Automatic feed treatment system and treatment method for livestock breeding

Abstract

The invention discloses an automatic feed processing system and method for livestock breeding, and the system comprises an Internet of Things equipment layer which is used for collecting the data of feed management, environment monitoring and animal health management; the data integration layer is used for receiving the sensor data from the Internet of Things equipment layer and performing data transmission through a standardized interface; the computing and storage layer comprises edge computing equipment and a cloud computing platform; the application layer displays the real-time data, the system state and the intelligent decision result through a visual interface, and the application layer comprises a data instrument panel, an alarm system and a decision support module and is used for helping a user to carry out decision analysis and operation. According to the automatic feed processing system and method for livestock breeding, the technical problems in multiple aspects of automatic feed processing, environment monitoring, animal health management, data integration, intelligent decision support and the like are solved, and accurate management and automatic optimization in the livestock breeding process are achieved.

Description

Technical Field

[0001] The invention relates to an automated feed processing system and method for animal husbandry. Background Art

[0002] Traditional animal husbandry management relies on manual operations in terms of feed delivery, environmental control, animal health monitoring, etc., which is inefficient and has certain management loopholes, making it difficult to meet the needs of modern and precision farming. Specifically, the existing technology faces the following major defects:

[0003] Inaccurate feed management: In traditional farming methods, feed delivery is usually done manually, and the amount and time of delivery are difficult to accurately control, which can easily lead to feed waste or animal malnutrition, affecting the growth and development of animals and production efficiency. In addition, manual feed management is inefficient and it is difficult to adjust the delivery strategy in time according to the growth needs of animals.

[0004] Limited environmental monitoring capabilities: In traditional breeding environments, environmental parameters such as temperature, humidity, and gas concentration are critical to the health and growth of animals. However, existing technologies often rely on manual observation or fixed control equipment, lacking the ability to monitor and automatically adjust environmental changes in real time. Fluctuations in environmental factors may trigger stress responses in animals, reduce production efficiency, and even cause disease transmission.

[0005] Incomplete animal health management: In the current breeding model, the health status of animals mainly relies on manual inspection and observation, which often makes it difficult to detect potential health problems of animals in a timely manner. The monitoring of important health indicators such as body temperature, weight, and activity level lacks systematization and automation, which can easily lead to delayed diagnosis of diseases and affect the overall breeding benefits.

[0006] Low data processing efficiency: A large amount of sensor data is generated during the breeding process, but existing technologies often do not have an effective mechanism to process and integrate data in real time. Diverse data sources cannot be efficiently aggregated, information islands are a serious problem, and it is difficult to achieve collaborative work between various types of equipment. Poor or delayed data transmission may reduce the response speed and accuracy of breeding management, thereby affecting breeding benefits and animal health.

[0007] Insufficient decision support: Traditional farming management methods rely on manual experience and lack scientific data analysis and intelligent decision support. It is difficult for managers to make optimized decisions based on real-time data, resulting in delayed management methods and poor results. The data utilization efficiency is not high, and there is a lack of intelligent predictive analysis and decision support tools, which leads to the inability to effectively improve the management efficiency and decision accuracy in the farming process.

[0008] Complex and error-prone operations: Existing farming management often relies on traditional information recording methods, such as manual records and paper forms, which are complex and error-prone. Due to the large amount of data and the wide variety of types, traditional manual operations are prone to information errors and omissions, which increases the difficulty of management in the farming process and affects the overall production efficiency. Summary of the invention

[0009] The purpose of the present invention is to provide an automated feed processing system and method for animal husbandry, which solves technical problems in multiple aspects such as automated feed processing, environmental monitoring, animal health management, data integration and intelligent decision support, and realizes precise management and automated optimization of the animal husbandry process.

[0010] The technical solution adopted by the present invention to solve its technical problem is:

[0011] An automated feed processing system for animal husbandry, comprising:

[0012] The IoT device layer is used to collect data on feed management, environmental monitoring and animal health management. The IoT device layer includes at least one environmental sensor, an animal monitoring sensor and a feed control device. The environmental sensor is used to monitor the temperature, humidity, gas concentration and other data of the breeding environment in real time. The animal monitoring sensor is used to collect data on the animal's body temperature, weight, activity level and other data. The feed control device is used to control the amount and time of feed delivery.

[0013] A data integration layer, which is used to receive sensor data from the IoT device layer and transmit data through a standardized interface. The data integration layer includes a data gateway, an API interface or a message queue system to ensure data sharing and transmission across systems;

[0014] The computing and storage layer includes edge computing devices and cloud computing platforms. The edge computing devices are used to pre-process, filter and perform preliminary analysis on the data collected by sensors in real time. The cloud computing platform is used to store historical data, perform big data analysis, machine learning and decision support.

[0015] The application layer displays real-time data, system status and intelligent decision-making results through a visual interface. The application layer includes a data dashboard, an alarm system and a decision support module to help users perform decision analysis and operations.

[0016] Preferably, the environmental sensors in the IoT device layer include temperature and humidity sensors, gas concentration sensors and light sensors, the animal monitoring sensors include body temperature sensors, motion sensors and food intake monitoring sensors, and the feed control equipment includes a feed delivery device and a flow meter.

[0017] Preferably, the data integration layer uses a message middleware system to realize data transmission between different systems, and the message middleware system is Kafka or RabbitMQ.

[0018] Preferably, the cloud computing platform uses a time series database to store sensor data and analyzes the data through a big data processing platform.

[0019] Preferably, the edge computing device includes computing nodes for data preprocessing, filtering and real-time analysis, and is able to quickly respond to data according to preset rules or algorithms, and upload the processed data to a cloud platform.

[0020] Preferably, the application layer includes a chart-based visualization interface for displaying real-time environmental data, animal health status, feed consumption and other information, and provides intelligent decision support functions to optimize feed delivery strategies.

[0021] Preferably, the cloud computing platform includes an intelligent prediction model for predicting the feed requirements of animals based on historical data and real-time data, and automatically adjusting the feed delivery amount.

[0022] Another technical problem to be solved by the present invention is to provide an automated feed processing method for animal husbandry, comprising the following steps:

[0023] Collect environmental data, animal health data, and feed consumption data through sensors in the IoT device layer. Environmental data includes temperature, humidity, gas concentration, etc. Animal health data includes body temperature, activity level, weight, etc. Feed consumption data includes feed input and remaining amount.

[0024] Transmit the collected environmental data, animal health data and feed consumption data to the data integration layer, using standardized interfaces or message queues for data transmission;

[0025] At the computing and storage layer, edge computing devices are used to perform preliminary processing and real-time analysis of sensor data, and cloud computing platforms are used to perform big data analysis and intelligent prediction to generate feed demand prediction models and environmental regulation strategies.

[0026] Based on the data analysis results, the system displays real-time data, analysis results and decision suggestions through the visual interface of the application layer. The system automatically adjusts the feed supply according to the feed demand forecast and adjusts the environmental parameters in real time to ensure the health of animals and the reasonable allocation of feed resources.

[0027] The adjusted feed delivery amount is automatically executed through the feed control equipment, and the environmental parameters such as temperature, humidity, and gas concentration are automatically adjusted through the environmental control equipment to optimize the breeding environment and improve feed utilization.

[0028] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the automated feed processing method for livestock breeding as described above is implemented.

[0029] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned method for automated feed processing in animal husbandry is implemented.

[0030] The beneficial effects of the present invention are:

[0031] Traditional feed delivery methods often rely on manual labor, which can easily lead to feed waste or animal malnutrition. Through IoT devices and automatic control systems, the amount and time of feed delivery can be dynamically adjusted according to real-time monitoring data to ensure accurate feed delivery and optimize feed utilization; factors such as temperature, humidity, and gas concentration in the breeding environment have an important impact on the health and growth of animals. The system collects environmental data in real time through environmental sensors, and maintains a suitable breeding environment through automatic adjustment mechanisms to avoid animal health problems caused by environmental discomfort.

[0032] In traditional farming, animal health monitoring relies on manual observation, which can easily miss potential health problems. Animal monitoring sensors can collect health indicators such as body temperature, weight, and activity in real time, detect animal health abnormalities in a timely manner, and reduce disease transmission and mortality. Data collected by multiple data sources (such as environmental sensors, animal monitoring sensors, and feed control equipment) are usually scattered, and traditional methods lack effective data sharing and integration mechanisms. Through the data integration layer (data gateway, API interface, or message queue system), data from different devices can be efficiently and stably integrated to ensure the collaborative work of the system.

[0033] A large amount of sensor data needs to be processed and analyzed quickly, and traditional methods cannot achieve a quick response. Through the combination of edge computing and cloud computing platforms, preliminary data processing and analysis can be performed locally, while big data analysis and machine learning can be performed in the cloud to provide intelligent decision-making support for the system, optimize feed delivery, environmental regulation and animal management strategies; traditional breeding management methods are complex and error-prone. The system provides a visual interface, data dashboard and alarm system at the application layer, allowing breeders to intuitively understand real-time data and system status, and can quickly respond to abnormal situations, provide decision support, and reduce management difficulty and operational errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flow chart of an automated feed processing system for animal husbandry. Specific implementation methods

[0036] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example with reference to the accompanying drawings in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that the drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, it should be noted that, unless otherwise clearly specified and defined, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two elements.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. Example

[0039] See also Figure 1 As shown, an automated feed processing system for animal husbandry includes:

[0040] The IoT device layer is used to collect data on feed management, environmental monitoring and animal health management. The IoT device layer includes at least one environmental sensor, an animal monitoring sensor and a feed control device. The environmental sensor is used to monitor the temperature, humidity, gas concentration and other data of the breeding environment in real time. The animal monitoring sensor is used to collect data on the animal's body temperature, weight, activity level and other data. The feed control device is used to control the amount and time of feed delivery.

[0041] A data integration layer, which is used to receive sensor data from the IoT device layer and transmit data through a standardized interface. The data integration layer includes a data gateway, an API interface or a message queue system to ensure data sharing and transmission across systems;

[0042] The computing and storage layer includes edge computing devices and cloud computing platforms. The edge computing devices are used to pre-process, filter and perform preliminary analysis on the data collected by sensors in real time. The cloud computing platform is used to store historical data, perform big data analysis, machine learning and decision support.

[0043] The application layer displays real-time data, system status and intelligent decision-making results through a visual interface. The application layer includes a data dashboard, an alarm system and a decision support module to help users perform decision analysis and operations.

[0044] By real-time monitoring of animal feed consumption, environmental parameters and animal health data through the IoT device layer, the amount and time of feed delivery can be adjusted in real time, thereby accurately controlling the use of feed, improving feed utilization and avoiding waste; environmental sensors can monitor key environmental data such as temperature, humidity, and gas concentration in real time, and through automatic adjustment, ensure the optimal state of the breeding environment, reduce the impact of adverse environmental factors on animals, and thus improve the health level and production efficiency of animals.

[0045] Data such as body temperature, weight, and activity level collected by animal monitoring sensors can help detect abnormalities in animal health in a timely manner, provide early warnings, reduce the occurrence of diseases, and improve animal productivity and survival rates; edge computing devices and cloud platforms at the computing and storage layer can perform real-time analysis and historical data storage, and use machine learning and big data analysis to optimize decision support systems, provide intelligent management decisions, and help farmers make more scientific decisions on feed placement, environmental adjustments, and other issues.

[0046] Through automated feed control equipment and environmental control equipment, the system can autonomously perform feed delivery and environmental adjustment, reduce manual operations, improve work efficiency and accuracy, and reduce management costs; the application layer's visual interface, data dashboard, and alarm system enable farmers to intuitively understand the system's operating status and various data, identify problems in a timely manner and take corresponding measures, facilitating monitoring and management; through refined management and intelligent optimization, the system can reduce feed costs, improve animal health and production efficiency, and ultimately improve the economic benefits of the entire breeding process.

[0047] The environmental sensors in the IoT device layer include temperature and humidity sensors, gas concentration sensors and light sensors; the animal monitoring sensors include body temperature sensors, motion sensors and food intake monitoring sensors; and the feed control equipment includes a feed delivery device and a flow meter.

[0048] Real-time monitoring of the temperature and humidity of the breeding environment to ensure the comfort of the animal's living environment and avoid affecting the health and production efficiency of the animals due to environmental discomfort; monitoring the concentration of harmful gases (such as ammonia, carbon dioxide, etc.) in the farm, timely detecting the accumulation of harmful gases, and avoiding threats to animal health; ensuring that the lighting conditions in the breeding environment are appropriate. Light has an important impact on the growth and behavior of animals. Light sensors can help adjust the light cycle and intensity.

[0049] By monitoring the animal's body temperature in real time, diseases or abnormal conditions, such as fever, can be detected in time, facilitating early intervention and treatment; monitoring the animal's activity level can help determine the animal's health status and whether the breeding environment is suitable. Changes in the animal's activity level can reflect its health status or abnormal behavior; real-time tracking of the animal's food intake can determine its feed consumption. Reduced food intake may be a sign of health problems, so problems can be detected and measures can be taken in a timely manner.

[0050] Automatically control the feed delivery to ensure that feed is supplied on time and in the right amount, avoiding waste or over-delivery. At the same time, the feed amount can be adjusted according to the needs and growth stage of the animals to improve resource utilization. Accurately measure the feed flow to ensure the accuracy and stability of the delivery system, which helps to achieve more refined management and optimize feed use.

[0051] Reducing human intervention through automated control systems not only improves work efficiency, but also reduces human errors and reduces management costs; data-driven decision support systems can optimize the breeding environment, animal health and feed input based on data collected by sensors, thereby improving overall economic benefits.

[0052] The data integration layer uses a message middleware system to realize data transmission between different systems, and the message middleware system is Kafka or RabbitMQ; the cloud computing platform uses a time series database to store sensor data and analyzes the data through a big data processing platform.

[0053] Using Kafka or RabbitMQ as message middleware can achieve efficient and asynchronous data transmission between different systems. These message middleware support high throughput and low latency data exchange, ensuring the real-time nature of data, especially for the large amount of sensor data generated by IoT devices, which can be quickly transmitted to the cloud for processing and storage.

[0054] Using a time series database to store sensor data can efficiently store, compress and query time series data. Time series databases are designed to process time-based continuous data. They can handle massive amounts of sensor data, support high-frequency data writing, and provide fast time range queries to help quickly obtain historical data and conduct trend analysis and prediction.

[0055] By using the big data processing platform to analyze the data stored in the time series database, it is possible to extract valuable information from a large amount of sensor data and perform pattern recognition, predictive analysis, etc. This provides decision makers with a data-driven basis, helps optimize system operation, improve efficiency, and conduct accurate early warning and prediction, thereby improving the intelligence level of the entire system.

[0056] The edge computing device includes computing nodes for data preprocessing, filtering and real-time analysis, and can quickly respond to data according to preset rules or algorithms, and upload the processed data to the cloud platform; the application layer includes a chart-based visualization interface for displaying real-time environmental data, animal health status, feed consumption and other information, and provides intelligent decision-making support functions to optimize feed delivery strategies; the cloud computing platform includes an intelligent prediction model for predicting animal feed needs based on historical data and real-time data, and automatically adjusting feed delivery.

[0057] Edge computing devices can greatly reduce latency and quickly respond to events triggered by preset rules or algorithms by preprocessing, filtering and real-time analysis of data locally. In this way, the system can respond to abnormal situations (such as animal health problems or environmental changes) in the first place, reducing latency caused by relying on cloud processing and ensuring real-time and high efficiency.

[0058] The visualization interface of the application layer displays key indicators such as environmental data, animal health status, feed consumption, etc. through charts, helping managers to monitor the operation of the system in real time. Based on these data, the system provides intelligent decision support, which can automatically optimize feed delivery strategies, reduce manual intervention, and ensure that animals receive accurate feed supply, thereby improving feeding efficiency and resource utilization.

[0059] The intelligent prediction model in the cloud computing platform can predict the future feed needs of animals by combining historical data and real-time data, and automatically adjust the feed supply according to these predictions. This intelligent adjustment not only reduces the error of manual operation, but also can more accurately match the needs of animals, avoid waste or shortage, and further improve the operational efficiency and economic benefits of the breeding system.

[0060] An automated feed processing method for animal husbandry comprises the following steps:

[0061] Collect environmental data, animal health data, and feed consumption data through sensors in the IoT device layer. Environmental data includes temperature, humidity, gas concentration, etc. Animal health data includes body temperature, activity level, weight, etc. Feed consumption data includes feed input and remaining amount.

[0062] Transmit the collected environmental data, animal health data and feed consumption data to the data integration layer, using standardized interfaces or message queues for data transmission;

[0063] At the computing and storage layer, edge computing devices are used to perform preliminary processing and real-time analysis of sensor data, and cloud computing platforms are used to perform big data analysis and intelligent prediction to generate feed demand prediction models and environmental regulation strategies.

[0064] Based on the data analysis results, the system displays real-time data, analysis results and decision suggestions through the visual interface of the application layer. The system automatically adjusts the feed supply according to the feed demand forecast and adjusts the environmental parameters in real time to ensure the health of animals and the reasonable allocation of feed resources.

[0065] The adjusted feed delivery amount is automatically executed through the feed control equipment, and the environmental parameters such as temperature, humidity, and gas concentration are automatically adjusted through the environmental control equipment to optimize the breeding environment and improve feed utilization.

[0066] Through real-time collection of environmental data, animal health data and feed consumption data through sensor data, the feed delivery amount is automatically adjusted in combination with the intelligent prediction model to ensure accurate feed delivery and avoid waste. In addition, the automatic adjustment of environmental parameters (such as temperature, humidity, gas concentration, etc.) further optimizes the breeding environment, improves feed utilization efficiency and reduces waste.

[0067] By real-time monitoring of animal health data (such as body temperature, activity level, weight, etc.), the system can detect and respond to abnormal animal health in a timely manner. Based on this data, the system can automatically adjust environmental parameters to provide animals with more suitable living conditions, thereby improving the health and production efficiency of animals.

[0068] This method realizes the automatic collection, analysis, prediction and execution of data, thus reducing manual intervention, avoiding human errors and greatly improving operational efficiency. Farmers can focus on higher-level management decisions, while daily operations and adjustments are automatically completed by the system, saving a lot of time and labor costs.

[0069] Data collection and transmission:

[0070] At the IoT device layer, various sensors (such as temperature and humidity sensors, gas concentration sensors, animal health monitoring equipment, etc.) are deployed, which will collect real-time environmental and animal health data as well as feed consumption. These data are transmitted to the data integration layer using standardized interfaces or message queues (such as Kafka, RabbitMQ, etc.) to ensure stable data transmission and real-time updates.

[0071] Data processing and analysis:

[0072] At the computing and storage layer, edge computing devices are used to perform preliminary processing on sensor data, such as noise removal, data cleaning, and simple real-time analysis. This can effectively reduce the burden on the cloud platform and quickly respond to local emergencies. The data is transmitted to the cloud computing platform, which uses big data processing technology to conduct in-depth analysis of the data and generate feed demand prediction models and environmental adjustment strategies. These strategies dynamically adjust feed delivery and environmental adjustment measures based on historical data, real-time data, and the prediction results of intelligent algorithms.

[0073] Intelligent decision making and visualization:

[0074] The application layer's visual interface displays real-time environmental data, animal health data, feed consumption, and analysis results and decision-making recommendations generated by the system. Managers can monitor and view data analysis results through the interface to further understand the current breeding status. Based on these analysis results, the system can automatically adjust the amount of feed and adjust environmental parameters such as temperature, humidity, and gas concentration to ensure that animals grow under optimal conditions.

[0075] Automatic execution control:

[0076] Feed control equipment accurately delivers the required amount of feed according to the system's automatic adjustment instructions. Environmental control equipment automatically adjusts parameters such as temperature, humidity, and gas concentration in the breeding environment according to real-time data to ensure that the breeding environment is always suitable for animal growth, improve feed utilization and animal health.

[0077] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for automated feed processing in livestock breeding as described above is implemented.

[0078] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for automated feed processing in animal husbandry as described above is implemented.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0080] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0081] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. An automated feed processing system for animal husbandry, characterized in that: Included are: The IoT device layer is used to collect data on feed management, environmental monitoring and animal health management. The IoT device layer includes at least one environmental sensor, an animal monitoring sensor and a feed control device. The environmental sensor is used to monitor the temperature, humidity, gas concentration and other data of the breeding environment in real time. The animal monitoring sensor is used to collect data on the animal's body temperature, weight, activity level and other data. The feed control device is used to control the amount and time of feed delivery. A data integration layer, which is used to receive sensor data from the IoT device layer and transmit the data through a standardized interface. The data integration layer includes a data gateway, an API interface or a message queue system to ensure data sharing and transmission across systems; The computing and storage layer includes edge computing devices and cloud computing platforms. The edge computing devices are used to pre-process, filter and perform preliminary analysis on the data collected by sensors in real time. The cloud computing platform is used to store historical data, perform big data analysis, machine learning and decision support. The application layer displays real-time data, system status and intelligent decision-making results through a visual interface. The application layer includes a data dashboard, an alarm system and a decision support module to help users perform decision analysis and operations.

2. The automated feed processing system for animal husbandry according to claim 1, characterized in that: The environmental sensors in the IoT device layer include temperature and humidity sensors, gas concentration sensors and light sensors; the animal monitoring sensors include body temperature sensors, motion sensors and food intake monitoring sensors; and the feed control equipment includes feed delivery devices and flow meters.

3. The automated feed processing system for animal husbandry according to claim 2, characterized in that: The data integration layer uses a message middleware system to realize data transmission between different systems, and the message middleware system is Kafka or RabbitMQ.

4. The automated feed processing system for animal husbandry according to claim 3, characterized in that: The cloud computing platform uses a time series database to store sensor data and analyzes the data through a big data processing platform.

5. The automated feed processing system for animal husbandry according to claim 1, characterized in that: The edge computing device includes computing nodes for data preprocessing, filtering and real-time analysis, and can quickly respond to data according to preset rules or algorithms, and upload the processed data to the cloud platform.

6. The automated feed processing system for animal husbandry according to claim 5, characterized in that: The application layer includes a chart-based visualization interface for displaying real-time environmental data, animal health status, feed consumption and other information, and provides intelligent decision support functions to optimize feed delivery strategies.

7. The automated feed processing system for animal husbandry according to claim 6, characterized in that: The cloud computing platform includes an intelligent prediction model for predicting the feed requirements of animals based on historical data and real-time data, and automatically adjusting the feed delivery amount.

8. A method for automated feed processing in animal husbandry, characterized in that: The following steps are involved: Collect environmental data, animal health data, and feed consumption data through sensors in the IoT device layer. Environmental data includes temperature, humidity, gas concentration, etc. Animal health data includes body temperature, activity level, weight, etc. Feed consumption data includes feed input and remaining amount. Transmit the collected environmental data, animal health data and feed consumption data to the data integration layer, using standardized interfaces or message queues for data transmission; At the computing and storage layer, edge computing devices are used to perform preliminary processing and real-time analysis of sensor data, and cloud computing platforms are used to perform big data analysis and intelligent prediction to generate feed demand prediction models and environmental regulation strategies. Based on the data analysis results, the system displays real-time data, analysis results and decision suggestions through the visual interface of the application layer. The system automatically adjusts the feed supply according to the feed demand forecast and adjusts the environmental parameters in real time to ensure the health of animals and the reasonable allocation of feed resources. The adjusted feed delivery amount is automatically executed through the feed control equipment, and the environmental parameters such as temperature, humidity, and gas concentration are automatically adjusted through the environmental control equipment to optimize the breeding environment and improve feed utilization.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for automated feed processing in animal husbandry as claimed in claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for automated feed processing in animal husbandry as claimed in claim 8 is implemented.

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