Intelligent automatic feed feeding system based on Internet of Things

Abstract

The invention is suitable for the technical field of intelligent feeding, and particularly relates to an intelligent automatic feed putting system based on the Internet of Things, in the system, a sensor network subsystem comprises various types of sensors and is used for detecting various parameters in the feed putting process, and an Internet of Things global regulation and control center control subsystem is used for connecting all devices and controlling the intelligent automatic feed putting system. The intelligent feeding subsystem is used for executing different feed feeding strategies according to feeding scenes, the self-cleaning and disinfecting subsystem is used for automatically disinfecting and cleaning feed pipelines regularly, and the data report subsystem is used for counting and generating feed feeding data reports. Through cooperative work of a plurality of modules such as data acquisition, processing, decision support, equipment control, communication and a user interface, comprehensive control and management of the feed putting process are realized, the feed putting efficiency and benefit are greatly improved, and the system has remarkable innovativeness and practicability.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent feeding, and in particular relates to an intelligent automatic feed delivery system based on the Internet of Things. Background Art

[0002] With the rapid development of the breeding industry, traditional feed delivery methods can no longer meet the requirements of modern farms for feed delivery accuracy and efficiency. Traditional feed delivery methods mainly rely on manual operation, which is not only labor-intensive, but also prone to feed waste and uneven delivery, affecting breeding results. Manual feed delivery usually requires a lot of labor, especially in large farms. The workload of feed delivery is huge and the labor intensity is high, which easily leads to worker fatigue, thus affecting the accuracy and consistency of delivery.

[0003] When feeding manually, it is difficult to accurately control the amount of feed each time, which can easily lead to overfeed or underfeed, resulting in feed waste or insufficient nutrition intake for pigs, which in turn affects the growth and health of pigs. Traditional feed delivery methods also have the problem of irregular delivery time, and it is impossible to adjust the delivery strategy according to the growth stage and actual needs of pigs, resulting in low feed utilization and low breeding efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent automatic feed delivery system based on the Internet of Things, which aims to solve the problem of raising pigs by manually delivering feed, which is labor-intensive, requires manual regular feed proportioning and delivery, has low efficiency, and if not managed properly, may lead to feed waste and increase breeding costs.

[0005] The present invention is implemented as follows: an intelligent automatic feed delivery system based on the Internet of Things, the system comprising: an Internet of Things global control central control subsystem, a sensor network subsystem, an intelligent delivery subsystem, a self-cleaning and disinfection subsystem and a data reporting subsystem, the sensor network subsystem comprising various types of sensors for detecting various parameters in the feed delivery process, the Internet of Things global control central control subsystem for connecting all devices to complete data transmission and processing, the intelligent delivery subsystem for executing different feed delivery strategies according to the feeding scene, the self-cleaning and disinfection subsystem for regularly automatically disinfecting and cleaning the feed pipeline, and the data reporting subsystem for statistics and generation of feed delivery data reports.

[0006] Preferably, the Internet of Things global control central control subsystem includes a data acquisition module, a data processing module, a decision support module, a device control module, a communication module and a user interface module. The data acquisition module is used to collect data generated by all devices, the data processing module is used to process and analyze the collected data, the decision support module is used to formulate a feed delivery strategy based on the data analysis results, the device control module is used to control each device according to the feed delivery strategy, the communication module is used for data transmission between devices, and the user interface module is used to provide a visual operation interface to the user.

[0007] Preferably, the intelligent delivery subsystem adopts the AFOA algorithm, which is used to formulate a feed delivery strategy according to scene conditions. The AFOA algorithm formulates a feed delivery strategy through machine learning and deep learning models. The formula of the AFOA algorithm is expressed as:

[0008] ;

[0009] in:

[0010] represents the time step, and all parameters vary with time.

[0011] and is the time-varying weight coefficient.

[0012] It is the amount of feed put in at the current moment, which is adjusted dynamically over time.

[0013] is a dynamic function of ideal feed requirement, based on body weight ,age , Environmental parameters Updated in real time. It is a dynamic function of health status indicator, based on body temperature , heart rate and respiratory rate Updated in real time.

[0014] Preferably, the sensor network subsystem includes at least a feed tower balance monitoring sensor, a feed feeding device, a water dial sensor and a temperature and humidity sensor. The feed tower balance monitoring sensor is used to detect the remaining amount of feed in the feed tower in real time, the feed feeding device is used to control the feeding speed and quantity of the feed, the water dial sensor is used to detect the flow and temperature of water, and the temperature and humidity sensor monitors the temperature and humidity of the environment in real time.

[0015] Preferably, the sensor network subsystem also includes a weight sensor, a high-definition camera, a gas sensor and a biosensor. The weight sensor is installed on the feed tower and the feed delivery equipment to monitor the feed balance and delivery amount. The high-definition camera is used to monitor the farmed animals and identify their behavior and health status. The biosensor is used to monitor the body temperature, heart rate and respiratory rate of the farmed animals. The gas sensor is installed in the ventilation system of the farm to monitor the concentration of harmful gases in the air.

[0016] Preferably, the self-cleaning and disinfection subsystem includes a self-cleaning nozzle and a disinfection nozzle. The material tower, conveying pipeline and delivery port are all provided with multiple self-cleaning nozzles and disinfection nozzles. When cleaning and disinfection are performed, the self-cleaning nozzle is first used for flushing, and then the disinfection liquid is sprayed through the disinfection nozzle, and the disinfection treatment is performed with the disinfectant liquid.

[0017] Preferably, the self-cleaning and disinfecting subsystem further comprises a disinfectant storage device and an intelligent control unit, wherein the disinfectant storage device is used to store the disinfectant, and the intelligent control unit is used to start the cleaning program and control the self-cleaning nozzle and the disinfecting nozzle to perform cleaning and disinfection.

[0018] Preferably, the intelligent control unit has a self-diagnosis function for monitoring the operating status of the cleaning and disinfection equipment in real time, and promptly giving an alarm or automatically adjusting the cleaning and disinfection strategy when an abnormality is found.

[0019] Preferably, the data report subsystem includes a data storage unit, a data processing unit and a report generation unit. The data storage unit is used to store various data collected during the operation of the system, including environmental temperature and humidity, feed input and pig health status data. The data processing unit is used to perform data processing. The report generation unit is used to present the processed data in the form of charts or text to obtain data reports.

[0020] Preferably, the data report is displayed through a user interface module, and the data report has a plurality of export formats, including at least PDF format, Excel format and CSV format.

[0021] The intelligent automatic feed delivery system based on the Internet of Things provided by the present invention realizes comprehensive monitoring and intelligent control of the feed delivery process through the Internet of Things technology, ensuring the accuracy and optimization of feed delivery. The system includes a data acquisition module, a data processing module, a decision support module, an equipment control module, a communication module and a user interface module. The present invention realizes comprehensive control and management of the feed delivery process through the collaborative work of multiple modules such as data acquisition, processing, decision support, equipment control, communication and user interface, greatly improving the efficiency and benefit of feed delivery, and has significant innovation and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is an architecture diagram of an intelligent automatic feed delivery system based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0025] like Figure 1 As shown, it is an architecture diagram of an intelligent automatic feed delivery system based on the Internet of Things provided by an embodiment of the present invention, the system includes: an Internet of Things global control central control subsystem, a sensor network subsystem, an intelligent delivery subsystem, a self-cleaning and disinfection subsystem and a data reporting subsystem, the sensor network subsystem includes various types of sensors for detecting various parameters in the feed delivery process, the Internet of Things global control central control subsystem is used to connect all devices, complete data transmission and processing, the intelligent delivery subsystem is used to execute different feed delivery strategies according to the feeding scene, the self-cleaning and disinfection subsystem is used to automatically disinfect and clean the feed pipeline regularly, and the data reporting subsystem is used to count and generate feed delivery data reports.

[0026] In this embodiment, the intelligent automatic feed delivery system based on the Internet of Things includes an Internet of Things global control central control subsystem, a sensor network subsystem, an intelligent delivery subsystem, a self-cleaning and disinfection subsystem, and a data reporting subsystem.

[0027] In this embodiment, the Internet of Things global control central control subsystem is used to connect and control various devices, realize real-time transmission and processing of data, and ensure the coordinated operation of various parts of the system. The Internet of Things global control central control subsystem includes a data acquisition module, a data processing module, a decision support module, a device control module, a communication module and a user interface module.

[0028] The data acquisition module collects various types of data through the sensor network subsystem. These data include but are not limited to the remaining feed in the feed tower, video monitoring of the feed delivery process, ambient temperature and humidity, pig weight and health status, etc., to ensure the real-time and accuracy of all data, and provide a basis for subsequent data processing and decision-making.

[0029] The data acquisition module uses a sensor network deployed throughout the farm to obtain real-time data such as ambient temperature and humidity, feed tower balance, video monitoring of the feed delivery process, pig weight and health status, etc. These data are transmitted to the central control platform in real time through wireless transmission technology for processing. Sensors include temperature and humidity sensors, weight sensors, high-definition cameras and biosensors to ensure the comprehensiveness and accuracy of the data. The collected data is transmitted to the central control platform in real time through NB-IoT and 5G wireless transmission technology.

[0030] The data processing module uses advanced data processing algorithms to clean, filter and analyze the collected data. Through in-depth analysis of the data, it can identify possible problems in the feed delivery process and provide corresponding solutions. After receiving the sensor data, the data is first cleaned and filtered to remove noise and abnormal data. Then, using big data analysis and machine learning algorithms, the data is deeply analyzed to identify possible problems in the feed delivery process and generate corresponding solutions. This module can also predict future feed demand through comprehensive analysis of historical data and real-time data, providing a reliable data foundation for the decision support module.

[0031] Based on the analysis results provided by the data processing module and combined with the intelligent delivery subsystem, the decision support module formulates a personalized feed delivery strategy, taking into account multiple factors such as environmental temperature and humidity, pig back fat, weight, epidemic diseases, age, gestation days and gestation stage, to ensure the accuracy and optimization of feed delivery, and can dynamically adjust the feed delivery strategy according to real-time data to cope with emergencies. The decision support module combines multiple factors such as environmental conditions, pig growth stage and health status to formulate personalized feed delivery strategies. This module uses advanced artificial intelligence algorithms to dynamically adjust the amount and time of feed delivery to ensure the accuracy and optimization of feed delivery; the decision support module formulates a personalized feed delivery strategy based on the analysis results provided by the data processing module, combined with environmental conditions, pig growth stage and health status. This module uses advanced artificial intelligence algorithms to dynamically adjust the amount and time of feed delivery to ensure the accuracy and optimization of feed delivery.

[0032] The equipment control module is responsible for converting the feed delivery strategy formulated by the decision support module into specific control instructions, sending them to each device, accurately controlling the operation of the feed feeding device, water dial and other equipment, ensuring that the feed is delivered to the target area as planned, and has a self-diagnosis function, real-time monitoring of the operating status of the equipment, and timely alarm when abnormalities are found; the equipment control module converts the feed delivery strategy formulated by the decision support module into specific control instructions, and sends them to each device through the communication module to ensure that the feed is delivered to the target area as planned. The communication module adopts the Internet of Things communication technology (NB-IoT, 5G) to achieve efficient data communication between the central control platform and various sensors and devices, and supports remote monitoring and control. The equipment control module converts the feed delivery strategy formulated by the decision support module into specific control instructions, and sends them to each device through the communication module to ensure that the feed is delivered to the target area as planned.

[0033] The communication module is responsible for realizing data communication between the central control platform and various sensors and devices. It adopts advanced Internet of Things communication technology to ensure the stability and reliability of data transmission and supports remote monitoring and control. Users can remotely access the central control platform through mobile devices or computers to view the system status and adjust the feed delivery strategy in real time. The communication module adopts Internet of Things communication technology to realize efficient data communication between the central control platform and various sensors and devices and supports remote monitoring and control. The user interface module provides users with a friendly and intuitive operation interface. Users can view various data, monitor the feed delivery process, adjust the feed delivery strategy, and support the generation and export of data reports. The system also supports self-cleaning and disinfection functions to ensure the hygiene and safety of the system.

[0034] The user interface module provides users with a friendly and intuitive operating interface. Users can view various data, monitor the feed delivery process, adjust feed delivery strategies, etc. through the interface. It also supports the generation and export of data reports. Users can easily view and analyze historical feed delivery data to provide data support for future feed delivery strategy optimization.

[0035] The IoT global control central control subsystem realizes comprehensive control and management of the intelligent feed automatic delivery system through the collaborative work of multiple modules such as data collection, processing, decision support, equipment control, communication and user interface, ensuring the efficient operation of the system and the accuracy of feed delivery.

[0036] In this embodiment, the sensor network subsystem includes a plurality of high-precision sensor devices and related supporting equipment. These sensor devices can monitor and feedback various key parameters in the feed delivery process in real time to ensure the accuracy and stability of the system operation.

[0037] The sensor network subsystem is an important part of the intelligent automatic feed delivery system. Through the deployment of multiple sensors at key locations in the farm, the whole process of feed delivery can be monitored and data collected in real time. The sensor network includes environmental sensors, feed tower sensors, video monitoring sensors and pig health status sensors. Environmental sensors are used to monitor environmental parameters such as temperature, humidity, and gas concentration in the farm to ensure the suitability of the feed delivery environment. The feed tower sensor monitors the remaining feed in the feed tower in real time to prevent feed shortage or over-delivery. The video monitoring sensor records the feed delivery process through a high-definition camera, providing real-time video data and historical video playback functions for management personnel to view and analyze at any time. The pig health status sensor provides real-time health data of pigs by monitoring the weight, activity and health indicators of pigs, helping to formulate a scientific feed delivery strategy. The data collected by all sensors are transmitted to the central control platform in real time through wireless transmission technology to ensure the timeliness and accuracy of the data, thereby achieving comprehensive coverage and refined management of the whole process of feed delivery.

[0038] The feed tower balance monitoring sensor is used to detect the remaining amount of feed in the feed tower in real time to prevent feed shortages; the video monitoring sensor provides real-time video streaming to monitor the entire process of feed delivery to ensure the accuracy and safety of delivery; the feed feeding device is equipped with a high-precision sensor that can accurately control the feed feeding speed and quantity to avoid waste and excessive delivery; the water dial sensor is used to detect the flow and temperature of water to ensure that the pigs have sufficient and appropriate drinking water; the temperature and humidity sensor monitors the temperature and humidity of the environment in real time to provide important environmental data support for the intelligent feed delivery strategy. These sensors realize real-time transmission and sharing of data through the Internet of Things technology, forming an efficient and reliable sensor network to ensure that all aspects of the feed delivery system are operating in the best state. This sensor network can not only improve the accuracy of feed delivery, but also timely discover and solve potential problems through data analysis and feedback, further optimize the feed delivery strategy, and improve the overall efficiency and benefits of the system.

[0039] Temperature and humidity sensors are installed in different areas of the farm to monitor changes in temperature and humidity. These sensors can transmit data to the central control platform in real time to ensure that environmental conditions are always in the best state.

[0040] Secondly, weight sensors are installed on feed towers and feed delivery equipment to monitor feed balance and feed delivery amount, ensuring the continuity and accuracy of feed supply; in order to monitor the health status and growth of pigs, high-definition cameras and biosensors are deployed in pig pens.

[0041] High-definition cameras can capture the pigs' activities in real time and analyze their behavior patterns and health status through image processing algorithms.

[0042] Biosensors provide more detailed health data by detecting physiological parameters such as body temperature, heart rate and respiratory rate of pigs. These data are transmitted to the central control platform in real time for analysis and processing through NB-IoT and 5G wireless transmission technology;

[0043] Gas sensors are installed in the ventilation system of the farm to monitor the concentration of harmful gases in the air, such as ammonia and carbon dioxide. Once the concentration of harmful gases is detected to exceed the standard, the system will automatically start the ventilation equipment to ensure that the air quality meets the standards.

[0044] The data from all sensors are cleaned, filtered and analyzed through the data processing module to generate detailed data reports and warning information.

[0045] In order to achieve full coverage and efficient data transmission, the sensor network adopts the Self-Organizing Network (SON) technology. This technology allows adaptive network configuration and optimization between sensor nodes to ensure the stability and efficiency of data transmission. Each sensor node is equipped with a backup power supply to ensure that it can still work normally in the event of a power outage. Users can view the data of various sensors in real time through the user interface module of the central control platform, monitor the feed delivery process, and adjust the feed delivery strategy according to actual needs. The system also supports the generation and export of data reports, and users can conduct comprehensive data analysis and decision support based on the reports.

[0046] In this embodiment, the intelligent delivery subsystem adopts the AFOA algorithm (Adaptive Feed Optimization Algorithm), which can dynamically adjust the feed delivery strategy according to different scenarios and real-time data to ensure the accuracy and optimization of feed delivery.

[0047] The AFOA algorithm obtains real-time data from the sensor network subsystem through the data acquisition module, and cleans, filters and analyzes the collected data. By adopting advanced data processing algorithms, AFOA can identify possible problems in the feed delivery process and provide corresponding solutions. For example, by analyzing the data from the temperature and humidity sensors, the feed delivery strategy can be adjusted to adapt to the current environmental conditions.

[0048] The data processing module can also predict possible future situations through analysis of historical data, providing a basis for decision support.

[0049] The AFOA algorithm develops a personalized feed delivery strategy based on a variety of factors, including but not limited to ambient temperature and humidity, pig back fat, weight, disease, age, gestation days, and gestation stage.

[0050] The AFOA algorithm can dynamically adjust the feed delivery strategy according to the needs of different scenarios. For example, in a high temperature and high humidity environment, the AFOA algorithm will reduce the amount of feed delivered to prevent feed deterioration; during the pregnancy period of pigs, the AFOA algorithm will adjust the nutritional composition and delivery amount of feed according to the number of days and stages of pregnancy to ensure the health of the pigs and the normal development of the fetus.

[0051] The AFOA algorithm achieves comprehensive control and management of the intelligent automatic feed delivery system through the collaborative work of multiple modules such as data acquisition, processing, decision support, equipment control, communication and user interface, ensuring the efficient operation of the system and the accuracy of feed delivery.

[0052] The flexible application of the AFOA algorithm in different scenarios has greatly improved the efficiency and benefits of feed delivery, and is significantly innovative and practical.

[0053] The AFOA algorithm combines environmental conditions, pig growth stage, health status, historical feed delivery data and other factors, and uses machine learning and artificial intelligence technology to generate personalized feed delivery strategies. In terms of environmental conditions, the algorithm can adjust the amount and time of feed delivery according to parameters such as temperature, humidity, and gas concentration to ensure that the feed is delivered in the best environment. In terms of the growth stage of pigs, the algorithm formulates different feed formulas and delivery plans based on the weight, age and health status of the pigs to meet the nutritional needs of different growth stages. The analysis of historical feed delivery data helps the algorithm identify regularities and anomalies in the delivery process and further optimize the delivery strategy. Through the comprehensive analysis of real-time data and historical data, the algorithm can predict future feed needs, dynamically adjust the delivery strategy, and ensure the efficiency and accuracy of feed delivery. The application of this intelligent delivery subsystem in different scenarios has greatly improved the efficiency and benefits of feed delivery, and has significant innovation and practicality.

[0054] The specific implementation process of the AFOA algorithm in the intelligent feed automatic delivery system. The algorithm dynamically adjusts and optimizes the feed delivery strategy by comprehensively analyzing multiple factors such as environmental parameters, pig health status and growth stage.

[0055] The AFOA algorithm uses a variety of advanced artificial intelligence technologies, including machine learning and deep learning models. First, the algorithm establishes a pig growth model and health status prediction model through comprehensive analysis of historical data and real-time data. These models can accurately predict the feed requirements and health status of pigs at different growth stages.

[0056] ;

[0057] in:

[0058] represents the time step, all parameters change with time; and is the time-varying weight coefficient; is the amount of feed put in at the current moment, which is adjusted dynamically over time; is a dynamic function of ideal feed requirement, based on body weight ,age , Environmental parameters Real-time updates; It is a dynamic function of health status indicator, based on body temperature , heart rate and respiratory rate Updated in real time.

[0059] The above objective function is not a fixed value, but a dynamic one. It will be adjusted according to the real-time changes in the ideal feed demand and health status indicators. By iteratively optimizing the objective function, the system can find the optimal feed delivery amount, thereby achieving personalized delivery management.

[0060] The specific formula and derivation process are as follows:

[0061] Objective function definition:

[0062] ;

[0063] in, Indicates The actual amount of feed input per pig, Indicates The ideal feed requirement per pig, Indicates Health status indicators of pigs, and is the weight coefficient, The number of pigs.

[0064] Calculation of ideal feed requirement:

[0065] Di = f(Wi,Ai,Ei);

[0066] in, Indicates The ideal feed requirement per pig, Wi represents the The weight of the pig, Ai represents the The function f(Wi,Ai,Ei) takes into account multiple key factors, including the weight (Wi), age (Ai) and environmental parameters (Ei) of the pig, and converts these input parameters into accurate ideal feed requirements (Di) through machine learning algorithms.

[0067] Health status indicator calculation:

[0068] Hi = g(Ti,Ri,Bi);

[0069] Among them, Ti represents the The body temperature of the first pig, Ri represents the The heart rate of the first pig, Bi represents the The respiratory rate of a pig, the overall meaning of the g(Ti,Ri,Bi) function is to comprehensively analyze multiple physiological parameters and obtain a comprehensive health status indicator Hi through a machine learning algorithm.

[0070] Optimization process:

[0071] Iteratively adjust the , so that the objective function reaches the minimum value. The specific steps are as follows:

[0072] ;

[0073] in, represents the amount of feed given to the i-th pig at time t+1, represents the amount of feed put into the i-th pig at the current time t, η is the learning rate, The objective function is The gradient reflects the difference between the current feed delivery and the ideal delivery, and guides the system on how to adjust the delivery to minimize the error. The whole formula describes an iterative optimization process. The system calculates the difference (gradient) between the current delivery and the ideal delivery, and then adjusts the delivery according to the learning rate to gradually approach the optimal solution.

[0074] About the specific implementation of feed delivery adjustment

[0075] In order to more clearly explain the specific implementation of feed delivery adjustment, we add the following optimization algorithms and steps:

[0076] (1) Dynamic parameter update mechanism:

[0077] Ideal feed requirement and health status indicators It is updated based on real-time collected data:

[0078] ;

[0079] ;

[0080] These parameters are collected in real time through sensors and IoT devices and serve as inputs to the optimization algorithms.

[0081] (2) Iterative optimization process:

[0082] Feed quantity The update uses the gradient descent method or other optimization algorithms for iterative adjustment. The specific formula is as follows:

[0083] ;

[0084] in, is the learning rate, which controls the step size in each iteration; is the objective function The gradient represents the difference between the current feed input and the ideal feed input.

[0085] (3) Iteration steps:

[0086] Initialization parameters:

[0087] ;

[0088] Collect real-time data and update parameters: ;

[0089] Calculate the gradient of the objective function ;

[0090] Update the delivery amount according to the gradient ;

[0091] Continue iterating until the convergence condition is met;

[0092] 3. Clarity about technical implementation

[0093] In order to further clarify the technical means, we added the following control process and convergence conditions:

[0094] (1) Data collection and processing:

[0095] weight : Real-time collection through electronic scales in IoT devices.

[0096] body temperature , heart rate , respiratory rate : Real-time collection through physiological sensors.

[0097] Environmental parameters : Collect temperature, humidity and other parameters through the environmental monitoring system.

[0098] (2) Optimize control process:

[0099] Data collection layer → parameter calculation layer → optimization decision layer → execution control layer;

[0100] Data collection layer: The weight, body temperature, heart rate, respiratory rate and environmental parameters of pigs are collected through sensors and IoT devices.

[0101] Parameter calculation layer: Calculate ideal feed requirements and health status indicators .

[0102] Optimize the decision layer: through the objective function Optimizing feed quantity using gradient descent method .

[0103] Execution control layer: adjust the feed input amount according to the optimization results.

[0104] (3) Convergence conditions:

[0105] In order to ensure the effectiveness of the algorithm, the following convergence conditions are added:

[0106] ;

[0107] in, is the convergence threshold of feed delivery (e.g. 0.1kg), is the convergence threshold of the objective function (e.g. 0.01).

[0108] In an embodiment of the present invention, the self-cleaning and disinfection subsystem module includes a plurality of high-efficiency nozzles, a disinfectant storage device and an intelligent control unit. The high-efficiency nozzles are installed at key parts of the feed delivery system, such as the feed tower, the feeding device and the conveying pipeline, and can cover the internal surface of the entire system. The disinfectant storage device is used to store high-efficiency and environmentally friendly disinfectants to ensure the continuity and effectiveness of the disinfection process. The intelligent control unit is connected to other modules of the system through the Internet of Things technology, and can automatically start the cleaning and disinfection program according to the preset cleaning and disinfection strategy or real-time monitoring data. When the system detects that the feed delivery is completed or in the intermittent period, the intelligent control unit will automatically start the cleaning program, and the high-efficiency nozzle will spray an appropriate amount of cleaning liquid to thoroughly clean the inside of the system and remove residual feed and dirt. Subsequently, the disinfectant storage device will supply disinfectant to the high-efficiency nozzle, and the nozzle will evenly spray the disinfectant to fully disinfect the inside of the system, kill bacteria and viruses, and prevent the spread of germs. The entire cleaning and disinfection process is monitored in real time by the intelligent control unit to ensure the execution effect of each link.

[0109] In addition, the intelligent control unit also has a self-diagnosis function, which can monitor the operating status of the cleaning and disinfection equipment in real time, and promptly alarm or automatically adjust the cleaning and disinfection strategy when abnormalities are found to ensure the stable operation of the system. Through the above self-cleaning and disinfection functions, the system can ensure the hygiene and safety of the feed delivery process while maintaining efficient operation, greatly improving the reliability and service life of the system.

[0110] The automatic cleaning device uses high-pressure water flow and special cleaning agents to regularly clean the feed delivery pipes and feed towers thoroughly to prevent the accumulation of feed residues and dirt. The disinfectant spraying device ensures that the disinfectant is evenly covered on the surface of the equipment by accurately controlling the spraying amount and spraying time, effectively killing bacteria and viruses. The ultraviolet disinfection device uses high-intensity ultraviolet lamps to perform secondary disinfection on the cleaned equipment to further improve the disinfection effect. The self-cleaning and disinfection subsystem monitors the cleaning status and disinfection effect of the equipment in real time through sensors to ensure the thoroughness and effectiveness of each cleaning and disinfection process. In addition, the system also supports remote monitoring and automatic alarm functions. When the equipment needs to be cleaned or disinfected, the system will automatically send a reminder notification to the management personnel. Through the above-mentioned technical means, the present invention effectively solves the sanitation problem of feed delivery equipment and ensures the safety and reliability of the feed delivery process.

[0111] Multiple self-cleaning and disinfecting nozzles are installed at key parts of the feed delivery equipment, such as the feed tower, delivery pipeline and delivery port. These nozzles can be connected to the central control system to realize scheduled or on-demand cleaning and disinfection operations; when the system detects that the feed delivery is completed or according to the preset time interval, the central control system will start the self-cleaning and disinfection program. First, the self-cleaning nozzle will spray high-pressure water or cleaning liquid to fully flush the feed tower, delivery pipeline and delivery port to remove residual feed and dirt. Then, the disinfection nozzle will spray disinfectant to ensure that all parts that come into contact with the feed are effectively disinfected to kill potential germs and viruses.

[0112] In addition, in order to improve the intelligence level of the system, the AFOA algorithm also integrates a self-learning function. By analyzing historical cleaning and disinfection data, the algorithm can optimize the cleaning and disinfection strategy, such as adjusting the frequency of cleaning and disinfection, selecting the most suitable cleaning and disinfection liquids, etc. In this way, the system can not only maintain efficient feed delivery, but also achieve the best cleaning and disinfection effect at the lowest cost.

[0113] In this embodiment, the system further includes a data reporting subsystem so that users can conveniently view and analyze historical data of feed delivery.

[0114] The data report subsystem includes a data storage unit, a data processing unit and a report generation unit.

[0115] The data storage unit is used to store various data collected during the operation of the system, including environmental temperature and humidity, feed input, pig health status, etc. The data processing unit analyzes and processes these data to extract valuable information.

[0116] The data processing unit can generate different types of data reports according to user needs, such as daily, weekly and monthly reports, etc. These reports can include statistical data on feed input, changing trends of environmental parameters, monitoring results of pig health status, etc.

[0117] The report generation unit is responsible for presenting the processed data in the form of charts, text, etc., and generating easy-to-understand reports. Users can select the required report type and time range through the system's user interface module, and export the report with one click. In addition, the data report export function module also supports multiple export formats, such as PDF, Excel, and CSV, and users can choose the appropriate format for export as needed. The exported reports can be used for internal analysis, management decisions, or to submit reports to regulatory authorities. The system also has automatic report generation and scheduled export functions. Users can preset the time and frequency of report generation. The system will automatically generate and export reports at the scheduled time, greatly improving the efficiency of data management.

[0118] Through the data report export function, users can fully understand the operation of the system and the effect of feed delivery, provide data support for optimizing feed delivery strategies, and ensure the efficient operation of the system and the accuracy of feed delivery. The introduction of this functional module not only improves the intelligence level of the system, but also provides users with convenient management tools, which is significantly practical and innovative.

[0119] The data report export function is designed to provide users with detailed and accurate feed delivery data analysis and records. This function is realized through the data report generation and export tool integrated in the user interface module. First, the system automatically collects and organizes data from various sensors and devices, including environmental temperature and humidity, feed silo remaining, feed delivery, pig health status and other parameters. Through the data processing module, these data will be further cleaned, filtered and analyzed to generate detailed data reports.

[0120] The report content includes but is not limited to feed delivery history, environmental parameter change trends, pig health status reports, etc. Users can use these reports to conduct comprehensive data analysis and decision support. In addition, the system also supports customized report functions. Users can customize the content and format of the report according to actual needs and generate personalized data reports.

[0121] In order to ensure the security and integrity of data, the system adopts a multi-level data encryption and backup mechanism to prevent data loss and leakage. Users can view and export data reports anytime and anywhere through a secure remote access method. Through the above technical means, the data report export function of the present invention provides users with a powerful data analysis and management tool, greatly improving the transparency and controllability of the feed delivery process, and has significant practicality and innovation.

[0122] After each feed delivery, the system automatically records relevant data, including actual feed consumption of each pig, delivery time, environmental parameters (such as temperature and humidity), and health indicators of the pigs (such as body temperature, heart rate, and respiratory rate). These data are collected in real time through sensors and stored in a central database.

[0123] In the process of generating data reports, the system first cleans and preprocesses the raw data to remove outliers and noise data. Then, the AFOA algorithm is used to model and analyze the data to identify problems and optimization space in the feed delivery process. For example, by analyzing the relationship between feed consumption and pig weight gain, the feed formula and delivery strategy can be optimized; by analyzing the impact of environmental parameters on the health status of pigs, the breeding environment can be adjusted to improve the health level of pigs. In order to further improve the intelligence level of the data report review function, the system also integrates a self-learning function. By continuously learning and accumulating historical data, the system can gradually optimize the data analysis model and improve the accuracy and practicality of the report. For example, the system can automatically identify the impact of seasonal changes on feed consumption and pig health, adjust the delivery strategy in advance, and avoid production fluctuations caused by environmental changes.

[0124] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent automatic feed delivery system based on the Internet of Things, characterized in that: The system includes: an Internet of Things global control central control subsystem, a sensor network subsystem, an intelligent delivery subsystem, a self-cleaning and disinfection subsystem and a data reporting subsystem. The sensor network subsystem includes various types of sensors for detecting various parameters in the feed delivery process. The Internet of Things global control central control subsystem is used to connect all devices to complete data transmission and processing. The intelligent delivery subsystem is used to execute different feed delivery strategies according to the feeding scene. The self-cleaning and disinfection subsystem is used to automatically disinfect and clean the feed pipeline regularly. The data reporting subsystem is used to count and generate feed delivery data reports.

2. The intelligent automatic feed delivery system based on the Internet of Things according to claim 1 is characterized in that: The Internet of Things global control central control subsystem includes a data acquisition module, a data processing module, a decision support module, a device control module, a communication module and a user interface module. The data acquisition module is used to collect data generated by all devices, the data processing module is used to process and analyze the collected data, the decision support module is used to formulate a feed delivery strategy based on the data analysis results, the device control module is used to control each device according to the feed delivery strategy, the communication module is used for data transmission between devices, and the user interface module is used to provide a visual operation interface to the user.

3. The intelligent automatic feed delivery system based on the Internet of Things according to claim 1 is characterized in that: The intelligent delivery subsystem adopts the AFOA algorithm, which is used to formulate a feed delivery strategy according to scene conditions. The AFOA algorithm formulates a feed delivery strategy through machine learning and deep learning models. The formula of the AFOA algorithm is expressed as follows: ； in: represents the time step, all parameters change with time; and is the time-varying weight coefficient; is the amount of feed put in at the current moment, which is adjusted dynamically over time; is a dynamic function of ideal feed requirement, based on body weight ,age , Environmental parameters Real-time updates; It is a dynamic function of health status indicator, based on body temperature , heart rate and respiratory rate Updated in real time.

4. The intelligent automatic feed delivery system based on the Internet of Things according to claim 1 is characterized in that: The sensor network subsystem includes at least a feed tower balance monitoring sensor, a feed feeding device, a water dial sensor and a temperature and humidity sensor. The feed tower balance monitoring sensor is used to detect the remaining amount of feed in the feed tower in real time. The feed feeding device is used to control the feeding speed and quantity of the feed. The water dial sensor is used to detect the flow and temperature of water. The temperature and humidity sensor monitors the temperature and humidity of the environment in real time.

5. The intelligent automatic feed delivery system based on the Internet of Things according to claim 4 is characterized in that: The sensor network subsystem also includes weight sensors, high-definition cameras, gas sensors and biosensors. Weight sensors are installed on feed towers and feed delivery equipment to monitor the remaining feed and the amount of feed delivered. High-definition cameras are used to monitor the farmed animals and identify their behavior and health status. Biosensors are used to monitor the body temperature, heart rate and respiratory rate of the farmed animals. Gas sensors are installed in the ventilation system of the farm to monitor the concentration of harmful gases in the air.

6. The intelligent automatic feed delivery system based on the Internet of Things according to claim 1 is characterized in that: The self-cleaning and disinfection subsystem includes self-cleaning nozzles and disinfection nozzles. The material tower, conveying pipeline and delivery port are all equipped with multiple self-cleaning nozzles and disinfection nozzles. When cleaning and disinfecting, they are first flushed with the self-cleaning nozzles, and then the disinfectant is sprayed with the disinfectant nozzles, and disinfection is carried out with the disinfectant.

7. The intelligent automatic feed delivery system based on the Internet of Things according to claim 6 is characterized in that: The self-cleaning and disinfection subsystem also includes a disinfectant storage device and an intelligent control unit. The disinfectant storage device is used to store disinfectant, and the intelligent control unit is used to start the cleaning program and control the self-cleaning nozzle and the disinfection nozzle to clean and disinfect.

8. The intelligent automatic feed delivery system based on the Internet of Things according to claim 7 is characterized in that: The intelligent control unit has a self-diagnosis function, which is used to monitor the operating status of the cleaning and disinfection equipment in real time, and promptly alarm or automatically adjust the cleaning and disinfection strategy when an abnormality is found.

9. The intelligent automatic feed delivery system based on the Internet of Things according to claim 1 is characterized in that: The data report subsystem includes a data storage unit, a data processing unit and a report generation unit. The data storage unit is used to store various data collected during the operation of the system, including environmental temperature and humidity, feed input and pig health status data. The data processing unit is used to process the data, and the report generation unit is used to present the processed data in the form of charts or text to obtain data reports.

10. The intelligent automatic feed delivery system based on the Internet of Things according to claim 9 is characterized in that: The data report is displayed through the user interface module, and the data report has multiple export formats, including at least PDF format, Excel format and CSV format.

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