Yak self-growth simulation system based on 3D twinning

Abstract

This invention relates to the field of yak farming and discloses a yak self-growth simulation system based on 3D twins. The system includes: a perception and data acquisition layer for automatically collecting yak-related data and environmental data via IoT devices, integrating multi-dimensional data, and creating a model library to provide data support and a model foundation; a digital twin engine, serving as the core of the system, enabling the binding and mapping of physical yaks to virtual models, growth simulation, and analytical decision-making; and a 3D twin scene and interaction platform for constructing a virtual pasture and providing visual interactive functions, ultimately achieving accurate mapping, growth prediction, decision support, and visual interaction for yak growth. Compared with existing technologies, this invention has the advantage of deeply integrating real yak data with a three-dimensional growth model to achieve dynamic simulation, prediction, and interactive analysis, thus solving problems such as opaque data, experience-dependent decision-making, and the lack of visibility into the growth process in traditional farming.

Description

Technical Field

[0001] This invention relates to the field of yak breeding technology, specifically to a 3D twin-based yak self-growth simulation system. Background Technology

[0002] Yak farming is a pillar industry in high-altitude and cold regions such as the Qinghai-Tibet Plateau. However, current farming management relies heavily on traditional experience, which presents several challenges: (1) Extensive management: It is difficult to accurately monitor and predict the feed intake, health status and growth rate of free-range yaks, and it is impossible to achieve precise feeding and cost control.

[0003] (2) Performance evaluation is lagging: It relies on manual methods such as phased weighing, the data is discontinuous, and it is difficult to predict the effect of individual fattening or the production performance of the group in advance.

[0004] (3) Lack of support for decision-making: It is impossible to effectively simulate the impact of different feeding strategies (such as grazing and supplementary feeding) and environmental changes (such as grassland conditions) on yak growth and economic benefits, resulting in high management decision-making risks.

[0005] (4) Difficulties in teaching and training: Traditional teaching cannot intuitively demonstrate the internal physiological changes of yaks and their interaction with the external environment, resulting in high training costs and low efficiency.

[0006] While existing livestock management software and simple 3D models exist, most offer limited functionality. For example, some 3D software is only used for cognitive learning, lacking high-fidelity simulation and prediction capabilities; and while some growth models can perform numerical simulations, they lack intuitive 3D visualization, making it difficult to bind them to specific individuals and dynamically display them. This invention aims to overcome these shortcomings by creating a digital platform that integrates precise mapping, real-time simulation, decision support, and immersive interaction. Summary of the Invention

[0007] (a) Technical problems to be solved The technical problem to be solved by this invention is to provide a 3D twin yak self-growth simulation system that can deeply integrate real yak data with a three-dimensional growth model to achieve dynamic simulation, prediction and interactive analysis, so as to solve the problems of data opacity, decision-making reliance on experience and invisible growth process in traditional breeding.

[0008] (II) Technical Solution To solve the above-mentioned technical problems, the technical solution provided by the present invention is: a 3D twin-based yak self-growth simulation system, comprising: The sensing and data acquisition layer is used to automatically collect yak-related data and environmental data through IoT devices; Multidimensional data fusion and model library provide data support and model foundation for the system; The digital twin engine, as the core of the system, enables the binding and mapping of physical yaks with virtual models, growth simulation, and analysis and decision-making. A 3D twin scene and interaction platform is used to build a virtual ranch, provide visualization and interactive functions, and ultimately achieve accurate mapping, growth prediction, decision support and visualization interaction of yak growth; The digital twin engine connects the perception and data acquisition layer with the multidimensional data fusion and model library, enabling real-time data synchronization and bidirectional driving between the physical yak and the virtual model.

[0009] As an improvement, the IoT devices in the sensing and data acquisition layer include collar sensors, cameras, drones, and environmental sensors. The collected yak-related data includes the yak's location, activity level, and body temperature, while the environmental data includes images of the surrounding vegetation.

[0010] As an improvement, the multidimensional data fusion and model library includes a three-dimensional model library, a growth model library, and a knowledge rule library; wherein, the three-dimensional model library is a high-precision parametric 3D model constructed based on yak breed, sex, and age, which supports dynamic morphological changes related to muscle development and fat deposition.

[0011] As an improvement, the growth model library integrates and improves existing physiological models, including a dry matter intake prediction module, an energy metabolism module, and a weight gain module.

[0012] As an improvement, the knowledge rule base stores rule data on the nutritional value and climate and environmental impact of different grassland types.

[0013] As an improvement, the digital twin engine includes a mapping module, a simulation module, and an analysis and decision-making module; wherein, the mapping module is used to bind the physical yak with the virtual model to ensure that the data of the two are synchronized in real time.

[0014] As an improvement, the simulation module is used to drive the virtual yak to perform growth and movement simulation in a three-dimensional scene based on real-time data and multi-dimensional data fusion collected by the perception and data acquisition layer and models in the model library.

[0015] As an improvement, the analysis and decision-making module is used to analyze the simulation results of the simulation module, predict the future growth trajectory of yaks, and evaluate the effects of different feeding and management schemes.

[0016] As an improvement, the 3D twin scene and interactive platform allows users to observe single or group virtual yaks from a free perspective, query relevant data, set up virtual experiments, and observe the corresponding prediction results. The virtual experiments include experiments related to adjusting the amount of supplementary feed and setting the slaughter time. The prediction results include changes in the growth status of yaks and economic benefit analysis results.

[0017] As an improvement, the data in the multi-dimensional data fusion and model library comes from multiple sources, including sensor data, remote sensing data, and manually entered data from the perception and data acquisition layer. It drives high-fidelity simulation of yak growth through a specific data fusion method.

[0018] (III) Beneficial Effects The advantages of this invention compared to the prior art are: Achieving precise mapping: Through IoT technology, the physiological, behavioral, and environmental data of physical yaks are synchronized with virtual 3D models in real time to create a digital twin.

[0019] Achieving growth prediction: Integrating mechanism and data-driven growth models, dynamic simulation and prediction of weight gain and body condition changes in individual yaks under different feeding conditions are achieved.

[0020] Enables decision support: Allows users to conduct "hypothesis analysis" on digital twins in a virtual environment, test the effects of different feeding programs and management strategies, and provide a scientific basis for optimizing production and reducing risks.

[0021] Achieve visual interaction: Through an immersive 3D interface, the growth process, herd dynamics, and breeding environment of yaks are intuitively displayed, serving teaching, training, and scientific research promotion. Attached Figure Description

[0022] Figure 1 This is a framework diagram of the yak self-growth simulation system based on 3D twins, which is the basis of this invention. Detailed Implementation

[0023] The invention will now be described in further detail with reference to specific embodiments, but this should not be construed as limiting the scope of the subject matter of the invention to the following embodiments.

[0024] A 3D twin-based simulation system for yak self-growth includes: (I) Sensing and Data Acquisition Layer: The sensing and data acquisition layer serves as the system's data input source, automatically collecting yak-related data and breeding environment data via IoT devices. This provides real-time and accurate foundational data support for subsequent simulations. The IoT devices include collar sensors, cameras, drones, and environmental sensors. The collected yak-related data includes the yak's location information (obtained via the collar sensor's GPS function), activity level (collected by the collar sensor's motion monitoring module), and body temperature (collected by the collar sensor's body temperature detection unit). Environmental data includes images of surrounding vegetation (acquired via cameras and drones) and grassland environmental parameters (temperature, humidity, light intensity, etc., collected by environmental sensors). All collected data is transmitted to the system backend in real time via a wireless communication module.

[0025] (II) Multidimensional Data Fusion and Model Library: The multidimensional data fusion and model library provides data support and model foundation for the system, used to store and integrate various data, models, and rules related to yak growth. Specifically, it includes three parts: a three-dimensional model library, a growth model library, and a knowledge rule base. 3D Model Library: Based on the characteristics of different breeds, sexes, and ages of yaks, high-precision parametric 3D models are constructed. These models support dynamic adjustment of morphological parameters according to changes in muscle development and fat deposition during the growth process of yaks, achieving precise matching with the growth state of actual yaks. Growth Model Library: Integrates and improves existing yak physiological models, including dry matter intake prediction module, energy metabolism module, and weight gain module. It can calculate the yak's nutrient intake, metabolic consumption, and weight change trend based on real-time data collected by the perception layer and environmental and nutritional data from the knowledge rule base. Knowledge rule base: Stores nutritional value parameters of different grassland types (such as alpine meadows, shrub grasslands, etc.), rules on the impact of climate environment (such as temperature and precipitation) on yak growth, feeding standard data, etc., to provide rule support for the calculation and simulation of growth models.

[0026] (III) Digital Twin Engine: The digital twin engine, as the core of the system, connects the perception and data acquisition layer with the multi-dimensional data fusion and model library, enabling real-time data synchronization and bidirectional driving between the physical yak and the virtual model. Specifically, it comprises three parts: a mapping module, a simulation module, and an analysis and decision-making module. Mapping module: Used to establish a unique binding relationship between the physical yak and the virtual 3D model, and to synchronize the physical yak data collected by the perception and data acquisition layer to the corresponding digital twin in real time, ensuring the consistency of the state between the virtual model and the physical yak; Simulation module: Based on real-time data collected by the perception and data acquisition layer, multi-dimensional data fusion, and growth models and knowledge rules in the model library, it drives the virtual yak to perform growth and movement simulation in a three-dimensional scene, dynamically simulating the yak's weight changes, morphological development, and behavioral state; Analysis and Decision Module: Performs in-depth analysis of the simulation results output by the simulation module, predicts the growth trajectory of yaks in the future based on the growth model, and compares and analyzes the simulation data corresponding to different feeding schemes and management strategies to evaluate the economic benefits and feasibility of each scheme.

[0027] (iv) 3D Twin Scenes and Interaction Platform: The 3D twin scene and interaction platform is used to construct a virtual ranch environment, providing users with visual interactive functions and serving as the interface between the system and the user. The platform allows users to observe the growth status of individual or group virtual yaks from a free perspective, and to query real-time data, historical growth curves, and simulation prediction data for the virtual yaks. It also supports users in setting up virtual experiments, such as adjusting supplementary feed amounts and setting different slaughter times. After the system drives the simulation module through a digital twin engine, the platform displays the corresponding prediction results in real time, including changes in yak growth status, weight gain trends, and economic benefit analysis results, allowing users to intuitively compare the effects of different solutions.

[0028] Example: Step 1: System Initialization and Digital Twin Creation Select a fully fattened yak (number YM001) as the target object and complete the initial configuration in this system: Manually enter YM001's basic information, including breed (e.g., Qinghai Plateau yak), age (18 months), initial weight (280kg), and health status (no disease record, good physical condition). Based on the entered basic information, the system matches the corresponding basic high-precision parametric 3D model from the multi-dimensional data fusion and model library's 3D model library, creates a digital twin of YM001, and completes the initial binding between the physical yak and the virtual model.

[0029] Step 2: Real-time data acquisition and synchronization Fit the smart collar sensor onto the YM001 to activate the relevant devices in the sensing and data acquisition layer: The collar sensor collects the GPS location information, exercise data, and body temperature data of the YM001 in real time and transmits them to the system via a wireless communication module; The drone regularly takes images of the vegetation in the grassland where YM001 is located, and the environmental sensors collect data on the temperature, humidity and light of the grassland. The mapping module of the digital twin engine synchronizes the real-time collected data to the digital twin of YM001, updates the state parameters of the virtual model, and ensures that the virtual model is consistent with the real-time state of the physical yak. ​​At the same time, the system calculates the remote sensing vegetation index (NDVI) based on grassland vegetation images and combines it with the nutritional value parameters of this type of grassland in the knowledge rule base to provide data support for subsequent forage estimation.

[0030] Step 3: Setting up the virtual experiment scene Technicians plan to test the effect of "daily supplemental feeding of 2 kg of concentrate" on the fattening effect of YM001, and will conduct a virtual experiment configuration in a 3D twin scene and interactive platform: Locate the digital twin of YM001 through the platform's interactive interface and enter the "Virtual Experiment Settings" module; Experimental parameters were set as follows: the amount of supplementary feed was 2 kg / day, the supplementary feeding period was 60 days, and other environmental conditions (such as grazing range and grassland condition) were kept consistent with the actual breeding environment. Once the experimental settings are submitted, the system triggers the simulation module of the digital twin engine to start working.

[0031] Step 4: Growth Simulation and Result Display The simulation module calls the growth model library (dry matter intake prediction module, energy metabolism module, weight gain module) in the multidimensional data fusion and model library, and combines the real-time data (exercise volume, body temperature) collected by the perception layer, pasture nutritional value data and set supplementary feeding parameters to calculate YM001's total daily nutrient intake and metabolic consumption. Based on the calculation results, the daily weight gain and body weight change trends of YM001 during a 60-day supplementary feeding cycle were dynamically simulated, and the 3D model of the digital twin was updated synchronously. According to the predicted weight gain and body condition changes, the muscle development degree and fat deposition status of the model were adjusted to achieve dynamic changes in morphology. The 3D twin scene and interactive platform display the simulation process in real time: users can observe the morphological changes of the YM001 digital twin from a free perspective, and view the daily weight gain curve, cumulative weight data and economic benefit analysis reports (such as feed costs, expected slaughter revenue, etc.) at any time.

[0032] Step 5: Results Analysis and Decision Implementation The analysis and decision-making module of the digital twin engine summarizes and analyzes the simulation results over 60 days and outputs an evaluation report: Under this supplementary feeding program, YM001 is expected to reach a weight of 350kg after 160 days, which meets the ideal slaughter weight standard, and the carcass quality is excellent, with a higher input-output ratio than the traditional feeding program. Based on the assessment report and the actual breeding conditions, the breeding management personnel determined to implement the supplementary feeding plan on the actual yak YM001. During the implementation of the scheme, the system continuously collects data through the perception layer and updates the simulation results in real time. If changes occur in grassland vegetation or fluctuations in the health status of yaks, the simulation predictions are adjusted in a timely manner to provide support for the dynamic optimization of breeding management.

[0033] Through the above implementation process, this system realizes virtual testing and precise decision-making for supplementary feeding strategies, effectively reducing the cost of trial and error in aquaculture, improving fattening effects and economic benefits, and fully verifying the system's practicality and reliability. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is defined by the appended claims and their equivalents. In short, if those skilled in the art are inspired by this and design similar structures and embodiments without departing from the inventive spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A 3D twin-based yak self-growth simulation system, characterized in that, include: The sensing and data acquisition layer is used to automatically collect yak-related data and environmental data through IoT devices; Multidimensional data fusion and model library provide data support and model foundation for the system; The digital twin engine, as the core of the system, enables the binding and mapping of physical yaks with virtual models, growth simulation, and analysis and decision-making. A 3D twin scene and interaction platform is used to build a virtual ranch, provide visualization and interactive functions, and ultimately achieve accurate mapping, growth prediction, decision support and visualization interaction of yak growth; The digital twin engine connects the perception and data acquisition layer with the multidimensional data fusion and model library, enabling real-time data synchronization and bidirectional driving between the physical yak and the virtual model.

2. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The IoT devices in the sensing and data acquisition layer include collar sensors, cameras, drones, and environmental sensors. The collected yak-related data includes the yak's location, activity level, and body temperature, while the environmental data includes images of the surrounding vegetation.

3. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The multidimensional data fusion and model library includes a three-dimensional model library, a growth model library, and a knowledge rule library; wherein, the three-dimensional model library is a high-precision parametric 3D model constructed based on yak breed, sex, and age, supporting dynamic morphological changes related to muscle development and fat deposition.

4. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The growth model library integrates and improves existing physiological models, including a dry matter intake prediction module, an energy metabolism module, and a weight gain module.

5. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The knowledge rule base stores rule data on the nutritional value and climate and environmental impact of different grassland types.

6. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The digital twin engine includes a mapping module, a simulation module, and an analysis and decision-making module; wherein, the mapping module is used to bind the physical yak with the virtual model to ensure that the data of the two are synchronized in real time.

7. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The simulation module is used to drive the virtual yak to simulate its growth and movement in a three-dimensional scene based on real-time data collected by the perception and data acquisition layer, multi-dimensional data fusion, and models in the model library.

8. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The analysis and decision-making module is used to analyze the simulation results of the simulation module, predict the future growth trajectory of yaks, and evaluate the effects of different feeding and management schemes.

9. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The 3D twin scene and interactive platform allows users to observe single or group virtual yaks from a free perspective, query relevant data, set up virtual experiments, and observe the corresponding prediction results. The virtual experiments include experiments related to adjusting the amount of supplementary feed and setting the slaughter time. The prediction results include changes in the growth status of yaks and economic benefit analysis results.

10. The yak self-growth simulation system based on 3D twins according to claim 1, characterized in that, The data in the multidimensional data fusion and model library comes from multiple sources, including sensor data, remote sensing data, and manually entered data from the perception and data acquisition layers. It drives high-fidelity simulation of yak growth through a specific data fusion method.

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