Feeding management system of intelligent cattle farm

Abstract

The invention provides an intelligent cattle farm feeding management system, which belongs to the technical field of intelligent agriculture and animal husbandry, and comprises a data acquisition module, a data processing module, a control module, a user interface module, a communication module and a storage module. The system obtains cattle farm environment and cattle physiological behavior data in real time through a multi-source sensor and image acquisition equipment, intelligent analysis and decision generation are carried out through algorithms such as machine learning, reinforcement learning and fuzzy logic, and automatic management of functions such as feed feeding, environment regulation and control and health early warning is achieved. The system has the characteristics of comprehensive perception, intelligent decision making and accurate execution, the cattle farm feeding efficiency can be effectively improved, cattle health is guaranteed, and the operation cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent agriculture and animal husbandry technology, and in particular to an intelligent cattle farm feeding and management system based on the Internet of Things, big data analysis and artificial intelligence. Background Technology

[0002] Traditional cattle farm management relies heavily on manual experience, leading to problems such as imprecise environmental control, unscientific feed application, and delayed disease detection. This results in resource waste, compromised cattle health, and low production efficiency. With the development of large-scale farming, traditional methods are no longer sufficient to meet the demands of modern animal husbandry for precision, automation, and intelligence. While some automated equipment has emerged, most systems are limited in function, lacking multi-source data fusion and intelligent decision-making capabilities, and thus failing to achieve collaborative optimization across the entire process.

[0003] Therefore, there is an urgent need in this field for an intelligent feeding management system that can integrate environmental monitoring, cattle behavior recognition, health early warning and resource regulation.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent feeding management system that integrates environmental monitoring, cattle behavior recognition, health early warning, and resource regulation.

[0006] To achieve the above objectives, the present invention provides the following solution: An intelligent cattle farm feeding and management system. It includes: The system includes a data acquisition module, a data processing module, a control module, a user interface module, a communication module, and a storage module. The data acquisition module is used to collect real-time environmental data and physiological data of cattle in the cattle farm. Environmental data includes temperature, humidity, air quality parameters, light intensity, and noise level. Physiological data includes weight, body temperature, heart rate, respiratory rate, activity level, feed intake, water intake, and excretion behavior. The data acquisition module includes multiple distributed sensor nodes, each equipped with a microcontroller and a wireless communication unit, used to transmit the collected data to the data processing module via the communication module. The data acquisition module also includes an image acquisition unit, used to capture video streams of cattle behavior via a camera and extract the cattle's posture and movement features using image processing technology. The data processing module is connected to the data acquisition module and is used to preprocess, extract features, and perform intelligent analysis on the acquired data to generate feeding management decisions. The data processing module includes a data preprocessing unit, a feature extraction unit, a decision generation unit, and an optimization unit. The data preprocessing unit filters, denoises, and normalizes the raw data to eliminate outliers and noise interference. The feature extraction unit extracts key features from the preprocessed data, including environmental features, cattle health features, and behavioral features. Based on the extracted features, the decision generation unit generates feeding management decisions through machine learning algorithms and optimization models. The decisions include feed rationing, water control, environmental regulation, disease warning, and growth prediction. The optimization unit uses a multi-objective optimization algorithm to adjust the decisions in real time to balance feeding costs, cattle health, and production efficiency. The control module is connected to the data processing module and is used to control the cattle farm feeding equipment according to the feeding management decisions. The control module includes a feed control unit, an environmental control unit, and a health intervention unit. The feed control unit controls the operation of automatic feeders and waterers and adjusts the feed type, feeding frequency, and water volume according to the output of the decision generation unit. The environmental control unit controls the ventilation system, heating system, cooling system, and lighting system to maintain the cattle farm environment within a preset range. When the health intervention unit detects abnormal health conditions in cattle, it triggers an alarm and controls isolation equipment or drug delivery equipment to intervene. The user interface module is connected to the data processing module and is used to display system status, real-time data, decision results and alarm information to the user, and to receive user input to adjust system parameters; the user interface module includes a graphical user interface and a mobile application interface, and supports data visualization, historical query and manual control functions. The communication module is used to realize data transmission between various modules of the system and communication with external systems; the communication module adopts wired communication protocol and wireless communication protocol to ensure real-time and reliable data transmission; The storage module is used to store the collected raw data, processed feature data, algorithm models, system configurations, and historical records; the storage module includes a local database and a cloud storage unit, supporting data backup, fast retrieval, and long-term archiving.

[0007] This system integrates data acquisition, intelligent processing, and control execution to achieve fully automated and intelligent management of cattle farms. It conforms to natural laws, effectively improves feeding efficiency, reduces labor costs, and ensures the health and welfare of cattle.

[0008] Optionally, the image acquisition unit in the data acquisition module uses a deep learning algorithm to recognize the behavior of cattle. The deep learning algorithm includes the following steps: Step 1: Data preparation. Collect a large amount of images and videos of cattle behavior, build a training dataset, and label the data. The label categories include eating, drinking, resting, exercising, and abnormal behavior. Step 2: Model training. A convolutional neural network (CNN) is used as the base model, and supervised learning is performed using the input training dataset. The CNN model includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layers are used to extract image features, the pooling layers are used to reduce feature dimensionality, and the fully connected layers are used for classification output. During training, a stochastic gradient descent optimizer is used, and the loss function is cross-entropy loss. The model parameters are adjusted through backpropagation until the model reaches a preset accuracy threshold on the validation set. Step 3: Behavior recognition. Input the real-time video stream into the trained CNN model. The model outputs the probability distribution of the cow's behavior. Determine the current behavior category based on the maximum probability. Step 4: Feature fusion. The behavior recognition results are fused with the physiological data collected by the sensors. A comprehensive behavior feature vector is generated through a multimodal data fusion algorithm for subsequent decision generation.

[0009] This algorithm improves the accuracy and efficiency of cattle monitoring through automated behavior analysis, conforms to the natural laws of machine learning, and enhances the intelligence level of the system.

[0010] Optionally, the decision generation unit in the data processing module uses a reinforcement learning algorithm to optimize feed allocation. The reinforcement learning algorithm includes the following steps: Step 1: Environmental modeling. The cattle farm feeding environment is modeled as a Markov decision process, where the state space includes the cattle's weight, body temperature, feed intake, ambient temperature and humidity, the action space includes feed type, feed amount and time interval, and the reward function is designed based on the cattle's growth rate, health indicators and feed cost. Step 2: Policy learning, using the Deep Q-Network algorithm for policy optimization; the DQN algorithm includes an experience replay mechanism and a target network. The experience replay mechanism stores historical state-action-reward sequences and uses random sampling to break data correlation. The target network is used to stabilize Q-value estimation. By iteratively updating the Q-network parameters, the cumulative reward is maximized, and the optimal feed allocation strategy is learned. Step 3: Decision execution. Based on the current state, the trained model is input, and the model outputs the optimal action, adjusting in real time to cope with environmental changes. Step 4: Policy evaluation. Periodically evaluate the policy using a simulation environment, verify the policy's robustness through sensitivity analysis, and update the model parameters based on the evaluation results.

[0011] This algorithm optimizes feeding decisions through adaptive learning, which conforms to the natural laws of reinforcement learning theory. It can dynamically adapt to changes in cattle needs and improve feeding efficiency.

[0012] Optionally, the optimization unit in the data processing module uses a multi-objective genetic algorithm to adjust environmental parameters. The multi-objective genetic algorithm includes the following steps: Step 1: Problem Definition. The environmental regulation problem is modeled as a multi-objective optimization problem. The objective functions include minimizing energy consumption, maximizing the comfort of the cattle, and minimizing equipment wear. The constraints include the safe range of environmental parameters and equipment operation limitations. Step 2: Population initialization. Randomly generate an initial population. Each individual represents a set of environmental control parameters, encoded as a binary string or a real number vector. Step 3: Fitness calculation. Calculate multiple objective function values ​​for each individual and use Pareto dominance relation to perform non-dominated ranking to determine the individual's fitness value. Step 4: Genetic operations, generating a new population through selection, crossover, and mutation; the selection operation uses tournament selection, prioritizing non-dominated solutions; the crossover operation uses simulated binary crossover, and the mutation operation uses polynomial mutation to maintain population diversity; Step 5: Iterative optimization. Repeat steps 3 and 4 until the maximum number of iterations or the convergence condition is reached, and output the Pareto optimal solution set. Step Six: Decision Selection. From the Pareto optimal solution set, select a solution as the environmental control parameter based on user preferences or real-time requirements, and transmit it to the control module for execution.

[0013] This algorithm balances multiple environmental management objectives through multi-objective optimization, conforms to the natural laws of evolutionary algorithms, and achieves synergistic optimization of energy efficiency and cattle welfare.

[0014] Optionally, the health intervention unit in the control module uses a fuzzy logic algorithm for disease early warning, and the fuzzy logic algorithm includes the following steps: Step 1: Fuzzification, converting the input variables into fuzzy sets; defining the membership functions of the input variables, for example, the membership functions of body temperature include three fuzzy sets: low, normal, and high, represented by triangular or trapezoidal functions; Step 2: Rule base construction. Based on expert knowledge, fuzzy rules are designed. The rule format is: if body temperature is high and heart rate is high, then the risk of disease is high. The rule base contains multiple rules, covering common disease warning scenarios. Step 3: Inference Engine. Fuzzy inference is performed using the Mamdani inference method. Fuzzy rules are applied to the input variables, the activation strength of the rules is calculated through min-max operations, and the output of all rules is aggregated. Step 4: Deblurring converts the fuzzy output into precise values, and the centroid method is used to calculate the disease risk index; Step 5: Decision triggering. If the disease risk index exceeds the preset threshold, an alarm will be triggered and health intervention measures will be initiated, such as controlling isolation equipment or sending data to the veterinary terminal.

[0015] This algorithm uses fuzzy logic to handle uncertainty and ambiguity, which aligns with the natural laws of human decision-making and improves the accuracy and timeliness of disease early warning.

[0016] Optionally, the user interface module uses a data visualization algorithm to generate a real-time monitoring dashboard, and the data visualization algorithm includes the following steps: Step 1: Data aggregation. Extract real-time and historical data from the storage module, use time series analysis methods to aggregate the data, and generate a statistical summary. Step 2: Visualization design. Based on user needs, design various chart types, including line charts to display changes in environmental parameters, bar charts to compare physiological data of cattle, and heat maps to show spatial distribution. Step 3: Rendering optimization. Perform high-performance rendering by dynamically adjusting visualization details using layer detail technology to ensure that the flow field interaction is maintained under large data volumes. Step 4: Interactive functions, integrating data processing mechanisms to support user click, zoom, and filter operations; when the user selects a specific cow or time range, the algorithm updates the visualization content in real time; Step 5: Alarm Integration. The alarm information generated by the data processing module is overlaid on the visualization interface, and color coding and pop-up windows are used to prompt the user.

[0017] This algorithm uses intuitive visualizations to help users quickly understand the system status, conforms to the natural laws of human-computer interaction, and enhances the system's usability and user experience.

[0018] Optionally, the communication module employs an adaptive routing algorithm to optimize data transmission, the adaptive routing algorithm comprising the following steps: Step 1: Network topology discovery. By broadcasting probe packets, the connection status and link quality of each node in the network are obtained, and a dynamic topology graph is constructed. Step 2: Route calculation. The algorithm is used to calculate the shortest path from the source node to the target node. The path weight is based on a comprehensive evaluation of link latency, bandwidth, and reliability. Step 3: Adaptive adjustment, real-time monitoring of network load and link failures. If path interruption or congestion is detected, route recalculation is triggered to select an alternative path. Step 4: Data fragmentation. Large blocks of data are fragmented into multiple data packets. Each data packet is assigned a sequence number and a checksum. Multipath transmission is used to improve reliability. Step 5: Confirmation mechanism, using the automatic retransmission request protocol to ensure reliable arrival of data packets; if the receiver does not confirm, the sender retransmits the data packet.

[0019] This algorithm optimizes data transmission through dynamic routing, ensuring efficient data transmission in the complex environment of a cattle farm. This conforms to the natural laws of network communication and improves the stability and real-time performance of the system.

[0020] Optionally, the storage module employs a data compression algorithm to reduce storage space usage. The data compression algorithm includes the following steps: Step 1: Data preprocessing, partitioning the stored data to process high-frequency and low-frequency data separately; Step 2: Compression encoding. For high-frequency data, a lossy compression algorithm is used to convert the data to the frequency domain and discard high-frequency components; for low-frequency data, a lossless compression algorithm is used to eliminate redundant information. Step 3: Optimize compression parameters. Adjust compression parameters using rate-distortion theory to balance compression ratio and data quality. For critical data, set a lower compression ratio to retain more details. Step 4: Store the index. Create an index for the compressed data to support fast retrieval and querying. Step 5: Regular cleanup. Based on the data lifecycle strategy, automatically delete expired data or archive it to low-cost storage media.

[0021] This algorithm reduces storage costs through efficient compression technology, conforms to the natural laws of information theory, and ensures the long-term economic efficiency of the system.

[0022] Optionally, the data processing module further includes a growth prediction unit, which uses a time series prediction algorithm to predict the weight gain of cattle. The time series prediction algorithm includes the following steps: Step 1: Data collection. Historical weight data, food intake, and environmental parameters are obtained from the storage module to construct a time series dataset. Step 2: Model selection. Use an autoregressive integral moving average model or a long short-term memory network for prediction. For nonlinear data, LSTM models are preferred, as they include input gates, forget gates, and output gates, and can capture long-term dependencies. Step 3: Model training. The time series data is divided into training and test sets. The training set is used to learn data patterns, and the test set is used to verify the model accuracy. During training, mean squared error is used as the loss function, and the model parameters are optimized through backpropagation and time backpropagation algorithms. Step 4: Prediction generation. Input the current time series data into the trained model. The model outputs the predicted weight value for a future period and calculates the prediction confidence interval. Step 5: Application of results. The predicted results are used to adjust feed allocation and formulate production plans to achieve precision feeding.

[0023] This algorithm provides scientific growth predictions through time series analysis, conforming to the natural laws of statistics and deep learning, and assisting users in making long-term management decisions.

[0024] Optionally, the system further includes an energy management module connected to the control module and the data processing module, used to optimize energy use in the cattle farm. The energy management module employs a dynamic programming algorithm for energy scheduling, and the dynamic programming algorithm includes the following steps: Step 1: State definition. Decompose the energy management problem into multiple stages, each stage representing a time interval. State variables include current energy reserves, equipment energy consumption, and external energy prices. Step 2: Decision variables, define the decision actions for each stage, such as starting / stopping equipment, switching energy sources; Step 3: State transition equation. Establish a state transition model to describe the changes in the state after the decision. For example, energy reserves are updated to the previous period's reserves plus the amount generated minus the amount consumed. Step 4: Value Function. Define the value function as the negative of the total cost, with the goal of minimizing the total cost. Step 5: Recursive solution. Use the Bellman equation for recursive calculation, working backward from the final stage to the initial stage to obtain the optimal decision sequence; Step Six: Real-time adjustments, updating status and decisions based on real-time data to achieve dynamic energy dispatch.

[0025] This algorithm optimizes energy allocation through dynamic programming, which aligns with the natural laws of operations research, reduces the operating costs of cattle farms, and improves the sustainability of the system.

[0026] Compared with the prior art, the present invention has the following beneficial effects: It enables real-time and accurate monitoring of the cattle farm environment and the condition of the cattle, improving the comprehensiveness and reliability of data collection; By using machine learning and multi-objective optimization algorithms, scientific feeding decisions are generated, significantly improving feed utilization and cattle health. By relying on the intelligent control module, the feeding equipment can be automatically controlled, reducing labor costs and operational errors; It has the ability to provide early disease warning and growth trend prediction, which helps in risk control and long-term planning in the breeding process; Energy management and data compression technologies have improved the economic efficiency and sustainability of system operation. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The purpose of this invention is to provide an intelligent feeding management system that integrates environmental monitoring, cattle behavior recognition, health early warning, and resource regulation.

[0031] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] Example 1: This embodiment provides an implementable intelligent cattle farm feeding management system, the system structure of which is as follows: Figure 1 As shown, the system is deployed in a standardized beef cattle farm with a stock of 500 head.

[0033] System hardware deployment: Data acquisition module: Thirty distributed sensor nodes were deployed inside the cattle shed, each node integrating: Temperature and humidity sensor (model: SHT35); Ammonia sensor (model: MQ-137); Noise sensor (model: INMP441); Light sensor (model: BH1750).

[0034] Ten weighing automatic feeders were installed in the feeding channel, each equipped with an RFID reader (model: UR200).

[0035] Eight high-definition network cameras (model: Hikvision DS-2CD2643G0-IZS) were installed in key locations in the cattle shed, supporting 1080P video capture.

[0036] Each cow wears a smart collar, which integrates: Body temperature sensor (accuracy ±0.1℃).

[0037] Triaxial accelerometer (sampling frequency 100Hz).

[0038] Heart rate monitoring module (photoelectric).

[0039] LoRa wireless communication module.

[0040] Software system configuration: The data processing module runs on a local server (configuration: Intel Xeon E5-2680 processor, 128GB RAM, RTX 8000 graphics card), and the software environment includes: Operating system: Ubuntu 20.04 LTS; Deep learning framework: TensorFlow 2.8; Database: MySQL 8.0 + Redis 6.2.

[0041] Algorithm model implementation details: The behavior recognition CNN model uses the ResNet-50 architecture and is trained on a self-built dataset of 100,000 cattle behavior images, achieving an accuracy of 94.2%.

[0042] The reinforcement learning feed allocation model has a state space dimension of 15 and an action space dimension of 8, and is implemented using Python's Stable-Baselines3 library.

[0043] The multi-objective genetic algorithm was developed based on the DEAP framework, with a population size of 100 and 500 iterations.

[0044] System workflow: Data collection phase: Sensor data is collected every 30 seconds and aggregated to the local server via a LoRaWAN gateway.

[0045] Video data is transmitted in real time, and key action segments are saved in H.264 format.

[0046] The smart collar data is synchronized every 5 minutes.

[0047] Data processing module: The data preprocessing unit performs the following on the raw data: Kalman filtering removes sensor noise; Z-score standardization; Outlier detection based on DBSCAN.

[0048] Feature extraction unit calculation: 24-hour activity level change trend; Statistics on the frequency of foraging behavior; Spatiotemporal distribution characteristics of environmental parameters.

[0049] Decision-making and implementation phase: Based on the decision-making results, the feed control unit controls the feeder to accurately dispense the mixed feed, with an error controlled within ±2%. The environmental control unit automatically adjusts based on the optimization results: Fan speed (adjustable from 0-3000 rpm); Water supply flow rate of the evaporative cooling pad.

[0050] Lighting intensity (0-500 lux graded control); The health intervention unit automatically activates when the disease risk index is >0.85. Isolation access control; Notify the veterinarian via text message.

[0051] Preparation of the drug delivery system.

[0052] Actual operating effect After a three-month trial run, the system achieved the following: Feed conversion rate increased by 12.3%.

[0053] The timely detection rate of diseases has increased to 92.5%.

[0054] Labor costs were reduced by 45%.

[0055] Energy consumption decreased by 18.7%.

[0056] System maintenance: The model is retrained weekly, and the weight parameters are updated.

[0057] The sensor accuracy is calibrated monthly.

[0058] The system data is backed up to cloud storage every quarter.

[0059] This embodiment demonstrates that the system of the present invention can operate stably in actual breeding environments, with all performance indicators meeting design requirements, effectively improving the level of intelligence and economic benefits of cattle farm feeding management. Those skilled in the art can appropriately adjust the hardware configuration and algorithm parameters according to the specific breeding scale and site conditions to achieve the technical effects of the present invention.

[0060] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0061] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An intelligent cattle farm feeding management system, characterized in that, include: The system includes a data acquisition module, a data processing module, a control module, a user interface module, a communication module, and a storage module. The data acquisition module is used to collect real-time environmental data and physiological data of cattle in the cattle farm. Environmental data includes temperature, humidity, air quality parameters, light intensity, and noise level. Physiological data includes weight, body temperature, heart rate, respiratory rate, activity level, feed intake, water intake, and excretion behavior. The data acquisition module includes multiple distributed sensor nodes, each equipped with a microcontroller and a wireless communication unit, used to transmit the collected data to the data processing module via the communication module. The data acquisition module also includes an image acquisition unit, used to capture video streams of cattle behavior via a camera and extract the cattle's posture and movement features using image processing technology. The data processing module is connected to the data acquisition module and is used to preprocess, extract features, and perform intelligent analysis on the acquired data to generate feeding and management decisions. The data processing module includes a data preprocessing unit, a feature extraction unit, a decision generation unit, and an optimization unit. The data preprocessing unit filters, denoises, and normalizes the raw data to eliminate outliers and noise interference. The feature extraction unit extracts key features from the preprocessed data, including environmental features, cattle health features, and behavioral features. The decision generation unit generates feeding and management decisions based on extracted features, using machine learning algorithms and optimization models. The decisions include feed rationing, water control, environmental regulation, disease warning, and growth prediction. The optimization unit uses a multi-objective optimization algorithm to adjust the decisions in real time to balance feeding costs, cattle health, and production efficiency. The control module is connected to the data processing module and is used to control the cattle farm feeding equipment according to the feeding management decisions. The control module includes a feed control unit, an environmental control unit, and a health intervention unit. The feed control unit controls the operation of automatic feeders and waterers and adjusts the feed type, feeding frequency, and water volume according to the output of the decision generation unit. The environmental control unit controls the ventilation system, heating system, cooling system, and lighting system to maintain the cattle farm environment within a preset range. When the health intervention unit detects abnormal health conditions in cattle, it triggers an alarm and controls isolation equipment or drug delivery equipment to intervene. The user interface module is connected to the data processing module and is used to display system status, real-time data, decision results and alarm information to the user, and to receive user input to adjust system parameters; the user interface module includes a graphical user interface and a mobile application interface, and supports data visualization, historical query and manual control functions. The communication module is used to realize data transmission between various modules of the system and communication with external systems; the communication module adopts wired communication protocol and wireless communication protocol to ensure real-time and reliable data transmission; The storage module is used to store the collected raw data, processed feature data, algorithm models, system configurations, and historical records; the storage module includes a local database and a cloud storage unit, supporting data backup, fast retrieval, and long-term archiving.

2. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The image acquisition unit in the data acquisition module uses a deep learning algorithm to recognize the behavior of cattle. The deep learning algorithm includes the following steps: Step 1: Data preparation. Collect a large amount of images and videos of cattle behavior, build a training dataset, and label the data. The label categories include eating, drinking, resting, exercising, and abnormal behavior. Step 2: Model training. A convolutional neural network (CNN) is used as the base model, and supervised learning is performed using the input training dataset. The CNN model includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layers are used to extract image features, the pooling layers are used to reduce feature dimensionality, and the fully connected layers are used for classification output. During training, a stochastic gradient descent optimizer is used, and the loss function is cross-entropy loss. The model parameters are adjusted through backpropagation until the model reaches a preset accuracy threshold on the validation set. Step 3: Behavior recognition. Input the real-time video stream into the trained CNN model. The model outputs the probability distribution of the cow's behavior. Determine the current behavior category based on the maximum probability. Step 4: Feature fusion. The behavior recognition results are fused with the physiological data collected by the sensors. A comprehensive behavior feature vector is generated through a multimodal data fusion algorithm for subsequent decision generation.

3. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The decision generation unit in the data processing module uses a reinforcement learning algorithm to optimize feed allocation. The reinforcement learning algorithm includes the following steps: Step 1: Environmental modeling. The cattle farm feeding environment is modeled as a Markov decision process, where the state space includes the cattle's weight, body temperature, feed intake, ambient temperature and humidity, the action space includes feed type, feed amount and time interval, and the reward function is designed based on the cattle's growth rate, health indicators and feed cost. Step 2: Policy learning, using the Deep Q-Network algorithm for policy optimization; the DQN algorithm includes an experience replay mechanism and a target network. The experience replay mechanism stores historical state-action-reward sequences and uses random sampling to break data correlation. The target network is used to stabilize Q-value estimation. By iteratively updating the Q-network parameters, the cumulative reward is maximized, and the optimal feed allocation strategy is learned. Step 3: Decision execution. Based on the current state, the trained model is input, and the model outputs the optimal action, adjusting in real time to cope with environmental changes. Step 4: Policy evaluation. Periodically evaluate the policy using a simulation environment, verify the policy's robustness through sensitivity analysis, and update the model parameters based on the evaluation results.

4. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The optimization unit in the data processing module uses a multi-objective genetic algorithm to adjust environmental parameters. The multi-objective genetic algorithm includes the following steps: Step 1: Problem Definition. The environmental regulation problem is modeled as a multi-objective optimization problem. The objective functions include minimizing energy consumption, maximizing the comfort of the cattle, and minimizing equipment wear. The constraints include the safe range of environmental parameters and equipment operation limitations. Step 2: Population initialization. Randomly generate an initial population. Each individual represents a set of environmental control parameters, encoded as a binary string or a real number vector. Step 3: Fitness calculation. Calculate multiple objective function values ​​for each individual and use Pareto dominance relation to perform non-dominated ranking to determine the individual's fitness value. Step 4: Genetic operations, generating a new population through selection, crossover, and mutation; the selection operation uses tournament selection, prioritizing non-dominated solutions; the crossover operation uses simulated binary crossover, and the mutation operation uses polynomial mutation to maintain population diversity; Step 5: Iterative optimization. Repeat steps 3 and 4 until the maximum number of iterations or the convergence condition is reached, and output the Pareto optimal solution set. Step Six: Decision Selection. From the Pareto optimal solution set, select a solution as the environmental control parameter based on user preferences or real-time requirements, and transmit it to the control module for execution.

5. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The health intervention unit in the control module uses a fuzzy logic algorithm for disease early warning, and the fuzzy logic algorithm includes the following steps: Step 1: Fuzzification, converting the input variables into fuzzy sets; defining the membership functions of the input variables, for example, the membership functions of body temperature include three fuzzy sets: low, normal, and high, represented by triangular or trapezoidal functions; Step 2: Rule base construction. Based on expert knowledge, fuzzy rules are designed. The rule format is: if body temperature is high and heart rate is high, then the risk of disease is high. The rule base contains multiple rules, covering common disease warning scenarios. Step 3: Inference Engine. Fuzzy inference is performed using the Mamdani inference method. Fuzzy rules are applied to the input variables, the activation strength of the rules is calculated through min-max operations, and the output of all rules is aggregated. Step 4: Deblurring converts the fuzzy output into precise values, and the centroid method is used to calculate the disease risk index; Step 5: Decision triggering. If the disease risk index exceeds the preset threshold, an alarm will be triggered and health intervention measures will be initiated, such as controlling isolation equipment or sending data to the veterinary terminal.

6. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The user interface module uses a data visualization algorithm to generate a real-time monitoring dashboard. The data visualization algorithm includes the following steps: Step 1: Data aggregation. Extract real-time and historical data from the storage module, use time series analysis methods to aggregate the data, and generate a statistical summary. Step 2: Visualization design. Based on user needs, design various chart types, including line charts to display changes in environmental parameters, bar charts to compare physiological data of cattle, and heat maps to show spatial distribution. Step 3: Rendering optimization. Perform high-performance rendering by dynamically adjusting visualization details using layer detail technology to ensure that the flow field interaction is maintained under large data volumes. Step 4: Interactive functions, integrating data processing mechanisms to support user click, zoom, and filter operations; when the user selects a specific cow or time range, the algorithm updates the visualization content in real time; Step 5: Alarm Integration. The alarm information generated by the data processing module is overlaid on the visualization interface, and color coding and pop-up windows are used to prompt the user.

7. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The communication module employs an adaptive routing algorithm to optimize data transmission. The adaptive routing algorithm includes the following steps: Step 1: Network topology discovery. By broadcasting probe packets, the connection status and link quality of each node in the network are obtained, and a dynamic topology graph is constructed. Step 2: Route calculation. The algorithm is used to calculate the shortest path from the source node to the target node. The path weight is based on a comprehensive evaluation of link latency, bandwidth, and reliability. Step 3: Adaptive adjustment, real-time monitoring of network load and link failures. If path interruption or congestion is detected, route recalculation is triggered to select an alternative path. Step 4: Data fragmentation. Large blocks of data are fragmented into multiple data packets. Each data packet is assigned a sequence number and a checksum. Multipath transmission is used to improve reliability. Step 5: Confirmation mechanism, using the automatic retransmission request protocol to ensure reliable arrival of data packets; if the receiver does not confirm, the sender retransmits the data packet.

8. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The storage module employs a data compression algorithm to reduce storage space usage. The data compression algorithm includes the following steps: Step 1: Data preprocessing, partitioning the stored data to process high-frequency and low-frequency data separately; Step 2: Compression encoding. For high-frequency data, a lossy compression algorithm is used to convert the data to the frequency domain and discard high-frequency components; for low-frequency data, a lossless compression algorithm is used to eliminate redundant information. Step 3: Optimize compression parameters. Adjust compression parameters using rate-distortion theory to balance compression ratio and data quality. For critical data, set a lower compression ratio to retain more details. Step 4: Store the index. Create an index for the compressed data to support fast retrieval and querying. Step 5: Regular cleanup. Based on the data lifecycle strategy, automatically delete expired data or archive it to low-cost storage media.

9. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The data processing module further includes a growth prediction unit, which uses a time series prediction algorithm to predict the weight gain of cattle. The time series prediction algorithm includes the following steps: Step 1: Data collection. Historical weight data, food intake, and environmental parameters are obtained from the storage module to construct a time series dataset. Step 2: Model selection. Use an autoregressive integral moving average model or a long short-term memory network for prediction. For nonlinear data, LSTM models are preferred, as they include input gates, forget gates, and output gates, and can capture long-term dependencies. Step 3: Model training. The time series data is divided into training and test sets. The training set is used to learn data patterns, and the test set is used to verify the model accuracy. During training, mean squared error is used as the loss function, and the model parameters are optimized through backpropagation and time backpropagation algorithms. Step 4: Prediction generation. Input the current time series data into the trained model. The model outputs the predicted weight value for a future period and calculates the prediction confidence interval. Step 5: Application of results. The predicted results are used to adjust feed allocation and formulate production plans to achieve precision feeding.

10. The intelligent cattle farm feeding management system according to claim 1, characterized in that, The system also includes an energy management module connected to the control module and the data processing module, used to optimize energy use in the cattle farm. The energy management module employs a dynamic programming algorithm for energy scheduling, and the dynamic programming algorithm includes the following steps: Step 1: State definition. Decompose the energy management problem into multiple stages, each stage representing a time interval. State variables include current energy reserves, equipment energy consumption, and external energy prices. Step 2: Decision variables, define the decision actions for each stage, such as starting / stopping equipment, switching energy sources; Step 3: State transition equation. Establish a state transition model to describe the changes in the state after the decision. For example, energy reserves are updated to the previous period's reserves plus the amount generated minus the amount consumed. Step 4: Value Function. Define the value function as the negative of the total cost, with the goal of minimizing the total cost. Step 5: Recursive solution. Use the Bellman equation for recursive calculation, working backward from the final stage to the initial stage to obtain the optimal decision sequence; Step Six: Real-time adjustments, updating status and decisions based on real-time data to achieve dynamic energy dispatch.

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