A computer simulation and regulation method and system for beef cattle fat metabolism

Abstract

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 feedback optimization. By reducing unnecessary fat deposition, improving feed conversion efficiency and improving 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.

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 object, the present application provides the following technical solutions:

[0006] According to the first aspect of the present application, the present application claims a computer simulation and regulation method for beef cattle fat metabolism, comprising the following steps:

[0007] S1, establishing a beef cattle population biology data acquisition and integration system, acquiring multi-dimensional biological data of beef cattle in different growth stages, including physiology, nutrition, behavior and meat production performance;

[0008] S2, constructing a beef cattle fat metabolism multi-scale biological network model, synthesizing the acquired multi-dimensional biological data into a unified, biologically interpretable computational model to simulate the synthesis, decomposition, transport and deposition process of fat in beef cattle;

[0009] S3, developing a beef cattle fat metabolism computer simulation and prediction engine, using the biological network model to simulate the dynamic changes of fat metabolism of beef cattle under different feeding conditions and physiological states, forming a simulation prediction result to predict the influence on growth performance and meat quality;

[0010] S4, designing an intelligent intervention strategy optimization engine, generating an intelligent intervention strategy for feeding management and additive application according to the simulation prediction result combined with the preset breeding target and constraint condition;

[0011] 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.

[0012] Further, the step S1 further comprises:

[0013] Obtaining the daily feed intake and water intake data of each beef cattle through automatic feeding equipment;

[0014] Obtaining key morphological parameters of beef cattle through a body shape measurement system based on computer vision;

[0015] Monitoring the activity intensity and duration of beef cattle through a pedometer or an acceleration sensor and recording the body weight change of beef cattle in real time through a weighing system to calculate the daily gain and feed conversion ratio;

[0016] 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;

[0017] Collecting beef cattle peripheral blood samples regularly, determining plasma physiological indicators, and quantitatively analyzing the hormone levels related to fat metabolism, growth and development and stress response in the plasma;

[0018] After the beef cattle reach the predetermined slaughter body weight, record the slaughter performance index, and collect the longissimus dorsi muscle sample for meat quality analysis.

[0019] Periodically collect fresh fecal samples of beef cattle, amplify the 16S rRNA gene by high-throughput sequencing platform, analyze the diversity index, species abundance, community structure composition and enrichment of key metabolic functional flora of intestinal microbial community.

[0020] Further, the step S2 further comprises:

[0021] Establish the absorption, transport of energy, protein, vitamins and minerals in the digestive tract of beef cattle and the metabolic pathway in the liver, muscle and adipose tissue;

[0022] The biochemical reaction equation set or dynamic system differential equation set based on matrix operation is used to describe and construct the multi-scale biological network model of beef cattle fat metabolism;

[0023] Identify the key genes and their transcription factors related to beef cattle fat metabolism, construct the regulation relationship network among genes, transcription factors and hormones, and describe the regulation mode of gene expression pattern;

[0024] Correlate the binding of pancreatic key hormones in plasma to receptors on target tissues, signal transduction pathways and downstream effector molecules, construct a dynamic model of hormone secretion, transport, receptor binding and signal cascade amplification, and explain the coordinated energy balance and fat metabolism of hormones in the whole body;

[0025] Correlate 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 identify the core microbial groups, specific genera or species significantly related to fat metabolism phenotype;

[0026] Construct the interaction network between the core microbial groups and the host fat metabolism pathway.

[0027] Further, the step S2 further comprises:

[0028] Different scale network modules are regarded as subsystems, and are connected through shared input and output variables or coupling parameters;

[0029] The gene regulation network is regarded as the regulation mechanism at the cellular 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;

[0030] 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.

[0031] Further, the step S3 further comprises:

[0032] S31, learning and calibrating unknown parameters in the beef cattle fat metabolism multi-scale biological network model by using collected experimental data;

[0033] S32, for metabolic pathways and hormone regulation networks with clear kinetic equations, numerical solutions of ordinary differential equations or partial differential equation systems are used to simulate the changes of metabolite concentrations, enzyme activities and hormone levels over time;

[0034] 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, and 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;

[0035] S34, using Monte Carlo simulation to quantify the uncertainty of the prediction results of the model, generating the probability distribution of the prediction results, and evaluating the risks that different intervention strategies may bring.

[0036] Further, the step S4 further comprises:

[0037] S41, defining a comprehensive optimization objective function, the input variables including 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 including at least the evaluation indexes of beef cattle growth performance, carcass quality, feeding cost and environmental impact;

[0038] 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;

[0039] S43, generating intervention strategies according to the output of the optimization algorithm, performing precise feed formula recommendation, functional additive application scheme, environmental control suggestion or individualized breeding management strategy.

[0040] Further, the step S42 further comprises:

[0041] By simulating the mechanism of natural selection and heredity, the global optimal solution is searched in the parameter space of feed formula and additive dosage, and different feed raw material ratios or specific additive amounts are used as gene coding;

[0042] The beef cattle feeding 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 the beef cattle are regarded as reward signals, and an optimal feeding strategy is learned through Q-learning, a deep Q network or a policy gradient algorithm;

[0043] The convex optimization or linear programming adopts a simplex method or an interior point method to solve an optimal formula under the constraint conditions of meeting nutritional requirements, raw material costs and availability.

[0044] Further, the step S43 further comprises:

[0045] The precise feed formula recommendation recommends an optimal daily ration composition including accurate proportions of different feed raw materials based on beef cattle breeds, growth stages, individual physiological states and predetermined production targets;

[0046] The functional additive application scheme recommends optimal additive amounts and addition timing of specific functional additives, including cysteamine hydrochloride, probiotics, enzyme preparations or organic trace elements;

[0047] The environmental regulation suggestion provides optimal environmental temperature, humidity and ventilation regulation parameter ranges to reduce stress and optimize energy utilization efficiency.

[0048] Further, the step S5 further comprises:

[0049] The generated feeding strategy and management scheme are deployed to actual beef cattle farms through an automatic feeding system, an environmental control system and an Internet of Things device;

[0050] Real-time data of growth performance, feed intake, health status and blood biochemical indicators of beef cattle are continuously collected, and the real-time data are obtained through a sensor network, visual recognition technology and biomarker detection technology;

[0051] 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 a preset value, a model correction process is started, and the biological network model parameters, the prediction engine algorithm or the optimization objective function are adjusted and recalibrated;

[0052] 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.

[0053] 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:

[0054] One or more processors;

[0055] a memory having stored thereon one or more programs, when executed by the one or more processors, cause the one or more processors to implement the computer simulation and regulation method for fat metabolism of beef cattle.

[0056] The present application belongs to the technical field of bioinformatics, and specifically relates to a computer simulation and regulation method and system for fat metabolism of beef cattle, 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 benefits 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 industry. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A computer simulation and regulation method for fat metabolism of beef cattle claimed in the embodiments of the present application;

[0058] Figure 2 A second workflow diagram of a computer simulation and regulation method for fat metabolism of beef cattle claimed in the embodiments of the present application;

[0059] Figure 3 A third workflow diagram of a computer simulation and regulation method for fat metabolism of beef cattle claimed in the embodiments of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a 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.

[0061] The terms "first", "second", "third", etc. in the present application are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. 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 "plurality" is at least two, such as two, three, etc., unless otherwise explicitly specified. 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 that includes 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.

[0062] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor does it necessarily refer to a separate or alternative embodiment in isolation from other embodiments. It is expressly understood that the embodiments described herein can be combined with other embodiments.

[0063] In the traditional beef cattle fattening management practice, in order to accelerate the growth process of beef cattle, the ranchers or breeders generally tend 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 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 in 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 technology.

[0064] The prior art has made many progresses in improving beef 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 proven 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 optimize growth performance and nutrient digestion and metabolism.

[0065] 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 fat metabolism, comprising the following steps:

[0066] S1, establishing a beef herd biology data acquisition and integration system to collect multi-dimensional biological data of beef in different growth stages, including physiology, nutrition, behavior and meat production performance;

[0067] S2, constructing a multi-scale beef fat metabolism 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 of beef fat in the body;

[0068] S3, developing a beef fat metabolism computer simulation and prediction engine, using the biological network model to simulate the dynamic changes of beef 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;

[0069] 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 goals and constraints;

[0070] S5, applying the intelligent intervention strategy to the actual breeding environment, and verifying and continuously optimizing the beef fat metabolism computer simulation and prediction engine through continuous monitoring and data feedback.

[0071] Furthermore, step S1 also includes:

[0072] Data on the daily feed intake and water consumption of each beef cattle are obtained through automated feeding equipment;

[0073] Key morphological parameters of beef cattle were obtained using a computer vision-based body measurement system.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] In this embodiment, phenotypic data of beef cattle are acquired in real time and without contact. Automated feeding equipment, such as individual feed supply stations equipped with RFID tag identification, can accurately record the daily feed intake and water consumption of each beef cattle, with a data accuracy of ±10 grams.

[0080] Meanwhile, computer vision-based body measurement systems, such as those using multiple high-definition cameras (e.g., 1920x1080 pixels, 30fps) in conjunction with deep learning algorithms (e.g., Mask R-CNN), can perform three-dimensional reconstruction and precise measurement of key morphological parameters of beef cattle, such as height, length, and chest circumference, with measurement errors controlled within ±1%. This system can also calculate body shape and leg scores by analyzing the cattle's posture and movement trajectories.

[0081] In addition, a pedometer or triaxial acceleration sensor integrated into the ear tag or neck collar, with a sampling frequency set to 100 Hz, is used to monitor the activity intensity, activity duration, and rumination behavior of beef cattle throughout the day, thereby quantifying their energy consumption. A real-time weighing system, typically a dynamic weighing platform integrated into the passageway 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 a local area network or wireless network to a central server and time-stamped for preliminary cleaning.

[0082] Secondly, the feed efficiency and nutrient conversion efficiency are evaluated. Feed samples are collected by batch or regularly, and routine nutrient component analysis is performed according to national standards (such as GB / T6432 for crude protein determination and GB / T6433 for 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 the content of vitamins A, D, and E. Digestibility is determined by implanting a fistula in a specific part of the beef cattle digestive tract (such as the rumen, the end of the duodenum, or the end of the ileum) to collect rumen fluid, small intestinal contents, and fecal samples. For example, the chromium oxide indicator method or acid insoluble ash (AIA) method is used to determine dry matter digestibility, organic matter digestibility, and crude protein digestibility. The determination of endogenous metabolites in feces, such as the analysis of steroid metabolites or short-chain fatty acids (SCFAs) in feces using HPLC-MS (high-performance liquid chromatography-mass spectrometry) technology, can further evaluate the energy and protein conversion efficiency of the feed and provide a deeper understanding of intestinal function.

[0083] Furthermore, peripheral blood samples are collected from the jugular vein of beef cattle regularly (e.g., every two weeks) using vacuum blood collection tubes (such as EDTA anticoagulant tubes or coagulation tubes). After plasma separation by centrifugation (3000g, 15 minutes), the glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total protein (TP), albumin (ALB), urea nitrogen (BUN), creatinine (CRE), homocysteine, and ammonia in the plasma are determined 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 liver and kidney function. At the same time, high-sensitivity enzyme-linked immunosorbent assay (ELISA) kits or radioimmunoassay (RIA) kits are used to quantitatively analyze the hormone levels in the plasma related to fat metabolism, growth and development, and stress response, including but not limited to insulin, glucagon, leptin, growth hormone (GH), insulin-like growth factor-1 (IGF-1), and adrenocortical hormone, to reveal the regulation mechanism of the endocrine system on fat metabolism.

[0084] Further, after the beef cattle reach the predetermined slaughter weight, evaluation data at the end of the final product is obtained. After the beef cattle are slaughtered, the carcass weight, carcass yield are recorded in detail. The backfat thickness is measured using ultrasonic waves (such as B-ultrasound) between the 12th and 13th ribs, or directly measured after slaughter, accurate to the nearest millimeter. The eye muscle area (REA) is calculated by tracing the cross-section of the carcass or using image analysis software. The net meat rate is also calculated by fine segmentation. Back longissimus dorsi muscle samples are collected for meat quality analysis, including meat color (L, a, b values are measured using a colorimeter), pH value (measured using a portable pH meter at 45 minutes and 24 hours after slaughter), drip loss (suspension method or bag method, water loss rate after 24 hours is measured), shear force (measured using a Warner-Bratzler shear force instrument, unit: N / cm², reflecting meat tenderness), 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 proportions) and amino acid composition are precisely quantified by gas chromatography-mass spectrometry (GC-MS) and amino acid automatic analyzer.

[0085] In addition, fresh fecal samples of beef cattle are collected regularly and immediately frozen at -80°C for preservation. Standard microbial DNA extraction kits are used to extract total microbial DNA. High-throughput sequencing platforms are used to amplify the V3-V4 or V4 hyper-variable region of the 16S rRNA gene for amplicon sequencing, using primers such as 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 DADA2 algorithm), operational taxonomic unit (OTU) clustering or amplicon sequence variant (ASV) identification. Subsequently, community data are analyzed, including alpha diversity indices (such as Shannon, Simpson indices), beta diversity analysis (such as PCoA), as well as community structure composition and relative abundance at the phylum, class, order, family, genus, and species levels. Through functional prediction tools such as PICRUSt2, the metabolic functions of intestinal microbes can be further inferred, and key metabolic pathways and functional flora related to fat metabolism can be identified.

[0086] 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 managed through a time series database, ensuring efficient storage, retrieval, and consistency of data, providing solid data support for subsequent model building.

[0087] Further, the step S2 further comprises:

[0088] 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;

[0089] The biochemical reaction equation set or dynamic system differential equation set based on matrix operation is used to describe and construct the multi-scale biological network model of beef cattle fat metabolism;

[0090] Identifying the key genes and transcription factors related to beef cattle fat metabolism, and constructing the regulation relationship network among genes, transcription factors and hormones to describe the regulation mode of gene expression pattern;

[0091] Associating the binding of pancreatic key hormones in plasma with 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;

[0092] 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 the core microbial groups, specific genera or species significantly related to fat metabolism phenotype;

[0093] Constructing the interaction network between the core microbial groups and the host fat metabolism pathway.

[0094] In this embodiment, multi-source biological data is fused into a unified computational model with biological interpretation to dynamically simulate the synthesis, decomposition, transport and deposition of fat in beef cattle.

[0095] First, the absorption, transport of energy (carbohydrate, fat), protein (amino acid), vitamin and mineral in the digestive tract of beef cattle, and the metabolic pathways in liver, muscle and adipose tissue are constructed. For example, the carbohydrate metabolic 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 metabolic 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, adipose triglyceride lipase ATGL). The protein metabolic pathway involves the absorption, transamination, deamination, urea cycle of amino acids and protein synthesis. 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 metabolic product 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 preliminarily estimated through literature data or experimental data.

[0096] 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 adipose 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, and the regulatory relationship of hormone (such as insulin, leptin) indirectly affecting gene expression through signal pathway is constructed. 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.

[0097] Further, the key hormones detected in 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.

[0098] 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 microbial community structure or functional features 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).

[0099] Further, the step S2 further comprises:

[0100] Different scale network modules are considered as subsystems, connected through shared input and output variables or coupling parameters;

[0101] Gene regulatory networks are considered as cell-level regulatory mechanisms, endocrine networks are considered as organ-level regulatory mechanisms, metabolic networks are considered as biochemical reaction-level execution mechanisms, and microbial interaction networks are considered as environmental-level modification mechanisms, with information transmission and collaboration through mathematical mapping relationships or multi-agent systems;

[0102] 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.

[0103] In this embodiment, the nutrient metabolism network, gene regulation network, endocrine regulation network and gut microbiota-host interaction network are integrated to form a comprehensive biological network model. The integration method includes:

[0104] 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.

[0105] Second, hierarchical modeling, the gene regulation network is regarded as the regulation mechanism at the cellular level, the endocrine network is regarded as the regulation mechanism at the organ / system level, the metabolic network is regarded as the execution mechanism at the biochemical reaction level, and the microbiota interaction network is regarded as the modification mechanism at the environmental level. Information transmission and cooperation are realized through mathematical mapping relationship or multi-agent system. 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 (such as regulation, catalysis, transport, and interaction) between them 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 realized by using Python programming language, combining scientific computing libraries such as Biopython and SciPy, and customizing scripts.

[0106] Further, with reference to Figure 2 , the step S3 further comprises:

[0107] S31, learning and calibrating unknown parameters in the beef fat metabolism multi-scale biological network model by using collected experimental data;

[0108] S32, for metabolic pathways and hormone regulation networks with explicit 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;

[0109] 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.

[0110] 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.

[0111] In this embodiment, the aforementioned biological network model is used to simulate the dynamic changes of beef cattle fat metabolism under different feeding conditions and physiological states, and accurately predict the comprehensive effects on growth performance and meat quality. The simulation and prediction engine includes:

[0112] First, the unknown parameters in the biological network model are learned and calibrated using the experimental data collected in the first step.

[0113] 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, a target function (such as mean square error MSE or root mean square error RMSE) is defined to minimize the error between the model prediction value and the actual observation value, and heuristic optimization algorithms such as gradient descent (e.g. Adam optimizer), particle swarm optimization (PSO) or genetic algorithm (GA) are used 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, identify key parameters, and optimize the calibration accuracy, focusing limited experimental resources on parameters that have the greatest impact on the model.

[0114] 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) to predict the effects of specific feeding conditions on meat quality.

[0115] 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.

[0116] Further, with reference to Figure 3 , the step S4 further comprises:

[0117] 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;

[0118] S42, according to the characteristics of the optimization problem, at least one optimization algorithm among genetic algorithm, reinforcement learning, convex optimization or linear programming is selected and implemented;

[0119] 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.

[0120] Further, the step S42 further comprises:

[0121] By simulating the mechanism of natural selection and heredity, the global optimal solution is searched in the parameter space of feed formula and additive dosage, and different feed raw material proportions or specific additive addition amounts are taken as gene coding;

[0122] 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, and the optimal feeding strategy is learned through Q-learning, deep Q network or policy gradient algorithm;

[0123] 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 requirements, raw material cost and availability.

[0124] Further, the step S43 further comprises:

[0125] The precise feed formula recommendation is based on beef cattle breed, growth stage, individual physiological state and predetermined production target, and the optimal daily ration composition is recommended, including the accurate proportion of different feed raw materials;

[0126] The functional additive application scheme recommends the optimal additive dosage and addition time of specific functional additives, including cysteamine hydrochloride, probiotics, enzyme preparations or organic trace elements;

[0127] The environmental regulation suggestion provides the optimal environmental temperature, humidity and ventilation regulation parameter range, reduces stress and optimizes energy utilization efficiency.

[0128] In this embodiment, according to the simulation prediction result, the optimal breeding management and additive application scheme is generated in combination with the preset breeding target and constraint condition. The intelligent intervention strategy optimization engine comprises:

[0129] Firstly, 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).

[0130] 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.

[0131] 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.

[0132] Moreover, based on the output of the optimization algorithm, specific and actionable intervention strategies are generated for implementation in actual farming environments. These strategies include: precise feed formulation recommendations, which recommend the optimal composition of daily rations, including the precise proportions of different feed ingredients (e.g., 55% corn, 20% soybean meal, 10% DDGS, 10% alfalfa meal, 5% premix) 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, which recommend the optimal dosage and timing of specific functional additives (e.g., cysteamine hydrochloride, recommended at 300 ppm in the post-fattening stage; specific probiotic strains, such as Bacillus subtilis, at a daily dosage of 1 x 10^9 CFU / head; enzyme preparations, such as cellulase, recommended at 500 g per ton of feed) to precisely influence fat metabolism through regulation of gut flora, digestive enzyme activity, or hormone levels. Environmental regulation recommendations, which provide 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 interventions for individuals with growth retardation or excessive obesity.

[0133] Further, the step S5 further comprises:

[0134] The generated feeding strategies and management plans are deployed to actual beef cattle farms through automated feeding systems, environmental control systems, and Internet of Things devices;

[0135] 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;

[0136] 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 a preset value, the model correction process is started to adjust and recalibrate the biological network model parameters, prediction engine algorithm, or optimization objective function;

[0137] The verified new biological data, model parameters, and optimization strategies are continuously integrated into the knowledge base of the system, enabling self-learning and iterative upgrading.

[0138] 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.

[0139] First, the feeding strategies and management plans generated by computer simulation and control methods are deployed to actual beef cattle farms through Internet of Things (IoT) technology.

[0140] Automated feeding systems, such as robotic feeding vehicles equipped with gravity sensors and precision feeding mechanisms, can automatically mix and deliver feed according to recommended feed formulas, ensuring that each beef cattle receives its customized ration. Intelligent environmental control systems, such as PLCs (programmable logic controllers) based on sensor feedback (temperature, humidity, ammonia concentration sensors), automatically adjust equipment such as ventilation fans, wet curtains, and heaters to maintain environmental parameters within recommended ranges.

[0141] Secondly, sensor networks, visual recognition technology, and biomarker detection technology are used to continuously collect real-time data on the growth performance, feed intake, water consumption, activity intensity, health status (such as body temperature and respiratory rate), and key blood biochemical indicators of beef cattle. For example, high-precision pedometers continuously monitor cattle activity, infrared thermal imaging cameras monitor body surface temperature, and non-contact sensors measure rumination time. This multi-dimensional real-time data is wirelessly transmitted to a central data platform for preliminary processing and visualization.

[0142] Furthermore, data on beef cattle growth performance, carcass quality, and meat quality obtained from actual production (e.g., average daily weight gain, intramuscular fat content, and backfat thickness of a batch of beef cattle under a specific intervention strategy) are compared and analyzed with the prediction results of the computer simulation model. If statistically significant differences exist (e.g., through t-tests or ANOVA analysis), a model correction process is initiated. This process involves adjusting the parameters of the biological network model in step two, optimizing the algorithm of the prediction engine in step three, or recalibrating the weights or constraints of the objective function in step four. This validation and correction process is iterative, ensuring that the model can continuously adapt to real-world farming environments and physiological changes in beef cattle, thereby improving the model's robustness and accuracy.

[0143] Finally, validated new biological data, model parameters, and optimization strategies are continuously integrated into the system's knowledge base. This means that each successful model validation and revision enriches the system's knowledge base, enabling it to learn and iteratively upgrade itself. For example, if a systematic deviation is found between the model's prediction and the response of a certain breed of beef cattle to a certain additive, the revised model parameters and new additive application strategies will be recorded, thereby improving the model's generalization ability and prediction accuracy across different breeds and environments, and achieving continuous intelligent evolution of the system.

[0144] According to a second embodiment of the present invention, the present invention claims protection for a computer simulation and regulation system for beef cattle fat metabolism, comprising:

[0145] one or more processors;

[0146] a memory having stored thereon one or more programs that, 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.

[0147] The following is explained and described with specific examples:

[0148] 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.

[0149] Experimental subjects: 120 beef cattle (Simmental and Limuzan cross, bulls) with an average weight of 350 kg 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.

[0150] The cattle in this group were managed using the computer simulation and regulation method described in the present application.

[0151] Data collection and integration:

[0152] The daily feed intake and water intake of each cow were recorded in real time through the RFID automated feeding system.

[0153] 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.

[0154] Blood samples were collected every two weeks to determine the levels of plasma glucose, triglycerides, insulin, leptin, and IGF-1.

[0155] Fecal samples were collected every month for 16S rRNA gene sequencing to analyze the intestinal microbial community structure.

[0156] Model construction and calibration:

[0157] Using the above collected data, a multi-scale biological network model of beef cattle fat metabolism was constructed and calibrated, including modules of nutrient metabolism, gene regulation, endocrine regulation, and intestinal microbe-host interaction. The model parameters were optimized through Bayesian inference combined with historical data.

[0158] Simulation and prediction:

[0159] Based on the constructed model and real-time data, the effects of different feed formulations and additive schemes on daily weight gain, backfat thickness, and intramuscular fat content of 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 muscle.

[0160] Optimization strategy generation:

[0161] Set optimization goals: maximize intramuscular fat content while ensuring daily weight gain of no less than 1.5 kg / day and feeding cost within a controllable range.

[0162] The intelligent intervention strategy optimization engine uses genetic algorithms to generate the optimal daily ration formulation and additive scheme under the constraints of feed raw material cost, nutritional requirements, and physiological limitations. The specific recommended scheme is: use the basic daily ration in the early fattening period (1-60 days), with crude protein content of 16% and energy density of 2.9 Mcal / kg; adjust the daily ration formulation in the later fattening period (61-120 days), reduce the crude protein content to 14%, increase the energy density to 3.1 Mcal / kg, and additionally add cysteamine hydrochloride (CA) 300 ppm.

[0163] Implementation and feedback:

[0164] The feed formulation is precisely delivered through automated feeding equipment, and cysteamine hydrochloride is mixed into the feed according to the recommended dosage.

[0165] Continuous monitoring of cattle growth performance and regular model verification. At the end of the fattening period, 30 cattle are slaughtered, and carcass performance and meat quality indicators are recorded in detail.

[0166] Traditional experience control strategy:

[0167] This group of cattle uses the traditional experiential feeding and management mode, and the feed formulation and additive application are based on industry general standards and the experience of breeding personnel.

[0168] Feed formulation:

[0169] In the early fattening period (1-60 days), the basic daily ration with crude protein content of 16% and energy density of 2.9 Mcal / kg is used.

[0170] In the later fattening period (61-120 days), the general fattening daily ration with crude protein content of 15% and energy density of 3.0 Mcal / kg is used, and no functional additives for regulating fat metabolism are added.

[0171] Management mode:

[0172] Cattle intake, body weight, and other data are still recorded manually or obtained through regular weighing, but systematic biological data collection and integration are not performed.

[0173] No computer simulation prediction and intelligent optimization were performed. The use of additives followed conventional livestock practices, lacking specificity.

[0174] Result evaluation:

[0175] At the end of the fattening period, 30 cattle were slaughtered, and carcass performance and meat quality indicators were recorded for comparison with Example One.

[0176] Comparison of experimental results:

[0177] Table 1 lists the main indicators of beef cattle growth performance and meat quality in Example One (using the method of the present application) and Comparative Example One (traditional experience method):

[0178] Table 1 Comparison of indicators of beef cattle growth performance and meat quality

[0179]

[0180] From the comparison data, it can be clearly seen that beef cattle raised using the computer simulation and control method disclosed in the present application (Example One) 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.

[0181] Specifically, the final body weight and average daily gain of beef cattle in Example One were significantly higher than those in Comparative Example One (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 One was significantly better than that of Comparative Example One (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.

[0182] In terms of slaughter performance, the carcass weight and carcass yield of Example One were significantly better than those of Comparative Example One (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 One was significantly lower than that of Comparative Example One (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.

[0183] In terms of meat quality, the advantages of the present application are more pronounced. Intramuscular fat content (IMF) is a key indicator of beef quality, particularly tenderness, juiciness and flavor, and the IMF content of Example One was 4.87%, much higher than 3.12% of Comparative Example One (P < 0.001), with a high 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 the beef of Example One was significantly reduced (35.6 N / cm 2 vs 42.1 N / cm 2 , P < 0.01), and the drip loss was also significantly reduced (2.1% vs 2.8%, P < 0.05), which directly confirmed the improvement in meat tenderness and water retention. 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.

[0184] 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, and for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, multiple 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 shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0185] 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 above integrated unit can be realized in the form of hardware or 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 contents 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.

[0186] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the specific embodiments described above. Any equivalent modification or substitution of the present application by those skilled in the art is also within the scope of the present application, and therefore, any equivalent transformation, modification, improvement, etc. made without departing from the spirit and principle range of the present application should be included in the scope of the present application.

Claims

1. A computer simulation and regulation method for fat metabolism in beef cattle, characterized in that, Includes 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, 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; Step S2 further includes: Establish pathways for the absorption, transport, and metabolism of energy, protein, vitamins, and minerals in the digestive tract of beef cattle, as well as in the liver, muscle, and adipose tissue; The multi-scale biological network model of beef cattle fat metabolism is constructed by describing a set of biochemical reaction equations or a set of dynamic system differential equations based on matrix operations. Identify key genes and their transcription factors related to fat metabolism in beef cattle, and construct a regulatory network of gene-gene, transcription factor-gene, and hormone-gene relationships to describe the regulated modes of gene expression. By associating key pancreatic hormones in plasma with their receptor binding, signal transduction pathways, and downstream effector molecules in their target tissues, a dynamic model of hormone secretion, transport, receptor binding, and signal cascade amplification is constructed to explain how hormones coordinate the body's energy balance and lipid metabolism. Correlation analysis was conducted between fecal microbiome sequencing data and growth performance, nutrient digestion and metabolism, blood biochemical indicators and slaughter meat quality indicators of beef cattle to identify core microbial groups, specific genera or species that are significantly related to lipid metabolism phenotype. Construct an interaction network between the core microbial groups and the host lipid metabolism pathway; Step S2 further includes: Network modules of different scales are treated as subsystems and connected through shared input / output variables or coupling parameters; Gene regulatory networks are used as regulatory mechanisms at the cellular level, endocrine networks as regulatory mechanisms at the organ level, metabolic networks as execution mechanisms at the biochemical reaction level, and microbial interaction networks as modification mechanisms at the environmental level. Information transmission and coordination are carried out through mathematical mapping relationships or multi-agent systems. Based on the heterogeneous graph model, different types of biological entities are regarded as nodes, and the 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.

2. The computer simulation and regulation method for beef cattle fat metabolism according to claim 1, characterized in that, Step S3 further includes: S31, using the collected experimental data to learn and calibrate the unknown parameters in the multi-scale biological network model of beef cattle fat metabolism; S32 uses ordinary differential equations or partial differential equations to numerically solve metabolic pathways and hormone regulatory networks with well-defined kinetic equations, simulating the changes in metabolite concentration, enzyme activity, and hormone levels over time. S33. For large 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 a 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 uses Monte Carlo simulation to quantify the uncertainty of the model's prediction results, generates the probability distribution of the prediction results, and assesses the potential risks of different intervention strategies.

3. The computer simulation and regulation method for beef cattle fat metabolism according to claim 1, characterized in that, Step S4 further includes: S41 defines a comprehensive optimization objective function. The input variables are adjustable factors including at least the proportion of feed components, the type and dosage of additives, the feeding frequency, and environmental control parameters. The output variables are evaluation indicators including at least the growth performance of beef cattle, carcass quality, feeding costs, and environmental impact. S42, Based on the characteristics of the optimization problem, select and implement at least one optimization algorithm among genetic algorithm, reinforcement learning, convex optimization, or linear programming; S43 generates intervention strategies based on the output of the optimization algorithm, and executes precise feed formulation recommendations, functional additive application plans, environmental control suggestions, or individualized breeding management strategies.

4. The computer simulation and regulation method for beef cattle fat metabolism according to claim 3, characterized in that, Step S42 further includes: By simulating the mechanisms of natural selection and heredity, the global optimal solution is searched in the parameter space of feed formulation and additive dosage, and different feed ingredient ratios or the amount of specific additives are used as gene encoding. 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.

5. The computer simulation and regulation method for beef cattle fat metabolism according to claim 4, 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.

6. The computer simulation and regulation method for beef cattle fat metabolism according to claim 1, characterized in that, 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.

7. A computer simulation and control system for beef cattle fat metabolism, characterized in that, 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 the computer simulation and regulation method for beef cattle fat metabolism according to any one of claims 1 to 6.

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