Kangaroo-derived microorganism targeted cattle stomach methane emission reduction device and method

Abstract

The invention discloses a kangaroo-derived microorganism targeted bovine stomach methane emission reduction device and method. The device comprises a sterile sampling tool, intelligent algorithm regulation and control equipment, a constant-temperature incubator, a microscope, a centrifugal machine, a bioreactor, a high-speed mixer and a gas chromatograph. The method comprises the following steps: collecting a sample from juvenile kangaroo excrement, and separating and purifying microorganisms by using a sterile sampling tool; culturing the separated special microorganisms in a constant-temperature incubator; introducing the cultured microorganisms into a bioreactor for simulating the cattle stomach environment, and observing the interaction between the cultured microorganisms and original methanogens; the method comprises the following steps: uniformly mixing microorganisms with a carrier by using a high-speed mixer to prepare a microbial preparation suitable for feeding, and filling the microbial preparation into a sealed container; a gas chromatograph is used for monitoring the methane emission amount of the cattle, and intelligent algorithm regulation and control equipment is used for timely adjusting a preparation method and a feeding scheme of a microbial preparation; and an efficient, specific and sustainable animal husbandry greenhouse gas emission reduction strategy is realized.

Description

Technical Field

[0001] The present invention relates to a device and method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms, and in particular to a device and method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms based on deep learning. Background Art

[0002] Against the backdrop of global climate change, the livestock industry, as a significant source of greenhouse gas emissions, is particularly important for research and development of emission reduction strategies. Methane, produced during fermentation in cattle stomachs, is a major component of livestock greenhouse gas emissions and contributes significantly to global warming. Therefore, exploring effective methane reduction technologies to mitigate the negative environmental impacts of livestock farming is a hot topic in current research.

[0003] Recent advances in microbial ecology have provided new insights into this issue. Certain microbes have been shown to influence fermentation processes in the cow's stomach, thereby regulating methane production. In particular, microbes isolated from the feces of juvenile kangaroos have been found to possess unique metabolic properties, enabling them to utilize hydrogen to produce acetic acid rather than methane, potentially opening the door to the development of novel strategies for methane reduction.

[0004] To achieve the effective application of this strategy, a series of technical difficulties need to be solved. First of all, how to efficiently and aseptically separate and purify these special microorganisms from kangaroo feces is the first step in technical implementation. Secondly, how to simulate the cow stomach environment under laboratory conditions and study the interaction between these microorganisms and the microorganisms that originally produce methane, as well as their impact on methane production, is the key to technical implementation. In addition, it is necessary to develop a microbial preparation suitable for feeding to ensure that cattle can ingest these microorganisms regularly and quantitatively without affecting their health and production performance. Finally, how to adjust the formula and feeding plan of the microbial preparation in time according to the actual application effect to optimize the emission reduction effect is also an important part of technical implementation that cannot be ignored. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a kangaroo-derived microbial targeted bovine gastric methane emission reduction device and method, aiming to develop an efficient, specific and sustainable livestock greenhouse gas emission reduction strategy to effectively inhibit the production of bovine gastric methane.

[0006] Technical solution: The method of reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to the present invention comprises:

[0007] (1) Collect samples from juvenile kangaroo feces and use sterile sampling tools to isolate and purify microorganisms;

[0008] (2) Cultivate the isolated special microorganisms in a constant temperature incubator;

[0009] (3) Introducing the cultured microorganisms into a bioreactor that simulates the bovine stomach environment to observe their interaction with the original methanogenic microorganisms;

[0010] (4) using a high-speed mixer to uniformly mix the microorganisms and the carrier to prepare a microbial preparation suitable for feeding, and then filling it into a sealed container;

[0011] (5) Use gas chromatographs to monitor methane emissions from cattle, and use intelligent algorithms to control equipment to timely adjust the preparation methods and feeding plans of microbial preparations;

[0012] A device for reducing methane emissions from bovine stomachs using kangaroo-derived microorganisms, comprising: a sterile sampling tool, an intelligent algorithm control device, a constant temperature incubator, a microscope, a centrifuge, a bioreactor, a high-speed mixer, and a gas chromatograph.

[0013] Preferably, the microorganism is a Wallaby Group 1 microorganism, which is repeatedly isolated and purified, and the morphological characteristics of the colony are observed.

[0014] Preferably, the constant temperature incubator monitors temperature and humidity, and the culture environment is carried out under anaerobic conditions to simulate the anaerobic environment in the kangaroo intestine.

[0015] Preferably, the bioreactor is set to a culture temperature, a pH probe is calibrated, and a pH value is set; the high-speed mixer is set to a rotation speed, and after a set time has elapsed, a discharging device is opened to discharge the material, and the carrier is diatomaceous earth and nutrient material.

[0016] Preferably, the intelligent algorithm control device adopts an APO optimization algorithm, including:

[0017] (51) The objective function comprehensively considers the total cost of preparing microbial preparations and implementing emission reduction programs, the methane emission reduction effect, the impact of the microbial preparation feeding program on cattle health and production performance, and the degree of automation and operational convenience of the entire preparation and feeding process;

[0018] (52) By simulating the life habits of the Atlantic puffin, including global search in flight and local exploitation while diving for food, adaptive adjustment of the search strategy is achieved; by introducing the behavioral transition coefficient B and parameter C, a smooth transition between global search and local exploitation is achieved, thereby adopting the most appropriate strategy in different search stages;

[0019] (53) The optimized APO algorithm was used, including but not limited to initializing the population, aerial search, diving predation, population merging, and subsequent aggregation foraging, intensified search, avoiding predators, and population merging again; each stage simulated the different behavioral strategies of the Atlantic puffin, and updated the position of the candidate solution through the position update equation; and the optimal solution was found by simulating natural behavior.

[0020] Preferably, the objective function is:

[0021]

[0022] Among them, E eff (x) is the core emission reduction efficiency item, C(x) is the life cycle cost of the microbial preparation, C max is the preset cost upper limit, γ is the cost sensitivity index, α, β, δ, ó are weights, S sys (x) is the system adaptability term, H dyn (x) is the dynamic operation robustness term.

[0023] Preferably, the APO algorithm in step (52) designs a behavior transition coefficient B, which is as follows:

[0024] B=2*log(1 / rand)*(1-t / T)

[0025] Where rand is a random number in the range (0, 1), t and T are the current and maximum iterations, respectively. In the APO algorithm, B is an adaptive parameter that is dynamically derived based on the ratio of the current to the maximum iterations. It also incorporates randomness to simulate the dynamic randomness of the behavior of the Atlantic puffin.

[0026] Preferably, the flight foraging behavior simulation algorithm of the Arctic puffin achieves a dynamic balance between global exploration and local exploitation; the high frequency of aerial flights in the initial stage corresponds to the algorithm's wide-area exploration of microbial formulations and feeding parameters, and the algorithm randomly samples the potential solution space through the Levy flight equation to find the optimal microbial combination for inhibiting methane production;

[0027] The diving foraging behavior focuses on high potential areas through the diving predation equation, and uses the speed coefficient S to dynamically adjust the parameter accuracy to ensure the directional optimization of the microbial metabolic pathway.

[0028] Preferably, during the air search phase, the position update equation is:

[0029]

[0030] R=round(0.5*(0.05+rand))*α, α~Normal(0,1)

[0031] Where R is a random integer between 1 and N. Indicates that the current i is a candidate solution in the population; is a candidate solution randomly selected from the current population; L(D) is the random number generated by Levy flight; D is the dimension; α is a random number that conforms to the standard normal distribution; rand generates random numbers between 0 and 1.

[0032] Preferably, during the diving predation phase, the position update equation is:

[0033]

[0034] S=tan((rand-0.5)*π)

[0035] in, is the new position of the i-th candidate solution after diving and predation, S is the speed coefficient, and rand generates a random number between 0 and 1.

[0036] In the gathering and foraging stage, the position update equation is:

[0037]

[0038] Where F is the cooperation factor that regulates the predation behavior of Atlantic puffins; F = 0.5; variables r1, r2, and r3 are random integers between 1 and N-1, and is a candidate solution randomly selected from the current population, and r1≠r2≠r3,

[0039] In the enhanced search phase, the position update equation is:

[0040]

[0041] Where T represents the total number of iterations, t represents the current iteration number, and f is the adaptive factor used to adjust the position of the Atlantic puffin in the water;

[0042] In the predator avoidance stage, the position update equation is:

[0043]

[0044] Among them, β is a random number uniformly distributed between 0 and 1; is the new position of the i-th candidate solution after diving and predation;

[0045] The population merging stage equation is described as follows:

[0046]

[0047] in, is the position or state of the i-th candidate solution or individual in the t+1th iteration, which is the new position obtained after the optimization algorithm updates; new is a candidate solution obtained after the update operation; sort is to sort the new population from small to large according to its fitness value, and select the new population.

[0048] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0049] (1) The present invention integrates, but is not limited to, a sterile sampling device, an intelligent algorithm control system, and a constant temperature culture environment to ensure that every step of the microbial separation, purification, culture, and subsequent application is highly accurate and efficient. The application of the intelligent algorithm control system enables dynamic optimization based on real-time data, enabling precise adjustment of the ratio of microbial preparations and feeding strategies, thereby continuously improving emission reduction efficiency. This is not only directly reflected in the significant improvement in methane emission reduction efficiency, but also demonstrates significant advantages in resource optimization and cost control.

[0050] (2) The present invention adopts the APO optimization algorithm, which achieves a dynamic balance between global search and local development by simulating the natural behavioral strategies of the Arctic puffin, significantly improving the efficiency and accuracy of cattle gastric methane emission reduction. The adaptive adjustment mechanism of the APO algorithm can dynamically optimize the formulation of microbial preparations and feeding plans based on real-time monitoring data, ensuring that while maximizing the methane emission reduction effect, the health and production performance of cattle are taken into account. Its global search capability achieves wide-area exploration through the Levy flight equation to avoid premature convergence; its local development capability focuses on high-potential areas through the dive predator equation to improve the accuracy of the solution. In addition, the algorithm further optimizes microbial metabolic pathways and feeding parameters through behavioral strategies such as concentrated foraging, enhanced search, and predator avoidance, strengthens the competitive inhibition relationship between acetogens and methanogens, and ensures the stability of the metabolic environment in the rumen. This dynamic optimization and real-time adjustment capability not only improves emission reduction efficiency, but also reduces operating costs, enhances the robustness and sustainability of the system, and provides an efficient, specific and sustainable technical means for greenhouse gas emission reduction in animal husbandry.

[0051] (3) The collection and utilization of kangaroo-derived microorganisms has minimal environmental impact, perfectly aligning with the concept of green and sustainable development. By systematically monitoring cattle methane emissions, physiological health, and production performance, emission reduction strategies can be flexibly adjusted to ensure that while effectively reducing greenhouse gas emissions, animal welfare and production efficiency are not compromised, achieving a harmonious unity between emission reduction and sustainable development of animal husbandry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a structural schematic diagram of the present invention.

[0053] Figure 2 This is a flow chart of the APO optimization algorithm described in the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0055] The present invention provides a method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms, comprising:

[0056] (1) Collect samples from juvenile kangaroo feces and use sterile sampling tools to isolate and purify microorganisms;

[0057] (2) Cultivate the isolated special microorganisms in a constant temperature incubator;

[0058] (3) Introducing the cultured microorganisms into a bioreactor that simulates the bovine stomach environment to observe their interaction with the original methanogenic microorganisms;

[0059] (4) using a high-speed mixer to uniformly mix the microorganisms and the carrier to prepare a microbial preparation suitable for feeding, and then filling it into a sealed container;

[0060] (5) Use gas chromatographs to monitor methane emissions from cattle, and use intelligent algorithms to control equipment to timely adjust the preparation methods and feeding plans of microbial preparations;

[0061] A device for reducing methane emissions from bovine stomachs using kangaroo-derived microorganisms, comprising: a sterile sampling tool, an intelligent algorithm control device, a constant temperature incubator, a microscope, a centrifuge, a bioreactor, a high-speed mixer, and a gas chromatograph.

[0062] The microorganisms are Wallaby Group 1 (WG-1) microorganisms, which are separated and purified repeatedly, and the morphological characteristics of the colonies are observed, and the individual morphological characteristics are detected by microscopy.

[0063] The constant temperature incubator monitors temperature and humidity, and the culture environment needs to be carried out under anaerobic conditions to simulate the anaerobic environment in the kangaroo intestine.

[0064] The temperature of the bioreactor is set to a suitable culture temperature, the pH probe is calibrated and set to a desired pH value, and samples are taken regularly to analyze the growth of microorganisms and metabolites.

[0065] The high-speed mixer is adjusted to a set speed, and a timer is started after reaching the set speed. After the set time is reached, the discharge device is opened to discharge the material. The carrier is diatomaceous earth and nutrient material.

[0066] The intelligent algorithm control device uses the APO optimization algorithm, and the implementation process is as follows:

[0067] (51) The objective function comprehensively considers the total cost of preparing the microbial preparation and implementing the emission reduction plan, the methane emission reduction effect, the impact of the microbial preparation feeding plan on cattle health and production performance, and the automation and operational convenience of the entire preparation and feeding process. These four aspects are balanced through their respective weight coefficients to ensure that while achieving methane emission reduction, cost-effectiveness, cattle health and production performance, and operational convenience are also taken into account, providing optimization direction for subsequent specific operations.

[0068] (52) The APO optimization algorithm simulates the life habits of the Atlantic puffin, including global search in the air and local development while diving for food, to achieve adaptive adjustment of the search strategy. By introducing the behavior transition coefficient B and parameter C, where parameter c is a preset threshold used to determine the current search stage of the algorithm, usually in the range of [0,1], in the APO optimization algorithm, parameter c is used together with the behavior transition coefficient b to dynamically adjust the algorithm's behavior pattern. The algorithm can smoothly transition between global search and local utilization, thereby adopting the most appropriate strategy in different search stages. It ensures that the optimal or near-optimal microbial preparation formula and feeding plan can be found.

[0069] (53) The optimized APO algorithm was used, including initialization of the population, aerial search, swooping predation, population merging, and subsequent aggregation foraging, intensified search, predator avoidance, and population merging again. Each stage simulated the different behavioral strategies of the Atlantic puffin and updated the position of the candidate solution through the position update equation. By simulating natural behavior to find the optimal solution, the optimal microbial preparation formula and feeding plan were obtained, making the entire method more efficient and adaptable.

[0070] The objective function of step (51) is as follows:

[0071]

[0072] Among them, E eff (x) is the core emission reduction efficiency item, C(x) is the life cycle cost of the microbial preparation, and C max is the preset cost upper limit, γ is the cost sensitivity index, α, β, δ, ó are weights, S sys (x) is the system adaptability term, H dyn (x) is the dynamic operation robustness term.

[0073] Among them, the core emission reduction efficiency item E eff The calculation formula for (x) is:

[0074]

[0075] Among them, Ebase is the baseline methane emission without adding microbial agents, E treated is the actual methane emission after adding microbial preparations, η bio is the microbial metabolic efficiency coefficient (dynamically corrected by measuring the hydrogen to acetic acid conversion rate using a gas chromatograph).

[0076] The cost-effectiveness constraint is C(x) is the life cycle cost of microbial preparations, C max is the preset cost upper limit, and γ is the cost sensitivity index.

[0077] Among them, the system adaptability term S sys The calculation formula for (x) is:

[0078] S sys (x) = w s1 ·ρ VFA +w s2 ·τ retention +w s3 ·σ pH

[0079] Among them, ρ VFA is the optimization degree of rumen volatile fatty acid (acetic acid / propionic acid) ratio, τ retention is the retention time of microbial preparation in the rumen, σ pH is the pH stability index of cattle stomach, w s1 , w s2 , w s3 is the weight coefficient.

[0080] Among them, the dynamic operation robustness term H dyn The calculation formula for (x) is:

[0081]

[0082] Among them, σ op is the standard deviation of the feeding plan execution deviation, and μ is the operation error tolerance coefficient.

[0083] The step (52) comprises:

[0084] (521) In the APO algorithm, the Atlantic puffin always tends to fly frequently in the air in the initial stage of the iteration to achieve global search, while in the later stage of the iteration, it prefers to dive frequently for food for local development. This search mechanism is inspired by the life habits of the Atlantic puffin. In the early stage, the Atlantic puffin prefers to look for suitable foraging waters, while in the later stage, it focuses on diving for food. Based on this behavior pattern, the APO algorithm designs a behavior transition coefficient B to achieve a smooth transition from global search to local utilization. It is defined as follows:

[0085] B=2*log(1 / rand)*(1-t / T)

[0086] Where rand is a random number in the range (0, 1), t and T are the current and maximum iterations, respectively. In the APO algorithm, B is an adaptive parameter. It is not arbitrarily selected but dynamically derived based on the ratio of the current to the maximum iterations. This randomness is incorporated to simulate the dynamic randomness of the behavior of the Atlantic puffin. This design allows the value of B to be dynamically adjusted as the iterations progress, adapting to the search requirements at different stages.

[0087] Exploration phase (B > C): The algorithm simulates the high-altitude flight behavior of an Arctic puffin and uses global search strategies such as Levy flight to expand the solution space and avoid premature convergence. This phase is suitable for the initial optimization stage, where high-intensity exploration is used to discover potential high-quality solutions.

[0088] Development phase (B≤C): The algorithm simulates the swooping predatory behavior of puffins, focusing on the neighborhood of the current optimal solution for a refined search, improving solution accuracy and convergence speed. This phase is suitable for the later optimization phase, where local development strategies are used to further optimize key methane emission reduction parameters.

[0089] (522) The flight foraging behavior of the Atlantic puffin simulates the algorithm’s dynamic balance between global exploration and local exploitation. The high-frequency aerial flights (global search) in the initial stage correspond to the algorithm’s wide-area exploration of microbial formulations and feeding parameters. By randomly sampling the potential solution space through the Levy flight equation, the algorithm seeks the optimal microbial combination to inhibit methane production.

[0090]

[0091] in, is the new position of the i-th Atlantic puffin (candidate solution) after the t+1th iteration (corresponding to the optimized microbial preparation formulation parameters), is the current position of the i-th candidate solution at the t-th iteration (current microbial preparation formulation parameters), is the position of another candidate solution randomly selected from the current population (used to introduce random perturbations), L(D) is the Levy flight step (simulating the step size distribution of random search), D is the problem dimension (such as the number of microbial species and the number of feeding parameters), and R is the random perturbation term.

[0092] (523) As iterations deepen, the diving foraging behavior (local development) focuses on high-potential areas through the diving predation equation and dynamically adjusts the parameter accuracy using the speed coefficient S to ensure the directional optimization of the microbial metabolic pathway (such as hydrogen to acetic acid conversion efficiency). This adaptive search mechanism directly serves the technical goal—to quickly locate the microbial formulation that maximizes the methane emission reduction effect through the algorithm.

[0093]

[0094] in, is the new position of the i-th candidate solution after diving predation (optimal formula after local development), and S is the speed coefficient.

[0095] The step (53) comprises:

[0096] (531) Each puffin represents a potential solution to the optimization. The generation process of the initial population is described by the following formula:

[0097]

[0098] in, represents the position of the i-th Atlantic puffin; rand generates a random number between 0 and 1; vb and lb represent the upper and lower bounds respectively; N is the number of individuals in the population.

[0099] (532) Aerial Search:

[0100] Atlantic puffins rely on unique flight and foraging strategies to navigate a challenging life. In their daily lives, they must flexibly adapt between the sea and air to meet their nutritional needs and adapt to a diverse environment. When navigating in the air, puffins employ two key strategies to cope with different situations, as shown in the figure below. The first strategy involves searching in the air, while the second involves diving underwater to find prey. These distinct behavioral strategies demonstrated during these two phases demonstrate the Atlantic puffins' adaptability to diverse situations, enabling their successful survival and reproduction.

[0101] During this phase, they focus on finding potential prey while remaining vigilant for nearby potential predators. Under favorable conditions, or when predators are scarce and fish are abundant, they skillfully accelerate toward the surface to better capture prey. The following is the position update equation associated with this strategy:

[0102]

[0103] R=round(0.5*(0.05+rand))*α

[0104] α~Normal(0,1)

[0105] Where R is a random integer between 1 and N, excluding Indicates that the current i is a candidate solution in the population is a candidate solution randomly selected from the current population, L(D) is the random number generated by Levy flight; D is the dimension; α is a random number that conforms to the standard normal distribution, is the new position of the i-th Atlantic puffin (candidate solution) after the t+1th iteration.

[0106] (533) Dive to prey:

[0107] Diving is a key strategy for Atlantic puffins as they rapidly change flight direction to expedite their food capture. To simulate this diving behavior, a velocity coefficient S is introduced to regulate the puffin's displacement during the dive. The following is the position update equation:

[0108]

[0109] S=tan((rand-0.5)*π)

[0110] In this flight strategy, the Atlantic puffin adjusts its displacement in the first stage by introducing a speed coefficient S, where π is a mathematical constant approximately equal to 3.14. S plays a key role in regulating the puffin's flight speed and direction. By adjusting the magnitude and direction of flight speed, S enables the puffin to flexibly adapt to different foraging needs. As the parameter S increases, the algorithm more closely matches the puffin's flight behavior, making it more flexible in the face of competition and uncertainty, and thus adapting to more complex aerial environments. Furthermore, the distributed nature of the parameter S increases the algorithm's randomness and diversity, enhancing the puffin's detection capabilities. Therefore, the inclusion of the diving foraging phase further improves the algorithm's search efficiency in the solution space and enhances its adaptability and search capabilities when handling diverse contexts.

[0111] (534) Population merger

[0112] To achieve optimal results in various scenarios, the algorithm chooses to merge the candidate locations generated in the two phases into a new solution. These solutions are then sorted according to fitness, and the top N individuals are selected to form a new population. The equation is described as follows:

[0113]

[0114] in, is the position (or state) of the i-th candidate solution or individual in the t+1th iteration. It is the new position obtained after the optimization algorithm updates. new is a candidate solution obtained after the update operation, which is different from the state before the update; sort is to sort the new population from small to large according to its fitness value.

[0115] (535) Gathering for food

[0116] Atlantic puffins typically gather near schools of fish near the water's surface to forage collectively, increasing their hunting efficiency and success rate. Puffins also observe the behavior of other puffins on the surface to determine the location of food resources. The position update formula is as follows:

[0117]

[0118] in, In the optimization algorithm, it represents the weight, parameter value or state of the i-th candidate solution in the t+1th iteration. F is the cooperation factor that regulates the predation behavior of the Atlantic puffin, and F = 0.5. The variables r1, r2, and r3 are random integers between 1 and N-1 (excluding i), and is a candidate solution randomly selected from the current population, and r1≠r2≠r3, The competitive inhibition relationship between acetogens and methanogens is strengthened by the cooperative factor F.

[0119] (536) Enhanced Search

[0120] After gathering to forage, Atlantic puffins may sense that food is running out in their current foraging area. To replenish their food supply, they must change their location or direction to intensify their search, seeking more food and meeting their nutritional needs. The position update equation for this phase is as follows:

[0121]

[0122] Where T represents the total number of iterations, and t represents the current iteration. rand is a random number that introduces some randomness into f. f is the adaptive factor used to adjust the Atlantic puffin's position in the water. This adaptive factor is inspired by the puffin's ability to adapt to its environment during foraging. As the number of iterations increases, parameter f gradually adjusts, allowing the puffin to decide whether to change its position to find more abundant food resources based on the progress and randomness of its search.

[0123] (537) Avoiding Predators

[0124] This strategy describes the behavior of Atlantic puffins when they detect a nearby predator. They warn other puffins of the danger by emitting specific sounds, or calls. This call acts as an alarm signal, alerting other puffins and prompting them to move away from the danger zone. At the same time, upon sensing the approach of a predator, the Atlantic puffins will quickly change their position, swimming along a longer route to safety to avoid danger. The following is the position update equation used by this strategy:

[0125]

[0126] In this strategy, β is a random number uniformly distributed between 0 and 1. When faced with dangerous situations, this strategy contains an ingenious balancing mechanism involving two different behavioral modes: gradual risk avoidance and rapid risk avoidance. In the algorithm, this dual-behavior strategy simulates how to escape from the local optimal solution in different situations. When rand ≥ 0.5, it means that a predator is approaching, and the Atlantic puffin will immediately avoid danger and drastically change its position. This is a method of using known information to enhance jumping ability, helping the algorithm to find a better solution in possible local optima. When rand < 0.5, the Atlantic puffin will choose to actively avoid predators and tend to behave more cautiously. It avoids potential dangers by randomly changing its posture. This is an exploratory method that allows the algorithm to search the surrounding environment more carefully.

[0127] (538) Population merger

[0128] In summary, puffins employ a variety of strategies when foraging underwater, including clustering foraging, intensified searching, and predator avoidance. These strategies can lead to different foraging outcomes under different conditions. This algorithm combines candidate positions from three different position equations into a single solution to achieve the best results under various circumstances. The solutions are then ranked according to fitness, and the optimal N individuals are selected. The equations are described as follows:

[0129]

[0130] The integrated strategy enables the model to consider multiple foraging scenarios simultaneously and ultimately select the most suitable location as the optimal solution, enhancing the puffins' foraging and survival capabilities in diverse situations.

[0131] This patent discloses a device and method for reducing methane emissions from the cattle stomach using kangaroo-derived microorganisms. The device comprises a sterile sampling tool, an intelligent algorithm-based control device, a constant-temperature incubator, a microscope, a centrifuge, a bioreactor, a high-speed mixer, and a gas chromatograph. The device aims to reduce greenhouse gas emissions from the livestock industry. Samples are collected from the feces of juvenile kangaroos, which contain specialized microorganisms that can produce acetic acid instead of methane from hydrogen. The collected fecal samples are isolated and purified using a sterile sampling tool and then cultured in a constant-temperature incubator. The cultured specialized microorganisms are introduced into a bioreactor designed to simulate the cattle stomach to observe their interaction with the original methane-producing microorganisms. A high-speed mixer is used to evenly mix the microorganisms with a carrier to prepare a microbial preparation suitable for feeding. The preparation is then filled into sealed containers to ensure that dairy cows receive the microbial preparation in a timely and quantitative manner. The cattle's methane emissions, health, and production performance are regularly monitored. Based on the monitoring results, the formulation of the microbial preparation and feeding regimen are adjusted to optimize emission reduction and ensure that the cattle's health and production performance are not affected.

[0132] This invention not only provides a set of practical and effective technical means for reducing greenhouse gas emissions in animal husbandry, but also opens up a new research direction for the in-depth exploration and efficient utilization of microbial resources. With the continuous improvement of technology and the continuous expansion of application fields, the strategy of reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms is expected to be widely accepted and applied worldwide, making an indelible contribution to addressing the challenges of climate change and promoting the green transformation of animal husbandry. At the same time, the successful practice of this strategy has also provided valuable experience and inspiration for greenhouse gas emission reduction work in other fields, further highlighting the huge potential and broad prospects of biotechnology in the field of environmental protection.

Claims

1. A method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms, characterized in that: include: (1) Collect samples from juvenile kangaroo feces and use sterile sampling tools to isolate and purify microorganisms; (2) Cultivate the isolated special microorganisms in a constant temperature incubator; (3) Introducing the cultured microorganisms into a bioreactor that simulates the bovine stomach environment to observe their interaction with the original methanogenic microorganisms; (4) using a high-speed mixer to uniformly mix the microorganisms and the carrier to prepare a microbial preparation suitable for feeding, and then filling it into a sealed container; (5) Use gas chromatographs to monitor methane emissions from cattle, and use intelligent algorithms to control equipment to timely adjust the preparation methods and feeding plans of microbial preparations; A device for reducing methane emissions from bovine stomachs using kangaroo-derived microorganisms, comprising: a sterile sampling tool, an intelligent algorithm control device, a constant temperature incubator, a microscope, a centrifuge, a bioreactor, a high-speed mixer, and a gas chromatograph.

2. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 1, characterized in that: The microorganisms are Wallaby Group 1 microorganisms, which are repeatedly isolated and purified, and the morphological characteristics of the colonies are observed.

3. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 1, characterized in that: The constant temperature incubator monitors temperature and humidity, and the culture environment is carried out under anaerobic conditions to simulate the anaerobic environment in the kangaroo intestine.

4. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 1, characterized in that: The bioreactor is set with a culture temperature, a pH probe is calibrated, and a pH value is set; the high-speed mixer is set with a rotation speed, and after reaching a set time, a discharging device is opened to discharge the material, and the carrier is diatomaceous earth and nutrient material.

5. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 1, characterized in that: The intelligent algorithm control device adopts APO optimization algorithm, including: (51) The objective function comprehensively considers the total cost of preparing microbial preparations and implementing emission reduction programs, the methane emission reduction effect, the impact of the microbial preparation feeding program on cattle health and production performance, and the degree of automation and operational convenience of the entire preparation and feeding process; (52) By simulating the life habits of the Atlantic puffin, including global search in flight and local exploitation while diving for food, adaptive adjustment of the search strategy is achieved; by introducing the behavioral transition coefficient B and parameter C, a smooth transition between global search and local exploitation is achieved, thereby adopting the most appropriate strategy in different search stages; (53) The optimized APO algorithm was used, including but not limited to initializing the population, aerial search, diving predation, population merging, and subsequent aggregation foraging, intensified search, avoiding predators, and population merging again; each stage simulated the different behavioral strategies of the Atlantic puffin, and updated the position of the candidate solution through the position update equation; and the optimal solution was found by simulating natural behavior.

6. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 5, characterized in that: The objective function is: Among them, E eff (x) is the core emission reduction efficiency item, C(x) is the life cycle cost of the microbial preparation, C max is the preset cost upper limit, γ is the cost sensitivity index, α, β, δ, ó are weights, S sys (x) is the system adaptability term, H dyn (x) is the dynamic operation robustness term.

7. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 5, characterized in that: In step (52), the APO algorithm designs a behavior transition coefficient B, which is as follows: B=2*log(1 / rand)*(1-t / T) Where rand is a random number in the range (0, 1), t and T are the current and maximum iterations, respectively. In the APO algorithm, B is an adaptive parameter that is dynamically derived based on the ratio of the current to the maximum iterations. It also incorporates randomness to simulate the dynamic randomness of the behavior of the Atlantic puffin.

8. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 5, characterized in that: The proposed algorithm for simulating the flight and foraging behavior of the Atlantic puffin achieves a dynamic balance between global exploration and local exploitation. The algorithm's high-frequency aerial flights in the initial phase allow for a wide-area exploration of microbial formulations and feeding parameters, randomly sampling the potential solution space through the Levy flight equation to find the optimal microbial combination for inhibiting methane production. The diving foraging behavior focuses on high potential areas through the diving predation equation, and uses the speed coefficient S to dynamically adjust the parameter accuracy to ensure the directional optimization of the microbial metabolic pathway.

9. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 5, characterized in that: In the air search phase, the position update equation is: R=round(0.5*(0.05+rand))*α,α~Normal(0,1) Where R is a random integer between 1 and N. Indicates that the current i is a candidate solution in the population; is a candidate solution randomly selected from the current population; L(D) is the random number generated by Levy flight; D is the dimension; α is a random number that conforms to the standard normal distribution; rand generates random numbers between 0 and 1.

10. The method for reducing bovine gastric methane emissions by targeting kangaroo-derived microorganisms according to claim 5, characterized in that: During the diving predation phase, the position update equation is: S=tan((rand-0.5)*π) in, is the new position of the i-th candidate solution after diving and predation, S is the speed coefficient, and rand generates a random number between 0 and 1. In the gathering and foraging stage, the position update equation is: Where F is the cooperation factor that regulates the predation behavior of Atlantic puffins; F = 0.5; variables r1, r2, and r3 are random integers between 1 and N-1, and is a candidate solution randomly selected from the current population, and r1≠r2≠r3, In the enhanced search phase, the position update equation is: Where T represents the total number of iterations, t represents the current iteration number, and f is the adaptive factor used to adjust the position of the Atlantic puffin in the water; In the predator avoidance stage, the position update equation is: Among them, β is a random number uniformly distributed between 0 and 1; is the new position of the i-th candidate solution after diving and predation; The population merging stage equation is described as follows: in, is the position or state of the i-th candidate solution or individual in the t+1th iteration, which is the new position obtained after the optimization algorithm updates; new is a candidate solution obtained after the update operation; sort is to sort the new population from small to large according to its fitness value, and select the new population.

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