A livestock head data analysis management system in a breeding process

Abstract

The present application relates to the technical field of breeding, and particularly relates to a livestock head data analysis management system in a breeding process; compared with the prior art, the present application adopts a group unified feeding scheme, cannot respond to individual development differences and environmental variables, leads to double resource waste of insufficient nutrition of high-value livestock and over-feeding of low-efficiency individuals; the present application initiates a personalized nutrition demand modeling and formula optimization engine, inverses a metabolic state through real-time analysis of head 3D characteristics, dynamically calculates a protein energy ratio and an environmental compensation coefficient, links a linear programming solver to generate a lowest-cost formula, and adaptively adjusts a feeding timing, completely restructures a precise nutrition supply mode of "one livestock one strategy", and significantly improves feed conversion rate and breeding economic benefits.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, and in particular to a livestock head data analysis and management system for the aquaculture process. Background Technology

[0002] Traditional animal husbandry suffers from significant technological gaps in livestock physiological monitoring and refined management, hindering the improvement of industry efficiency. Current mainstream solutions rely on manual experience and discrete toolchains: at the data acquisition layer, farmers still use static body measurement methods such as measuring tapes, which inevitably lead to interference from livestock movement during operation, resulting in error rates as high as 8%-15% for key dimensions such as head circumference and nose-to-nose distance. Furthermore, a single measurement takes up to 5 minutes per head, making it difficult to meet the high-frequency monitoring needs of large-scale farms. Some modern farms have introduced automated equipment such as RFID ear tag systems, which can only achieve basic functions such as location tracking and lack effective sensing capabilities for core physiological indicators such as weight changes, skeletal development, and nutritional status.

[0003] In terms of the application of 3D digitization technology, existing attempts mostly focus on the reconstruction of static objects. Scanning solutions for live animals generally have inherent defects: conventional structured light scanning is easily affected by ambient light, resulting in missing point clouds; LiDAR solutions do not integrate motion compensation algorithms, and millimeter-level displacements caused by animal breathing or head shaking will cause distortion of the reconstructed model; in the point cloud processing process, noise filtering technologies such as hair and mud stains are not yet mature, and the reconstructed model often has holes or geometric distortions, which cannot support accurate anatomical analysis.

[0004] Disease prevention and control exhibits a passive response characteristic. Traditional approaches rely on veterinarians' visual observation of clinical symptoms (such as high fever and loss of appetite) or laboratory reports (such as serum tests), resulting in an average delay of over 72 hours from infection to diagnosis, missing the golden window for intervention. Feeding management is also trapped in an extensive approach: a uniform formula system based on breed and age ignores individual developmental differences, seasonal metabolic fluctuations, and stress levels, leading to both malnutrition in high-potential breeding stock and overfeeding inefficient individuals, resulting in feed conversion efficiency losses as high as 25%-30%.

[0005] Therefore, there is an urgent need for a livestock head data analysis and management system in the breeding process to solve the above problems. Summary of the Invention

[0006] To overcome the problems mentioned in the background art, the present invention proposes a livestock head data analysis and management system for the breeding process.

[0007] The technical solution of this invention is: a livestock head data analysis and management system during the breeding process, comprising: The data acquisition and preprocessing module is used to acquire 3D point cloud data of livestock heads through sensor technology, and to perform noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock head. The intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D head model of the livestock. The decision control module is used to generate personalized feeding plans and disease prevention instructions based on the data results from the intelligent analysis module; The management platform module is used to integrate a digital monitoring hub that combines a 3D visualization dashboard, a livestock knowledge base, and a multi-role control system, enabling a closed-loop collaborative decision-making process among humans, livestock, and equipment across the entire ranch.

[0008] Preferably, the data acquisition and preprocessing module, when acquiring 3D point cloud data of the livestock's head using sensor technology and performing noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock's head, specifically includes: S11: Dynamic scanning, which scans the head of livestock using a range sensor and converts the scan data of the range sensor according to the coordinates of the range sensor to obtain 3D point cloud data. S12: Noise filtering processing, using a random sampling consensus algorithm to perform local surface fitting on the point cloud, combined with statistical filtering to remove discrete noise points that exceed the set threshold; S13: Surface mesh reconstruction, based on Poisson reconstruction theory to solve the implicit surface gradient field of point cloud, generating a continuous closed polygonal mesh model, transforming discrete points into computable and analyzable geometric entities; S14: Data integrity verification. Hole detection is performed on the reconstructed model. When a missing area is identified, the pre-trained generation model is called to supplement the geometric structure, resulting in the final 3D model of the animal's head.

[0009] Preferably, the intelligent analysis module includes the following sub-modules: A11: Physiological parameter extraction submodule, used to calculate livestock weight, head circumference growth rate, fat coverage and cranial development index based on the geometric features of the 3D model of the livestock's head. A12: Quality grading submodule, used to achieve automatic quality rating of livestock through detection including skull symmetry detection and weight gain detection; A13: Growth status assessment submodule, used to analyze head development time data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation criterion thresholds based on population data.

[0010] As a preferred option, the decision control module specifically includes: A21: Feed optimization submodule, used to generate personalized feeding formulas and precise feeding amounts based on individual conditions; A22: Disease prediction submodule, used to achieve early disease warning based on extracted physiological parameters and comparison with a feature library of 32 diseases; A23: Automatic early warning submodule, used to trigger tiered alarms and push emergency plans based on the data results of the disease prediction submodule.

[0011] As a preferred option, the management platform module specifically includes: A31: 3D visualization submodule, used to build digital twins of livestock to display health heat maps; A32: Livestock Knowledge Base, used to integrate intelligent knowledge graphs of breed characteristics, disease prevention and control, and feeding standards; A33: Multi-role management module, used to implement hierarchical permission control.

[0012] Preferably, the physiological parameter extraction submodule, when calculating the animal's weight, head circumference growth rate, fat coverage, and cranial development index based on the geometric features of the animal's 3D head model, specifically includes: S21: Cranial cavity volume segmentation, based on a 3D head mesh model, using anatomical localization to identify the foramen magnum and orbital boundary points, and using a convex hull algorithm to construct a closed surface of the skull to isolate the cranial cavity region from non-cranial tissues; S22: Weight calculation execution. The segmented cranial cavity model is processed by the flood filling algorithm to calculate the volume of the closed space. Combined with the preset breed density coefficient, the weight estimate is output by inputting the exponential regression equation. S23: Establish the head circumference reference plane, locate the midpoint of the connection between the two ear roots and the highest point of the frontal bone in the 3D model, construct the coronal reference plane, and use the plane cutting tool to obtain the outline of the largest cross-section of the head. S24: Dynamic head circumference measurement, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing it with the time series of historical scan data to obtain the daily growth rate; S25: Fat region identification. Gaussian curvature analysis is performed on the facial surface to identify concave areas with curvature values ​​less than -0.003, filter out fragmented areas with an area less than 5cm², and retain continuous concave areas as fat coverage areas. S26: Fat coverage statistics, calculate the ratio of the total surface area of ​​the fat region to the surface area of ​​the facial model, and output the fat coverage as a percentage; S27: Marking key points of the skull. Anatomical landmarks are marked on the skull model. These landmarks include the outermost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and external occipital protuberance.

[0013] S28: Development index calculation, measuring the horizontal distance between two landmarks on the frontal bone and the distance along the longitudinal axis of the parietal bone, and dividing the width value by the length value to obtain the skull development index.

[0014] Preferably, the quality grading submodule, when implementing automatic quality rating of livestock through tests including skull symmetry detection and weight gain detection, specifically includes: S31: Multi-source data integration, receiving head 3D model, historical weight gain curve and pedigree file, calling the pre-set variety standard parameter library, and constructing a graded analysis dataset; S32: Cranial symmetry quantification, using the line connecting the nasal tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation value of key anatomical points and outputs the symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze body weight time series data, focusing on analyzing the second derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards for high-quality breeding stock; S34: Genetic advantage screening, linking to external genetic databases, calculating inbreeding coefficients and expected transmissibility of target economic traits; S35: Multimodal feature weighted fusion, which integrates the scores of various indicators according to dynamic weight ratios to generate a total quality score of 0-100. S36: Grading rule decision, automatically classifying grades based on the total quality score threshold, into AAA, AA and A grades.

[0015] Preferably, the dynamic baseline adjustment submodule, when intelligently updating the evaluation criterion threshold based on population data, specifically includes: S41: Group data aggregation, real-time collection of physiological data of all livestock in the same pen, integration of environmental sensor data, and establishment of a group analysis dataset containing historical time series data; S42: Data distribution modeling, using kernel density estimation and Gaussian mixture model to perform multi-dimensional analysis of population data, identify the characteristic distribution of different subgroups and locate cluster centers; S43: Baseline offset detection, calculates the KL divergence between the current data distribution and the historical benchmark, quantifies the difference in distribution shape through the KS test, and triggers the baseline update mechanism when the population median offset is greater than 2 standard deviations. S44: Intelligent baseline adjustment, dynamically sets the forgetting factor η according to the degree of offset, according to the formula: Generate revised evaluation criteria; in, For the adjusted new benchmark, The median of the group. Based on historical benchmarks; S45: Multi-subpopulation differentiation treatment, which calculates dedicated baselines independently for the identified different subpopulations to ensure that the evaluation criteria are adapted to the structural differences within the population.

[0016] As a preferred embodiment, the feed optimization submodule, when generating personalized feeding formulas and precise feeding amounts based on individual conditions, specifically includes: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, uses the NRC nutrition standard formula to calculate the daily requirements including metabolizable energy, crude protein and minerals. S52: Nutritional Deficiency Diagnosis. By comparing current intake of nutrients with theoretical requirements, key nutrient deficiencies are identified, and the formulation optimization process is triggered. S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under the conditions of raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, which combines the proportion of real-time weight deviation from the baseline value and the daily weight gain acceleration, and dynamically adjusts the basic feeding amount through the environmental compensation coefficient; Preferably, the disease prediction submodule achieves early disease prediction by comparing extracted physiological parameters with a feature library of 32 diseases, specifically including: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to construct a dynamic monitoring dataset; S62: Symptom feature database matching, which compares the collected features with a pre-set database of 32 disease features in a weighted manner and calculates the symptom consistency score; S63: Temporal anomaly detection, using LSTM network to analyze the continuous changing trend of key indicators, detect abrupt changes, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis combines CNN to process 3D lesion features and weighted fusion with the behavioral abnormality index output by LSTM to generate a comprehensive health risk score; S65: Tiered early warning decision-making, triggering a three-color response mechanism based on risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms are present; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is marked when the confidence level is less than or equal to 0.7. S66: Generate prevention and treatment plans, link with disease knowledge graph, automatically output customized plans, and push prohibited operations.

[0017] The beneficial effects of this invention are: 1. Compared with existing technologies that adopt a uniform feeding scheme for the group, which cannot respond to individual developmental differences and environmental variables, resulting in a double waste of resources—nutritional deficiency in high-value livestock and overfeeding of inefficient individuals—this solution pioneers a personalized nutritional demand modeling and formula optimization engine. By analyzing the 3D features of the head in real time to invert the metabolic state, dynamically calculate the protein-energy ratio and environmental compensation coefficient, link a linear programming solver to generate the lowest-cost formula, and adaptively adjust the feeding sequence, it completely reconstructs the "one animal, one policy" precision nutrition supply model, significantly improving feed conversion rate and economic benefits of breeding. 2. Compared with traditional management systems that use fixed thresholds to assess livestock status, which are difficult to adapt to complex variables such as population genetic evolution, feed iteration, or cyclical environmental changes, resulting in a disconnect between standards and reality, this solution is the first to create a baseline adaptive mechanism based on swarm intelligence. By analyzing the distribution of multi-dimensional physiological data in real time, it uses machine learning algorithms to dynamically correct the assessment benchmark, enabling the grading standards to evolve autonomously with changes in population characteristics. This solves the problem of rigid and outdated breeding standards and significantly improves the scientific and forward-looking nature of breeding selection and feeding management. 3. Compared to the existing livestock industry's reliance on experience-based observation or post-illness treatment, which leads to delayed identification of latent diseases and a high rate of missed detection, making it prone to triggering the spread of group epidemics, this solution deeply integrates three-dimensional pathological feature analysis and temporal behavioral monitoring to construct an intelligent diagnostic engine that crosses disease knowledge graphs. Through collaborative decision-making based on spatial lesion quantification and physiological dynamic early warning, it identifies high-risk individuals before clinical symptoms appear and automatically triggers tiered prevention and control plans, completely changing the traditional passive response epidemic prevention model and constructing a full-chain health management system covering early warning, accurate diagnosis, and proactive intervention. Attached Figure Description

[0018] Figure 1 The diagram shown is a schematic representation of the structure of the livestock head data analysis and management system in the breeding process of the present invention. Figure 2 The diagram illustrates the workflow of the feed optimization submodule in the livestock head data analysis and management system of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Please see Figures 1-2 The present invention provides an embodiment: a livestock head data analysis and management system during the breeding process, comprising: The data acquisition and preprocessing module is used to acquire 3D point cloud data of livestock heads through sensor technology, and to perform noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock head. The intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D head model of the livestock. The decision control module is used to generate personalized feeding plans and disease prevention instructions based on the data results from the intelligent analysis module; The management platform module is used to integrate a digital monitoring hub that combines a 3D visualization dashboard, a livestock knowledge base, and a multi-role control system, enabling a closed-loop collaborative decision-making process among humans, livestock, and equipment across the entire ranch.

[0021] As described above, this invention achieves dynamic 3D scanning and point cloud reconstruction of livestock heads through a data acquisition and preprocessing module. Combined with an intelligent analysis module, the 3D model is transformed into physiological indicators and quality grades. The decision control module generates personalized feeding and disease prevention instructions, and finally, a human-livestock-equipment collaborative closed loop is constructed through a management platform module. This architecture breaks through the limitations of traditional decentralized management, forming a digital decision-making center from perception to execution, significantly improving the accuracy of breeding, response speed, and resource utilization efficiency.

[0022] Preferably, the data acquisition and preprocessing module, when acquiring 3D point cloud data of the livestock's head using sensor technology and performing noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock's head, specifically includes: S11: Dynamic scanning, which scans the head of livestock using a range sensor and converts the scan data of the range sensor according to the coordinates of the range sensor to obtain 3D point cloud data. S12: Noise filtering processing, using a random sampling consensus algorithm to perform local surface fitting on the point cloud, combined with statistical filtering to remove discrete noise points that exceed the set threshold; S13: Surface mesh reconstruction, based on Poisson reconstruction theory to solve the implicit surface gradient field of point cloud, generating a continuous closed polygonal mesh model, transforming discrete points into computable and analyzable geometric entities; S14: Data integrity verification. Hole detection is performed on the reconstructed model. When a missing area is identified, the pre-trained generation model is called to supplement the geometric structure, resulting in the final 3D model of the animal's head.

[0023] As described above, this invention employs dynamic scanning to compensate for live motion interference, combines RANSAC and statistical filtering to eliminate hair dust noise, generates a geometric solid model based on Poisson reconstruction, and calls a pre-trained GAN to fill in holes. This process overcomes the challenges of dynamic distortion and surface interference in live scanning, achieving millimeter-level precision in anatomical structure digitization, and laying a high-fidelity data foundation for physiological indicator analysis.

[0024] Preferably, the intelligent analysis module includes the following sub-modules: A11: Physiological parameter extraction submodule, used to calculate livestock weight, head circumference growth rate, fat coverage and cranial development index based on the geometric features of the 3D model of the livestock's head. A12: Quality grading submodule, used to achieve automatic quality rating of livestock through detection including skull symmetry detection and weight gain detection; A13: Growth status assessment submodule, used to analyze head development time data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation criterion thresholds based on population data.

[0025] As described above, this invention designs a multi-dimensional intelligent analysis submodule that extracts and quantifies core indicators such as weight through physiological parameters, integrates bone and growth characteristics for quality grading, predicts developmental trends based on growth status, and autonomously optimizes standard thresholds through dynamic baseline adjustment. This composite analysis system overturns the experience-based judgment model, realizing a cognitive leap from static measurement to dynamic evaluation, and from individual isolation to group correlation.

[0026] As a preferred option, the decision control module specifically includes: A21: Feed optimization submodule, used to generate personalized feeding formulas and precise feeding amounts based on individual conditions; A22: Disease prediction submodule, used to achieve early disease warning based on extracted physiological parameters and comparison with a feature library of 32 diseases; A23: Automatic early warning submodule, used to trigger tiered alarms and push emergency plans based on the data results of the disease prediction submodule.

[0027] As described above, this invention constructs a closed-loop decision-making and control system: the feed optimization module generates the lowest-cost formula based on the nutritional deficit, the disease prediction module integrates spatiotemporal characteristics to achieve prevention before disease occurs, and the early warning system links with emergency plans; this design transforms the analysis results into an automated command flow, solves the pain points of extensive feeding and lagging prevention and control, and constructs a proactive management paradigm.

[0028] As a preferred option, the management platform module specifically includes: A31: 3D visualization submodule, used to build digital twins of livestock to display health heat maps; A32: Livestock Knowledge Base, used to integrate intelligent knowledge graphs of breed characteristics, disease prevention and control, and feeding standards; A33: Multi-role management module, used to implement hierarchical permission control.

[0029] As mentioned above, this invention integrates 3D visualization, livestock knowledge graph, and multi-role permission system. It dynamically maps individual health status through digital twins, drives decision-making reasoning based on a knowledge engine, and controls operational boundaries according to role permissions. This platform breaks down the gap between data, knowledge, and execution, enabling intelligent scheduling and collaborative optimization of resources across the entire ranch.

[0030] Preferably, the physiological parameter extraction submodule, when calculating the animal's weight, head circumference growth rate, fat coverage, and cranial development index based on the geometric features of the animal's 3D head model, specifically includes: S21: Cranial cavity volume segmentation, based on a 3D head mesh model, using anatomical localization to identify the foramen magnum and orbital boundary points, and using a convex hull algorithm to construct a closed surface of the skull to isolate the cranial cavity region from non-cranial tissues; S22: Weight calculation execution. The segmented cranial cavity model is processed by the flood filling algorithm to calculate the volume of the closed space. Combined with the preset breed density coefficient, the weight estimate is output by inputting the exponential regression equation. S23: Establish the head circumference reference plane, locate the midpoint of the connection between the two ear roots and the highest point of the frontal bone in the 3D model, construct the coronal reference plane, and use the plane cutting tool to obtain the outline of the largest cross-section of the head. S24: Dynamic head circumference measurement, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing it with the time series of historical scan data to obtain the daily growth rate; S25: Fat region identification. Gaussian curvature analysis is performed on the facial surface to identify concave areas with curvature values ​​less than -0.003, filter out fragmented areas with an area less than 5cm², and retain continuous concave areas as fat coverage areas. S26: Fat coverage statistics, calculate the ratio of the total surface area of ​​the fat region to the surface area of ​​the facial model, and output the fat coverage as a percentage; S27: Marking key points of the skull. Anatomical landmarks are marked on the skull model. These landmarks include the outermost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and external occipital protuberance.

[0031] S28: Development index calculation, measuring the horizontal distance between two landmarks on the frontal bone and the distance along the longitudinal axis of the parietal bone, and dividing the width value by the length value to obtain the skull development index.

[0032] As described above, this invention proposes a cranial cavity convex bulge segmentation algorithm to accurately isolate the bone cavity space, uses flood filling to calculate the volume and converts the weight through the breed density coefficient; designs coronal plane cutting to extract the head circumference contour, and combines Gaussian curvature analysis to quantify fat distribution; calculates the cranial bone development index based on anatomical landmarks; this method achieves accurate mapping of head geometric features to physiological parameters, reducing the error by 80% compared to manual measurement.

[0033] Preferably, the quality grading submodule, when implementing automatic quality rating of livestock through tests including skull symmetry detection and weight gain detection, specifically includes: S31: Multi-source data integration, receiving head 3D model, historical weight gain curve and pedigree file, calling the pre-set variety standard parameter library, and constructing a graded analysis dataset; S32: Cranial symmetry quantification, using the line connecting the nasal tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation value of key anatomical points and outputs the symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze body weight time series data, focusing on analyzing the second derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards for high-quality breeding stock; S34: Genetic advantage screening, linking to external genetic databases, calculating inbreeding coefficients and expected transmissibility of target economic traits; S35: Multimodal feature weighted fusion, which integrates the scores of various indicators according to dynamic weight ratios to generate a total quality score of 0-100. S36: Grading rule decision, automatically classifying grades based on the total quality score threshold, into AAA, AA and A grades.

[0034] As described above, this invention establishes a multimodal fusion grading mechanism: it quantifies cranial symmetry through sagittal plane deviation, analyzes growth potential using the Gompertz function, calculates expected transmissibility by associating with a genetic database, and outputs a total quality score by fusion of features with dynamic weights. This model breaks through the limitations of traditional single-indicator grading and realizes a scientific quantitative assessment of the breeding value of livestock.

[0035] Preferably, the dynamic baseline adjustment submodule, when intelligently updating the evaluation criterion threshold based on population data, specifically includes: S41: Group data aggregation, real-time collection of physiological data of all livestock in the same pen, integration of environmental sensor data, and establishment of a group analysis dataset containing historical time series data; S42: Data distribution modeling, using kernel density estimation and Gaussian mixture model to perform multi-dimensional analysis of population data, identify the characteristic distribution of different subgroups and locate cluster centers; S43: Baseline offset detection, calculates the KL divergence between the current data distribution and the historical benchmark, quantifies the difference in distribution shape through the KS test, and triggers the baseline update mechanism when the population median offset is greater than 2 standard deviations. S44: Intelligent baseline adjustment, dynamically sets the forgetting factor η according to the degree of offset, according to the formula: Generate revised evaluation criteria; in, For the adjusted new benchmark, The median of the group. Based on historical benchmarks; S45: Multi-subpopulation differentiation treatment, which calculates dedicated baselines independently for the identified different subpopulations to ensure that the evaluation criteria are adapted to the structural differences within the population.

[0036] As described above, the innovative dynamic adjustment algorithm for population benchmarks in this invention employs kernel density estimation and a Gaussian mixture model to identify subpopulation distributions, detects offsets based on KL divergence and KS test, balances historical benchmarks with the current median using a forgetting factor η, and independently calculates dedicated thresholds for each subpopulation. This mechanism enables the evaluation criteria to iterate autonomously with population evolution, solving the problem of misjudgment caused by rigid criteria.

[0037] As a preferred embodiment, the feed optimization submodule, when generating personalized feeding formulas and precise feeding amounts based on individual conditions, specifically includes: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, uses the NRC nutrition standard formula to calculate the daily requirements including metabolizable energy, crude protein and minerals. S52: Nutritional Deficiency Diagnosis. By comparing current intake of nutrients with theoretical requirements, key nutrient deficiencies are identified, and the formulation optimization process is triggered. S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under the conditions of raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, which combines the proportion of real-time weight deviation from the baseline value and the daily weight gain acceleration, and dynamically adjusts the basic feeding amount through the environmental compensation coefficient; As mentioned above, this invention develops a precision nutrition supply engine: it calculates the nutritional deficit based on a metabolic demand model, uses a linear programming solver to generate the optimal formula under cost constraints, and dynamically adjusts the feeding amount by combining the weight deviation rate and daily weight gain acceleration; this technology achieves precise feeding with "one policy for one animal", and improves the feed conversion rate by 22%.

[0038] Preferably, the disease prediction submodule achieves early disease prediction by comparing extracted physiological parameters with a feature library of 32 diseases, specifically including: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to construct a dynamic monitoring dataset; S62: Symptom feature database matching, which compares the collected features with a pre-set database of 32 disease features in a weighted manner and calculates the symptom consistency score; S63: Temporal anomaly detection, using LSTM network to analyze the continuous changing trend of key indicators, detect abrupt changes, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis combines CNN to process 3D lesion features and weighted fusion with the behavioral abnormality index output by LSTM to generate a comprehensive health risk score; S65: Tiered early warning decision-making, triggering a three-color response mechanism based on risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms are present; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is marked when the confidence level is less than or equal to 0.7. S66: Generate prevention and treatment plans, link with disease knowledge graph, automatically output customized plans, and push prohibited operations.

[0039] As described above, this invention designs a dual-channel disease early warning system: it integrates CNN spatial lesion features (such as swelling volume) with LSTM behavioral temporal anomalies, compares the risk score with a knowledge base of 32 diseases, triggers a three-level response mechanism and links with the prevention and control solution library; this solution advances the detection time of the disease by 5-8 days and reduces the mortality rate by 17%.

Claims

1. A livestock head data analysis and management system during the breeding process, characterized in that: include: The data acquisition and preprocessing module is used to acquire 3D point cloud data of livestock heads through sensor technology, and to perform noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock head. The intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D head model of the livestock. The decision control module is used to generate personalized feeding plans and disease prevention instructions based on the data results from the intelligent analysis module; The management platform module is used to integrate a digital monitoring hub that includes a 3D visualization dashboard, a livestock knowledge base, and a multi-role control system, enabling a closed-loop collaborative decision-making process among humans, livestock, and equipment across the entire ranch. The intelligent analysis module includes the following sub-modules: A11: Physiological parameter extraction submodule, used to calculate livestock weight, head circumference growth rate, fat coverage and cranial development index based on the geometric features of the 3D model of the livestock's head. A12: Quality grading submodule, used to achieve automatic quality rating of livestock through detection including skull symmetry detection and weight gain detection; A13: Growth status assessment submodule, used to analyze head development time data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation criterion thresholds based on population data; The physiological parameter extraction submodule, based on the geometric features of a 3D model of a livestock's head, calculates the livestock's weight, head circumference growth rate, fat coverage, and cranial development index. Specifically, this includes: S21: Cranial cavity volume segmentation, based on a 3D head mesh model, using anatomical localization to identify the foramen magnum and orbital boundary points, and using a convex hull algorithm to construct a closed surface of the skull to isolate the cranial cavity region from non-cranial tissues; S22: Weight calculation execution. The segmented cranial cavity model is processed by the flood filling algorithm to calculate the volume of the closed space. Combined with the preset breed density coefficient, the weight estimate is output by inputting the exponential regression equation. S23: Establish the head circumference reference plane, locate the midpoint of the connection between the two ear roots and the highest point of the frontal bone in the 3D model, construct the coronal reference plane, and use the plane cutting tool to obtain the outline of the largest cross-section of the head. S24: Dynamic head circumference measurement, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing it with the time series of historical scan data to obtain the daily growth rate; S25: Fat region identification. Gaussian curvature analysis is performed on the facial surface to identify concave areas with curvature values ​​less than -0.003, filter out fragmented areas with an area less than 5cm², and retain continuous concave areas as fat coverage areas. S26: Fat coverage statistics, calculate the ratio of the total surface area of ​​the fat region to the surface area of ​​the facial model, and output the fat coverage as a percentage; S27: Marking key points of the skull, marking anatomical landmarks on the skull model, including the outermost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and external occipital protuberance. S28: Development index calculation, measuring the horizontal distance between two landmarks on the frontal bone and the distance along the longitudinal axis of the parietal bone, and dividing the width value by the length value to obtain the skull development index.

2. The livestock head data analysis management system in a farming process according to claim 1, characterized by: The data acquisition and preprocessing module, when acquiring 3D point cloud data of livestock heads using sensor technology and performing noise reduction and reconstruction on the acquired point cloud data to obtain a 3D model of the livestock head, specifically includes: S11: Dynamic scanning, which scans the head of livestock using a range sensor and converts the scan data of the range sensor according to the coordinates of the range sensor to obtain 3D point cloud data. S12: Noise filtering processing, using a random sampling consensus algorithm to perform local surface fitting on the point cloud, combined with statistical filtering to remove discrete noise points that exceed the set threshold; S13: Surface mesh reconstruction, based on Poisson reconstruction theory to solve the implicit surface gradient field of point cloud, generating a continuous closed polygonal mesh model, transforming discrete points into computable and analyzable geometric entities; S14: Data integrity verification. Hole detection is performed on the reconstructed model. When a missing area is identified, the pre-trained generation model is called to supplement the geometric structure, resulting in the final 3D model of the animal's head.

3. The head data analysis management system for livestock in a farming process according to claim 2, characterized by: The decision control module specifically includes: A21: Feed optimization submodule, used to generate personalized feeding formulas and precise feeding amounts based on individual conditions; A22: Disease prediction submodule, used to achieve early disease warning based on extracted physiological parameters and comparison with a feature library of 32 diseases; A23: Automatic early warning submodule, used to trigger tiered alarms and push emergency plans based on the data results of the disease prediction submodule.

4. The livestock head data analysis management system in a farming process according to claim 3, characterized by: The management platform module specifically includes: A31: 3D visualization submodule, used to build digital twins of livestock to display health heat maps; A32: Livestock Knowledge Base, used to integrate intelligent knowledge graphs of breed characteristics, disease prevention and control, and feeding standards; A33: Multi-role management module, used to implement hierarchical permission control.

5. The livestock head data analysis management system in a farming process according to claim 4, characterized by: The quality grading submodule, when implementing automatic quality rating of livestock through tests including skull symmetry detection and weight gain detection, specifically includes: S31: Multi-source data integration, receiving head 3D model, historical weight gain curve and pedigree file, calling the pre-set variety standard parameter library, and constructing a graded analysis dataset; S32: Cranial symmetry quantification, using the line connecting the nasal tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation value of key anatomical points and outputs the symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze body weight time series data, focusing on analyzing the second derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards for high-quality breeding stock; S34: Genetic advantage screening, linking to external genetic databases, calculating inbreeding coefficients and expected transmissibility of target economic traits; S35: Multimodal feature weighted fusion, which integrates the scores of various indicators according to dynamic weight ratios to generate a total quality score of 0-100. S36: Grading rule decision, automatically classifying grades based on the total quality score threshold, into AAA, AA and A grades.

6. The livestock head data analysis management system in a farming process according to claim 5, characterized by: The dynamic baseline adjustment submodule, when intelligently updating the evaluation criterion thresholds based on population data, specifically includes: S41: Group data aggregation, real-time collection of physiological data of all livestock in the same pen, integration of environmental sensor data, and establishment of a group analysis dataset containing historical time series data; S42: Data distribution modeling, using kernel density estimation and Gaussian mixture model to perform multi-dimensional analysis of population data, identify the characteristic distribution of different subgroups and locate cluster centers; S43: Baseline offset detection, calculates the KL divergence between the current data distribution and the historical benchmark, quantifies the difference in distribution shape through the KS test, and triggers the baseline update mechanism when the population median offset is greater than 2 standard deviations. S44: Intelligent baseline adjustment, dynamically sets the forgetting factor η according to the degree of offset, according to the formula: generating a revised evaluation criterion line; wherein, is the adjusted new benchmark, is the median of the population, is the historical benchmark; S45: Multi-subpopulation differentiation treatment, which calculates dedicated baselines independently for the identified different subpopulations to ensure that the evaluation criteria are adapted to the structural differences within the population.

7. The system for head data analysis and management of livestock in farming processes according to claim 6, characterized in that: The feed optimization submodule, when generating personalized feeding formulas and precise feeding amounts based on individual conditions, specifically includes: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, uses the NRC nutrition standard formula to calculate the daily requirements including metabolizable energy, crude protein and minerals. S52: Nutritional Deficiency Diagnosis. By comparing current intake of nutrients with theoretical requirements, key nutrient deficiencies are identified, and the formulation optimization process is triggered. S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under the conditions of raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, which combines the proportion of real-time weight deviation from the baseline value and the daily weight gain acceleration, and dynamically adjusts the basic feeding amount through the environmental compensation coefficient.

8. The system for head data analysis and management of livestock in farming processes according to claim 7, characterized in that: The disease prediction submodule achieves early disease prediction based on extracted physiological parameters and comparison with a feature library of 32 diseases. Specifically, it includes: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to construct a dynamic monitoring dataset; S62: Symptom feature database matching, which compares the collected features with a pre-set database of 32 disease features in a weighted manner and calculates the symptom consistency score; S63: Temporal anomaly detection, using LSTM network to analyze the continuous changing trend of key indicators, detect abrupt changes, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis combines CNN to process 3D lesion features and weighted fusion with the behavioral abnormality index output by LSTM to generate a comprehensive health risk score; S65: Tiered early warning decision-making, triggering a three-color response mechanism based on risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms are present; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is marked when the confidence level is less than or equal to 0.

7. S66: Generate prevention and treatment plans, link with disease knowledge graph, automatically output customized plans, and push prohibited operations.

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