Intelligent control method and system for hatching duck egg hatching environment

Abstract

The invention discloses a duck egg incubation environment intelligent control method and system, and the method comprises the steps: monitoring an incubator environment in real time through a temperature and humidity sensor, automatically adjusting a heater, a humidifier and a dehumidifier, and optimizing an incubation condition through an intelligent algorithm; the electric egg turning device adjusts the egg turning angle and frequency according to embryonic development, and the optical sensor detects the abnormal egg shape and gives an alarm; analyzing historical fault data through machine learning to predict equipment faults, and configuring automatic switching of redundant equipment; hatching data are uploaded to the cloud platform through the Internet of Things, and a data instrument panel, a trend chart and remote monitoring are provided. Energy distribution is optimized through an intelligent algorithm, and high-power equipment is turned off in a non-critical period; data encryption processing is carried out, graphic interface setting parameters are provided, optimal setting is automatically recommended, and 24 / 7 technical support is provided; according to the intelligent control method and system for the hatching duck egg hatching environment, an intelligent control algorithm based on machine learning is developed, and hatching parameters are automatically adjusted to adapt to various environmental conditions.

Description

Technical Field

[0001] The invention belongs to the field of intelligent control of a breeding duck egg hatching environment, and in particular relates to an intelligent control method and system for a breeding duck egg hatching environment. Background Art

[0002] Intelligent control of the incubation environment for breeding duck eggs uses advanced technical means such as sensors, automated control systems and artificial intelligence to achieve precise management and adjustment of the incubation environment, ensuring that temperature, humidity, ventilation and other conditions are always in the best state during the incubation process to improve the hatching rate and the hatching quality of the eggs. The temperature in the incubator is monitored in real time through a temperature sensor, and the heating or cooling equipment is automatically adjusted according to the preset optimal incubation temperature to ensure temperature stability; the humidity in the incubator is controlled by a humidity sensor, and the humidifier or dehumidifier is adjusted to ensure that the humidity meets the needs of duck egg hatching; the ventilation equipment is automatically adjusted according to the needs of the eggs and environmental conditions to maintain air circulation and ensure sufficient oxygen; the egg turning system is intelligently controlled to turn the duck eggs regularly and evenly to promote embryonic development; through the Internet of Things technology, various data during the incubation process can be monitored and recorded in real time, and the incubator can remotely control the incubation environment through mobile phones or computers.

[0003] However, although the existing intelligent control systems for the incubation environment of breeding duck eggs have advantages in improving hatching rates and management efficiency, they also have some defects and challenges. Most of the current intelligent control systems are designed based on standardized environmental conditions and may have poor adaptability to some special climatic and geographical conditions. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent control method and system for the incubation environment of breeding duck eggs, and to develop an intelligent control algorithm based on machine learning so that the system can automatically adjust the incubation parameters according to different environmental conditions to adapt to various geographical and climatic environments.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] The intelligent control method for the hatching environment of breeding duck eggs includes:

[0007] The environmental parameters in the incubator are monitored in real time through multiple temperature and humidity sensors. The working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the needs of duck egg hatching. The incubation conditions are automatically adjusted according to the real-time environmental data through the intelligent adjustment algorithm, and self-learning optimization is performed based on historical data.

[0008] The electric egg-turning device automatically turns the duck eggs over, and adjusts the turning angle and frequency according to the embryonic development stage. At the same time, the built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions;

[0009] Real-time monitoring of the working status of each device in the incubator, including temperature and humidity sensors, fans, and heaters. Analyze historical fault data through machine learning algorithms to predict upcoming equipment failures and configure redundant equipment. When the main device fails, the system automatically switches to the backup device.

[0010] The temperature, humidity, carbon dioxide concentration, and egg-turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, allowing users to view the incubator's various indicators, historical data, and trend charts in real time, and remotely monitor the incubator's working status through a mobile phone app or PC and adjust it at any time;

[0011] Adjust the working intensity of the equipment according to the incubation process, optimize energy distribution based on environmental conditions and equipment operating status, and shut down high-power equipment during non-critical periods of the incubation process through intelligent algorithms;

[0012] The data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms, provides 24 / 7 technical support, and generates solutions through remote diagnosis.

[0013] As a preferred method, the environmental parameters in the incubator are monitored in real time by multiple temperature and humidity sensors, the working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the hatching requirements of duck eggs, and the hatching conditions are automatically adjusted according to the real-time environmental data by an intelligent adjustment algorithm. At the same time, the method of self-learning optimization based on historical data is as follows:

[0014] Deploy multiple temperature and humidity sensors at different locations in the incubator to collect temperature and humidity environmental data in real time;

[0015] According to the temperature and humidity requirements during the incubation of duck eggs, the system automatically adjusts the working status of the heater, humidifier, and dehumidifier through real-time monitoring data. When the temperature is lower than the preset range, the heater is automatically started; when the humidity is too low, the humidifier is started; when the humidity is too high, the dehumidifier is enabled for adjustment;

[0016] By integrating fuzzy logic, PID control or other adaptive control methods, the system adjusts the incubation environment conditions according to the real-time data provided by sensors;

[0017] By recording historical data, long-term data accumulation is carried out, and based on historical data, regression analysis, deep learning model and machine learning algorithms are used for optimization.

[0018] As a preferred method, the duck eggs are automatically turned over by an electric egg turning device, and the turning angle and frequency are adjusted according to the embryonic development stage. At the same time, a built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions. The method is as follows:

[0019] The electric egg turning device automatically turns the duck eggs over, and adjusts the angle and frequency of turning the eggs according to the embryonic development stage of the duck eggs;

[0020] By combining the temperature and humidity sensors with other sensors, combined with the temperature, humidity, and CO2 concentration incubation environment data, the different stages of embryo development can be identified and judged. Combined with historical data and models, the egg turning angle and frequency during the incubation process can be predicted and adaptively adjusted.

[0021] Integrate cameras, lasers or infrared sensors into the egg-turning device to monitor the shape of each duck egg in real time. Through image processing and machine vision algorithms, analyze the shape of the duck egg and identify whether there are deformations, cracks, or irregular shells.

[0022] When the optical sensor detects an abnormal egg shape, the system identifies it and confirms the type of abnormality. After discovering the abnormality, the system will sound an alarm to notify the user or hatcher;

[0023] When an egg-shaped anomaly is detected, the system generates repair suggestions as follows:

[0024] Crack repair, which generates guidance on how to repair cracks, including using appropriate repair agents or avoiding excessive flipping;

[0025] Correction of irregular shapes: when an abnormal egg shape is detected, it is recommended to reduce the frequency or angle of egg turning;

[0026] After handling abnormal situations, the system adjusts the egg turning frequency and angle based on real-time data.

[0027] As a preferred method, the working status of each device in the incubator is monitored in real time, including temperature and humidity sensors, fans, and heaters. The historical fault data is analyzed through machine learning algorithms to predict upcoming equipment failures, and redundant equipment is configured. When the main device fails, the system automatically switches to the backup device as follows:

[0028] By installing various sensors and monitoring systems, the working status of each device in the incubator is monitored in real time, including:

[0029] Temperature and humidity sensors to monitor changes in temperature and humidity in the incubation environment;

[0030] Fans: monitor the fan speed, operating status, and whether there is noise or abnormally slow speed;

[0031] Heater, real-time tracking of heater temperature setting, heating status, and power consumption;

[0032] The operating data of the equipment, including power, operating time, temperature and humidity, is collected and recorded regularly;

[0033] Collect and analyze past equipment failure data, including failure type, frequency, and equipment operation mode. Use decision trees, random forests, support vector machines, and neural network machine learning algorithms to extract failure modes and patterns from historical data. Based on machine learning models, analyze equipment operation data and predict whether equipment will fail.

[0034] Set up temperature and humidity sensors, fans, and heaters as redundant devices as backup for the main devices. The redundant devices work synchronously with the main devices. When the system detects that the main device fails, the backup device immediately takes over the work.

[0035] As a preferred method, the temperature, humidity, carbon dioxide concentration, and egg turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, and the user can view the various indicators, historical data, and trend charts of the incubator in real time, and remotely monitor the working status of the incubator through a mobile phone app or PC and adjust it at any time. The method is as follows:

[0036] Deploy temperature and humidity, carbon dioxide concentration, and egg-turning device sensors in the incubator to monitor various indicators in real time, and transmit the real-time monitoring data to the cloud platform through the Internet of Things technology;

[0037] The cloud platform receives data from various sensors and performs real-time storage, processing and analysis. The cloud platform generates an intuitive dashboard through data visualization technology, through which users can view various real-time data, including temperature, humidity, carbon dioxide concentration and egg-turning cycle;

[0038] Users can log in to the cloud platform through the mobile phone app or PC to check the real-time status of the incubator at any time. Through the remote operation interface, users can adjust the temperature, humidity, and carbon dioxide concentration parameters of the incubator in real time. The adjustment functions are as follows:

[0039] Temperature regulation: according to the incubation requirements, users can adjust the working mode of the heater through the cloud platform;

[0040] Humidity regulation, adjusting the humidity of the incubator through remote control of the water addition system or humidifier;

[0041] Carbon dioxide concentration control: remotely adjust the ventilation system or control the carbon dioxide emission equipment according to the carbon dioxide concentration in the incubator;

[0042] Egg turning cycle adjustment: when the egg turning cycle or time needs to be adjusted during the hatching process, the user can adjust the working cycle of the egg turning machine through the platform.

[0043] As a preferred method, the working intensity of the equipment is adjusted according to the incubation process, and the energy distribution is optimized based on the environmental conditions and the operating status of the equipment. Through the intelligent algorithm, the method of shutting down the high-power equipment during the non-critical period of the incubation process is:

[0044] According to the embryo development, pre-incubation and post-incubation stages in the incubation process, it is determined that one of the stages has higher requirements for temperature and humidity parameters, while another stage has lower requirements for equipment working intensity;

[0045] Determine the period of high demand for stable temperature and humidity during the early stages of embryonic development during incubation, and the period after incubation when equipment demand decreases to a non-critical period, and predict the intensity of equipment demand based on different incubation stages and environmental conditions;

[0046] Adopt intelligent scheduling systems based on machine learning or optimization algorithms to analyze and optimize energy distribution in real time, calculate the current energy consumption of each device by real-time monitoring of the energy efficiency and load of the equipment, and automatically adjust the working intensity of the equipment according to demand;

[0047] When the incubator is in the late incubation period and the temperature and humidity are stable, the intelligent system will judge and shut down unnecessary high-power equipment based on the algorithm;

[0048] According to different incubation processes and environmental requirements, intelligent algorithms automatically adjust the working status of high-power equipment in different time periods.

[0049] As a preference, data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms and provides 24 / 7 technical support. The method of generating solutions through remote diagnosis is as follows:

[0050] The data transmitted from the device to the cloud platform is encrypted using a strong encryption algorithm. The user data, incubation parameters, and historical records stored on the cloud platform are encrypted and stored, and the incubation parameters, device status records, and user data are automatically backed up regularly.

[0051] The system uses a simple graphical interface. Users can set and adjust incubation parameters through an intuitive interface. It monitors environmental data, incubation progress, and equipment status information in real time, and automatically recommends the optimal incubation parameter settings based on machine learning and data analysis:

[0052] When the temperature and humidity are low, the system automatically recommends increasing the power of the heater and humidifier;

[0053] Towards the end of incubation, intelligent algorithms reduce the power requirements of the device, reducing energy consumption;

[0054] Users contact the technical support team through the cloud platform, which provides 24 / 7 online help. The system sets up an automatic diagnosis function to monitor the status of the device in real time and identify faults or anomalies. The remote technical support team develops targeted solutions based on the diagnostic information provided by the system.

[0055] The intelligent control system for the hatching environment of breeding duck eggs includes:

[0056] Environmental monitoring and adjustment module, which uses temperature and humidity sensors to monitor the temperature and humidity in the incubator in real time, automatically adjusts the heater, humidifier, and fan equipment according to preset parameters, and has a built-in carbon dioxide sensor to monitor gas concentration in real time, and adjusts light intensity and air pressure according to the incubation process;

[0057] Automatic egg turning and egg shape detection module, used for built-in egg turning device, the system automatically controls the egg turning cycle, and dynamically adjusts the egg turning angle and frequency according to the incubation stage. It uses optical sensors or image recognition technology to detect whether there are cracks or irregular shapes in the eggshell, and automatically alarms and prompts the user to handle it when problems are detected;

[0058] The fault monitoring and early warning module is used to monitor the working status of each device in real time, and issue an alarm and send a notification to the user end when a fault is identified. It also configures redundant equipment and automatically switches to the backup equipment when the main equipment fails.

[0059] Intelligent control and optimization module, which is used to dynamically adjust the incubation conditions according to environmental parameters, incubation stage and historical data through fuzzy control and machine learning algorithms, and automatically adjust the power according to the real-time demand during the incubation process;

[0060] The cloud platform and data analysis module is used to upload real-time data to the cloud platform through wireless networks for centralized storage and management, automatically generate hatching reports through data analysis tools, and use machine learning models to predict hatching success rates;

[0061] The security and privacy protection module is used to encrypt data transmission and storage, regularly back up cloud data, and protect the security of the system through passwords, fingerprint recognition or two-step verification.

[0062] 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. When the processor executes the program, an intelligent control method and system for the hatching environment of breeding duck eggs as described above is implemented.

[0063] 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, an intelligent control method and system for the hatching environment of breeding duck eggs is implemented.

[0064] The beneficial effects of the present invention are:

[0065] By using multiple temperature and humidity sensors to monitor the environmental parameters in the incubator in real time, the system can automatically adjust the working status of the heater, humidifier, and dehumidifier according to the real-time data; based on real-time environmental data and historical data, the system can self-learn and optimize through intelligent algorithms, and can continuously improve the accuracy and adaptability of incubation conditions to ensure that the most suitable environment is provided at different incubation stages; the electric egg turning device can automatically adjust the egg turning angle and frequency according to the embryonic development stage, and the built-in optical sensor checks the egg shape to ensure the scientificity and accuracy of the egg turning process; the machine learning algorithm can predict possible equipment failures by analyzing historical failure data and configure redundant equipment in advance; the temperature, humidity, and carbon dioxide during the incubation process are automatically adjusted. Data such as carbon dioxide concentration and egg-turning cycle are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive dashboard, where users can view the incubator's various indicators, historical data and trend charts in real time. The system automatically adjusts the equipment's working intensity according to the incubation process, especially shutting down high-power equipment during non-critical periods. The cloud platform encrypts the transmitted data to ensure the security of user data. The cloud platform provides a graphical operation interface, where users can easily set and adjust incubation parameters. The system provides 24-hour technical support and generates solutions through remote diagnosis. By analyzing the equipment's historical fault data through machine learning algorithms, the system can predict equipment failures in advance and take preventive measures to avoid the impact of sudden equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The present invention is a schematic diagram of the process flow of the intelligent control system for the hatching environment of breeding duck eggs. DETAILED DESCRIPTION

[0067] The principles and features of the present invention are described below, and the examples 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 more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0068] 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.

[0069] Example

[0070] The intelligent control method for the hatching environment of breeding duck eggs includes:

[0071] The environmental parameters in the incubator are monitored in real time through multiple temperature and humidity sensors. The working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the needs of duck egg hatching. The incubation conditions are automatically adjusted according to the real-time environmental data through the intelligent adjustment algorithm, and self-learning optimization is performed based on historical data.

[0072] The electric egg-turning device automatically turns the duck eggs over, and adjusts the turning angle and frequency according to the embryonic development stage. At the same time, the built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions;

[0073] Real-time monitoring of the working status of each device in the incubator, including temperature and humidity sensors, fans, and heaters. Analyze historical fault data through machine learning algorithms to predict upcoming equipment failures and configure redundant equipment. When the main device fails, the system automatically switches to the backup device.

[0074] The temperature, humidity, carbon dioxide concentration, and egg-turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, allowing users to view the incubator's various indicators, historical data, and trend charts in real time, and remotely monitor the incubator's working status through a mobile phone app or PC and adjust it at any time;

[0075] Adjust the working intensity of the equipment according to the incubation process, optimize energy distribution based on environmental conditions and equipment operating status, and shut down high-power equipment during non-critical periods of the incubation process through intelligent algorithms;

[0076] The data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms, provides 24 / 7 technical support, and generates solutions through remote diagnosis.

[0077] The system uses multiple temperature and humidity sensors to monitor the temperature, humidity, carbon dioxide concentration and other environmental parameters in the incubator in real time. The system will automatically adjust the working status of heaters, humidifiers, dehumidifiers and other equipment according to the requirements of duck egg hatching. The system not only adjusts environmental conditions based on real-time data, but also uses historical data for self-learning optimization. The electric egg-turning device automatically adjusts the egg-turning angle and frequency according to the development stage of the embryo. This precise egg-turning control can promote uniform heating of the embryo and improve hatching efficiency. At the same time, the built-in optical sensor can detect whether the egg shape is normal in real time. The system can identify anomalies and issue alarms, provide repair suggestions, and help users take timely measures. By analyzing historical fault data through machine learning algorithms, the system can predict possible equipment failures. The temperature during the incubation process Important data such as humidity, carbon dioxide concentration, and egg-turning cycle are transmitted to the cloud platform through the Internet of Things technology. The cloud platform generates an intuitive dashboard, where users can view the status of all equipment and key data in the incubation process in real time; through the mobile app or PC, users can remotely monitor and adjust the working status of the incubator; the system can adjust the working intensity of the equipment according to the different stages of the incubation process; the data transmitted to the cloud is encrypted through encryption algorithms to ensure the security of user data information; the system simplifies the user's setting process through a graphical operation interface, and the intelligent algorithm will automatically recommend the optimal incubation settings based on actual conditions, reducing the complexity of user operations; it provides 24-hour technical support, and users can get help and solutions at any time when they encounter any problems.

[0078] The environmental parameters in the incubator are monitored in real time through multiple temperature and humidity sensors. The working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the needs of duck egg hatching. The incubation conditions are automatically adjusted according to the real-time environmental data through the intelligent adjustment algorithm. At the same time, the method of self-learning optimization based on historical data is as follows:

[0079] Deploy multiple temperature and humidity sensors at different locations in the incubator to collect temperature and humidity environmental data in real time;

[0080] According to the temperature and humidity requirements during the incubation of duck eggs, the system automatically adjusts the working status of the heater, humidifier, and dehumidifier through real-time monitoring data. When the temperature is lower than the preset range, the heater is automatically started; when the humidity is too low, the humidifier is started; when the humidity is too high, the dehumidifier is enabled for adjustment;

[0081] By integrating fuzzy logic, PID control or other adaptive control methods, the system adjusts the incubation environment conditions according to the real-time data provided by sensors;

[0082] By recording historical data, long-term data accumulation is carried out, and based on historical data, regression analysis, deep learning model and machine learning algorithms are used for optimization.

[0083] Through real-time monitoring and automatic adjustment, it can ensure that the environment in the incubator is always maintained under conditions suitable for duck egg hatching, avoiding errors caused by human operation; fuzzy logic and PID control algorithms can accurately control complex and nonlinear environmental changes, avoiding sudden fluctuations in temperature and humidity, thereby keeping the environmental conditions in the incubator more stable; through the accumulation and analysis of historical data, the system can continuously learn and optimize incubation conditions. As the use time increases, the system will become more and more intelligent, and the hatching success rate will continue to improve; regression analysis and deep learning models can predict future environmental changes, adjust incubation conditions in advance, and avoid the impact of temperature and humidity fluctuations on the incubation process; ensuring that the incubation environment is always in the best condition can effectively reduce the risk of hatching failure, and by intelligently adjusting the working status of the equipment, it can save energy, reduce electricity waste, and reduce operating costs; users can check the status of the incubator at any time through the mobile phone App or PC, and make adjustments based on real-time data, providing great flexibility.

[0084] The electric egg turning device automatically turns the duck eggs, and adjusts the turning angle and frequency according to the embryonic development stage. At the same time, the built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions. The method is as follows:

[0085] The electric egg turning device automatically turns the duck eggs over, and adjusts the angle and frequency of turning the eggs according to the embryonic development stage of the duck eggs;

[0086] By combining the temperature and humidity sensors with other sensors, combined with the temperature, humidity, and CO2 concentration incubation environment data, the different stages of embryo development can be identified and judged. Combined with historical data and models, the egg turning angle and frequency during the incubation process can be predicted and adaptively adjusted.

[0087] Integrate cameras, lasers or infrared sensors into the egg-turning device to monitor the shape of each duck egg in real time. Through image processing and machine vision algorithms, analyze the shape of the duck egg and identify whether there are deformations, cracks, or irregular shells.

[0088] When the optical sensor detects an abnormal egg shape, the system identifies it and confirms the type of abnormality. After discovering the abnormality, the system will sound an alarm to notify the user or hatcher;

[0089] When an egg-shaped anomaly is detected, the system generates repair suggestions as follows:

[0090] Crack repair, which generates guidance on how to repair cracks, including using appropriate repair agents or avoiding excessive flipping;

[0091] Correction of irregular shapes: when an abnormal egg shape is detected, it is recommended to reduce the frequency or angle of egg turning;

[0092] After handling abnormal situations, the system adjusts the egg turning frequency and angle based on real-time data.

[0093] The electric egg turning device reduces the burden of manual egg turning, and the automatic egg turning ensures the consistency and regularity of the egg turning process, reducing errors in human operation; by adjusting the egg turning strategy according to the different stages of embryonic development, the specific conditions required for embryonic development can be better met, thereby improving the hatching success rate; combined with environmental data and embryonic development stages, the system can accurately adjust the frequency and angle of egg turning to ensure that every detail of the incubation process meets the requirements for healthy embryonic development; through intelligent control of egg turning, the negative impact on embryonic development caused by frequent egg turning or inappropriate angles is avoided, the incubation process is optimized, and the hatching success rate is improved; the optical sensor can monitor each duck egg in real time, and promptly detect any morphological abnormalities (such as cracks, irregular shapes, etc.), to ensure that each egg during the incubation process is in the best condition; by early detection and treatment of egg shape abnormalities, It can prevent abnormal eggs from further damaging embryonic development and ensure the quality of hatching; the system can detect problems in a timely manner and provide repair suggestions, greatly reducing errors and omissions in human intervention and improving the accuracy of hatching management; by identifying and processing egg shape abnormalities, it can significantly reduce the risk of hatching failure and improve the hatching success rate; by accurately adjusting the frequency and angle of egg turning, it avoids further deterioration of egg shape abnormalities due to excessive turning, and helps the embryo to develop smoothly; the system can flexibly adjust strategies based on real-time data and test results to provide more personalized hatching management solutions; the automated and integrated management system greatly reduces human intervention, and the incubator only needs to make decisions and process according to the information provided by the system, saving time and energy; through the system's intelligent algorithms and automated operations, the impact of human factors on the incubation process is reduced, and the consistency and stability of incubation are improved.

[0094] Real-time monitoring of the working status of each device in the incubator, including temperature and humidity sensors, fans, and heaters. Analyze historical fault data through machine learning algorithms, predict upcoming equipment failures, and configure redundant equipment. When the main device fails, the system automatically switches to the backup device as follows:

[0095] By installing various sensors and monitoring systems, the working status of each device in the incubator is monitored in real time, including:

[0096] Temperature and humidity sensors to monitor changes in temperature and humidity in the incubation environment;

[0097] Fans: monitor the fan speed, operating status, and whether there is noise or abnormally slow speed;

[0098] Heater, real-time tracking of heater temperature setting, heating status, and power consumption;

[0099] The operating data of the equipment, including power, operating time, temperature and humidity, is collected and recorded regularly;

[0100] Collect and analyze past equipment failure data, including failure type, frequency, and equipment operation mode. Use decision trees, random forests, support vector machines, and neural network machine learning algorithms to extract failure modes and patterns from historical data. Based on machine learning models, analyze equipment operation data and predict whether equipment will fail.

[0101] Set up temperature and humidity sensors, fans, and heaters as redundant devices as backup for the main devices. The redundant devices work synchronously with the main devices. When the system detects that the main device fails, the backup device immediately takes over the work.

[0102] By monitoring the temperature and humidity sensors, fans and heaters, the equipment status can be tracked in real time, and equipment abnormalities (such as fan speed too slow, heater temperature unstable, etc.) can be discovered in time; backup equipment (such as redundant temperature and humidity sensors, fans, heaters, etc.) can be set up to ensure seamless switching when the main equipment fails; by collecting the equipment's operating data and historical fault records, the equipment's failure mode can be deeply understood, helping maintenance personnel to better perform predictive maintenance, rather than waiting until the equipment fails to repair it; by accurately monitoring the status of temperature and humidity, fans and heaters, the incubator can continuously optimize the incubation environment conditions, thereby ensuring that the incubator environment is always kept in the best condition; the solution greatly reduces the need for manual intervention through machine learning and automatic switching of redundant equipment; by learning from historical fault data, the machine learning model can analyze the potential patterns of equipment failures, thereby helping the system make more optimized decisions.

[0103] The temperature, humidity, carbon dioxide concentration, and egg-turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, and users can view the incubator's various indicators, historical data, and trend charts in real time. They can also remotely monitor the incubator's working status through a mobile phone app or PC and adjust it at any time. The method is as follows:

[0104] Deploy temperature and humidity, carbon dioxide concentration, and egg-turning device sensors in the incubator to monitor various indicators in real time, and transmit the real-time monitoring data to the cloud platform through the Internet of Things technology;

[0105] The cloud platform receives data from various sensors and performs real-time storage, processing and analysis. The cloud platform generates an intuitive dashboard through data visualization technology, through which users can view various real-time data, including temperature, humidity, carbon dioxide concentration and egg-turning cycle;

[0106] Users can log in to the cloud platform through the mobile phone app or PC to check the real-time status of the incubator at any time. Through the remote operation interface, users can adjust the temperature, humidity, and carbon dioxide concentration parameters of the incubator in real time. The adjustment functions are as follows:

[0107] Temperature regulation: according to the incubation requirements, users can adjust the working mode of the heater through the cloud platform;

[0108] Humidity regulation, adjusting the humidity of the incubator through remote control of the water addition system or humidifier;

[0109] Carbon dioxide concentration control: remotely adjust the ventilation system or control the carbon dioxide emission equipment according to the carbon dioxide concentration in the incubator;

[0110] Egg turning cycle adjustment: when the egg turning cycle or time needs to be adjusted during the hatching process, the user can adjust the working cycle of the egg turning machine through the platform.

[0111] The sensors monitor the key indicators in the incubator (temperature, humidity, carbon dioxide concentration, egg turning cycle) in real time and transmit the data to the cloud platform. Through the mobile app or PC, users can log in to the cloud platform anytime and anywhere to check the status of the incubator and control it remotely. Remote control of the ventilation system or carbon dioxide emission equipment can effectively adjust the carbon dioxide concentration in the incubator to prevent excessive concentration from having a negative impact on the embryo. The real-time monitoring and remote adjustment functions ensure that the incubator environment is always kept in the best state, such as suitable temperature and humidity, low carbon dioxide concentration, etc., to maximize the hatching success rate. The system automatically records and processes data, generates trend charts and historical data reports, and centrally manages multiple key parameters such as temperature and humidity, carbon dioxide concentration, and egg turning cycle, making the hatching process more efficient and orderly, and avoiding mistakes caused by complex operations. By recording and analyzing historical data, users can identify the best operating mode during the hatching process. Whether it is an incubation task that is sensitive to changes in temperature and humidity, or an operation that requires a specific egg turning cycle, this solution can provide highly customized solutions to meet different types of hatching needs. The cloud platform provides an intuitive data dashboard, so users can easily view the equipment status and make necessary adjustments.

[0112] Adjust the working intensity of the equipment according to the incubation process, optimize energy distribution based on environmental conditions and equipment operating status, and use intelligent algorithms to shut down high-power equipment during non-critical periods during the incubation process:

[0113] According to the embryo development, pre-incubation and post-incubation stages in the incubation process, it is determined that one of the stages has higher requirements for temperature and humidity parameters, while another stage has lower requirements for equipment working intensity;

[0114] Determine the period of high demand for stable temperature and humidity during the early stages of embryonic development during incubation, and the period after incubation when equipment demand decreases to a non-critical period, and predict the intensity of equipment demand based on different incubation stages and environmental conditions;

[0115] Adopt intelligent scheduling systems based on machine learning or optimization algorithms to analyze and optimize energy distribution in real time, calculate the current energy consumption of each device by real-time monitoring of the energy efficiency and load of the equipment, and automatically adjust the working intensity of the equipment according to demand;

[0116] When the incubator is in the late incubation period and the temperature and humidity are stable, the intelligent system will judge and shut down unnecessary high-power equipment based on the algorithm;

[0117] According to different incubation processes and environmental requirements, intelligent algorithms automatically adjust the working status of high-power equipment in different time periods.

[0118] Through machine learning or optimization algorithms, the intelligent scheduling system can analyze the operating status and energy efficiency of incubator equipment in real time, calculate energy consumption, and automatically adjust the working intensity of the equipment according to equipment requirements, avoiding unnecessary continuous operation of high-power equipment and significantly reducing energy consumption; by optimizing the operating mode of the equipment and avoiding unnecessary high-power work, it can reduce excessive use of equipment and extend the service life of the equipment; by analyzing the temperature and humidity requirements of different stages of the incubation process (such as early embryonic development, pre-incubation, and late incubation), the intelligent system can accurately adjust the working intensity of the equipment to ensure the optimal environmental conditions at each stage, thereby improving the hatching success rate; through the intelligent scheduling system, users no longer need to manually adjust the working mode of the equipment, reducing the workload of managers; the intelligent system The system adjusts temperature and humidity according to the needs of the embryo at different stages of incubation to avoid environmental fluctuations caused by excessive or insufficient equipment work, thereby ensuring that the embryo is always in stable environmental conditions throughout the incubation process; the system can predict equipment needs based on real-time and historical data of the incubation environment and make dynamic adjustments. Through machine learning algorithms, the system can continuously optimize energy distribution and equipment scheduling strategies to improve overall operational efficiency; different incubation tasks may have different environmental requirements, and intelligent algorithms can automatically adjust equipment operation strategies according to task types to ensure flexibility and adaptability; users do not need to pay attention to the specific operation of each device, they only need to observe the incubation status through a simple monitoring interface, the system will automatically optimize equipment operation, reduce human intervention, and improve the convenience of incubation management.

[0119] The data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms and provides 24 / 7 technical support. The method of generating solutions through remote diagnosis is as follows:

[0120] The data transmitted from the device to the cloud platform is encrypted using a strong encryption algorithm. The user data, incubation parameters, and historical records stored on the cloud platform are encrypted and stored, and the incubation parameters, device status records, and user data are automatically backed up regularly.

[0121] The system uses a simple graphical interface. Users can set and adjust incubation parameters through an intuitive interface. It monitors environmental data, incubation progress, and equipment status information in real time, and automatically recommends the optimal incubation parameter settings based on machine learning and data analysis:

[0122] When the temperature and humidity are low, the system automatically recommends increasing the power of the heater and humidifier;

[0123] Towards the end of incubation, intelligent algorithms reduce the power requirements of the device, reducing energy consumption;

[0124] Users contact the technical support team through the cloud platform, which provides 24 / 7 online help. The system sets up an automatic diagnosis function to monitor the status of the device in real time and identify faults or anomalies. The remote technical support team develops targeted solutions based on the diagnostic information provided by the system.

[0125] The solution ensures that data transmission and storage from the device to the cloud platform are encrypted with a strong algorithm, which guarantees the confidentiality and security of user data and prevents data from being illegally accessed or tampered with. Users can easily set and adjust incubation parameters through a graphical interface. The interface is simple and intuitive, easy to operate, and reduces the user's learning cost. The system automatically analyzes the environment and equipment status based on real-time data and machine learning technology, and recommends the optimal incubation parameters. By monitoring the equipment status and environmental data in real time, the system can intelligently adjust the power requirements of the equipment at different incubation stages. Users can contact the technical support team through the cloud platform at any time to get all-weather help. Due to intelligent recommendations and automated adjustments, users do not need to manually set parameters frequently, reducing tedious operation steps. Regular cloud data backup ensures the reliability and stability of the system and reduces the impact on operations when data is lost or fails. The intelligent system adjusts the power of the equipment according to actual needs, thereby avoiding excessive consumption of electricity and resources. This not only helps to reduce energy costs, but also makes the system more environmentally friendly. The system can flexibly adjust settings according to user needs, environmental conditions and incubation stages, and can adapt to incubation tasks of different scales and environmental conditions.

[0126] The intelligent control system for the hatching environment of breeding duck eggs includes:

[0127] Environmental monitoring and adjustment module, which uses temperature and humidity sensors to monitor the temperature and humidity in the incubator in real time, automatically adjusts the heater, humidifier, and fan equipment according to preset parameters, and has a built-in carbon dioxide sensor to monitor gas concentration in real time, and adjusts light intensity and air pressure according to the incubation process;

[0128] Automatic egg turning and egg shape detection module, used for built-in egg turning device, the system automatically controls the egg turning cycle, and dynamically adjusts the egg turning angle and frequency according to the incubation stage. It uses optical sensors or image recognition technology to detect whether there are cracks or irregular shapes in the eggshell, and automatically alarms and prompts the user to handle it when problems are detected;

[0129] The fault monitoring and early warning module is used to monitor the working status of each device in real time, and issue an alarm and send a notification to the user end when a fault is identified. It also configures redundant equipment and automatically switches to the backup equipment when the main equipment fails.

[0130] Intelligent control and optimization module, which is used to dynamically adjust the incubation conditions according to environmental parameters, incubation stage and historical data through fuzzy control and machine learning algorithms, and automatically adjust the power according to the real-time demand during the incubation process;

[0131] The cloud platform and data analysis module is used to upload real-time data to the cloud platform through wireless networks for centralized storage and management, automatically generate hatching reports through data analysis tools, and use machine learning models to predict hatching success rates;

[0132] The security and privacy protection module is used to encrypt data transmission and storage, regularly back up cloud data, and protect the security of the system through passwords, fingerprint recognition or two-step verification.

[0133] By real-time monitoring of temperature, humidity, carbon dioxide concentration and air pressure, the system can automatically adjust heaters, humidifiers, fans and other equipment according to the set incubation parameters; the system monitors the status of the equipment in real time, and can promptly issue an alarm when a fault is identified, and send a notification to the user end, configure redundant equipment to ensure that when the main equipment fails, it can automatically switch to the backup equipment, thereby minimizing the risk of interruption of the incubation process; the intelligent control module combines fuzzy control and machine learning algorithms to dynamically adjust the incubation conditions according to environmental data, incubation stage and historical data; upload real-time data to the cloud platform via wireless network for centralized storage, management and analysis; use encryption technology to ensure the security of data transmission and storage to prevent data leakage or tampering; the system displays the incubation status, environmental data and equipment information through a graphical interface, and users can easily adjust the set parameters.

[0134] 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 computer program, the intelligent control method and system for the hatching environment of breeding duck eggs as described above are implemented.

[0135] 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 and system for intelligent control of the hatching environment of breeding duck eggs as described above are implemented.

[0136] 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).

[0137] 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.

[0138] 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 intelligent control method for the hatching environment of breeding duck eggs, characterized in that: Included are: The environmental parameters in the incubator are monitored in real time through multiple temperature and humidity sensors. The working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the needs of duck egg hatching. The incubation conditions are automatically adjusted according to the real-time environmental data through the intelligent adjustment algorithm, and self-learning optimization is performed based on historical data. The electric egg-turning device automatically turns the duck eggs over, and adjusts the turning angle and frequency according to the embryonic development stage. At the same time, the built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions; Real-time monitoring of the working status of each device in the incubator, including temperature and humidity sensors, fans, and heaters. Analyze historical fault data through machine learning algorithms to predict upcoming equipment failures and configure redundant equipment. When the main device fails, the system automatically switches to the backup device. The temperature, humidity, carbon dioxide concentration, and egg-turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, allowing users to view the incubator's various indicators, historical data, and trend charts in real time, and remotely monitor the incubator's working status through a mobile phone app or PC and adjust it at any time; Adjust the working intensity of the equipment according to the incubation process, optimize energy distribution based on environmental conditions and equipment operating status, and shut down high-power equipment during non-critical periods of the incubation process through intelligent algorithms; The data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms, provides 24 / 7 technical support, and generates solutions through remote diagnosis.

2. The method for intelligently controlling the hatching environment of breeding duck eggs according to claim 1, characterized in that: The environmental parameters in the incubator are monitored in real time through multiple temperature and humidity sensors. The working status of the heater, humidifier, and dehumidifier are automatically adjusted according to the needs of duck egg hatching. The incubation conditions are automatically adjusted according to the real-time environmental data through the intelligent adjustment algorithm. At the same time, the method of self-learning optimization based on historical data is as follows: Deploy multiple temperature and humidity sensors at different locations in the incubator to collect temperature and humidity environmental data in real time; According to the temperature and humidity requirements during the incubation of duck eggs, the system automatically adjusts the working status of the heater, humidifier, and dehumidifier through real-time monitoring data. When the temperature is lower than the preset range, the heater is automatically started; when the humidity is too low, the humidifier is started; when the humidity is too high, the dehumidifier is enabled for adjustment; By integrating fuzzy logic, PID control or other adaptive control methods, the system adjusts the incubation environment conditions according to the real-time data provided by sensors; By recording historical data, long-term data accumulation is carried out, and based on historical data, regression analysis, deep learning model and machine learning algorithms are used for optimization.

3. The method for intelligently controlling the hatching environment of duck eggs according to claim 2, characterized in that: The electric egg turning device automatically turns the duck eggs, and adjusts the turning angle and frequency according to the embryonic development stage. At the same time, the built-in optical sensor detects whether the egg shape is normal. When an abnormality is identified, the system issues an alarm and provides repair suggestions. The method is as follows: The electric egg turning device automatically turns the duck eggs over, and adjusts the angle and frequency of turning the eggs according to the embryonic development stage of the duck eggs; By combining the temperature and humidity sensors with other sensors, combined with the temperature, humidity, and CO2 concentration incubation environment data, the different stages of embryo development can be identified and judged. Combined with historical data and models, the egg turning angle and frequency during the incubation process can be predicted and adaptively adjusted. Integrate cameras, lasers or infrared sensors into the egg-turning device to monitor the shape of each duck egg in real time. Through image processing and machine vision algorithms, analyze the shape of the duck egg and identify whether there are deformations, cracks, or irregular shells. When the optical sensor detects an abnormal egg shape, the system identifies it and confirms the type of abnormality. After discovering the abnormality, the system will sound an alarm to notify the user or hatcher; When an egg-shaped anomaly is detected, the system generates repair suggestions as follows: Crack repair, which generates guidance on how to repair cracks, including using appropriate repair agents or avoiding excessive flipping; Correction of irregular shapes: when an abnormal egg shape is detected, it is recommended to reduce the frequency or angle of egg turning; After handling the abnormal situation, the system adjusts the egg turning frequency and angle based on real-time data.

4. The method for intelligently controlling the hatching environment of breeding duck eggs according to claim 3, characterized in that: Real-time monitoring of the working status of each device in the incubator, including temperature and humidity sensors, fans, and heaters. Analyze historical fault data through machine learning algorithms, predict upcoming equipment failures, and configure redundant equipment. When the main device fails, the system automatically switches to the backup device as follows: By installing various sensors and monitoring systems, the working status of each device in the incubator is monitored in real time, including: Temperature and humidity sensors to monitor changes in temperature and humidity in the incubation environment; Fans: monitor the fan speed, operating status, and whether there is noise or abnormally slow speed; Heater, real-time tracking of heater temperature setting, heating status, and power consumption; The operating data of the equipment, including power, operating time, temperature and humidity, is collected and recorded regularly; Collect and analyze past equipment failure data, including failure type, frequency, and equipment operation mode. Use decision trees, random forests, support vector machines, and neural network machine learning algorithms to extract failure modes and patterns from historical data. Based on machine learning models, analyze equipment operation data and predict whether equipment will fail. Set up temperature and humidity sensors, fans, and heaters as redundant devices as backup for the main devices. The redundant devices work synchronously with the main devices. When the system detects that the main device fails, the backup device immediately takes over the work.

5. The method for intelligently controlling the hatching environment of breeding duck eggs according to claim 4, characterized in that: The temperature, humidity, carbon dioxide concentration, and egg-turning cycle data during the incubation process are transmitted to the cloud platform through the Internet of Things. The cloud platform generates an intuitive data dashboard, and users can view the incubator's various indicators, historical data, and trend charts in real time. They can also remotely monitor the incubator's working status through a mobile phone app or PC and adjust it at any time. The method is as follows: Deploy temperature and humidity, carbon dioxide concentration, and egg-turning device sensors in the incubator to monitor various indicators in real time, and transmit the real-time monitoring data to the cloud platform through the Internet of Things technology; The cloud platform receives data from various sensors and performs real-time storage, processing and analysis. The cloud platform generates an intuitive dashboard through data visualization technology, through which users can view various real-time data, including temperature, humidity, carbon dioxide concentration and egg-turning cycle; Users can log in to the cloud platform through the mobile phone app or PC to check the real-time status of the incubator at any time. Through the remote operation interface, users can adjust the temperature, humidity, and carbon dioxide concentration parameters of the incubator in real time. The adjustment functions are as follows: Temperature regulation: according to the incubation requirements, users can adjust the working mode of the heater through the cloud platform; Humidity regulation, adjusting the humidity of the incubator through remote control of the water addition system or humidifier; Carbon dioxide concentration control: remotely adjust the ventilation system or control the carbon dioxide emission equipment according to the carbon dioxide concentration in the incubator; Egg turning cycle adjustment: when the egg turning cycle or time needs to be adjusted during the hatching process, the user can adjust the working cycle of the egg turning machine through the platform.

6. The method for intelligently controlling the hatching environment of breeding duck eggs according to claim 5, characterized in that: Adjust the working intensity of the equipment according to the incubation process, optimize energy distribution based on environmental conditions and equipment operating status, and use intelligent algorithms to shut down high-power equipment during non-critical periods during the incubation process: According to the embryo development, pre-incubation and post-incubation stages in the incubation process, it is determined that one of the stages has higher requirements for temperature and humidity parameters, while another stage has lower requirements for equipment working intensity; Determine the period of high demand for stable temperature and humidity during the early stages of embryonic development during incubation, and the period after incubation when equipment demand decreases to a non-critical period, and predict the intensity of equipment demand based on different incubation stages and environmental conditions; Adopt intelligent scheduling systems based on machine learning or optimization algorithms to analyze and optimize energy distribution in real time, calculate the current energy consumption of each device by real-time monitoring of the energy efficiency and load of the equipment, and automatically adjust the working intensity of the equipment according to demand; When the incubator is in the late incubation period and the temperature and humidity are stable, the intelligent system will judge and shut down unnecessary high-power equipment based on the algorithm; According to different incubation processes and environmental requirements, intelligent algorithms automatically adjust the working status of high-power equipment in different time periods.

7. The method for intelligently controlling the hatching environment of breeding duck eggs according to claim 6, characterized in that: The data transmitted in the cloud is encrypted. The cloud platform uses encryption algorithms to store user data and regularly backs up cloud data. It uses an intuitive and easy-to-use operation interface. Users set and adjust incubation parameters through a graphical interface. The system automatically recommends the optimal settings through intelligent algorithms and provides 24 / 7 technical support. The method of generating solutions through remote diagnosis is as follows: The data transmitted from the device to the cloud platform is encrypted using a strong encryption algorithm. The user data, incubation parameters, and historical records stored on the cloud platform are encrypted and stored, and the incubation parameters, device status records, and user data are automatically backed up regularly. The system uses a simple graphical interface. Users can set and adjust incubation parameters through an intuitive interface. It monitors environmental data, incubation progress, and equipment status information in real time, and automatically recommends the optimal incubation parameter settings based on machine learning and data analysis: When the temperature and humidity are low, the system automatically recommends increasing the power of the heater and humidifier; Towards the end of incubation, intelligent algorithms reduce the power requirements of the device, reducing energy consumption; Users contact the technical support team through the cloud platform, which provides 24 / 7 online help. The system sets up an automatic diagnosis function to monitor the status of the device in real time and identify faults or anomalies. The remote technical support team develops targeted solutions based on the diagnostic information provided by the system.

8. The intelligent control system for the hatching environment of breeding duck eggs is characterized by: Included are: Environmental monitoring and adjustment module, which uses temperature and humidity sensors to monitor the temperature and humidity in the incubator in real time, automatically adjusts the heater, humidifier, and fan equipment according to preset parameters, and has a built-in carbon dioxide sensor to monitor gas concentration in real time, and adjusts light intensity and air pressure according to the incubation process; Automatic egg turning and egg shape detection module, used for built-in egg turning device, the system automatically controls the egg turning cycle, and dynamically adjusts the egg turning angle and frequency according to the incubation stage. It uses optical sensors or image recognition technology to detect whether there are cracks or irregular shapes in the eggshell, and automatically alarms and prompts the user to handle it when problems are detected; The fault monitoring and early warning module is used to monitor the working status of each device in real time, and issue an alarm and send a notification to the user end when a fault is identified. It also configures redundant equipment and automatically switches to the backup equipment when the main equipment fails. Intelligent control and optimization module, which is used to dynamically adjust the incubation conditions according to environmental parameters, incubation stage and historical data through fuzzy control and machine learning algorithms, and automatically adjust the power according to the real-time demand during the incubation process; The cloud platform and data analysis module is used to upload real-time data to the cloud platform through wireless networks for centralized storage and management, automatically generate hatching reports through data analysis tools, and use machine learning models to predict hatching success rates; The security and privacy protection module is used to encrypt data transmission and storage, regularly back up cloud data, and protect the security of the system through passwords, fingerprint recognition or two-step verification.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the intelligent control method for the hatching environment of breeding duck eggs as described in any one of claims 1 to 7 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 intelligent control method for the hatching environment of breeding duck eggs as described in any one of claims 1 to 7 is implemented.

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