Livestock head data analysis and management system in breeding process

Abstract

The invention relates to the technical field of breeding, in particular to a livestock head data analysis and management system in the breeding process. Compared with the prior art that a group unified feeding scheme is adopted, individual development differences and environment variables cannot be responded, and double resources are wasted due to insufficient nutrition of high-value livestock and excessive feeding of low-efficiency individuals; according to the scheme, a personalized nutrition demand modeling and formula optimization engine is initiated for the first time, the metabolic state is inversed by analyzing 3D features of the head in real time, the protein energy ratio and the environment compensation coefficient are dynamically calculated, a linear programming solver is linked to generate the lowest-cost formula, the feeding time sequence is adaptively adjusted, and the accurate nutrition supply mode of one animal and one strategy is thoroughly reconstructed; the feed conversion rate and the breeding economic benefit are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of breeding technology, and in particular to a livestock head data analysis and management system in a breeding process. Background Art

[0002] Traditional animal husbandry faces significant technological gaps in livestock physiological monitoring and refined management, hindering improvements in industry efficiency. Current mainstream solutions rely on manual experience and discrete tool chains. At the data collection level, farmers still use static body measurement methods such as tape measures. This inevitably interferes with livestock movement, leading to errors of up to 8%-15% in key dimensions like head circumference and nose-to-nose distance. A single measurement can take up to five minutes per head, making it difficult to meet the high-frequency monitoring needs of large-scale ranches. Automated equipment introduced by some modern ranches, such as RFID ear tags, only provides basic functions such as location tracking and lacks effective sensing of core physiological indicators such as weight changes, bone development, and nutritional status.

[0003] In terms of the application of three-dimensional digital technology, existing attempts have mostly focused on the reconstruction of static objects. Scanning solutions for live livestock generally have inherent defects: conventional structured light scanning is easily affected by ambient light interference, resulting in missing point clouds; because the lidar solution does not integrate motion compensation algorithms, 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 technology such as hair and mud stains is still immature, and the reconstructed model often has holes or geometric distortion, which cannot support accurate anatomical analysis.

[0004] Disease prevention and control efforts are characterized by a passive response. Traditional approaches rely on veterinary observation of clinical symptoms (such as high fever and refusal to eat) or laboratory reports (such as serological testing). This results in an average delay of over 72 hours from infection to diagnosis, missing the golden window for intervention. Feeding management, on the other hand, is mired in a rudimentary, extensive system: a uniform formula system based on breed and age disregards individual developmental differences, seasonal metabolic fluctuations, and stress. This results in both nutritional deficiencies in high-potential breeding stocks and overfeeding in inefficient individuals, resulting in feed conversion efficiency losses of up to 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] In order to overcome the problems raised in the above background technology, the present invention proposes a livestock head data analysis and management system in the breeding process.

[0007] The technical solution of the present invention is: a livestock head data analysis and management system in the breeding process, comprising: The data acquisition and preprocessing module is used to collect 3D point cloud data of the livestock head through sensor technology, and to perform noise reduction and reconstruction on the collected point cloud data to obtain a 3D model of the livestock head; An intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D model of the head; The decision-making control module is used to generate personalized feeding plans and disease prevention and control instructions based on the data results of the intelligent analysis module; The management platform module is used to integrate the digital supervision center of the three-dimensional visualization dashboard, livestock knowledge base and multi-role control system, realizing a closed loop of collaborative decision-making among people, livestock and equipment on the entire ranch.

[0008] Preferably, the data acquisition and preprocessing module collects 3D point cloud data of the livestock's head through sensor technology, performs noise reduction and reconstruction on the collected point cloud data, and obtains a 3D model of the livestock's head, specifically including: S11: Dynamic scanning: Scan the head of the livestock using the ranging sensor and convert the scanning data of the ranging sensor according to the coordinates of the ranging sensor to obtain 3D point cloud data; S12: Noise filtering, 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, solves the implicit surface gradient field of the point cloud, generates a continuous closed polygonal mesh model, and transforms discrete points into geometric entities that can be calculated and analyzed; S14: Data integrity verification, hole detection on the reconstructed model, and when missing areas are identified, the pre-trained generative model is called to supplement the geometric structure to obtain the final 3D model of the livestock head.

[0009] Preferably, the intelligent analysis module includes the following submodules: A11: Physiological parameter extraction submodule, used to calculate the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's 3D head model; A12: Quality grading submodule, used to automatically grade livestock quality through tests including skull symmetry and weight gain. A13: Growth status assessment submodule, used to analyze head development time series data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation standard threshold based on group data.

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

[0011] As a preference, the management platform module specifically includes: A31: 3D visualization submodule, used to build livestock digital twins to display health heat maps; A32: Livestock knowledge base, which integrates an intelligent knowledge graph 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 calculates the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's head 3D model, specifically including: S21: Cranial cavity volume segmentation, based on the 3D head mesh model, uses the anatomical positioning method to identify the foramen magnum and orbital boundary points, and constructs the skull closed surface using the convex hull algorithm to isolate the cranial cavity area from non-cranial tissue; S22: Weight calculation execution: the segmented cranial cavity model is processed by flood filling algorithm to calculate the volume of the enclosed space. Combined with the preset species density coefficient, the exponential regression equation is input to output the weight estimation; 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 maximum cross-sectional contour line of the head. S24: Dynamic measurement of head circumference, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing the time series of historical scan data to obtain the daily growth rate; S25: Fat area identification: Gaussian curvature analysis is performed on the facial surface to identify concave areas with a curvature value less than -0.003, and fragmented areas with an area less than 5 cm² are filtered out. Continuous concave areas are retained as fat-covered 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 in percentage form; S27: Skull key point calibration: mark anatomical landmarks on the skull model. The anatomical landmarks include the outermost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and the external occipital protuberance.

[0013] S28: Development index calculation: measure the horizontal distance between the two landmark points of the frontal bone and the distance between the longitudinal axes of the parietal bones, and divide the width value by the length value to obtain the skull development index.

[0014] Preferably, the quality grading submodule, when realizing automatic quality grading of livestock through detection including skull symmetry detection and weight gain detection, specifically includes: S31: Multi-source data integration, receiving head 3D models, historical weight gain curves and pedigree archives, calling the preset breed standard parameter library, and building a hierarchical analysis data set; S32: Quantification of skull symmetry, using the line connecting the nose tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation values ​​of key anatomical points, and outputs a symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze weight time series data, focusing on analyzing the second-order derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards of 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, combining the scores of various indicators according to the dynamic weight ratio to generate a total quality score of 0-100; S36: Grading rule decision, automatically dividing the grades into AAA, AA and A according to the total quality score threshold.

[0015] Preferably, when the dynamic baseline adjustment submodule intelligently updates the evaluation standard threshold according to the group data, it 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 data set 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 subpopulations and locate cluster centers; S43: Baseline shift detection, calculates the KL divergence of 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 shift is greater than 2 standard deviations; S44: Intelligent baseline adjustment, dynamically setting the forgetting factor η according to the degree of offset, according to the formula: , generate the revised evaluation standard line; in, The new benchmark after adjustment is is the group median, For historical benchmarks; S45: Differentiated treatment of multiple subpopulations, independently calculating dedicated baselines for the identified different subpopulations to ensure that the evaluation criteria adapt to the structural differences within the population.

[0016] Preferably, the feed optimization submodule generates personalized feeding formulas and precise feeding amounts based on individual status, specifically including: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, applies NRC nutrition standard formulas to calculate daily requirements including metabolizable energy, crude protein, and minerals; S52: Nutritional gap diagnosis, comparing current intake with theoretical requirements, identifying key nutrient gaps, and triggering the formula optimization process; S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, combining the ratio of real-time body weight deviation from the baseline value and daily weight gain acceleration, dynamically adjusts the basic feeding amount through the environmental compensation coefficient; Preferably, the disease prediction submodule compares 32 disease feature libraries based on the extracted physiological parameters to achieve early disease prediction, specifically including: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to build a dynamic monitoring data set; S62: Symptom feature library matching: weighted comparison of the collected features with the preset 32 ​​disease feature libraries to calculate the symptom consistency score; S63: Time series anomaly detection, using LSTM networks to analyze the continuous change trends of key indicators, detect mutations, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis, combining 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: A graded early warning decision-making system triggers a three-color response mechanism based on the risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms occur; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is flagged when the confidence level is less than 0.7. S66: Generate prevention and treatment plans, link with disease knowledge graph, automatically output customized plans, and push taboo operations.

[0017] Beneficial effects of the present invention: 1. Compared with existing technologies that use uniform group feeding plans, which cannot respond to individual developmental differences and environmental variables, resulting in double resource waste due to malnutrition of high-value livestock and overfeeding of inefficient individuals, this solution pioneers personalized nutritional demand modeling and recipe optimization engines. By analyzing the metabolic state through real-time analysis of 3D head features, dynamically calculating the protein-energy ratio and environmental compensation coefficient, and linking a linear programming solver to generate the lowest-cost recipe, it also adaptively adjusts the feeding schedule, completely reconstructing the "one animal, one policy" precise nutritional supply model, significantly improving feed conversion rate and breeding economic benefits. 2. Compared to 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 iterations, or cyclical environmental changes, resulting in a disconnect between standards and reality, this solution pioneers a baseline adaptive mechanism based on swarm intelligence. By analyzing the distribution of multi-dimensional physiological data in real time and applying machine learning algorithms to dynamically modify assessment benchmarks, the grading standards evolve autonomously as population characteristics change. This solves the problem of rigid and lagging breeding standards and significantly improves the scientific and forward-looking nature of breeding selection and feeding management. 3. Compared with the existing aquaculture industry that relies on empirical observation or post-onset treatment, the identification of latent diseases is delayed and the missed detection rate is high, which can easily lead to the spread of mass epidemics. This solution deeply integrates three-dimensional pathological feature analysis and temporal behavior monitoring to build an intelligent diagnosis engine across disease knowledge graphs. Through the collaborative decision-making of spatial lesion quantification and physiological dynamic warning, it locks high-risk individuals before clinical symptoms appear and automatically triggers a graded prevention and control plan. This completely changes the traditional passive response epidemic prevention model and builds a full-chain health management system covering early warning, accurate diagnosis and active intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Shown is a schematic diagram of the structure of the livestock head data analysis and management system in the breeding process of the present invention; Figure 2 Shown is a schematic diagram of the workflow of the feed optimization submodule in the livestock head data analysis and management system during the breeding process of the present invention. DETAILED DESCRIPTION

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

[0020] See also Figure 1-Figure 2 The present invention provides an embodiment: a livestock head data analysis and management system in a breeding process, comprising: The data acquisition and preprocessing module is used to collect 3D point cloud data of the livestock head through sensor technology, and to perform noise reduction and reconstruction on the collected point cloud data to obtain a 3D model of the livestock head; An intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D model of the head; The decision-making control module is used to generate personalized feeding plans and disease prevention and control instructions based on the data results of the intelligent analysis module; The management platform module is used to integrate the digital supervision center of the three-dimensional visualization dashboard, livestock knowledge base and multi-role control system, realizing a closed loop of collaborative decision-making among people, livestock and equipment on the entire ranch.

[0021] As mentioned above, the present invention realizes dynamic three-dimensional scanning and point cloud reconstruction of livestock heads through the data acquisition and preprocessing module, and converts the three-dimensional model into physiological indicators and quality grades in combination with the intelligent analysis module. The decision-making control module generates personalized feeding and disease prevention and control instructions, and finally builds a human-livestock-equipment collaborative closed loop through the management platform module. This architecture breaks through the limitations of traditional decentralized management and forms a digital decision-making center from perception to execution, significantly improving the accuracy, response speed and resource utilization efficiency of breeding.

[0022] Preferably, the data acquisition and preprocessing module collects 3D point cloud data of the livestock's head through sensor technology, performs noise reduction and reconstruction on the collected point cloud data, and obtains a 3D model of the livestock's head, specifically including: S11: Dynamic scanning: Scan the head of the livestock using the ranging sensor and convert the scanning data of the ranging sensor according to the coordinates of the ranging sensor to obtain 3D point cloud data; S12: Noise filtering, 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, solves the implicit surface gradient field of the point cloud, generates a continuous closed polygonal mesh model, and transforms discrete points into geometric entities that can be calculated and analyzed; S14: Data integrity verification, hole detection on the reconstructed model, and when missing areas are identified, the pre-trained generative model is called to supplement the geometric structure to obtain the final 3D model of the livestock head.

[0023] As mentioned above, the present invention uses dynamic scanning to compensate for in vivo motion interference, combines RANSAC with statistical filtering to eliminate hair and dust noise, generates a geometric solid model based on Poisson reconstruction, and calls a pre-trained GAN to fill holes; this process overcomes the problems of dynamic distortion and surface interference in in vivo scanning, achieves millimeter-level precision digitization of anatomical structures, and lays a high-fidelity data foundation for the analysis of physiological indicators.

[0024] Preferably, the intelligent analysis module includes the following submodules: A11: Physiological parameter extraction submodule, used to calculate the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's 3D head model; A12: Quality grading submodule, used to automatically grade livestock quality through tests including skull symmetry and weight gain. A13: Growth status assessment submodule, used to analyze head development time series data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation standard threshold based on group data.

[0025] As mentioned above, the present invention designs a multi-dimensional intelligent analysis sub-module, which extracts and quantifies core indicators such as weight through physiological parameters, integrates bone and growth characteristics into quality grading, predicts developmental trends based on growth status, and dynamically adjusts baselines to autonomously optimize standard thresholds; this composite analysis system subverts the empirical judgment model and realizes a cognitive leap from static measurement to dynamic evaluation, and from individual isolation to group association.

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

[0027] As mentioned above, the present invention constructs a closed-loop decision-making and control chain: the feed optimization module generates the lowest-cost formula based on nutritional gaps, the disease prediction module integrates temporal and spatial characteristics to achieve prevention before disease occurs, and the early warning system is linked to the emergency plan; this design converts the analysis results into an automated instruction flow, solves the pain points of extensive feeding and lagging prevention and control, and builds a proactive management paradigm.

[0028] As a preference, the management platform module specifically includes: A31: 3D visualization submodule, used to build livestock digital twins to display health heat maps; A32: Livestock knowledge base, which integrates an intelligent knowledge graph 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, the present invention integrates three-dimensional visualization, livestock knowledge graph and multi-role permission system, dynamically maps individual health status through digital twins, drives decision reasoning based on knowledge engines, and controls operation boundaries according to role permissions. The platform breaks through the data-knowledge-execution fault and realizes intelligent scheduling and collaborative optimization of all ranch resources.

[0030] Preferably, the physiological parameter extraction submodule calculates the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's head 3D model, specifically including: S21: Cranial cavity volume segmentation, based on the 3D head mesh model, uses the anatomical positioning method to identify the foramen magnum and orbital boundary points, and constructs the skull closed surface using the convex hull algorithm to isolate the cranial cavity area from non-cranial tissue; S22: Weight calculation execution: the segmented cranial cavity model is processed by flood filling algorithm to calculate the volume of the enclosed space. Combined with the preset species density coefficient, the exponential regression equation is input to output the weight estimation; 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 maximum cross-sectional contour line of the head. S24: Dynamic measurement of head circumference, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing the time series of historical scan data to obtain the daily growth rate; S25: Fat area identification: Gaussian curvature analysis is performed on the facial surface to identify concave areas with a curvature value less than -0.003, and fragmented areas with an area less than 5 cm² are filtered out. Continuous concave areas are retained as fat-covered 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 in percentage form; S27: Skull key point calibration: mark anatomical landmarks on the skull model. The anatomical landmarks include the outermost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and the external occipital protuberance.

[0031] S28: Development index calculation: measure the horizontal distance between the two landmark points of the frontal bone and the distance between the longitudinal axes of the parietal bones, and divide the width value by the length value to obtain the skull development index.

[0032] As mentioned above, the present invention proposes a cranial cavity convex hull segmentation algorithm to accurately isolate the bone cavity space, uses flood filling to calculate the volume and converts the weight through the species density coefficient; designs a coronal plane cutting to extract the head circumference contour, combines Gaussian curvature analysis to quantify fat distribution; and calculates the skull 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 realizing automatic quality grading of livestock through detection including skull symmetry detection and weight gain detection, specifically includes: S31: Multi-source data integration, receiving head 3D models, historical weight gain curves and pedigree archives, calling the preset breed standard parameter library, and building a hierarchical analysis data set; S32: Quantification of skull symmetry, using the line connecting the nose tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation values ​​of key anatomical points, and outputs a symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze weight time series data, focusing on analyzing the second-order derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards of 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, combining the scores of various indicators according to the dynamic weight ratio to generate a total quality score of 0-100; S36: Grading rule decision, automatically dividing the grades into AAA, AA and A according to the total quality score threshold.

[0034] As mentioned above, the present invention establishes a multimodal fusion grading mechanism: skull symmetry is quantified by sagittal plane deviation, growth potential is analyzed using the Gompertz function, the expected transmission force is calculated by associating the genetic database, and the total quality score is output according to the dynamic weight fusion characteristics; this model breaks through the limitations of traditional single indicator grading and realizes the scientific quantitative evaluation of the breeding value of breeding livestock.

[0035] Preferably, when the dynamic baseline adjustment submodule intelligently updates the evaluation standard threshold according to the group data, it 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 data set 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 subpopulations and locate cluster centers; S43: Baseline shift detection, calculates the KL divergence of 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 shift is greater than 2 standard deviations; S44: Intelligent baseline adjustment, dynamically setting the forgetting factor η according to the degree of offset, according to the formula: , generate the revised evaluation standard line; in, The new benchmark after adjustment is is the group median, For historical benchmarks; S45: Differentiated treatment of multiple subpopulations, independently calculating dedicated baselines for the identified different subpopulations to ensure that the evaluation criteria adapt to the structural differences within the population.

[0036] As mentioned above, the present invention innovates the dynamic adjustment algorithm of group benchmarks: kernel density estimation and Gaussian mixture model are used to identify subpopulation distribution, offset is detected based on KL divergence and KS test, forgetting factor η is used to balance historical benchmarks and current medians, and dedicated thresholds are calculated independently for each subpopulation; this mechanism enables the evaluation criteria to iterate autonomously with the evolution of the population, solving the problem of misjudgment caused by rigid standards.

[0037] Preferably, the feed optimization submodule generates personalized feeding formulas and precise feeding amounts based on individual status, specifically including: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, applies NRC nutrition standard formulas to calculate daily requirements including metabolizable energy, crude protein, and minerals; S52: Nutritional gap diagnosis, comparing current intake with theoretical requirements, identifying key nutrient gaps, and triggering the formula optimization process; S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, combining the ratio of real-time body weight deviation from the baseline value and daily weight gain acceleration, dynamically adjusts the basic feeding amount through the environmental compensation coefficient; As mentioned above, the present invention develops a precise nutrition supply engine: it calculates the nutritional gap based on the metabolic demand model, applies a linear programming solver to generate the optimal formula under cost constraints, and dynamically adjusts the feeding amount based on the weight deviation rate and daily weight gain acceleration; this technology realizes the precise feeding of "one animal, one policy", and improves the feed conversion rate by 22%.

[0038] Preferably, the disease prediction submodule compares 32 disease feature libraries based on the extracted physiological parameters to achieve early disease prediction, specifically including: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to build a dynamic monitoring data set; S62: Symptom feature library matching: weighted comparison of the collected features with the preset 32 ​​disease feature libraries to calculate the symptom consistency score; S63: Time series anomaly detection, using LSTM networks to analyze the continuous change trends of key indicators, detect mutations, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis, combining 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: A graded early warning decision-making system triggers a three-color response mechanism based on the risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms occur; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is flagged when the confidence level is less than 0.7. S66: Generate prevention and treatment plans, link with disease knowledge graph, automatically output customized plans, and push taboo operations.

[0039] As mentioned above, the present invention designs a dual-channel disease early warning system: it integrates CNN spatial lesion characteristics (such as swelling volume) with LSTM behavioral temporal anomalies, calculates risk scores by comparing 32 disease knowledge bases, triggers a three-level response mechanism, and links to a library of prevention and control solutions; this solution advances disease detection by 5-8 days and reduces mortality by 17%.

Claims

1. A livestock head data analysis and management system in the breeding process, characterized by: include: The data acquisition and preprocessing module is used to collect 3D point cloud data of the livestock head through sensor technology, and to perform noise reduction and reconstruction on the collected point cloud data to obtain a 3D model of the livestock head; An intelligent analysis module is used to analyze the physiological indicators and quality grades of livestock based on the 3D model of the head; The decision-making control module is used to generate personalized feeding plans and disease prevention and control instructions based on the data results of the intelligent analysis module; The management platform module is used to integrate the digital supervision center of the three-dimensional visualization dashboard, livestock knowledge base and multi-role control system, realizing a closed loop of collaborative decision-making among people, livestock and equipment on the entire ranch.

2. The livestock head data analysis and management system in the breeding process according to claim 1, characterized in that: The data acquisition and preprocessing module collects 3D point cloud data of the livestock's head through sensor technology, performs noise reduction and reconstruction on the collected point cloud data, and obtains a 3D model of the livestock's head. Specifically, it includes: S11: Dynamic scanning: Scan the head of the livestock using the ranging sensor and convert the scanning data of the ranging sensor according to the coordinates of the ranging sensor to obtain 3D point cloud data; S12: Noise filtering, 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, solves the implicit surface gradient field of the point cloud, generates a continuous closed polygonal mesh model, and transforms discrete points into geometric entities that can be calculated and analyzed; S14: Data integrity verification, hole detection on the reconstructed model, and when missing areas are identified, the pre-trained generative model is called to supplement the geometric structure to obtain the final 3D model of the livestock head.

3. The livestock head data analysis and management system in the breeding process according to claim 2, characterized in that: The intelligent analysis module includes the following sub-modules: A11: Physiological parameter extraction submodule, used to calculate the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's 3D head model; A12: Quality grading submodule, used to automatically grade livestock quality through tests including skull symmetry and weight gain. A13: Growth status assessment submodule, used to analyze head development time series data and predict sexual maturity and nutritional status; A14: Dynamic baseline adjustment submodule, used to intelligently update the evaluation standard threshold based on group data.

4. The livestock head data analysis and management system in the breeding process according to claim 3, characterized in that: The decision control module specifically includes: A21: Feed optimization submodule, used to generate personalized feeding formulas and precise feeding amounts based on individual status; A22: Disease prediction submodule, used to achieve early disease warning based on extracted physiological parameters and comparison of 32 disease feature libraries; A23: Automatic early warning submodule, used to trigger graded alarms and push emergency plans based on the data results of the disease prediction submodule.

5. The livestock head data analysis and management system in the breeding process according to claim 4, characterized in that: The management platform modules specifically include: A31: 3D visualization submodule, used to build livestock digital twins to display health heat maps; A32: Livestock knowledge base, which integrates intelligent knowledge graphs on breed characteristics, disease prevention and control, and feeding standards; A33: Multi-role management module, used to implement hierarchical permission control.

6. The livestock head data analysis and management system in the breeding process according to claim 5, characterized in that: The physiological parameter extraction submodule calculates the livestock's weight, head circumference growth rate, head coverage rate, and cranial development index based on the geometric features of the livestock's 3D head model. Specifically, it includes: S21: Cranial cavity volume segmentation, based on the 3D head mesh model, uses the anatomical positioning method to identify the foramen magnum and orbital boundary points, and constructs the skull closed surface using the convex hull algorithm to isolate the cranial cavity area from non-cranial tissue; S22: Weight calculation execution: the segmented cranial cavity model is processed by flood filling algorithm to calculate the volume of the enclosed space. Combined with the preset species density coefficient, the exponential regression equation is input to output the weight estimation; 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 maximum cross-sectional contour line of the head. S24: Dynamic measurement of head circumference, automatically marking 32 equally divided points along the contour line, accumulating the Euclidean distance between adjacent points to calculate the circumference value, and comparing the time series of historical scan data to obtain the daily growth rate; S25: Fat area identification: Gaussian curvature analysis is performed on the facial surface to identify concave areas with a curvature value less than -0.003, and fragmented areas with an area less than 5 cm² are filtered out. Continuous concave areas are retained as fat-covered 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 in percentage form; S27: Skull key point calibration: mark anatomical landmarks on the skull model. The anatomical landmarks include the lateralmost points of the bilateral zygomatic arches and the highest points of the frontoparietal suture and the external occipital protuberance.

7. S28: Development index calculation: Measure the horizontal distance between the two landmarks on the frontal bone and the longitudinal axis of the parietal bone, and divide the width by the length to calculate the skull development index.

8. The livestock head data analysis and management system in the breeding process according to claim 6, characterized in that: The quality grading submodule automatically grades livestock quality through tests including skull symmetry and weight gain. Specifically, it includes: S31: Multi-source data integration, receiving head 3D models, historical weight gain curves and pedigree archives, calling the preset breed standard parameter library, and building a hierarchical analysis data set; S32: Quantification of skull symmetry, using the line connecting the nose tip and the occipital crest as the sagittal plane segmentation line, calculates the spatial coordinate deviation values ​​of key anatomical points, and outputs a symmetry index; S33: Growth potential assessment, fitting the Gompertz growth function to analyze weight time series data, focusing on analyzing the second-order derivative of the recent weight gain curve to determine whether the growth acceleration meets the standards of 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, combining the scores of various indicators according to the dynamic weight ratio to generate a total quality score of 0-100; S36: Grading rule decision, automatically dividing the grades into AAA, AA and A according to the total quality score threshold.

9. The livestock head data analysis and management system in the breeding process according to claim 7, characterized in that: The dynamic baseline adjustment submodule intelligently updates the evaluation standard threshold based on group data, specifically including: 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 data set 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 subpopulations and locate cluster centers; S43: Baseline shift detection, calculates the KL divergence of 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 shift is greater than 2 standard deviations; S44: Intelligent benchmark adjustment, dynamically setting the forgetting factor η according to the degree of deviation, and generating a revised evaluation standard line; in, The new benchmark after adjustment is is the group median, For historical benchmarks; S45: Differentiated treatment of multiple subpopulations, independently calculating dedicated baselines for the identified different subpopulations to ensure that the evaluation criteria adapt to the structural differences within the population.

10. The livestock head data analysis and management system in the breeding process according to claim 8, characterized in that: The feed optimization submodule generates personalized feeding formulas and precise feeding amounts based on individual status, specifically including: S51: Individual nutrition modeling, based on real-time weight, daily weight gain trend, body fat percentage and environmental data, applies NRC nutrition standard formulas to calculate daily requirements including metabolizable energy, crude protein, and minerals; S52: Nutritional gap diagnosis, comparing current intake with theoretical requirements, identifying key nutrient gaps, and triggering the formula optimization process; S53: Dynamic recipe generation, using a linear programming solver to calculate the optimal raw material ratio combination under raw material inventory and nutritional constraints; S54: Adaptive calculation of feeding amount, combining the ratio of real-time body weight deviation from the baseline value and the daily weight gain acceleration, dynamically adjusts the basic feeding amount through the environmental compensation coefficient.

11. The livestock head data analysis and management system in the breeding process according to claim 9, characterized in that: The disease prediction submodule compares 32 disease feature libraries based on extracted physiological parameters to achieve early disease prediction, specifically including: S61: Multi-source data integration, real-time aggregation of head 3D scan features, environmental sensor data and historical physiological indicators to build a dynamic monitoring data set; S62: Symptom feature library matching: weighted comparison of the collected features with the preset 32 ​​disease feature libraries to calculate the symptom consistency score; S63: Time series anomaly detection, using LSTM networks to analyze the continuous change trends of key indicators, detect mutations, and locate abnormal time nodes; S64: Spatial-temporal fusion analysis, combining 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: A graded early warning decision-making system triggers a three-color response mechanism based on the risk score: a red alert is activated when the confidence level is greater than 0.9 and acute symptoms occur; a yellow alert is activated when the confidence level is greater than 0.7; and a potential risk is flagged when the confidence level is less than 0.

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

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