Accurate management and control system and method for livestock breeding in alpine grassland

Abstract

The invention relates to the technical field of agriculture and animal husbandry management and control, and discloses an accurate management and control system and method for livestock breeding in alpine grassland, and the system comprises a data collection and monitoring module which is used for collecting meteorological data, grassland data and livestock data in real time; the data processing and analyzing module is used for cleaning, storing and preliminarily analyzing the collected original data; the ecological model and livestock growth model module is used for establishing a grassland productivity model and a livestock growth dynamic model; and the optimal control decision module is used for generating an accurate grazing intensity and feed supply control scheme based on an optimal control theory, and the optimal control decision module comprises an optimal control algorithm unit. According to the invention, through the ecological model and the livestock growth model based on real-time data, in combination with the optimal control decision and feedback mechanism, precise adjustment of grazing intensity and feed supply is realized, and sustainable management of grassland and livestock is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural and livestock management, and specifically to a precise control system and method for livestock feeding in alpine grasslands. Background Art

[0002] In modern agriculture and animal husbandry, the management of livestock feeding in alpine grasslands faces more and more challenges. With climate change and the limitedness of grassland resources, how to scientifically and reasonably manage grasslands and livestock has become the key to improving production efficiency and protecting the ecological environment.

[0003] Currently, many agricultural management systems use fixed algorithms and rules to determine grazing intensity and feed supply. These systems estimate the basic needs of grasslands and livestock through information provided by sensors such as meteorological data and grassland monitoring. However, these systems lack real-time interaction with the growth status of livestock and grassland resources, and often can only provide management plans based on historical data. Some systems also incorporate simple grazing intensity adjustment models that can make some basic adjustments according to climate change and grassland conditions to avoid overgrazing.

[0004] There are still some deficiencies in the prior art. Many systems rely on fixed rules or algorithms and fail to fully consider the real-time changes in grassland productivity and livestock growth status. This overly simplified management model has led to two main problems: on the one hand, grasslands are prone to overgrazing and grassland productivity cannot recover; on the other hand, the nutritional needs of livestock cannot be precisely met, resulting in growth restrictions or health problems. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a precise control system and method for livestock feeding in alpine grasslands, which solves the problem that the grazing intensity and feed supply in the prior art cannot be adjusted precisely in real time according to grassland productivity and livestock growth status.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A precise control system for livestock feeding in alpine grasslands includes:

[0007] A data collection and monitoring module for real-time collection of meteorological data, grassland data, and livestock data;

[0008] A data processing and analysis module for cleaning, storing, and preliminarily analyzing the collected raw data;

[0009] An ecological model and livestock growth model module for establishing a grassland productivity model and a livestock growth dynamic model;

[0010] An optimal control decision-making module for generating precise grazing intensity and feed supply control plans based on the optimal control theory;

[0011] An execution and control module for performing grazing and feed management operations according to the optimal control decision;

[0012] A feedback and optimization module for optimizing the control strategy in real time according to the system feedback.

[0013] Preferably, the data acquisition and monitoring module includes:

[0014] A meteorological sensor for collecting data on temperature, humidity, wind speed, and precipitation;

[0015] A grassland monitoring sensor for collecting grassland humidity, grassland quality, and grassland biomass;

[0016] A livestock health monitoring device for collecting livestock weight, health status, feed intake, and activity level;

[0017] Internet of Things technology for transmitting the above data to the data processing and analysis module in real time.

[0018] Preferably, the data processing and analysis module includes:

[0019] A data cleaning unit for removing invalid data and supplementing missing data;

[0020] A data storage unit for storing the data after cleaning;

[0021] A data analysis unit for performing a preliminary analysis of grassland productivity and livestock growth and providing real-time status data for the subsequent decision-making module.

[0022] Preferably, the ecological model and livestock growth model module includes:

[0023] A grassland productivity model for describing the relationship between grassland productivity and climate, grazing intensity, and ecological carrying capacity;

[0024] A livestock growth model for describing the impact of livestock weight, activity level, and feed supply on livestock growth;

[0025] This model uses mathematical models of ecology and animal husbandry and can predict the changing trends of grasslands and livestock in real time.

[0026] Preferably, the optimal control decision module includes:

[0027] An optimal control algorithm unit for making optimal decisions based on the grassland productivity and livestock growth models;

[0028] A target function calculation unit for generating an optimal control target according to the weight relationship between livestock growth and grassland productivity;

[0029] Optimization algorithm unit, for performing real-time optimization according to dynamic programming or model predictive control algorithms.

[0030] Preferably, the execution and control module includes:

[0031] Grazing intensity control unit, for adjusting the grazing area and grazing time according to the optimal control decision;

[0032] Feed supply management unit, for automatically adjusting the feed supply according to the feed demand of livestock;

[0033] Operation interface, for displaying the operation status and decision results in real time and for manual intervention.

[0034] Preferably, the feedback and optimization module includes:

[0035] Real-time monitoring unit, for monitoring the feedback data of livestock growth and grassland quality;

[0036] Optimization adjustment unit, for adjusting the optimal control decision according to the monitoring results and feeding back to the optimal control decision module.

[0037] Preferably, the feedback and optimization module further includes:

[0038] Data mining unit, for mining potential laws from historical data and providing a more accurate optimization strategy for feedback based on this;

[0039] Machine learning-based feedback algorithm, for automatically adjusting parameters according to the system operation data and further optimizing grassland productivity and livestock health management.

[0040] Preferably, the system further includes:

[0041] User interface, for displaying the operation status, data analysis results and optimization suggestions of the system;

[0042] Alarm system, for giving an alarm when grassland degradation or abnormal livestock health is detected.

[0043] The present invention also provides a precise control method for raising livestock in alpine grasslands, including the following steps:

[0044] Real-time collection of meteorological data, grassland data and livestock data;

[0045] Cleaning and storing the collected original data, and preliminarily analyzing the grassland productivity and livestock growth status;

[0046] Generating dynamic predictions of grasslands and livestock according to the grassland productivity model and livestock growth model;

[0047] Based on the optimal control theory, calculate the optimal grazing intensity and feed supply quantity to form an optimal control decision;

[0048] Execute the optimal control decision and conduct precise management by adjusting the grazing intensity and feed supply quantity;

[0049] Adjust the system decision according to the real-time feedback information and continuously optimize the feeding management strategy.

[0050] The present invention provides a precise control system and method for livestock feeding in alpine grasslands. It has the following beneficial effects:

[0051] 1. By combining the grassland productivity model and the livestock growth model, the present invention realizes the accurate prediction and dynamic adjustment of the grazing intensity and feed supply. The system adjusts the strategy according to real-time data, avoiding overgrazing or grassland degradation in traditional grazing methods. This intelligent management effectively ensures the sustainability of grassland and livestock resources.

[0052] 2. With the help of the optimal control theory and the adaptive feedback mechanism, the present invention can adjust the management decision in a timely manner according to environmental changes. Compared with the static control in traditional technologies, the present invention provides a flexible dynamic optimization method, which can respond to the changes of grassland and livestock in real time, improving the management efficiency and accuracy.

[0053] 3. The present invention continuously optimizes the grazing intensity and feed supply quantity through a closed-loop feedback mechanism to ensure the best ratio of livestock growth and grassland resources. Compared with the management methods lacking effective feedback in the prior art, this innovation makes the management process more adaptable and resistant to interference, improving the stability and long-term sustainability of the entire system. Description of the Drawings

[0054] Figure 1 It is the system structure diagram of the present invention;

[0055] Figure 2 It is the module architecture diagram of data collection and monitoring of the present invention;

[0056] Figure 3 It is the module architecture diagram of data processing and analysis of the present invention;

[0057] Figure 4 It is the module architecture diagram of the ecological model and the livestock growth model of the present invention;

[0058] Figure 5 It is the module architecture diagram of the optimal control decision of the present invention;

[0059] Figure 6 It is the module architecture diagram of execution and control of the present invention;

[0060] Figure 7This is the module architecture diagram for the feedback and optimization of the present invention;

[0061] Figure 8 This is the method flowchart of the present invention. Detailed implementation manners

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0063] Please refer to the Figure 1 - appendix Figure 7 , the embodiments of the present invention provide a precise control system for livestock feeding in alpine grasslands, including:

[0064] A data acquisition and monitoring module, which is used to collect meteorological data, grassland data and livestock data in real time;

[0065] Meteorological sensors are used to monitor the impact of climate change on grassland productivity and livestock growth. The accurate acquisition of meteorological data is crucial for subsequent grassland management and livestock feeding. Meteorological sensors include temperature sensors, humidity sensors, anemometers, precipitation sensors, etc. Each sensor will collect environmental data in real time and transmit it to the data processing module through a wireless transmission protocol (such as LoRa, NB-IoT).

[0066] Temperature sensor (T(t)): It monitors the change of environmental temperature and has a direct impact on grassland growth and livestock activity. The accuracy should reach ±0.2 °C, and the measuring range is from -40 °C to 50 °C.

[0067] Humidity sensor (H(t)): It monitors the air humidity and affects the water supply of the grassland and the digestion and absorption of livestock feed. The accuracy should reach ±3%, and the measuring range is from 0% to 100%.

[0068] Anemometer (W(t)): It measures the wind speed and affects the physiological activities of livestock and the evapotranspiration of the grassland. The accuracy should reach ±0.1 m / s, and the measuring range is from 0 to 30 m / s.

[0069] Precipitation sensor (R(t)): It monitors the change of precipitation and directly affects the water content of the grassland. The accuracy should reach ±1 mm, and the measuring range is from 0 to 300 mm.

[0070] Formula representation of meteorological data:

[0071] T(t), H(t), W(t), R(t);

[0072] Where: T(t): environmental temperature, in degrees Celsius (°C); H(t): air humidity, in percentage (%); W(t): wind speed, in meters per second (m / s); R(t): precipitation, in millimeters (mm).

[0073] These meteorological data will be used in the grassland productivity prediction model and livestock growth model, affecting factors such as grassland water management, feed digestion, and livestock activities.

[0074] Grassland monitoring sensors include soil moisture sensors, grassland biomass sensors, and photosynthetic efficiency sensors, which are used to comprehensively monitor the growth status, quality, and biomass of the grassland, etc. Through the monitoring and analysis of real-time data, the grazing intensity can be adjusted in real time to avoid grassland degradation and ensure the sustainable production of the grassland.

[0075] Soil moisture sensor (S(t)): Real-time monitor the water content of the soil, which affects the water supply of the grassland. The accuracy should reach ±2%, and the measurement range is 0% to 100%.

[0076] Grassland biomass sensor (B(t)): Used to evaluate the biomass of the grassland, especially the amount of grass available for livestock to eat. The accuracy should reach ±5%, and the measurement range is 0 to 5000 kg / ha.

[0077] Grassland quality sensor (Q(t)): Monitor the grassland quality, especially the density of grass seeds, nutrient content, etc. The accuracy should reach ±3%, and the measurement range is 0 to 1000 kg / m 2 。

[0078] Formula representation of grassland data:

[0079] S(t)1, B(t), Q(t);

[0080] Where: S(t)1: soil moisture, in percentage (%); B(t): grassland biomass, in kilograms per hectare (kg / ha); Q(t): grassland quality, in kilograms per square meter (kg / m 2 )。

[0081] These data will affect the growth rate of the grassland, determine the grazing intensity, and provide a reference for the design of grassland restoration strategies.

[0082] Livestock health monitoring equipment includes smart collars, body temperature sensors, activity monitoring equipment, etc. These devices can collect data on the health status, activity level, feed intake, etc. of livestock in real time, helping to detect livestock health problems in a timely manner, so as to intervene and adjust management strategies.

[0083] Body temperature sensor (T h(t)): Used to monitor the body temperature changes of livestock in real time and promptly detect health abnormalities such as fever or hypothermia. The accuracy should reach ±0.1°C, and the measurement range is from 30°C to 45°C.

[0084] Activity monitoring device (A h (t)): Used to monitor the activity level of livestock. The change in activity level is closely related to the health status of livestock. The accuracy should reach ±5%, and the measurement range is from 0 to 200m 2 / day.

[0085] Feed intake monitoring (F h (t)): Used to monitor the feed intake of livestock, which can directly affect the nutritional status of livestock. The accuracy should reach ±10%, and the measurement range is from 0 to 50kg / day.

[0086] Formula representation of livestock health data:

[0087] T h (t), A h (t), F h (t);

[0088] Where: T h (t): Body temperature of livestock, in degrees Celsius (°C); A h (t): Activity level of livestock, in square meters per day (m 2 / day); F h (t): Feed intake of livestock, in kilograms (kg).

[0089] These data will provide real-time feedback on the growth status of livestock, support optimal control decisions, and adjust grazing intensity and feed supply.

[0090] Internet of Things (IoT) technology transmits sensor data to the data processing and analysis module in real time through wireless communication protocols (such as LoRa, NB-IoT, etc.). IoT technology ensures the real-time collection and transmission of grassland and livestock data, and can guarantee the stability and reliability of system data transmission even in alpine or remote areas.

[0091] Data processing and analysis module, used to clean, store, and perform preliminary analysis on the collected raw data;

[0092] The main task of the data processing and analysis module is to ensure that the raw data transmitted from the data collection and monitoring module is cleaned, stored, analyzed, and provide accurate data support for subsequent optimal control decisions based on the analysis results. To ensure the efficient operation and precise control of the system, the data processing and analysis module needs to comprehensively and systematically process various data from grassland, meteorology, and livestock.

[0093] First, all the collected raw data will be transmitted to the data cleaning unit. The data cleaning unit is mainly responsible for removing incomplete, inaccurate or inconsistent data and filling in missing values as needed. Common data cleaning operations include denoising, missing value filling, and outlier detection. For the handling of missing data, the system can adopt interpolation methods (such as linear interpolation or polynomial interpolation) or estimation methods based on historical data.

[0094] For example, the missing livestock activity data T h (t) can be supplemented by interpolating the data at the previous and subsequent time points. In some embodiments, the handling of missing data may adopt filling methods based on data patterns, such as techniques like mean filling and local trend filling.

[0095] The cleaned data will enter the data storage unit. All data will be stored in an orderly manner according to categories when stored, ensuring efficient data reading and use. The storage unit will distinguish and store data according to different data categories of grassland, meteorology, and livestock. Data storage should not only ensure its persistence but also improve access efficiency. Specifically, meteorological data, grassland growth data, and livestock health data will be stored separately in different database tables.

[0096] The real-time data that has been cleaned and stored will then be input into the data analysis unit. In the data analysis unit, grassland productivity analysis and livestock growth analysis are the main tasks. By analyzing the growth status of the grassland and livestock, the system can calculate the current grassland productivity and livestock growth rate in real time, thereby providing a decision-making basis for the optimal control decision-making module.

[0097] The grassland productivity model can predict the production capacity of the grassland based on input data (such as meteorological data, grassland humidity, grazing intensity, etc.). In this embodiment, the change in grassland productivity is described by the following differential equation:

[0098]

[0099] Where: P(t): The productivity of the grassland, representing the amount of grass per unit area; r: The natural growth rate of the grassland, reflecting the growth ability of the grassland without grazing pressure; K: The ecological carrying capacity (maximum productivity) of the grassland, that is, the productivity when the grassland reaches the maximum growth amount; α: The influence coefficient of grazing intensity on grassland productivity, indicating the suppression effect of grazing on grassland growth. The larger the value, the greater the grazing pressure; G(t): Grazing intensity, in units of heads per mu, representing the number of livestock grazing on the grassland during a certain time period, which changes with time t; β1: The influence coefficient of climate change on grassland productivity, indicating the adjustment effect of climate change on grassland productivity; C(t): Climate change factor, in dimensionless units, representing the impact of environmental climate on grassland growth. The higher the value, the more favorable the climate is for grassland growth.

[0100] The terms in the above equation represent the natural growth of the grassland, the negative impact of grazing, and the positive impact of climate respectively. When conducting the analysis, the data processing and analysis module updates the calculation results of grassland productivity in real time based on the current climate data, grassland quality data, and grazing intensity data.

[0101] The growth state of livestock is analyzed through a livestock growth model, which can calculate the weight change, activity level, and feed demand of livestock, etc. The formula of the livestock growth model is:

[0102]

[0103] Where: S(t)2: The growth state of livestock, with the unit of weight (kg); β2: Feed conversion rate coefficient, indicating the efficiency of converting unit feed into weight; F(t): Feed supply of livestock, changing with time, representing the daily feed intake of livestock; A(t): Activity level of livestock, representing the daily activity intensity of livestock; δ: Metabolic consumption rate, representing the weight loss consumed by livestock to maintain life activities.

[0104] The above formula describes that the weight of livestock increases with the changes in feed intake and activity level, and also takes into account the basic metabolic requirements of livestock. By analyzing the livestock growth model in real time, the data processing and analysis module can predict the future weight change and feed demand of livestock, so as to guide the subsequent feed delivery decision-making.

[0105] In this embodiment, the data analysis unit also adopts machine learning algorithms and regression analysis models to further improve the accuracy of grassland productivity and livestock growth prediction. The data analysis module can perform in-depth learning based on historical data to discover potential data patterns. For example, a regression analysis model is used to predict the productivity of grasslands under different climate conditions, or a neural network algorithm is used to predict the complex relationship between livestock weight and feed delivery amount.

[0106] In actual operation, the analysis results are continuously iteratively updated through algorithms. With the accumulation of data, the model can better adapt to the actual environment and provide more accurate predictions.

[0107] The data processing and analysis module ensures the accurate collection and effective utilization of grassland productivity, livestock growth state, and environmental data through cleaning, storage, and analysis processes.

[0108] The ecological model and livestock growth model module are used to establish a grassland productivity model and a livestock growth dynamic model;

[0109] This module can accurately predict the productivity of grasslands and the growth trends of livestock, thereby providing real-time data support for subsequent optimal control decisions. The ecological model and the livestock growth model can not only dynamically adjust the productivity of grasslands according to factors such as climate change and grazing intensity, but also accurately calculate the weight gain and feed requirements of livestock to ensure the balance between grasslands and livestock.

[0110] In the aforementioned data processing and analysis module, data related to grassland productivity and livestock growth have been cleaned and stored. At this time, these data will be used as inputs into the ecological model and the livestock growth model module. After calculation and prediction, the output results will be used to guide the adjustment of grazing intensity and feed supply in the optimal control decision module.

[0111] The main function of the ecological model is to predict changes in grassland productivity based on factors such as the natural growth of grasslands, grazing intensity, and climate change.

[0112] In this embodiment, the ecological model and the livestock growth model cooperate with each other and jointly act on the decision-making process of the system. The ecological model predicts the change trend of grassland productivity based on real-time climate data, grazing intensity, and grassland quality, while the livestock growth model predicts the weight change of livestock based on the productivity of the grassland (the amount of grass available for feeding livestock) and the activity level of livestock. The collaborative work of the two ensures the balance between grasslands and livestock and prevents overgrazing of grasslands or insufficient nutrition of livestock.

[0113] For example, when the predicted value of grassland productivity is low, the ecological model will recommend reducing the grazing intensity, and the livestock growth model will accordingly adjust the feed supply of livestock to ensure that livestock can still grow healthily under reduced grazing.

[0114] The ecological model and the livestock growth model in this embodiment have a real-time update function. The system will dynamically adjust the parameters in the model according to newly collected data. For example, real-time updated meteorological data will affect the prediction results of grassland productivity, and changes in the health data of livestock will affect the activity level and feed requirements in the growth model.

[0115] In a possible implementation, the model not only makes predictions based on real-time data, but also optimizes the accuracy of model predictions through machine learning algorithms. For example, the system can train regression models or neural networks based on historical data to identify the complex relationships between grasslands and livestock, thereby improving prediction ability and control accuracy.

[0116] The ecological model and the livestock growth model module in this embodiment provide a method for accurately predicting the states of grasslands and livestock by comprehensively considering various factors such as grassland productivity, grazing intensity, and climate change.

[0117] The optimal control decision-making module is used to generate precise grazing intensity and feed supply control schemes based on the optimal control theory;

[0118] The function of the optimal control decision-making module is to generate precise management decisions based on the aforementioned grassland productivity model and livestock growth model through the optimal control algorithm according to the real-time input data. By optimizing the grazing intensity and feed supply volume, the optimal control decision-making module can maximize the system benefits while ensuring the healthy growth of livestock and the sustainable utilization of grassland resources.

[0119] In the aforementioned data processing and analysis module, relevant data such as grassland productivity and livestock growth status have been processed and transmitted to the optimal control decision-making module. Based on this data, the module calculates the optimal control strategy to adjust the grazing intensity and feed supply volume, thereby ensuring the coordinated development of grassland and livestock.

[0120] The core task of the optimal control objective function is to optimize the overall benefit of the system by adjusting control variables (such as grazing intensity and feed supply volume). By maximizing the growth benefit of livestock and the productivity of grassland, the objective function can achieve the optimal allocation of resources during the control process. The form of the objective function is as follows:

[0121]

[0122] Where: J: The objective function, representing the overall benefit of the system, measuring the comprehensive benefit between livestock growth and grassland productivity; S(t)2: The growth status of livestock, in units of body weight (kg), changing with time t, representing the growth of livestock body weight; P(t): The productivity of grassland, changing with time t, representing the grass amount of grassland; θ1: The weight coefficient of livestock growth, in dimensionless units, reflecting the importance of livestock growth in system optimization; θ2: The weight coefficient of grassland productivity, in dimensionless units, reflecting the importance of grassland productivity in system optimization; T: The control time window, in units of days, representing the time range of the control process.

[0123] The design of the objective function can be dynamically adjusted according to the relative importance of livestock growth and grassland productivity. The system can flexibly control the trade-off between livestock and grassland by adjusting the values of θ1 and θ2 to cope with different management objectives.

[0124] To ensure the maintenance of ecological balance during the operation of the system, the optimal control decision-making module needs to consider a series of constraint conditions. The constraint conditions cover aspects such as the ecological carrying capacity of grassland, the feed demand of livestock, and the sustainable utilization of resources. Common constraint conditions are as follows:

[0125] P(t) ≤ K;

[0126]

[0127] Among them: P(t): The productivity of the grassland, representing the amount of grass in the unit area of the grassland, which changes with time t; K: The ecological carrying capacity of the grassland, that is, the maximum productivity that the grassland can carry, ensuring that the grassland productivity does not exceed its maximum carrying capacity; D(t): The feed demand of livestock, which changes with time, and the feed demand of livestock is related to factors such as its weight and activity level; γ: The feed conversion coefficient, representing the feed demand corresponding to the unit weight; S(t)2: The growth state of livestock, with the unit of weight (kg), which is the basis for calculating the feed demand of livestock; A(t): The activity level of livestock, reflecting the daily activity intensity of livestock; A max : The maximum activity level of livestock, representing the maximum activity intensity that livestock can reach when moving.

[0128] The constraint conditions ensure that the productivity of the grassland does not exceed its ecological carrying capacity, while the feed demand of livestock is met, avoiding overgrazing or feed waste. These constraint conditions guarantee the rationality of resource allocation and the long-term sustainability of the system during operation.

[0129] In this embodiment, the optimal control decision-making module realizes the optimal adjustment of control variables (grazing intensity and feed supply amount) through dynamic programming (DP) and model predictive control (MPC) algorithms.

[0130] The dynamic programming (DP) method is applicable to control problems that need to decompose the problem into multiple stages and optimize step by step. DP can calculate the optimal decision for each stage according to the current state, and find the overall optimal strategy through the backward iteration method.

[0131] The model predictive control (MPC) method is a control strategy based on system state prediction, which can predict the future system changes in real time according to the current input and state, and make optimal decisions on this basis. MPC can handle complex multivariable control problems and adjust the control strategy according to real-time feedback. The MPC method is particularly suitable for the dynamic environment of livestock breeding in alpine grasslands because it can cope with uncertainty factors such as climate change and grassland productivity fluctuations.

[0132] The output of the optimal control decision-making module will directly affect the execution and control module. The execution and control module adjusts the grazing area and feed delivery amount according to the optimal control decision, and simultaneously monitors the growth states of the grassland and livestock in real time. The system continuously collects the state data after execution through the feedback mechanism and transmits it back to the optimal control decision-making module for optimal adjustment.

[0133] The optimal control decision-making module in this embodiment dynamically adjusts the grazing intensity and feed supply amount through an optimization algorithm based on the objective function and constraint conditions, ensuring the rational allocation of resources and the sustainable development of the grassland and livestock.

[0134] An execution and control module for performing grazing and feed management operations according to the optimal control decision;

[0135] The core task of the execution and control module is to convert the control strategy output by the optimal control decision module into specific operations. By precisely adjusting the grazing intensity and feed supply, it realizes the healthy growth of livestock and the sustainable utilization of grasslands. The execution and control module is the execution layer of the system. It not only performs physical operations based on the calculation results but also feeds back the execution status to the optimal control decision module in real time through a feedback mechanism, so that the system can continuously adjust and optimize.

[0136] The aforementioned optimal control decision module generates optimal decisions based on information such as grassland productivity and livestock growth status. Subsequently, these decisions are transmitted to the execution and control module, which adjusts the grazing area, feed supply, and other management measures based on these decisions, thus ensuring the best balance between grasslands and livestock.

[0137] The main function of the grazing intensity control unit is to dynamically adjust the grazing area and grazing time according to the grazing intensity data output by the optimal control decision module. The grazing intensity control unit ensures the reasonable allocation of grassland resources, avoids overgrazing of grasslands, and reasonably adjusts the grazing intensity according to changes in grassland productivity. In specific implementation, the grazing intensity control unit calculates the optimal grazing intensity based on the following formula:

[0138]

[0139] Where: G(t): Grazing intensity, in units of heads per mu, representing the number of livestock on the grassland within a certain time period, changing with time t; P(t): Grassland productivity, representing the amount of grass per unit area of the grassland, changing with time; K: Ecological carrying capacity of the grassland, that is, the maximum productivity that the grassland can carry; G max : Maximum grazing intensity, in units of heads per mu, representing the maximum grazing amount allowed by the system at a certain moment; min: Minimum value function, indicating that the grazing intensity G(t) cannot exceed the maximum allowable value G max .

[0140] This formula ensures that the grazing intensity does not exceed the productivity carrying capacity of the grassland. The grazing intensity G(t) is dynamically adjusted according to the grassland productivity P(t), avoiding excessive consumption of grassland resources, ensuring the effective restoration of grasslands and maintaining ecological balance.

[0141] The feed supply management unit adjusts the type and supply amount of feed according to the feed demand and health status of livestock. This unit combines the growth status, activity level, and feed conversion rate of livestock to dynamically adjust the daily feed supply amount, ensuring that livestock obtain sufficient nutrition and preventing the health of livestock from being affected due to insufficient or excessive feed supply.

[0142] The feed supply management unit uses the following formula to calculate the daily required amount of feed:

[0143] F(t) = β2·S(t)·A(t);

[0144] Where: F(t): The feed supply amount of livestock, which changes with time t; β2: The feed conversion rate coefficient, indicating the feed demand corresponding to unit body weight; S(t): The growth state of livestock, in unit of body weight (kg), changing with time; A(t): The activity amount of livestock, reflecting the daily activity intensity of livestock.

[0145] This formula dynamically adjusts the feed supply amount by combining the body weight and activity amount of livestock. The activity amount and body weight of livestock will affect their daily energy demand, and the feed conversion rate coefficient β reflects the feed conversion efficiency of different livestock.

[0146] The operation interface provides a user interaction platform for the system. Breeders can monitor the running state of the system in real time through this platform. Through the operation interface, users can view data such as the productivity of the grassland, the growth status of livestock, and the feed supply amount, and can manually adjust certain parameters (such as the feed supply amount, grazing area, etc.). Generally, the data displayed by the operation interface is visualized through charts or graphical interfaces for easy viewing and operation by users.

[0147] As an option, the operation interface not only displays real-time data, but also can show information such as the historical data and trend analysis of the system to help users judge the effect of the current management strategy. When the system automatically adjusts the grazing intensity or feed supply amount, the operation interface will also display the current control state in real time.

[0148] The feedback and adjustment mechanism is an important part of the execution and control module. This mechanism evaluates the effectiveness of the current control strategy by collecting execution data in real time, such as grassland biomass, livestock body weight, activity amount, etc. These feedback data will be transmitted back to the optimal control decision module for strategy adjustment.

[0149] Specifically, when the grassland productivity is lower than expected or the health status of livestock is not good, the feedback mechanism will be activated and adjust the grazing intensity or feed supply amount. For example, if the system detects a decrease in the grass amount of the grassland or slow growth of livestock body weight, the execution and control module will reduce the grazing intensity or increase the feed supply amount according to the feedback information to ensure the balanced operation of the system.

[0150] This feedback mechanism operates through a dynamic closed-loop system, adjusting various parameters in real time to ensure the health of the grassland and livestock and the reasonable utilization of resources. The sensitivity and reaction speed of the feedback mechanism are crucial for the stability and efficiency of the entire system.

[0151] In this embodiment, the execution and control module realizes the precise management of grasslands and livestock by integrating a grazing intensity control unit, a feed supply management unit, an operation interface, and a feedback mechanism.

[0152] A feedback and optimization module, which is used to optimize the control strategy in real time according to the system feedback;

[0153] The feedback and optimization module is mainly responsible for collecting the feedback data of the execution and control module in real time, and dynamically adjusting the optimal control decision based on these feedback data to ensure that the system always maintains efficient operation and the best state under changing environmental conditions. The goal of the feedback and optimization module is to achieve the adaptive ability of the system, enabling it to automatically optimize the management strategy according to the health status of livestock and grasslands, feeding requirements, and environmental changes.

[0154] This module works closely with the aforementioned execution and control module and the optimal control decision module to jointly complete the feedback, analysis, and optimization adjustment of data. The execution and control module adjusts the grazing intensity and feed supply according to the control strategy provided by the optimal control decision module, while the feedback and optimization module further optimizes these control strategies based on the actual execution results, forming a closed-loop control system.

[0155] The feedback and optimization module in this embodiment ensures the dynamic regulation and efficient optimization of the system through the following main functions:

[0156] Real-time monitoring and data acquisition;

[0157] Real-time feedback and adaptive adjustment;

[0158] Optimization algorithms and adjustment strategies;

[0159] Closed-loop control mechanism and self-optimization.

[0160] These functions work together to ensure the reasonable allocation of livestock and grassland resources and the stable operation of the system through continuous monitoring and optimization adjustment.

[0161] First of all, the feedback and optimization module collects feedback data by real-time monitoring the status of the execution and control module. These data come from different sensors and monitoring devices, including livestock weight, activity level, health status, grassland productivity, grazing intensity, and feed supply. The real-time data will be processed and analyzed, and then used as the basis for optimization decisions.

[0162] After receiving the feedback data, the feedback and optimization module dynamically adjusts the optimal control strategy based on the current grassland productivity, the health status of livestock, and the actual implementation. For example, if the monitored grassland productivity is lower than expected, the system adjusts the grazing intensity G(t) according to the change in grassland productivity to reduce the grazing pressure. On the other hand, when the weight of the livestock does not reach the expected growth, the system optimizes and adjusts the feed supply F(t) to ensure that the livestock receives sufficient nutritional support.

[0163] The core formula of the feedback and optimization module is as follows:

[0164]

[0165] Where: ΔG(t): The adjusted grazing intensity, in units of heads per mu, representing the number of livestock on the grassland during a certain period; ΔP(t): The change in grassland productivity, representing the change in the amount of grass per unit area of the grassland; K: The ecological carrying capacity of the grassland, that is, the maximum productivity that the grassland can bear; G max : The maximum grazing intensity, in units of heads per mu, representing the maximum grazing intensity allowed by the system at a certain moment; ΔG max : The maximum adjustment amount of the grazing intensity, in units of heads per mu, representing the maximum allowable adjustment range of the grazing intensity.

[0166] This formula ensures that when the grassland productivity changes, the system can timely adjust the grazing intensity to avoid grassland degradation caused by overgrazing.

[0167] The feedback and optimization module adopts an optimization algorithm based on machine learning to improve the accuracy of feedback control and the system response speed. Specifically, the system continuously optimizes the control strategy by analyzing historical data and real-time feedback, using regression analysis, neural networks, or deep learning models.

[0168] The specific optimization and adjustment formula is:

[0169]

[0170] Where: ΔF(t): The adjusted feed supply, representing the feed supply to livestock during a certain period. It is adjusted according to the actual needs of livestock to ensure that livestock can obtain appropriate nutrition; S(t)2: The growth status of livestock, in units of weight (kg), which changes with time t. It is the basis for adjusting the feed supply, representing the nutritional and growth needs of livestock; S max : The maximum growth status of livestock, in units of weight (kg), representing the state when livestock reaches the maximum weight, which is a parameter for measuring the upper limit of livestock feed demand; A(t): The activity level of livestock, reflecting the daily activity intensity of livestock. The greater the activity level, the more energy livestock consumes and the more feed is needed; A max:The maximum activity level of livestock, representing the maximum activity intensity that livestock can reach, is a reference for the upper limit of feed demand; θ1: The weight coefficient of the growth state of livestock, dimensionless, indicating the degree of influence of the growth state of livestock on feed demand. This coefficient can be adjusted according to actual needs to reflect the demand degree of livestock weight for feed supply; θ3: The weight coefficient of the activity level of livestock, dimensionless, indicating the degree of influence of the activity level of livestock on feed demand.

[0171] This formula dynamically adjusts the feed supply according to data such as the weight and activity level of livestock to ensure that livestock obtain sufficient nutrition under different activity levels and growth states.

[0172] The feedback and optimization module forms a closed-loop control mechanism to ensure that every operation of the execution and control module can be timely fed back to the system and self-adjusted. Whenever the system detects abnormal changes in livestock health or grassland productivity, the feedback and optimization module will immediately perform corresponding optimization adjustments according to the changes. Through this self-optimization mechanism, the system can adjust the control strategy according to real-time feedback, thereby ensuring the rational use of resources and the healthy growth of livestock.

[0173] In this embodiment, the feedback and optimization module ensures that the system can continuously optimize control decisions in a dynamic environment by collecting feedback data in real time, combining optimization algorithms and adaptive adjustment strategies.

[0174] The precise control method for raising livestock in alpine grasslands described below can be correspondingly referred to the precise control system for raising livestock in alpine grasslands described above.

[0175] Please refer to the appendix Figure 8 , the present invention also provides a precise control method for raising livestock in alpine grasslands, including the following steps:

[0176] S1. Real-time data collection: Real-time collection of meteorological data, grassland data and livestock data;

[0177] S2. Data cleaning and preliminary analysis: Clean and store the collected raw data, and conduct preliminary analysis on grassland productivity and livestock growth state;

[0178] S3. Dynamic prediction generation: Generate dynamic predictions of grasslands and livestock according to the grassland productivity model and livestock growth model;

[0179] S4. Optimal control decision calculation: Based on the optimal control theory, calculate the optimal grazing intensity and feed supply amount to form an optimal control decision;

[0180] S5. Execution of control decision: Execute the optimal control decision and perform precise management by adjusting the grazing intensity and feed supply amount;

[0181] S6. Real-time feedback and optimization adjustment: Adjust the system decision according to the real-time feedback information and continuously optimize the feeding management strategy.

[0182] The method of this embodiment can be used to implement the above system embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0183] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise control system for livestock feeding in alpine grasslands, characterized in that, Including: A data collection and monitoring module for real-time collection of meteorological data, grassland data, and livestock data; A data processing and analysis module for cleaning, storing, and preliminarily analyzing the collected raw data; An ecological model and livestock growth model module for establishing a grassland productivity model and a livestock growth dynamic model; An optimal control decision-making module for generating precise grazing intensity and feed supply control schemes based on the optimal control theory. The optimal control decision-making module includes: an optimal control algorithm unit for making optimal decisions based on the grassland productivity and livestock growth models; a target function calculation unit for generating an optimal control target according to the weight relationship between livestock growth and grassland productivity; an optimization algorithm unit for performing real-time optimization according to dynamic programming or model predictive control algorithms. By maximizing the growth benefits of livestock and the productivity of the grassland, the target function can achieve the optimal allocation of resources during the control process. The form of the target function is: Where: J: The target function, representing the overall benefit of the system, measuring the comprehensive benefit between livestock growth and grassland productivity; S(t)2: The growth state of livestock, in units of body weight (kg), changing with time t, representing the growth of livestock weight; P(t): The productivity of the grassland, changing with time t, representing the grass amount of the grassland; θ1: The weight coefficient of livestock growth, dimensionless, reflecting the importance of livestock growth in system optimization; θ2: The weight coefficient of grassland productivity, dimensionless, reflecting the importance of grassland productivity in system optimization; T: The control time window, in units of days, representing the time range of the control process. By adjusting the values of θ1 and θ2, the trade-off between livestock and grassland can be flexibly controlled to meet different management objectives; The optimal control decision-making module also includes constraint conditions, and the form of the constraint conditions is: P(t) ≤ K; Where: P(t): The productivity of the grassland, representing the amount of grass per unit area of the grassland, which changes with time t; K: The ecological carrying capacity of the grassland, that is, the maximum productivity that the grassland can carry, ensuring that the grassland productivity does not exceed its maximum carrying capacity; D(t): The feed demand of livestock, which changes with time, and the feed demand of livestock is related to factors such as its weight and activity level; γ: The feed conversion coefficient, representing the feed demand corresponding to unit weight; S(t)2: The growth state of livestock, in units of weight (kg), which is the basis for calculating the feed demand of livestock; A(t): The activity level of livestock, reflecting the daily activity intensity of livestock; A max : The maximum activity level of livestock, representing the maximum activity intensity that livestock can reach during activity. The constraint condition avoids overgrazing or feed waste; An execution and control module for performing grazing and feed management operations according to the optimal control decision; A feedback and optimization module for real-time optimizing the control strategy according to system feedback.

2. The precise control system for livestock breeding in alpine grasslands according to claim 1, wherein The data collection and monitoring module includes: Meteorological sensors for collecting temperature, humidity, wind speed, and precipitation data; Grassland monitoring sensors for collecting grassland humidity, grassland quality, and grassland biomass; Livestock health monitoring equipment for collecting livestock weight, health status, feed intake, and activity level; Internet of Things technology for real-time transmitting the above data to the data processing and analysis module.

3. The precision control system for livestock breeding in alpine grasslands according to claim 1, characterized in that The data processing and analysis module includes: A data cleaning unit for removing invalid data and supplementing missing data; A data storage unit for storing the data after cleaning; A data analysis unit for performing preliminary analysis of grassland productivity and livestock growth, providing real-time status data for the subsequent decision-making module.

4. The precision control system for livestock breeding in alpine grasslands according to claim 1, wherein The ecological model and livestock growth model module includes: A grassland productivity model for describing the relationship between grassland productivity, climate, grazing intensity, and ecological carrying capacity; A livestock growth model for describing the impact of livestock weight, activity level, and feed supply on livestock growth; This model uses mathematical models of ecology and animal husbandry and can real-time predict the changing trends of grassland and livestock.

5. The precise control system for livestock feeding in alpine grasslands according to claim 1, characterized in that, The execution and control module includes: The grazing intensity control unit is used to adjust the grazing area and grazing time according to the optimal control decision. The grazing intensity control unit calculates the optimal grazing intensity based on the following formula: Among them: G(t): grazing intensity, with the unit of heads per mu, representing the number of livestock on the grassland within a certain time period, changing with time t; P(t): grassland productivity, representing the amount of grass per unit area of the grassland, changing with time; K: ecological carrying capacity of the grassland, that is, the maximum productivity that the grassland can carry; G max : maximum grazing intensity, with the unit of heads per mu, representing the maximum grazing amount allowed by the system at a certain moment; min: minimum value function, indicating that the grazing intensity G(t) cannot exceed the maximum allowed value G max ; Grazing intensity G(t) will be dynamically adjusted according to grassland productivity P(t) to avoid excessive consumption of grassland resources; The feed supply management unit is used to automatically adjust the feed supply according to the feed requirements of the livestock. The feed supply management unit uses the following formula to calculate the daily feed required: F(t) = β2·S(t)·A(t); Among them: F(t): livestock feed supply, which changes with time t; β2: feed conversion coefficient, which indicates the feed demand per unit body weight; S(t): livestock growth status, in units of body weight (kg), which changes with time; A(t): livestock activity, which reflects the intensity of livestock daily activities; By combining the weight and activity of livestock, the feed supply is adjusted dynamically. The activity and weight of livestock will affect their daily energy demand, and the feed conversion coefficient β reflects the conversion efficiency of different livestock to feed. The operation interface is used to display the operation status and decision results in real time and to perform manual intervention.

6. The precise control system for livestock feeding in alpine grasslands according to claim 1, wherein The feedback and optimization module includes: Real-time monitoring units to monitor livestock growth and feedback data on grassland quality; The optimization adjustment unit is used to adjust the optimal control decision according to the monitoring results and feed back to the optimal control decision module. The optimization adjustment formula is: Among them: ΔF(t): The adjusted feed supply amount, which represents the feed supply amount for livestock within a certain time period. It is adjusted according to the actual needs of livestock to ensure that livestock can obtain appropriate nutrition; S(t)2: The growth state of livestock, in units of body weight (kg), which changes with time t. It is the basis for adjusting the feed supply amount and represents the nutritional and growth needs of livestock; S max : The maximum growth state of livestock, in units of body weight (kg), which represents the state when livestock reaches its maximum body weight and is a parameter for measuring the upper limit of livestock feed demand; A(t): The activity level of livestock, which reflects the daily activity intensity of livestock. The greater the activity level, the more energy livestock consumes and the more feed is needed; A max : The maximum activity level of livestock, which represents the maximum activity intensity that livestock can reach and is a reference for the upper limit of feed demand; θ1: The weight coefficient of the growth state of livestock, in dimensionless units, which represents the degree of influence of the growth state of livestock on feed demand. This coefficient can be adjusted according to actual needs and reflects the demand degree of livestock body weight for feed supply; θ3: The weight coefficient of the activity level of livestock, in dimensionless units, which represents the degree of influence of the activity level of livestock on feed demand. The formula dynamically adjusts feed supply based on livestock weight, activity level and other data to ensure that livestock receive adequate nutrition at different activity levels and growth states.

7. The precise control system for livestock feeding in alpine grasslands according to claim 6, characterized in that, The feedback and optimization module further includes: Data mining unit, used to mine potential patterns from historical data and provide more accurate optimization strategies for feedback; A machine learning-based feedback algorithm is used to automatically adjust parameters based on system operating data to further optimize grassland productivity and livestock health management.

8. The precise control system for livestock feeding in alpine grasslands according to claim 1, wherein, The system further comprises: User interaction interface, used to display the system's operating status, data analysis results, and optimization suggestions; An alarm system is used to sound the alarm when grassland degradation or abnormal livestock health is detected.

9. A precise control method for livestock feeding in alpine grasslands, characterized in that, The precise control system for livestock breeding in alpine grasslands according to any one of claims 1 to 8 comprises the following steps: Collect weather data, grassland data and livestock data in real time; Clean and store the collected raw data, and conduct preliminary analysis on grassland productivity and livestock growth status; Generate grassland and livestock dynamic forecasts based on grassland productivity models and livestock growth models; Based on the optimal control theory, the optimal grazing intensity and feed supply are calculated to form the optimal control decision; Execute optimal control decisions and manage precisely by adjusting grazing intensity and feed supply; Adjust system decisions based on real-time feedback information and continuously optimize feeding and management strategies.

Images