Computer simulation and regulation method and system for beef cattle fat metabolism

Abstract

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of bioinformatics, and particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism. BACKGROUND

[0002] Compared with other livestock industry, beef cattle breeding has inherent characteristics such as long breeding cycle, low reproductive efficiency, and high feeding cost. Effectively improving the growth performance of beef cattle, reducing the breeding cost, and significantly improving the beef quality have become a major challenge and research focus in the industry.

[0003] However, as consumers increasingly demand higher quality meat products and the beef industry moves towards intelligent and precision farming, the existing technical solutions based on experience, trial and error, and single or few factor regulation gradually reveal their inherent limitations at the principle level. Specifically, although the industry has recognized that beef fat metabolism is an extremely complex and multi-factor intertwined biological process involving multiple influences such as nutrient intake, digestion and absorption, endocrine regulation, gene expression, and environmental factors, current regulation strategies still mainly rely on empirical adjustment and effect verification of specific feed ingredients (such as protein, energy, specific vitamins and minerals) or new additives (such as cysteamine hydrochloride). The essential feature of this method is its post-verification and local optimization: first, actual feeding experiments are conducted, and then the effects of a single or few interventions are evaluated through detection of beef growth performance, carcass quality, blood biochemical indicators, and even fecal microbial communities. This research approach, although indispensable, has significant problems such as resource consumption, long cycle, and poor results extrapolation. Each attempt of a new diet formula, additive type, or feeding scheme requires a significant investment of time, manpower, and animal resources for long-term experiments, and the experimental results are often limited to specific beef breeds, growth stages, and environmental conditions, making it difficult to accurately generalize to more extensive farming practices. The deeper contradiction lies in the fact that the precise regulation of beef fat metabolism is not a linear effect of a single factor, but a complex nonlinear interaction network of multiple pathways, genes, and hormones. The existing empirical or few-factor-based regulation strategies are difficult to capture and effectively utilize this complex internal relationship. For example, high-energy feed may promote fat deposition, but excessive energy restriction may affect growth rate and feed conversion rate; a certain additive may be beneficial for intramuscular fat deposition, but its effectiveness may vary greatly in different genotypes of beef cattle or at different feeding stages, and even cause secondary metabolic problems. This trade-off and synergistic optimization between multiple objectives (such as fat reduction, meat increase, speed increase, cost reduction, and quality optimization) is a fundamental problem that current traditional methods cannot effectively solve. The core bottleneck is the lack of a tool and method that can simulate, predict, and quantify the effects of various interventions on the overall fat metabolism network of beef cattle from a system level, thereby enabling accurate, dynamic, and personalized management of beef fat metabolism processes.

[0004] Therefore, how to break through the limitations of existing empirical and local optimization, build a digital model that can integrate multi-dimensional data such as nutrition, metabolism, endocrine, and gene expression, accurately predict the effects of different feeding conditions and additives on beef fat metabolism based on computer simulation technology, and then design intelligent intervention strategies based on this model to achieve efficient and precise fat reduction and meat increase, has become a key challenge and technical problem that needs to be solved for those skilled in the art. SUMMARY

[0005] To achieve the above objectives, this application provides the following technical solution: According to a first aspect of the present invention, the present invention claims protection for a computer simulation and regulation method for beef cattle fat metabolism, comprising the following steps: S1. Establish a biological data collection and integration system for beef cattle populations to collect multi-dimensional biological data on the physiology, nutrition, behavior and meat production performance of beef cattle at different growth stages. S2. Construct a multi-scale biological network model of beef cattle fat metabolism, and synthesize the collected multi-dimensional biological data into a unified computational model with biological interpretability to simulate the synthesis, decomposition, transport and deposition of fat in beef cattle. S3, Develop a computer simulation and prediction engine for beef cattle fat metabolism, using the biological network model to simulate the dynamic changes in fat metabolism in beef cattle under different feeding conditions and physiological states, and form simulation prediction results to predict the impact on growth performance and meat quality. S4, Design an intelligent intervention strategy optimization engine, which generates intelligent intervention strategies for feeding management and additive application based on the simulation prediction results and in combination with preset breeding goals and constraints. S5. The intelligent intervention strategy is applied to the actual breeding environment, and the computer simulation and prediction engine for beef cattle fat metabolism is verified and continuously optimized through continuous monitoring and data feedback.

[0006] Furthermore, step S1 also includes: Data on the daily feed intake and water consumption of each beef cattle are obtained through automated feeding equipment; Key morphological parameters of beef cattle were obtained using a computer vision-based body measurement system. The activity intensity and duration of beef cattle are monitored by pedometers or accelerometers, and the weight changes of beef cattle are recorded in real time by a weighing system to calculate the daily weight gain and feed conversion ratio. Routine nutrient composition analysis was performed on the feed ingested by beef cattle. Nutrient digestibility at different stages was determined by collecting rumen fluid, small intestinal contents and fecal samples. Peripheral blood samples were collected from beef cattle regularly to measure plasma physiological indicators and quantitatively analyze the levels of hormones in the plasma related to lipid metabolism, growth and development and stress response. After the beef cattle reach the predetermined slaughter weight, the slaughter performance indicators are recorded, and samples of the longissimus dorsi muscle are collected for meat quality analysis. Fresh fecal samples from beef cattle were collected regularly, and the 16S rRNA gene was amplicon sequenced using a high-throughput sequencing platform to analyze the diversity index, species abundance, community structure composition, and enrichment of key metabolic functional bacteria in the gut microbiota.

[0007] Further, the step S2 further comprises: Establishing the absorption, transport of energy, protein, vitamins and minerals in the digestive tract of beef cattle, and the metabolic pathways in the liver, muscle and adipose tissue; The biochemical reaction equation set or dynamic system differential equation set based on matrix operation is used to describe and construct the beef cattle fat metabolism multi-scale biological network model; The key genes and their transcription factors related to beef cattle fat metabolism are identified, and the regulation relationship network among genes, transcription factors, and hormones is constructed to describe the regulated mode of gene expression pattern; The association between the key hormones in the plasma and the receptors on the target tissues, the signal transduction pathway, and the downstream effector molecules is associated, and a dynamic model of hormone secretion, transport, receptor binding, and signal cascade amplification is constructed to explain the coordinated energy balance and fat metabolism of hormones in the whole body; The fecal microbiome sequencing data is associated with the growth performance of beef cattle, nutrient digestion and metabolism, blood biochemical indicators, and slaughter meat quality indicators, and the core microbial groups, specific genera or species significantly related to fat metabolism phenotype are identified; The interaction network between the core microbial groups and the host fat metabolism pathway is constructed.

[0008] Further, the step S2 further comprises: The network modules of different scales are regarded as subsystems, and are connected through shared input and output variables or coupling parameters; The gene regulation network is regarded as the regulation mechanism at the cell level, the endocrine network is regarded as the regulation mechanism at the organ level, the metabolic network is regarded as the execution mechanism at the biochemical reaction level, and the microbial interaction network is regarded as the modification mechanism at the environmental level, and information transmission and cooperation are realized through mathematical mapping relationship or multi-agent system; Based on the heterogeneous graph model, different types of biological entities are regarded as nodes, and different types of relationships between them are regarded as edges, and a heterogeneous graph structure is constructed and feature learning and relationship inference are performed through a deep learning model.

[0009] Further, the step S3 further comprises: S31, learning and calibrating unknown parameters in the beef cattle fat metabolism multi-scale biological network model by using the collected experimental data; S32, for the metabolic pathways and hormone regulation networks with clear kinetic equations, ordinary differential equations or partial differential equation systems are used for numerical solution to simulate the changes of metabolite concentration, enzyme activity and hormone level with time; S33, for large-scale metabolic networks, flux balance analysis or metabolic control analysis methods are used to predict the flux distribution of each metabolic pathway and the flux rate of fat synthesis or decomposition under given nutrient intake. For complex nonlinear relationships, deep learning prediction models are used to learn from time series data and high-dimensional features to predict the impact of specific feeding conditions on beef cattle weight gain, feed conversion rate, backfat thickness, eye muscle area, and intramuscular fat content. S34, Monte Carlo simulation is used to quantify the uncertainty of the model's prediction results, generate the probability distribution of the prediction results, and evaluate the risks that different intervention strategies may bring.

[0010] Further, the step S4 further comprises: S41, define a comprehensive optimization objective function, the input variables include at least the proportion of feed components, the type and dosage of additives, the feeding frequency, and the controllable factors of environmental control parameters, and the output variables include at least the evaluation indexes of beef cattle growth performance, carcass quality, feeding cost and environmental impact; S42, according to the characteristics of the optimization problem, at least one optimization algorithm of genetic algorithm, reinforcement learning, convex optimization or linear programming is selected and implemented; S43, generate intervention strategies according to the output of the optimization algorithm, execute precise feed formula recommendation, functional additive application scheme, environmental control suggestion or individualized breeding management strategy.

[0011] Further, the step S42 further comprises: By simulating the mechanism of natural selection and heredity, search for the global optimal solution in the parameter space of feed formula and additive dosage, and take different feed raw material proportions or specific additive amounts as gene coding; The beef cattle breeding environment is regarded as a Markov decision process, the feeding decision is regarded as the action of an intelligent agent, and the growth performance and meat quality of beef cattle are regarded as reward signals. Through Q-learning, deep Q network or policy gradient algorithm, the optimal feeding strategy is learned; The convex optimization or linear programming adopts simplex method or interior point method to solve the optimal formula under the constraint conditions of meeting nutritional needs, raw material cost and availability.

[0012] Further, the step S43 further comprises: The precise feed formula recommendation is based on beef cattle breed, growth stage, individual physiological state and predetermined production target to recommend the optimal daily ration composition, including the accurate proportion of different feed raw materials; The functional additive application scheme recommends the best additive amount and timing of specific functional additives, including cysteamine hydrochloride, probiotics, enzyme preparations or organic trace elements; The environmental regulation suggestion provides the optimal environmental temperature, humidity, ventilation regulation parameter range, reduces stress and optimizes energy utilization efficiency.

[0013] Further, the step S5 further comprises: The generated feeding strategy and management scheme are deployed to an actual beef cattle farm through an automatic feeding system, an environmental control system and Internet of Things equipment. Real-time data of growth performance, feed intake, health status and blood biochemical indicators of beef cattle are continuously collected through a sensor network, visual recognition technology and biomarker detection technology. The beef cattle growth performance, carcass quality and meat quality data obtained in actual production are compared and analyzed with the prediction results of the computer simulation model, and if the difference is greater than a preset value, a model correction process is started to adjust and recalibrate the biological network model parameters, the prediction engine algorithm or the optimization objective function. The verified new biological data, model parameters and optimization strategies are continuously integrated into the knowledge base of the system to realize self-learning and iterative upgrading.

[0014] According to the second aspect of the present application, the present application claims to protect a computer simulation and regulation system for beef cattle fat metabolism, comprising: One or more processors; A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the computer simulation and regulation method for beef cattle fat metabolism.

[0015] The present application belongs to the technical field of bioinformatics, and specifically relates to a computer simulation and regulation method and system for beef cattle fat metabolism, aiming to solve the problems of excessive fat deposition, low feed conversion efficiency and multi-objective optimization difficulty in existing beef cattle breeding. By establishing a biological data acquisition system, constructing a multi-scale biological network model, developing a computer simulation and prediction engine, designing an intelligent intervention strategy optimization engine, and implementing and feeding back optimization, the present application reduces unnecessary fat deposition, improves feed conversion efficiency and improves beef quality. The present application not only significantly improves the economic benefit of beef cattle breeding, but also responds to the demand of consumers for high-quality and safe beef products, and has important value for promoting the intelligent upgrading and sustainable development of the beef cattle industry. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A work flow chart of the computer simulation and regulation method for beef cattle fat metabolism claimed by the embodiments of the present application; Figure 2A second workflow diagram of a computer simulation and regulation method for fat metabolism of beef cattle according to the embodiment of the present application is requested to protect; Figure 3 A third workflow diagram of a computer simulation and regulation method for fat metabolism of beef cattle according to the embodiment of the present application is requested to protect. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] The terms "first", "second", "third" in the present application are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0019] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of other embodiments. It is explicitly understood that the embodiments described herein can be combined with other embodiments.

[0020] In the traditional beef cattle fattening management practice, in order to accelerate the growth process of beef cattle, the pasture or breeder generally tends to increase the feeding proportion of concentrated feed. Although this can increase the weight gain speed of beef cattle to a certain extent, the high energy characteristics often lead to excessive deposition of fat in beef cattle, especially in the subcutaneous and internal organs. This improper fat distribution not only directly affects the commodity grade and quality evaluation of the carcass, but also significantly reduces the feed conversion efficiency, thereby negatively affecting the breeding benefit. Looking back at history, some fattening farms have tried to solve the problem of fat deposition by illegally adding "lean meat powder" substances such as clenbuterol hydrochloride and ractopamine to the feed, in order to achieve the effect of "reducing fat and increasing meat". How to develop and apply new green, efficient and residue-free fat-reducing and meat-increasing feed additives under the premise of ensuring food safety, to meet the current breeding industry's pursuit of high-efficiency and high-quality production mode, and to improve the overall level of beef cattle breeding, has become a key problem to be solved in the field of animal husbandry science and technology.

[0021] The prior art has made many progress in improving beef cattle fat metabolism and meat quality. For example, precise regulation of energy and protein levels in feed has been shown to directly affect beef composition. High-protein diets generally help promote body protein deposition, thereby reducing the proportion of body fat and intramuscular fat; while high-energy diets are more likely to promote fat deposition. In addition, the balance of amino acids in the diet, the addition of minerals (such as calcium, selenium) and vitamins (such as vitamin E, vitamin C) have been shown to significantly improve beef tenderness, color stability, fat oxidation and drip loss. In terms of biological principles, fat deposition in the animal body is a dynamic balance process, the core of which is the synthesis and metabolism of fat. In this process, the expression levels of various enzymes, hormones and related genes play a key regulatory role. Therefore, by specifically regulating the expression levels of these biological indicators, it is considered to be an effective way to improve fat synthesis and optimize intramuscular fat deposition, thereby improving beef quality. Accordingly, functional additives such as cysteamine hydrochloride are introduced into ruminant production in order to intervene in specific physiological and biochemical processes to achieve the purpose of optimizing growth performance and nutrient digestion and metabolism.

[0022] According to the first embodiment of the present application, with reference to Figure 1 The present application claims a computer simulation and control method for beef cattle fat metabolism, comprising the following steps: S1, establishing a beef cattle population biology data acquisition and integration system to collect multi-dimensional biological data of beef cattle in different growth stages, including physiology, nutrition, behavior and meat production performance; S2, construct a multi-scale biological network model of beef cattle fat metabolism, synthesize the collected multi-dimensional biological data into a unified, biologically interpretable computational model to simulate the synthesis, decomposition, transport and deposition of fat in beef cattle; S3, develop a beef cattle fat metabolism computer simulation and prediction engine, use the biological network model to simulate the dynamic changes of beef cattle fat metabolism under different feeding conditions and physiological states, and form a simulation prediction result to predict the influence on growth performance and meat quality; S4, design an intelligent intervention strategy optimization engine, generate intelligent intervention strategies for feeding management and additive application according to the simulation prediction results combined with preset breeding targets and constraint conditions; S5, apply the intelligent intervention strategy to the actual breeding environment, and verify and continuously optimize the beef cattle fat metabolism computer simulation and prediction engine through continuous monitoring and data feedback.

[0023] Further, the step S1 further comprises: Obtain the daily feed intake and water intake data of each beef cattle through an automated feeding device; Obtain the key morphological parameters of beef cattle through a computer vision-based body shape measurement system; Monitor the activity intensity and duration of beef cattle through a pedometer or an acceleration sensor, and record the body weight changes of beef cattle in real time through a weighing system to calculate the daily gain and feed conversion ratio; Conduct routine nutrient component analysis on the feed ingested by beef cattle, and measure the nutrient digestion rates at different stages by collecting rumen fluid, small intestine contents and fecal samples; Collect beef cattle peripheral blood samples regularly, measure plasma physiological indicators, and quantitatively analyze the hormone levels in the plasma related to fat metabolism, growth and development, and stress response; After beef cattle reach the predetermined slaughter weight, record the slaughter performance indicators, and collect longissimus dorsi muscle samples for meat quality analysis; Collect fresh fecal samples from beef cattle regularly, amplify the 16S rRNA gene through a high-throughput sequencing platform, and analyze the diversity index, species abundance, community structure composition and enrichment of key metabolic functional flora of intestinal microbial communities.

[0024] In this embodiment, the phenotype data of beef cattle is obtained in real time and non-contact. The automated feeding device, such as an individual concentrate feeding station equipped with RFID tag recognition function, can accurately record the daily feed intake and water intake of each beef cattle, with a data accuracy of ±10 grams.

[0025] Meanwhile, body measurement system based on computer vision, such as using multiple high-definition cameras (e.g., resolution of 1920x1080 pixels, frame rate of 30 fps) combined with deep learning algorithms (such as MaskR-CNN), can reconstruct and accurately measure key morphological parameters of beef cattle such as body height, body length, chest circumference, etc., with a measurement error controlled within ±1%. This system can also calculate body shape score and leg score by analyzing the posture and movement trajectory of the cattle.

[0026] In addition, the pedometer or three-axis acceleration sensor integrated in the ear tag or neck ring, with a sampling frequency set to 100 Hz, is used to monitor the activity intensity, activity duration and rumination behavior of beef cattle all day long, and then quantify their energy consumption. The real-time weighing system, usually a dynamic weighing platform integrated in the channel or feeding area, has an accuracy of ±0.5 kg and can continuously record the weight changes of beef cattle, providing raw data for calculating average daily gain (ADG) and feed conversion ratio (FCR). All data are collected through local area network or wireless network to the central server, and time stamp alignment and preliminary cleaning are performed.

[0027] Secondly, evaluate the feed efficiency and nutrient conversion efficiency. Feed samples are collected by batch or regularly, and routine nutrient analysis is performed according to national standards (such as GB / T6432 crude protein determination, GB / T6433 crude fat determination), including dry matter (DM), crude protein (CP), crude fat (EE), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude ash, calcium, phosphorus and other mineral elements, as well as vitamin A, D, E content. Digestibility is determined by implanting a fistula in a specific part of the beef cattle digestive tract (such as rumen, distal duodenum or distal ileum) to collect rumen fluid, small intestine contents and fecal samples. For example, by using the chromium oxide indicator method or the acid insoluble ash (AIA) method, the dry matter digestibility, organic matter digestibility, crude protein digestibility, etc. can be determined. The determination of endogenous metabolites in feces, such as the analysis of steroid metabolites or short-chain fatty acids (SCFAs) in feces by HPLC-MS (high performance liquid chromatography-mass spectrometry) technology, can further evaluate the energy and protein conversion efficiency of feed and provide a deeper understanding of intestinal function.

[0028] Further, peripheral blood samples are collected from the jugular vein of the beef cattle periodically (e.g., once every two weeks) using vacuum blood collection tubes (e.g., EDTA anticoagulation tubes or coagulation tubes). After the plasma is separated by centrifugation (3000g, 15 minutes), the glucose, total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total protein (TP), albumin (ALB), urea nitrogen (BUN), creatinine (CRE), homocysteine, and ammonia, etc. are determined by using a fully automatic biochemical analyzer (e.g., Hitachi 7180 type fully automatic biochemical analyzer). These indicators can directly reflect the metabolic status of carbohydrates, fats and proteins, as well as the liver and kidney functions. At the same time, the hormone levels related to fat metabolism, growth and development, and stress response in the plasma are quantitatively analyzed by using high-sensitivity enzyme-linked immunosorbent assay (ELISA) kits or radioimmunoassay (RIA) kits, including but not limited to insulin, glucagon, leptin, growth hormone (GH), insulin-like growth factor-1 (IGF-1), and adrenocortical hormone, so as to reveal the regulation mechanism of the endocrine system on fat metabolism.

[0029] Further, after the beef cattle reach the predetermined slaughter weight, the evaluation data at the end of the final product are obtained. After the beef cattle are slaughtered, the carcass weight and the carcass yield are recorded in detail. The backfat thickness is measured using ultrasonic waves (e.g., B-ultrasound) between the 12th and 13th ribs, or directly measured after slaughter, with an accuracy of millimeters. The ribeye area (REA) is calculated by tracing the cross-section of the carcass or using image analysis software. The net meat yield is also calculated by fine segmentation. The longissimus dorsi muscle sample is collected for meat quality analysis, including meat color (L, a, and b values are measured using a color difference meter), pH value (measured using a portable pH meter at 45 minutes and 24 hours after slaughter), drip loss (suspension method or bagging method, water loss rate after 24 hours is measured), shear force (measured using a Warner-Bratzler shear force instrument, unit: N / cm², reflecting the tenderness of the meat), intramuscular fat (IMF) content (quantified by Soxhlet extraction method or near-infrared spectroscopy (NIRS), with an accuracy of 0.1%). The fatty acid composition (saturated fatty acids SFA, monounsaturated fatty acids MUFA, polyunsaturated fatty acids PUFA, and their ratios) and amino acid composition are precisely quantified by gas chromatography-mass spectrometry (GC-MS) and an amino acid automatic analyzer.

[0030] In addition, fresh fecal samples of beef cattle are collected regularly and immediately stored at -80°C. Total DNA of microorganisms is extracted using standard microbial DNA extraction kits. The V3-V4 or V4 hyper-variable region of the 16S rRNA gene is amplified and sequenced using a high-throughput sequencing platform, and the primers used are, for example, 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). Sequencing data are subjected to bioinformatics methods for quality control (such as FastQC), sequence denoising (such as the DADA2 algorithm), operational taxonomic unit (OTU) clustering or amplicon sequence variant (ASV) identification. Subsequently, community data are analyzed, including α diversity indices (such as the Shannon and Simpson indices), β diversity analysis (such as PCoA), and community structure composition and relative abundance at the phylum, class, order, family, genus and species levels. The metabolic functions of intestinal microorganisms can be further inferred by functional prediction tools such as PICRUSt2, to identify key metabolic pathways and functional flora related to fat metabolism.

[0031] All the above data are integrated into a high-capacity, highly scalable distributed database management system through a unified data interface (API) and data standards (such as XML or JSON format), and are managed by a time series database, ensuring efficient storage, retrieval and consistency of data, and providing solid data support for subsequent model construction.

[0032] Further, the step S2 further comprises: Establishing the absorption, transport of energy, protein, vitamins and minerals in the digestive tract of beef cattle, and the metabolic pathways in the liver, muscle and adipose tissue; Using biochemical reaction equations or dynamic system differential equations based on matrix operations to describe and construct the multi-scale biological network model of fat metabolism in beef cattle; Identifying key genes and their transcription factors related to fat metabolism in beef cattle, and constructing the regulatory relationship network among genes, transcription factors and hormones to describe the regulated mode of gene expression patterns; Associating the binding of pancreatic key hormones in plasma to their receptors on target tissues, signal transduction pathways and downstream effector molecules, and constructing a dynamic model of hormone secretion, transport, receptor binding and signal cascade amplification to explain the coordinated energy balance and fat metabolism of hormones throughout the body; Correlating the fecal microbiome sequencing data with the growth performance of beef cattle, nutrient digestion and metabolism, blood biochemical indicators, and slaughter meat quality indicators, and identifying core microbial groups, specific genera or species that are significantly related to fat metabolism phenotypes; Constructing the interaction network between the core microbiota and the host lipid metabolism pathway.

[0033] In this embodiment, multi-source biological data is fused into a unified, biologically interpretable computational model to dynamically simulate the synthesis, decomposition, transport and deposition of fat in beef cattle.

[0034] First, the absorption, transport and metabolism pathways of energy (carbohydrates, fats), proteins (amino acids), vitamins and minerals in the digestive tract of beef cattle, liver, muscle and adipose tissue are constructed. For example, the carbohydrate metabolism pathway covers glycolysis, gluconeogenesis, tricarboxylic acid cycle, pentose phosphate pathway, involving various metabolites such as glucose, pyruvate, acetyl-CoA, and key enzymes such as hexokinase, phosphofructokinase, pyruvate dehydrogenase complex. The fat metabolism pathway includes de novo fatty acid synthesis (involving acetyl-CoA carboxylase ACC, fatty acid synthase FAS), beta-oxidation (involving carnitine palmitoyltransferase CPT-I / II), triglyceride synthesis (involving glycerol-3-phosphate acyltransferase) and hydrolysis (involving hormone-sensitive lipase HSL, triglyceride lipase ATGL). The protein metabolism pathway involves the absorption, transamination, deamination, urea cycle and synthesis of amino acids. The network model is constructed using biochemical reaction equations described by constraint-based metabolic analysis (such as flux balance analysis FBA in COBRA toolbox), where each node represents a metabolite or enzyme, and each edge represents a biochemical reaction or material transport process. Each reaction is associated with stoichiometric coefficients, reversibility and corresponding reaction rate constants or transport coefficients, which can be initially estimated from literature data or experimental data.

[0035] Second, core genes such as peroxisome proliferator-activated receptor gamma (PPARγ), sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), hormone-sensitive lipase (HSL) and triglyceride lipase (ATGL) are identified and incorporated. At the same time, the binding relationship between transcription factors (such as liver X receptor LXR, retinol X receptor RXR) and the promoter regions of these genes is constructed, as well as the regulatory relationship of hormone (such as insulin, leptin) indirectly affecting gene expression through signal pathways. The regulatory network can be represented by a graph model with weighted connections, where nodes represent genes, transcription factors or hormones, edges represent regulatory relationships, and edge weights can quantify the strength of regulation (for example, based on the correlation of gene expression or the predicted strength of transcription factor binding sites). By integrating experimental data such as RNA-seq or qPCR, the gene expression pattern can be dynamically updated and reflect its response under different nutritional status or physiological stages.

[0036] Further, the key hormones detected in the plasma, such as insulin, leptin, growth hormone, and adrenocorticotropic hormone, are associated with their specific receptors on target tissues (e.g., liver, muscle, adipocytes), signal transduction pathways (e.g., PI3K / AKT pathway, MAPK pathway), and downstream effector molecules. A dynamic model of hormone secretion kinetics, transport in blood, binding to target cell receptors, and signal cascade amplification is constructed. This module uses a system dynamics model, represented by an ordinary differential equation (ODE) system, to characterize the dynamic changes in hormone concentration over time, receptor binding rate, signal pathway activation level, and influence on downstream metabolic enzyme activity. For example, after insulin binds to its receptor, it activates the PI3K / AKT pathway, which in turn promotes glucose uptake, fatty acid synthesis, and inhibits lipolysis. Model parameters such as hormone synthesis rate, degradation rate, receptor affinity, dissociation constant, etc. can be determined from published literature or through specific in vitro experiments.

[0037] In addition, the fecal microbiome sequencing data obtained in the first step is subjected to multivariate correlation analysis with the growth performance, nutrient digestion and metabolism, blood biochemical indicators, and slaughter meat quality indicators of beef cattle. By calculating the Spearman or Pearson correlation coefficient, core microbial groups (such as specific genera or species) that are significantly correlated with fat metabolism phenotypes such as intramuscular fat content and backfat thickness are identified. Machine learning algorithms such as random forests or support vector machines are used to construct prediction models, taking the microbial community structure or functional characteristics as input to predict host fat metabolism-related phenotypes. Further, an interaction network between these core microbial groups and host fat metabolism pathways is constructed. For example, the production of short-chain fatty acids (SCFAs) such as butyric acid, propionic acid, and acetic acid mediates host energy metabolism and fat synthesis pathways, or affects bile acid metabolism and steroid metabolism. The interaction network can be constructed using partial correlation networks or co-expression networks, or more complex graph neural network-based models, where nodes can represent microbial species, microbial genes, or host metabolic pathways, and edges represent their interactions (such as metabolic product exchange, signal transduction).

[0038] Further, the step S2 further comprises: The network modules of different scales are regarded as subsystems, connected through shared input and output variables or coupling parameters; The gene regulation network is regarded as a regulatory mechanism at the cellular level, the endocrine network is regarded as a regulatory mechanism at the organ level, the metabolic network is regarded as an execution mechanism at the biochemical reaction level, and the microbial interaction network is regarded as a modification mechanism at the environmental level, and information transmission and collaboration are achieved through mathematical mapping relationships or multi-agent systems; Based on the heterogeneous graph model, different types of biological entities are regarded as nodes, and different types of relationships between them are regarded as edges. A heterogeneous graph structure is constructed, and feature learning and relationship inference are performed through a deep learning model.

[0039] In this embodiment, the nutrient metabolism network, gene regulation network, endocrine regulation network, and gut microbe-host interaction network are integrated to form a comprehensive biological network model. The integration method includes: First, modular connection, different scale network modules are regarded as independent subsystems, and are connected through shared input / output variables or coupling parameters. For example, the amount of nutrient intake is the input of the metabolic network, the concentration of metabolic products affects gene expression, gene expression regulates enzyme synthesis and activity, and enzyme activity in turn affects metabolic reaction rate.

[0040] Second, hierarchical modeling, the gene regulation network is regarded as a regulatory mechanism at the cellular level, the endocrine network is regarded as a regulatory mechanism at the organ / system level, the metabolic network is regarded as an execution mechanism at the biochemical reaction level, and the microbe interaction network is regarded as a modification mechanism at the environmental level. Information transmission and cooperation are performed through mathematical mapping relationships or multi-agent systems. Third, based on the heterogeneous graph model, different types of biological entities (such as genes, proteins, metabolites, hormones, and microorganisms) are regarded as nodes, and different types of relationships between them (such as regulation, catalysis, transport, and interaction) are regarded as edges. A unified heterogeneous graph structure is constructed, and feature learning and relationship inference are performed through deep learning models such as graph convolution network (GCN) or graph attention network (GAT) to capture deeper complex biological associations. The multi-scale biological network model is implemented using Python programming language, combined with scientific computing libraries such as Biopython and SciPy, and customized scripts.

[0041] Further, with reference to Figure 2 , the step S3 further includes: S31, learning and calibrating unknown parameters in the beef cattle fat metabolism multi-scale biological network model using collected experimental data; S32, for metabolic pathways and hormone regulation networks with explicit kinetic equations, numerical solutions of ordinary differential equations or partial differential equation systems are used to simulate the changes of metabolite concentration, enzyme activity, and hormone level over time; S33, for large-scale metabolic networks, flux balance analysis or metabolic control analysis methods are used to predict the flux distribution of each metabolic pathway and the flux rate of fat synthesis or decomposition under given nutrient intake. For complex nonlinear relationships, deep learning prediction models are used to learn time series data and high-dimensional features to predict the effects of specific feeding conditions on beef cattle weight gain, feed conversion rate, backfat thickness, eye muscle area, and intramuscular fat content. S34, using Monte Carlo simulation to quantify the uncertainty of the model's prediction results, generate the probability distribution of the prediction results, and evaluate the risks that different intervention strategies may bring.

[0042] In this embodiment, the biological network model is used to simulate the dynamic changes of fat metabolism of beef cattle under different feeding conditions and physiological states, and accurately predict the comprehensive influence on growth performance and meat quality. The simulation and prediction engine includes: First, the unknown parameters in the biological network model are learned and calibrated using the experimental data collected in the first step.

[0043] The parameter learning method includes Bayesian inference, which uses prior knowledge and observed data to update the posterior distribution of model parameters, and samples the probability distribution of parameters through Markov Chain Monte Carlo (MCMC) method; based on the optimization of parameter estimation, by defining a target function (such as mean square error MSE or root mean square error RMSE), minimizing the error between the model prediction value and the actual observation value, using gradient descent (such as Adam optimizer), particle swarm optimization (PSO) or genetic algorithm (GA) and other heuristic optimization algorithms to search for the optimal parameter combination. During the parameter calibration process, k-fold cross-validation is usually used to evaluate the generalization ability of the model. In addition, sensitivity analysis (such as Morris method or Sobol index method) is performed to evaluate the sensitivity of the model output to each input parameter, to identify key parameters and optimize the calibration accuracy, and to concentrate limited experimental resources on the parameters that have the greatest impact on the model.

[0044] Secondly, multiple simulation functions are provided. For metabolic pathways and hormone regulation networks with explicit kinetic equations, ordinary differential equations (ODE) or partial differential equations (PDE) systems are used for numerical solution, such as using the Runge-Kutta method, to simulate the dynamic changes of metabolite concentrations, enzyme activities and hormone levels over time. For large metabolic networks, constraint-based metabolic analysis methods such as flux balance analysis (FBA) and metabolic control analysis (MCA) are used to predict the flux distribution of each metabolic pathway and the steady-state flux rate of fat synthesis or decomposition under given nutrient intake (such as glucose, amino acid, fatty acid input flux in feed). In addition, for complex nonlinear relationships or modules that are difficult to establish accurate kinetic equations (such as intestinal microbe-host interaction effects), deep learning prediction models such as long short-term memory networks (LSTM) or variational autoencoders (VAE) are used. LSTM networks can learn from time series data (such as daily feed intake, body weight, blood indicators) to predict future changes in beef cattle weight growth curve, feed conversion rate, backfat thickness, eye muscle area and intramuscular fat content. VAE is used for dimensionality reduction and feature learning of high-dimensional biological features (such as microbiome data), and then predicts the impact of specific feeding conditions on meat quality.

[0045] Furthermore, the inherent variability of biological systems and errors in data collection are considered. Monte Carlo simulation or other stochastic simulation methods are used to quantify the uncertainty of the model's prediction results. For example, by setting reasonable probability distributions (such as normal distribution, uniform distribution) for the model input parameters (such as feed nutrient content, individual physiological parameters), 1000 or more times of repeated simulation is performed to generate the probability distribution of the prediction results (such as the prediction interval of intramuscular fat content). By analyzing these distributions, the confidence interval of the prediction results can be calculated, and the risks that different intervention strategies may bring, such as feed waste, carcass quality decline, animal health problems (such as metabolic diseases), etc. These risk assessments can provide more comprehensive information for decision-makers to assist them in risk management.

[0046] Further, with reference to Figure 3 , the step S4 further comprises: S41, defining a comprehensive optimization objective function, the input variables including at least the proportion of feed components, the types and dosages of additives, the feeding frequency, and the controllable factors of environmental control parameters, and the output variables including at least the evaluation indexes of beef cattle growth performance, carcass quality, feeding cost and environmental impact; S42, according to the characteristics of the optimization problem, at least one optimization algorithm is selected and implemented from genetic algorithm, reinforcement learning, convex optimization or linear programming; S43, generating an intervention strategy according to the output of the optimization algorithm, performing precise feed formula recommendation, functional additive application scheme, environmental regulation suggestion or individualized breeding management strategy.

[0047] Further, the step S42 further comprises: By simulating the mechanism of natural selection and heredity, searching for the global optimal solution in the parameter space of feed formula and additive dosage, different feed raw material proportions or specific additive addition amounts are taken as gene coding; The beef cattle breeding environment is regarded as a Markov decision process, the feeding decision is regarded as the action of an agent, and the growth performance and meat quality of beef cattle are regarded as reward signals. An optimal feeding strategy is learned through Q-learning, deep Q network or policy gradient algorithm; The convex optimization or linear programming adopts simplex method or interior point method to solve the optimal formula under the constraint conditions of meeting nutritional needs, raw material cost and availability.

[0048] Further, the step S43 further comprises: The precise feed formula recommendation is based on beef cattle breed, growth stage, individual physiological state and predetermined production target to recommend the optimal daily ration composition, including the accurate proportion of different feed raw materials; The functional additive application scheme recommends the best additive dosage and addition time of specific functional additives, including cysteamine hydrochloride, probiotics, enzyme preparations or organic trace elements; The environmental regulation suggestion provides the optimal environmental temperature, humidity and ventilation regulation parameter range to reduce stress and optimize energy utilization efficiency.

[0049] In this embodiment, according to the simulation prediction result, the optimal breeding management and additive application scheme are generated in combination with the preset breeding target and constraint condition. The intelligent intervention strategy optimization engine comprises: First, a comprehensive optimization objective function is defined, the input variables of which are a series of controllable factors including but not limited to feed component proportion, additive type and dosage, feeding frequency, environmental control parameters (such as temperature, humidity, ventilation volume), and the output variables are beef cattle growth performance (such as daily gain, feed conversion rate), carcass quality (such as intramuscular fat content, backfat thickness, eye muscle area), breeding cost (such as feed cost, labor cost) and environmental impact (such as methane emission).

[0050] The objective function realizes the trade-off and collaborative optimization among multiple objectives through weighted summation, Pareto optimization or utility function-based methods. For example, a specific optimization objective can be set as: maximizing average daily gain, maximizing intramuscular fat content, and minimizing backfat thickness, under the constraints that the total feeding cost does not exceed X yuan per head and the methane emission is less than Y kg per head per year.

[0051] Secondly, according to the characteristics of the optimization problem, suitable optimization algorithms are selected and implemented: genetic algorithm (GA) searches for global optimal solutions in the discrete or continuous parameter space of feed formula, additive dosage, etc. by simulating the mechanism of natural selection and inheritance. For example, the ratio (percentage) of different feed raw materials (such as corn, soybean meal, alfalfa meal, DDGS) and the amount of cysteamine hydrochloride added (0 ppm, 100 ppm, 300 ppm, 500 ppm) are encoded as genes on chromosomes. Through operations such as selection (e.g. roulette wheel selection), crossover (e.g. single-point crossover), mutation (e.g. Gaussian mutation), etc., a better solution set is iteratively generated until the convergence condition is met. Reinforcement learning (RL) regards the beef cattle feeding environment as a Markov decision process (MDP), in which the current physiological state of beef cattle (such as body weight, feed intake, blood indicators) constitutes the state space, and the feeding decision (such as feed formula adjustment, additive increase or decrease) is regarded as the action of the agent, while the growth performance and meat quality of beef cattle (such as the time required to reach a certain intramuscular fat content) are regarded as the reward signal. Through Q-learning, deep Q network (DQN) or policy gradient algorithm (such as PPO), the agent learns the optimal feeding strategy in the simulated environment, and realizes the dynamic adjustment of the feeding scheme according to the real-time state of beef cattle. For problems such as feed formula optimization that have linear or convex characteristics, convex optimization or linear programming methods (such as simplex method, interior point method) are used to quickly solve the optimal formula under the constraints of meeting nutritional requirements (such as crude protein content not less than 14%, energy density not less than 2.8 Mcal / kg), raw material cost, availability, etc.

[0052] Moreover, based on the output of the optimization algorithm, specific and actionable intervention strategies are generated to facilitate implementation in actual farms. These strategies include: precise feed formulation recommendations, recommending the optimal daily ration composition, including the precise proportions of different feed ingredients (e.g., corn 55%, soybean meal 20%, DDGS 10%, alfalfa meal 10%, premix 5%), based on beef cattle breed (e.g., Angus, Simmental), growth stage (e.g., pre-fattening, mid-fattening, post-fattening), individual physiological state (e.g., health status, body weight), and predetermined production goals. Functional additive application schemes, recommending the optimal dosage and timing of specific functional additives (e.g., cysteamine hydrochloride, recommended at 300 ppm in the post-fattening period; specific probiotic strains, such as Bacillus subtilis, with a daily additive dose of 1x10^9 CFU / head; enzyme preparations, such as cellulase, recommended at 500 g per ton of feed) to precisely influence fat metabolism by regulating gut flora, digestive enzyme activity, or hormone levels. Environmental regulation recommendations, providing optimal environmental temperature (e.g., 18-22°C), humidity (e.g., 60-70%), ventilation (e.g., 10 air changes per hour), and other regulation parameter ranges to reduce heat or cold stress and optimize energy utilization efficiency. For beef cattle with specific body conditions or special physiological needs, personalized farming management strategies can also be provided, such as targeted intervention for individuals with growth retardation or excessive obesity.

[0053] Further, the step S5 further comprises: The generated feeding strategies and management schemes are deployed to actual beef cattle farms through automated feeding systems, environmental control systems, and Internet of Things devices; Real-time data on beef cattle growth performance, feed intake, health status, and blood biochemical indicators are continuously collected through sensor networks, visual recognition technology, and biomarker detection technology; The beef cattle growth performance, carcass quality, and meat quality data obtained in actual production are compared and analyzed with the prediction results of the computer simulation model. If the difference is greater than the preset value, the model correction process is started to adjust and recalibrate the biological network model parameters, prediction engine algorithm, or optimization objective function; The verified new biological data, model parameters, and optimization strategies are continuously integrated into the knowledge base of the system, realizing self-learning and iterative upgrading.

[0054] In this embodiment, the generated intelligent intervention strategies are applied to actual farming environments, and the entire system is verified and continuously optimized through continuous monitoring and data feedback.

[0055] First, the feeding strategies and management schemes generated by computer simulation and regulation methods are deployed to actual beef cattle farms through Internet of Things technology.

[0056] Using an automated feeding system, such as a robotic feeding cart equipped with a gravity sensor and precise feeding mechanism, can automatically mix and dispense feed according to the recommended feed formula, ensuring that each beef cow receives its customized daily ration. Using an intelligent environmental control system, such as a PLC (Programmable Logic Controller) based on sensor feedback (temperature, humidity, ammonia concentration sensors), automatically adjusts ventilation fans, wet curtains, heaters, and other equipment to maintain environmental parameters within the recommended range.

[0057] Secondly, using sensor networks, visual recognition technology and biomarker detection technology to continuously collect real-time data of beef cattle growth performance, feed intake, water intake, activity intensity, health status (such as body temperature, respiratory rate) and key blood biochemical indicators, etc. For example, high-precision pedometers continuously monitor cattle activity, infrared thermal imaging cameras monitor body temperature, and non-contact sensors measure rumination time. These multi-dimensional real-time data are transmitted wirelessly to the central data platform and preliminarily processed and visualized.

[0058] Furthermore, the beef cattle growth performance, carcass quality and meat quality data obtained in actual production (for example, the average daily gain, intramuscular fat content, backfat thickness of a batch of beef cattle under a certain intervention strategy) are compared and analyzed with the prediction results of the computer simulation model. If there is a statistically significant difference (for example, through t-test or ANOVA analysis), the model correction process is started. This process involves adjusting the parameters of the biological network model in the second step, optimizing the algorithm of the prediction engine in the third step, or recalibrating the weights or constraints of the optimization objective function in the fourth step. The verification and correction process is iterative, ensuring that the model can continuously adapt to the real breeding environment and physiological changes of beef cattle, improving the robustness and accuracy of the model.

[0059] Finally, the verified new biological data, model parameters and optimization strategies are continuously integrated into the knowledge base of the system. This means that each successful model verification and correction enriches the system's knowledge reserve, enabling the system to learn and iteratively upgrade itself. For example, if it is found that the response of a certain breed of beef cattle to a certain additive is systematically different from the model prediction, the corrected model parameters and new additive application strategy will be recorded, thereby improving the model's generalization ability and prediction accuracy in different breeds and different environments, and realizing the continuous intelligent evolution of the system.

[0060] According to the second embodiment of the present application, the present application claims to protect a computer simulation and regulation system for beef cattle fat metabolism, comprising: one or more processors; A memory having stored thereon one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the computer simulation and regulation method for beef cattle fat metabolism.

[0061] The following is explained and described with specific examples: The computer simulation and regulation method for beef cattle fat metabolism of the present application is applied in a specific implementation scenario to evaluate its effect in optimizing intramuscular fat deposition.

[0062] Experimental subjects: 120 beef cattle with an average weight of 350 kg (Simmental and Limuzan cross, bulls) were selected and randomly divided into two groups, 60 in each group, with a feeding period of 120 days (finishing period). The feeding environment (temperature 20±2°C, humidity 65±5%) and basic diet (corn, soybean meal, alfalfa meal, DDGS) of the two groups of cattle were kept consistent.

[0063] The cattle in this group were managed using the computer simulation and regulation method described in the present application.

[0064] Data collection and integration: The daily feed intake and water intake of each cow were recorded in real time through the RFID automated feeding system.

[0065] The body height, body length, and chest circumference of the cattle were measured every week through the computer vision system, and the body shape score was calculated.

[0066] Blood samples were collected every two weeks to determine the plasma glucose, triglyceride, insulin, leptin, and IGF-1 levels.

[0067] Fecal samples were collected every month for 16S rRNA gene sequencing to analyze the intestinal microbial community structure.

[0068] Model construction and calibration: Using the above collected data, a multi-scale biological network model of beef cattle fat metabolism was constructed and calibrated, including nutrient metabolism, gene regulation, endocrine regulation, and intestinal microbe-host interaction modules. Model parameters were optimized through Bayesian inference combined with historical data.

[0069] Simulation and prediction: Based on the constructed model and real-time data, the effects of different feed formulations and additive schemes on the daily weight gain, backfat thickness, and intramuscular fat content of the cattle were simulated and predicted. For example, through simulation, it was found that adding an appropriate amount of cysteamine hydrochloride (CA) in the later fattening period (last 60 days) can effectively guide fat deposition into the intramuscular.

[0070] Optimization strategy generation: Set optimization goal: maximize intramuscular fat content while ensuring daily weight gain of no less than 1.5 kg / day and feeding cost within controllable range.

[0071] The intelligent intervention strategy optimization engine uses genetic algorithm to generate the optimal diet formula and additive scheme under the constraints of feed raw material cost, nutritional requirements and physiological limitations. The specific recommended scheme is: in the early fattening period (1-60 days), the basic diet is used, with crude protein content of 16% and energy density of 2.9 Mcal / kg; in the later fattening period (61-120 days), the diet formula is adjusted, the crude protein content is reduced to 14%, the energy density is increased to 3.1 Mcal / kg, and cysteamine hydrochloride (CA) 300 ppm is additionally added.

[0072] Implementation and feedback: The feed formula is precisely delivered through automated feeding equipment, and cysteamine hydrochloride is mixed into the feed according to the recommended dose.

[0073] The growth performance of cattle is continuously monitored, and the model is verified regularly. At the end of the fattening period, 30 cattle are slaughtered, and carcass performance and meat quality indicators are recorded in detail.

[0074] Traditional experience control strategy: The cattle in this group use the traditional experience-based feeding and management mode, and the feed formula and additive application are based on industry general standards and the experience of breeders.

[0075] Feed formula: In the early fattening period (1-60 days), the basic diet with crude protein content of 16% and energy density of 2.9 Mcal / kg is used.

[0076] In the later fattening period (61-120 days), the general fattening diet with crude protein content of 15% and energy density of 3.0 Mcal / kg is used, and no functional additives for specifically regulating fat metabolism are added.

[0077] Management mode: Cattle intake, body weight and other data are still recorded manually or obtained through regular weighing, but no systematic biological data collection and integration is performed.

[0078] No computer simulation prediction and intelligent optimization is performed. The use of additives follows conventional animal husbandry practices and lacks specificity.

[0079] Result evaluation: At the end of the fattening period, 30 cattle are slaughtered, and carcass performance and meat quality indicators are recorded, which are compared with Example One.

[0080] Comparison of experimental results: Table 1 lists the main indicators of beef cattle growth performance and meat quality of Example 1 (using the method of the present application) and Comparative Example 1 (traditional empirical method) in detail: Table 1 Comparison of indicators of beef cattle growth performance and meat quality

[0081] From the comparison data, it can be clearly seen that the beef cattle raised by the computer simulation and control method disclosed in the present application (Example 1) have significant advantages in many key indicators. Although the total cost of feeding is not significantly different from the traditional method group (P>0.05), the present application has significantly improved the growth performance and meat quality of beef cattle.

[0082] Specifically, the final body weight and average daily gain of beef cattle in Example 1 were significantly higher than those in Comparative Example 1 (P<0.05), indicating that the method can effectively promote the growth of beef cattle. More importantly, the feed conversion rate (feed to weight ratio) of Example 1 was significantly better than that of Comparative Example 1 (6.25 vs 6.83, P<0.01), which directly reflects the improvement of feed utilization efficiency, meaning that less feed is needed to produce the same amount of weight gain, thereby reducing the resource consumption per unit of meat production.

[0083] In terms of slaughter performance, the carcass weight and carcass yield of Example 1 were significantly better than those of Comparative Example 1 (P<0.05), reflecting higher economic output. Most notably, the present application successfully achieved precise regulation of fat deposition patterns: the backfat thickness of Example 1 was significantly lower than that of Comparative Example 1 (10.2mm vs 12.5mm, P<0.01), indicating that excessive fat deposition was effectively inhibited. At the same time, the eye muscle area was significantly increased (98.7cm 2 vs 92.1cm 2 , P<0.01), which is highly related to meat value.

[0084] In terms of meat quality, the advantages of the present application are more significant. Intramuscular fat content (IMF) is a key indicator for measuring beef quality, especially tenderness, juiciness and flavor, and the IMF content of Example 1 reached 4.87%, much higher than that of Comparative Example 1 (3.12%) (P<0.001), with an increase of 56%. This means that the sensory quality of beef has been significantly improved by the method of the present application. Correspondingly, the shear force of beef in Example 1 was significantly reduced (35.6N / cm 2 vs 42.1N / cm 2, P<0.01), and drip loss was also significantly reduced (2.1% vs 2.8%, P<0.05), which directly reflected the improvement of meat tenderness and water-holding capacity. In terms of fatty acid composition, the beef of Example One had lower saturated fatty acid (SFA) content and higher monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) content (P<0.05 or P<0.01), which was not only more beneficial to consumer health, but also endowed the beef with more rich flavor characteristics.

[0085] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0086] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0087] The specific embodiments of the application are described in detail above, but only as an example, and the present application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modification or substitution of the application is also within the scope of the present application, therefore, any equivalent transformation and modification, improvement, etc. made without departing from the spirit and principle range of the present application should be covered in the scope of the present application.

Claims

1. A computer simulation and regulation method for beef cattle fat metabolism, characterized in that, The method comprises the following steps: S1, establishing a beef cattle population biology data collection and integration system to collect multi-dimensional biological data of beef cattle in different growth stages, including physiological, nutritional, behavioral and meat production performance data; S2, constructing a beef cattle fat metabolism multi-scale biological network model, synthesizing the collected multi-dimensional biological data into a unified, biologically interpretable computational model to simulate the synthesis, decomposition, transport and deposition processes of fat in beef cattle; S3, developing a beef cattle fat metabolism computer simulation and prediction engine, using the biological network model to simulate the dynamic changes of beef cattle fat metabolism under different feeding conditions and physiological states, and forming a simulation prediction result to predict the influence on growth performance and meat quality; S4, designing an intelligent intervention strategy optimization engine to generate intelligent intervention strategies for feeding management and additive application according to the simulation prediction results, combined with preset breeding targets and constraints; S5, applying the intelligent intervention strategy to the actual breeding environment, and verifying and continuously optimizing the beef cattle fat metabolism computer simulation and prediction engine through continuous monitoring and data feedback.

2. The computer simulation and regulation method for beef cattle fat metabolism according to claim 1, characterized in that, The step S1 further comprises: Obtaining the daily feed intake and water intake data of each beef cattle through automatic feeding equipment; Obtaining key morphological parameters of beef cattle through a computer vision-based body shape measurement system; Monitoring the activity intensity and duration of beef cattle through a pedometer or an acceleration sensor and recording the body weight changes of beef cattle in real time through a weighing system to calculate the daily gain and feed conversion ratio; Performing routine nutrient component analysis on the feed ingested by beef cattle, determining the nutrient digestion rate at different stages by collecting rumen fluid, small intestine contents and fecal samples; Collecting beef cattle peripheral blood samples regularly to determine plasma physiological indicators and quantitatively analyze the hormone levels related to fat metabolism, growth and development and stress response in the plasma; After beef cattle reach the predetermined slaughter weight, recording the slaughter performance indicators and collecting longissimus dorsi muscle samples for meat quality analysis; Collecting fresh fecal samples of beef cattle regularly, amplifying the 16S rRNA gene through a high-throughput sequencing platform, and analyzing the diversity index, species abundance, community structure composition and enrichment of key metabolic functional flora of intestinal microbial communities.

3. The computer simulation and regulation method for beef cattle fat metabolism according to claim 2, characterized in that, The step S2 further comprises: Establishing the absorption, transport of energy, protein, vitamins and minerals in the digestive tract of beef cattle and the metabolic pathways in the liver, muscle and adipose tissue; Using biochemical reaction equation sets or dynamic system differential equation sets based on matrix operations to describe and construct the beef cattle fat metabolism multi-scale biological network model; Identifying key genes and their transcription factors related to beef cattle fat metabolism, constructing the regulation relationship network among genes, transcription factors and hormones to describe the regulated mode of gene expression patterns; Associating the binding of key plasma hormones to their receptors on target tissues, signal transduction pathways and downstream effector molecules to construct a dynamic model of hormone secretion, transport, receptor binding and signal cascade amplification to explain the coordinated energy balance and fat metabolism of hormones throughout the body; Correlate the fecal microbiome sequencing data with the growth performance, nutrient digestion and metabolism, blood biochemical indicators, and carcass quality indicators of beef cattle to identify core microbial groups, specific genera or species that are significantly associated with fat metabolism phenotypes; Construct an interaction network between the core microbial groups and the host fat metabolism pathways.

4. The computer simulation and regulation method for beef cattle fat metabolism according to claim 3, characterized in that, The step S2 further comprises: Different scale network modules are regarded as subsystems, connected through shared input and output variables or coupling parameters; The gene regulation network is regarded as a regulation mechanism at the cellular level, the endocrine network is regarded as a regulation mechanism at the organ level, the metabolic network is regarded as an execution mechanism at the biochemical reaction level, and the microbial interaction network is regarded as a modification mechanism at the environmental level, and information transmission and cooperation are realized through mathematical mapping relationships or multi-agent systems; Based on the heterogeneous graph model, different types of biological entities are regarded as nodes, and different types of relationships between them are regarded as edges, a heterogeneous graph structure is constructed, and feature learning and relationship inference are performed through a deep learning model.

5. The in-silico simulation and regulation method for beef cattle fat metabolism as claimed in claim 1 wherein, The step S3 further comprises: S31, learning and calibrating unknown parameters in the beef cattle fat metabolism multi-scale biological network model using collected experimental data; S32, for metabolic pathways and hormone regulation networks with clear kinetic equations, numerical solutions are obtained using ordinary differential equations or partial differential equation systems to simulate the changes of metabolite concentrations, enzyme activities and hormone levels over time; S33, for large-scale metabolic networks, flux balance analysis or metabolic control analysis methods are used to predict the flux distribution of each metabolic pathway and the flux rate of fat synthesis or decomposition under given nutrient intake, and for complex nonlinear relationships, deep learning prediction models are used to learn time series data and high-dimensional features to predict the effects of specific feeding conditions on beef cattle weight gain, feed conversion rate, backfat thickness, eye muscle area and intramuscular fat content; S34, using Monte Carlo simulation to quantify the uncertainty of the model's prediction results, generating the probability distribution of the prediction results, and evaluating the risks that different intervention strategies may bring.

6. The in-silico simulation and regulation method for beef cattle fat metabolism as claimed in claim 1 wherein, The step S4 further comprises: S41, define a comprehensive optimization objective function, the input variables include at least the proportion of feed components, the type and dosage of additives, the feeding frequency, and the controllable factors of environmental control parameters, and the output variables include at least the evaluation indexes of beef cattle growth performance, carcass quality, feeding cost and environmental impact; S42, according to the characteristics of the optimization problem, at least one optimization algorithm is selected and implemented from genetic algorithm, reinforcement learning, convex optimization or linear programming; S43, generate intervention strategies according to the output of the optimization algorithm, implement precise feed formula recommendation, functional additive application scheme, environmental control suggestion or individualized breeding management strategy.

7. The computer simulation and regulation method for beef cattle fat metabolism according to claim 6, characterized in that, The step S42 further comprises: Search for the global optimal solution in the parameter space of feed formula and additive dosage by simulating the mechanism of natural selection and heredity, and use different feed raw material ratios or specific additive amounts as gene codes. The beef cattle feeding environment is regarded as a Markov decision process, the feeding decision is regarded as the action of the agent, and the growth performance and meat quality of beef cattle are regarded as reward signals. The optimal feeding strategy is learned through Q-learning, deep Q network or policy gradient algorithm. The convex optimization or linear programming uses the simplex method or interior point method to solve for the optimal formula under the constraints of nutritional requirements, raw material costs, and availability.

8. The computer simulation and regulation method for beef cattle fat metabolism according to claim 7, characterized in that, Step S43 further includes: The recommended precision feed formulation is based on the breed of beef cattle, growth stage, individual physiological state and predetermined production target, and recommends the optimal diet composition, including the precise proportion of different feed ingredients. The functional additive application scheme recommends the optimal dosage and timing of specific functional additives, including cysteine ​​hydrochloride, probiotics, enzyme preparations, or organic trace elements. The environmental control recommendations provide optimal ranges for temperature, humidity, and ventilation control parameters to reduce stress and optimize energy efficiency.

9. The in-silico simulation and regulation method for beef cattle fat metabolism as claimed in claim 1 wherein, Step S5 further includes: The generated feeding strategies and management plans are then deployed to actual beef cattle farms through automated feeding systems, environmental control systems, and IoT devices. Real-time data on growth performance, feed intake, health status, and blood biochemical indicators of beef cattle are continuously collected. This real-time data is obtained through sensor networks, visual recognition technology, and biomarker detection technology. The data on beef cattle growth performance, carcass quality, and meat quality obtained from actual production are compared and analyzed with the prediction results of the computer simulation model. If the difference is greater than the preset value, the model correction process is initiated to adjust and recalibrate the parameters of the biological network model, the prediction engine algorithm, or the optimization objective function. Validated new biological data, model parameters, and optimization strategies are continuously integrated into the system's knowledge base to enable self-learning and iterative upgrades.

10. A computer simulation and regulation system for beef cattle fat metabolism, characterized by, include: One or more processors; A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement a computer simulation and control method for beef cattle fat metabolism according to any one of claims 1 to 9.

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