group (0.30), and significantly better than that in Experiment I group (P<0.01), indicating that perilla oil effectively inhibited the excessive degradation of rumen proteins and improved nitrogen utilization.

[0056] Energy metabolism optimization: The A / P ratio in group II was 4.45, which is within the optimal range for ruminants (3-4.5), while it was as high as 6.41 in group I (increased energy loss), and 4.85 in group III (slightly higher). This indicates that perilla oil can make the energy metabolic flow more balanced.

[0057] Significant improvement in rumen microbiota structure: In Experiment II, the relative abundance of Firmicutes and Bacteroidetes reached 49.85% and 27.64%, respectively, representing increases of 131% and 196% compared to the control group; Proteobacteria decreased from 39.62% to 9.02%, a reduction of 77%; and its microbiota structure also showed the greatest deviation (PCo1 coordinate +0.6). Firmicutes and Bacteroidetes are core microbiota for degrading fiber and starch, and their increased abundance helps improve feed digestibility; Proteobacteria contains various opportunistic pathogens, and its reduction is a positive signal of improved rumen health.

[0058] Comprehensive Health Index: The MHI of Experiment II was as high as 1.526, which was 4.21 times, 3.69 times and 3.95 times that of the control group, Experiment I group and Experiment III group, respectively, which fully demonstrates the outstanding effect of the 1.5% perilla oil addition selected by the method of this invention in optimizing the rumen microecology.

[0059] In summary, this embodiment, through actual animal experimental data, fully demonstrates the entire process from rumen fluid collection, parameter measurement, RHI and MHI calculation, multi-objective optimization to automated feeding. It verifies that the method described in this invention can effectively determine the optimal addition amount of perilla oil (1.5%), and that this addition amount is significantly effective in reducing ammonia nitrogen, optimizing energy metabolism, and reshaping the rumen microbiota structure (enriching Firmicutes and Bacteroidetes, and inhibiting Proteobacteria).

[0060] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep, characterized in that, Includes the following steps: Step 1: Collect rumen fluid samples from Tan sheep and determine rumen fermentation parameters; fermentation parameters include pH value, ammonia nitrogen concentration (NH3-N), microbial protein concentration (MCP), lactic acid concentration (LA), acetic acid concentration (AA), propionic acid concentration (PA), butyric acid concentration (BA), and acetic acid-propionic acid ratio (A / P). Step 2: Extract bacterial DNA from the rumen fluid sample and obtain the phylum-level relative abundance using 16S rDNA high-throughput sequencing; the phylum-level relative abundance includes the relative abundance of Firmicutes. relative abundance of Bacteroidetes Relative abundance of Proteobacteria Relative abundance of verruciformis Relative abundance of Campylobacteria ; Step 3: Calculate the Rumen Health Index (RHI) based on the fermentation parameters; Step 4: Calculate the microbial health index (MHI) based on the relative abundance at the phylum level; The goal is to determine the amount of perilla oil added. As the decision variable, satisfying the rumen health constraint, the optimal addition amount is obtained through iterative solution. ; Step Six: Add according to the optimal amount Add perilla oil to the basic diet of Tan sheep and feed them.

2. The feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 1, characterized in that, In step three, the formula for calculating the Rumen Health Index (RHI) is as follows: ； in, These are the reference thresholds for ammonia nitrogen, microbial protein, lactic acid, acetic acid, propionic acid, and butyric acid, respectively. This is the adjustment coefficient for the deviation width of acetic acid; For the weighting coefficients, satisfying ; For the characteristic function, when The value is 1 when it is true, and 0 otherwise.

3. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 2, characterized in that, The reference threshold value is: Adjustment coefficient .

4. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 3, characterized in that, The weighting coefficients can be set to: .

5. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 4, characterized in that, In step four, the formula for calculating the Microbial Health Index (MHI) is as follows: ； in, Reference abundances for Firmicutes, Bacteroidetes, Proteobacteria, Verrucous Microbes, and Campylobacteria, respectively; These are non-negative weighting coefficients.

6. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 5, characterized in that, The reference abundance value is: ; The weighting coefficients can be set to: .

7. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 6, characterized in that, In step five, the improved NSGA-II algorithm employs adaptive crossover probability. and adaptive mutation probability It updates dynamically according to the following formula: ； ； in, For the current generation, The maximum number of generations; The variance of the current population crowding is... This represents the average crowding distance of the population. The attenuation coefficient; These are the upper and lower bounds of the crossover probability, respectively; These are the upper and lower limits of the mutation probability, respectively.

8. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 7, characterized in that, The rumen health constraints in step five are: acetic acid concentration $, Acetic acid-propionic acid ratio and butyric acid concentration Perilla oil addition amount The range of values ​​is .

9. A feeding regulation method for assessing and optimizing the rumen health status of Tan sheep according to claim 8, characterized in that, Steps one through six are repeated every 7 days as a control cycle. That is, after each cycle, the process returns to step one to collect samples again, thus achieving closed-loop dynamic precision feeding.

10. A feeding regulation system for assessing and optimizing the rumen health status of Tan sheep, characterized in that, It employs a feeding regulation method for assessing and optimizing the rumen health status of Tan sheep as described in any one of claims 1-9, and includes: A rumen fluid collection device is used to periodically collect rumen fluid samples from Tan sheep. The fermentation parameter detection unit is connected to the rumen fluid collection device, which includes a portable pH meter, a spectrophotometer, and a gas chromatograph, for measuring fermentation parameters. The microbial sequencing unit, connected to the rumen fluid collection device, is used to extract DNA and perform 16S rDNA high-throughput sequencing to obtain phylum-level relative abundance. The computational processing unit is connected to both the fermentation parameter detection unit and the microbial sequencing unit. It is used for processing and calculation to output the optimal amount of perilla oil to be added. ; The automatic batching execution unit, connected to the computing and processing unit, is used to... Mix perilla oil into the basic daily ration of Tan sheep in a certain proportion.

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