Intelligent environment control system and method for silkworm feeding

Abstract

The invention relates to the technical field of environment control, in particular to an intelligent environment control system and method for silkworm feeding. The method comprises the following steps of performing real-time monitoring on a silkworm feeding community, and performing classification treatment on the growth stages of silkworms to obtain data of the growth stages of the silkworms; environment demand difference analysis among different silkworms is carried out on the growth stage data of the silkworms, and aggregation behavior environment interaction effect evaluation among different silkworm growth stages is carried out to obtain aggregation behavior environment interaction effect data; according to the gathering behavior environment interaction effect data, environment global balance intelligent control optimization of silkworm feeding is carried out, and environment intelligent control optimization data is obtained; and according to the environment intelligent control optimization data, carrying out automatic silkworm feeding environment control management strategy design so as to execute silkworm feeding intelligent environment control. According to the invention, the intelligent environment control technology is optimized, so that the intelligent environment control technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental control, and particularly to an intelligent environmental control method and system for silkworm breeding. Background Art

[0002] Silkworm breeding is an agricultural activity with extremely high requirements for environmental conditions. In the past, the silkworm breeding method relied on natural conditions and was easily affected by seasonal changes and climate fluctuations. In order to improve the efficiency and stability of silkworm breeding, intelligent environmental control technology has emerged. This method is applicable to silkworm breeding in greenhouses. Through modern scientific and technological means such as sensor networks, the Internet of Things, big data, and artificial intelligence, key parameters such as temperature, humidity, light, and ventilation in the breeding environment are monitored and regulated in real time to ensure the best conditions for silkworm growth. Specifically, the intelligent environmental control system can automatically adjust environmental parameters according to different growth stages of silkworms, making them always within the most suitable range, thereby reducing the occurrence of diseases and improving the growth rate and health status of silkworms. In addition, through data analysis and prediction, scientific management suggestions can also be provided for farmers to optimize breeding strategies and maximize economic benefits. The intelligent environmental control method not only improves the automation level and refined management ability of silkworm breeding, but also significantly reduces the labor intensity and error rate of manual operations, promoting the transformation and development of traditional agriculture towards modernization and intelligence. However, there are problems in a traditional intelligent environmental control method for silkworm breeding that it cannot accurately regulate the local area environment changes caused by the aggregation behavior of silkworms during the silkworm breeding process, and the effect of balancing the whole area silkworm breeding environment control is poor. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent environmental control system and method for silkworm breeding to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent environmental control method for silkworm breeding, the method includes the following steps: Step S1: Obtain silkworm growth cycle data; monitor the silkworm breeding community in real time to obtain a set of silkworm breeding community monitoring images; classify the set of silkworm breeding community monitoring images according to the silkworm growth cycle data to obtain silkworm growth stage data; Step S2: Analyze the environmental demand differences among different silkworms for the silkworm growth stage data to obtain silkworm environmental demand difference data; identify the silkworm aggregation behavior for the set of silkworm breeding community monitoring images to obtain silkworm aggregation behavior data; evaluate the aggregation behavior environment interaction effect among different silkworm growth stages for the silkworm environmental demand difference data according to the silkworm aggregation behavior data to obtain aggregation behavior environment interaction effect data; Step S3: Perform behavioral activity environment demand regulation on the mulberry silkworm environmental demand difference data based on the agglomeration behavior environment interaction effect data to obtain the behavioral activity environment demand regulation data; perform intelligent control optimization of the overall environmental balance for mulberry silkworm rearing based on the behavioral activity environment demand regulation data to obtain the environmental intelligent control optimization data; Step S4: Design an automated mulberry silkworm rearing environment control management strategy based on the environmental intelligent control optimization data to obtain the mulberry silkworm rearing environment control management strategy, and send the mulberry silkworm rearing environment control management strategy to the cloud platform to execute the intelligent environmental control for mulberry silkworm rearing.

[0005] The present invention understands the complete life cycle of silkworms from hatching to becoming moths and the time characteristics of each stage. These data can help farmers and breeders formulate precise feeding plans and schedules, thereby maximizing production efficiency and product quality. Through real-time monitoring, abnormal situations in the process of silkworm breeding, such as diseases, food supply problems, or environmental changes, can be detected and addressed in a timely manner. This helps reduce losses and improve breeding efficiency. Through the image set, the quantity, distribution, and health status of silkworms in the community can be quantitatively analyzed, providing objective data support for breeding management. This monitoring method is more accurate and reliable than traditional visual inspections. Classifying the monitoring images according to the growth stages of silkworms can accurately judge the status and needs of silkworms at each stage. This classification can guide feeding plans, disease prevention and treatment, as well as timely harvesting and processing arrangements. Obtaining detailed growth stage data helps analyze and optimize the breeding process. By comparing the data of different growth stages, potential optimization points can be found, such as improving the feed formula, optimizing temperature and humidity control, etc., thereby increasing the growth rate and yield of silkworms. By analyzing the differences in environmental requirements at different silkworm growth stages, the ideal conditions of environmental factors such as temperature, humidity, and light for silkworms at each stage can be determined. This helps breeders optimize the breeding environment and provide more suitable growth conditions, thus promoting the healthy growth and high yield of silkworms. Aggregation behavior data can reveal the aggregation patterns and behavioral characteristics of silkworms at different growth stages. These data not only help understand the interaction methods of silkworms in the group but also provide optimization space for breeders, such as adjusting the breeding density, improving air circulation, etc., to reduce the risk of disease transmission and enhance production efficiency. Evaluating the interaction effect between aggregation behavior and environmental requirements can help understand how environmental factors affect the aggregation behavior of silkworms and how the aggregation behavior in turn affects the environment. This in-depth analysis helps optimize the design and management strategies of the breeding system, thereby improving the overall breeding efficiency and health level. Based on the data of the interaction effect between aggregation behavior and the environment, the environmental requirements can be regulated to precisely adjust the breeding environment to meet the environmental and physiological needs of silkworms at each different growth stage during aggregation behavior. This data-driven regulation method can maximize the growth rate, health status, and yield of silkworms while reducing energy and resource waste. Using the data of behavior, activity, and environmental requirement regulation for environmental intelligent control optimization can achieve the global balance of the breeding environment. This means that while taking into account the environmental requirements during silkworm aggregation, it does not affect the environmental requirements of silkworms in non-aggregation areas. According to the environmental intelligent control optimization data, precise and efficient silkworm breeding environment control and management strategies are designed. These strategies are based on real-time monitoring and data analysis and can targetedly adjust parameters such as temperature, humidity, and light in the breeding environment to maximize the growth needs of silkworms. Through automated control, breeders can reduce the need for manual intervention, improve management efficiency, and ensure that silkworms grow under the best environmental conditions.Send the designed feeding environment control and management strategy to the cloud platform for execution to achieve the intelligent management of mulberry silkworm feeding. The cloud platform can respond to data in real time and automatically adjust the breeding environment according to the preset strategy to maintain a stable optimized state. This intelligent environment control not only improves the consistency and predictability of production, but also reduces the errors of manual operations, lowers the operation cost and energy consumption. Therefore, the present invention is an optimized treatment for a traditional intelligent environment control method for mulberry silkworm feeding, solving the problems existing in the traditional intelligent environment control method for mulberry silkworm feeding, that is, during the mulberry silkworm feeding process, the local area environment change caused by the aggregation behavior of mulberry silkworms cannot be accurately regulated in the local area, and the balance control effect of the whole area mulberry silkworm feeding environment is poor, improving the accuracy of local area environment regulation for the local area environment change caused by the aggregation behavior of mulberry silkworms, and enhancing the balance control ability of the whole area mulberry silkworm feeding environment.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain the mulberry silkworm growth cycle data; Step S12: Real-time monitor the mulberry silkworm feeding community through an electronic monitoring device to obtain a set of mulberry silkworm feeding community monitoring images; Step S13: Perform image sharpening processing on the set of mulberry silkworm feeding community monitoring images to obtain a set of sharpened images of the mulberry silkworm community monitoring; Step S14: Classify the set of sharpened images of the mulberry silkworm community according to the mulberry silkworm growth cycle data to obtain the data of the growth stage where the mulberry silkworms are located.

[0007] Through the detailed mulberry silkworm growth cycle data, the present invention enables the breeders to accurately understand the development process of mulberry silkworms from hatching to moth emergence and the duration of their key growth stages. These data provide a basis for formulating optimized feeding plans and help ensure the best management at each stage of the breeding process. Real-time monitoring of the mulberry silkworm feeding community through an electronic monitoring device allows breeders to detect and handle potential health problems or environmental changes in a timely manner, and to judge the current physical signs of individual mulberry silkworms. This timely response ability helps reduce losses and improve production efficiency, ensuring that mulberry silkworms grow rapidly under good growth conditions. Performing image sharpening processing on the set of monitoring images can enhance the clarity and details of the images, enabling breeders to more accurately observe and analyze the state and health of the mulberry silkworm community. This processing improves the quality and reliability of the data and helps accurately identify abnormal situations in the mulberry silkworm population. Classifying the sharpened image set according to the growth cycle data enables breeders to accurately judge the state of mulberry silkworms at each stage. This classification helps formulate targeted management strategies, including feeding, disease prevention and harvesting arrangements, so as to maximize production efficiency and product quality.

[0008] Preferably, step S2 includes the following steps: Step S21: Analyze the environmental requirement differences among different growth stages of the silkworms based on the silkworm growth cycle data to obtain the silkworm environmental requirement difference data; Step S22: Quantify the phased clustering differences of the silkworm environmental requirement difference data to obtain the environmental requirement difference clustering quantification data; Step S23: Identify the silkworm aggregation behaviors from the silkworm breeding community monitoring image set to obtain the silkworm aggregation behavior data; Step S24: Evaluate the aggregation behavior environmental interaction effects among different growth stages of the silkworm based on the silkworm aggregation behavior data and the environmental requirement difference clustering quantification data to obtain the aggregation behavior environmental interaction effect data.

[0009] By analyzing the environmental requirement differences among different growth stages of the silkworms, the breeder can understand the specific requirements of silkworms for factors such as temperature, humidity, and light at each stage. This data helps to optimize the breeding environment, provide the most suitable growth conditions, and thus promote the healthy growth and high yield of silkworms. Quantifying the phased clustering differences of the environmental requirement difference data can help breeders more systematically understand and manage the changing needs of silkworms at different growth stages. This quantitative analysis provides a basis for customized breeding strategies, enabling breeders to more effectively adjust and optimize the breeding environment. On the basis of clustering, it is necessary to quantify the environmental requirement differences between different stages. This includes measuring and describing the degree of difference between different clustering groups. By quantifying the differences, the degree and pattern of changes in environmental requirements between each stage can be accurately understood. By identifying the aggregation behaviors of silkworms, the aggregation patterns and behavioral characteristics of silkworm groups under different times and environmental conditions can be understood. These data not only help to evaluate the adaptability and stability of the breeding environment, but also provide optimization suggestions for breeders, such as adjusting the breeding density or improving air circulation, to improve production efficiency and health levels. Evaluating the interaction effects between aggregation behaviors and environmental requirement differences can reveal how environmental factors affect the group behaviors of silkworms, and how group behaviors in turn affect the environment. This in-depth analysis helps to optimize the design and management strategies of the breeding system, thereby improving the overall breeding efficiency and health status.

[0010] Preferably, step S24 includes the following steps: Step S241: Calculate the aggregation density of the silkworm aggregation behavior data to obtain the silkworm aggregation density data; calculate the aggregation peristaltic friction frequency of the silkworm aggregation behavior data to obtain the silkworm aggregation peristaltic friction frequency data; Step S242: Simulate the aggregation temperature effect based on the silkworm aggregation density data and the silkworm aggregation peristaltic friction frequency data to obtain the aggregation temperature effect simulation data; Step S243: Calculate the aggregation effect temperature increment for the simulated data of the aggregation temperature effect to obtain the aggregation effect temperature increment data; Step S244: Based on the aggregation effect temperature increment data, perform a stress state simulation between different silkworm growth stages on the clustering quantization data of environmental demand differences to obtain the silkworm stress state simulation data; Step S245: Calculate the difference in silkworm vitality reduction between different growth stages for the silkworm stress state simulation data to obtain the silkworm vitality reduction difference data; Step S246: Based on the aggregation effect temperature increment data, the silkworm stress state simulation data, and the silkworm vitality reduction difference data, evaluate the aggregation behavior environment interaction effect between different silkworm growth stages on the clustering quantization data of environmental demand differences to obtain the aggregation behavior environment interaction effect data.

[0011] The present invention can understand in detail the aggregation behavior characteristics of the silkworm population by calculating the aggregation density and peristaltic friction frequency of silkworms. The aggregation density data reflects the degree of density of the silkworm population, while the peristaltic friction frequency indicates the activity level of movement within the silkworm population. These data provide a basis for subsequent steps to help understand how the group behavior responds to environmental changes. Based on the aggregation density and peristaltic friction frequency data, a simulation of the aggregation temperature effect is carried out, which can simulate the thermal effect of the silkworm population at different densities and movement frequencies. This simulation provides information on the internal temperature distribution and changes within the silkworm population, helping to optimize the temperature control strategy in the breeding environment to ensure that silkworms grow within an appropriate temperature range. Calculating the aggregation effect temperature increment based on the simulated data of the aggregation temperature effect can quantify the impact of the silkworm population on the environmental temperature. These data provide a basis for understanding and predicting the stress response of silkworms under different aggregation conditions, helping to adjust the breeding environment to reduce stress and improve production efficiency. Based on the aggregation effect temperature increment data, a simulation of the silkworm stress state is carried out, which can simulate the stress response of silkworms at different growth stages. These simulation data provide in-depth insights into the physiological and behavioral changes of silkworms in different aggregation environments, providing support for formulating personalized management strategies. Calculating the difference in vitality reduction of silkworms at different growth stages can quantify the potential impact of the aggregation behavior on the growth and health status of silkworms. These data help breeders better understand how the aggregation behavior affects the growth efficiency and yield of silkworms, and then optimize the breeding environment and management measures. Considering comprehensively the aggregation effect temperature increment, the silkworm stress state simulation, and the vitality reduction difference data, evaluate the interaction effect between the aggregation behavior and the environmental demand difference. This evaluation provides a profound understanding of how the silkworm population behavior affects the breeding environment, providing a scientific basis and data support for optimizing the breeding system.

[0012] Preferably, calculating the variance of the difference in vitality reduction between different silkworm growth stages for the silkworm stress state simulation data includes the following steps: Evaluate the slowdown tendency of the silkworm peristalsis frequency among different silkworm growth stages based on the simulated data of the silkworm stress state, and obtain the slowdown tendency data of the silkworm peristalsis frequency; Calculate the slowdown fluctuation range of the silkworm peristalsis frequency for different growth stages based on the slowdown tendency data of the silkworm peristalsis frequency, and obtain the slowdown fluctuation range; Perform a logarithmic transformation of the slowdown fluctuation on the slowdown tendency data of the silkworm peristalsis frequency according to the slowdown fluctuation range, and obtain the logarithmically transformed slowdown fluctuation data; Analyze the skewness of the time series distribution of the logarithmically transformed slowdown fluctuation data, and obtain the skewness data of the time series distribution of the slowdown; Calculate the approximate numerical range of the slowdown based on the skewness data of the time series distribution of the slowdown for the logarithmically transformed slowdown fluctuation data, and obtain the approximate numerical range of the slowdown; Conduct a non-linear slowdown constraint analysis on the slowdown fluctuation range based on the grey relational analysis method and the approximate numerical range of the slowdown, and obtain the slowdown correlation data of the peristalsis frequency; Calculate the difference in the reduction of silkworm vitality among different growth stages based on the slowdown correlation data of the peristalsis frequency, and obtain the difference data in the reduction of silkworm vitality;

[0013] The present invention evaluates the slowdown tendency of the silkworm peristalsis frequency under stress conditions at different silkworm growth stages, that is, the trend of frequency change over time. Understand the change characteristics of the silkworm peristalsis frequency at different growth stages, and provide basic data for subsequent steps. According to the slowdown tendency data of the peristalsis frequency, calculate the fluctuation range of the silkworm peristalsis frequency at different growth stages. Determine the fluctuation range of the silkworm peristalsis frequency, reveal the amplitude and trend of the change in the silkworm peristalsis frequency at different growth stages, and provide a data basis for subsequent analysis. Use the slowdown fluctuation range data of the peristalsis frequency to perform a logarithmic transformation to make the data more linear and suitable for further statistical analysis, reduce the volatility of the data, more accurately reflect the change trend of the peristalsis frequency among different growth stages, and provide a more stable and reliable data basis for subsequent analysis. Analyze the skewness of the time series distribution of the logarithmically transformed slowdown fluctuation data, that is, the distribution characteristics of the data in the time series. Understand the distribution characteristics and skewness degree of the data at different growth stages, and help to understand the non-uniformity of the frequency change and its biological interpretation. According to the skewness data of the time series distribution of the slowdown, calculate the approximate numerical range of the logarithmically transformed slowdown fluctuation data. Provide a more specific numerical range to reflect the specific amplitude and direction of the change in the silkworm peristalsis frequency at different growth stages. Based on the grey relational analysis method and the approximate numerical range of the slowdown, calculate the difference in the reduction of silkworm vitality among different growth stages. Quantify the difference in the reduction of silkworm vitality under stress conditions at different growth stages, and reveal the potential impact of frequency change on the growth and health status of silkworms.

[0014] Preferably, step S3 includes the following steps: Step S31: Regulate the environmental requirements for behavioral activities of the mulberry silkworm environmental requirement difference data based on the agglomeration behavior environment interaction effect data to obtain the regulated data for environmental requirements of behavioral activities; wherein the regulated data for environmental requirements of behavioral activities includes the regulated data for temperature of behavioral activities and the regulated data for air flow of behavioral activities. Step S32: Calculate the influence degree on the adjacent activity area of the mulberry silkworm feeding aggregation behavior according to the regulated data for environmental requirements of behavioral activities to obtain the environmental influence data for the adjacent activity area. Step S33: Identify the influence degree of the azimuth distribution environment of the adjacent activity area of the mulberry silkworm feeding aggregation behavior on the environmental influence data for the adjacent activity area to obtain the influence degree data for the azimuth distribution environment. Step S34: Perform intelligent control optimization of the overall environmental balance for mulberry silkworm feeding according to the influence degree data for the azimuth distribution environment and the regulated data for environmental requirements of behavioral activities to obtain the optimized data for intelligent environmental control.

[0015] The present invention adjusts the temperature and air flow in the breeding environment according to the agglomeration behavior characteristics of the mulberry silkworm population to ensure that the mulberry silkworms grow and reproduce under suitable environmental conditions, thereby improving production efficiency and quality. Understanding the impact of the agglomeration behavior of mulberry silkworms on the surrounding environment, including the diffusion and regulation effects of temperature and air flow, helps to optimize the breeding space layout and management strategies to maximize the growth requirements of mulberry silkworms. Determining the differences in environmental impacts in different directions helps to optimize the space layout and regulation strategies to provide a balanced growth environment and ensure that the mulberry silkworms remain healthy and vigorous throughout the breeding process. Through the intelligent control system, the dynamic regulation of the breeding environment is realized, and the stability and balance of environmental parameters within the ideal range are maintained, improving the growth efficiency and production results of mulberry silkworms.

[0016] Preferably, step S31 includes the following steps: Step S311: Analyze the abnormal temperature fluctuations of the agglomeration behavior during the mulberry silkworm feeding process according to the agglomeration behavior environment interaction effect data to obtain the abnormal temperature fluctuation data of the agglomeration behavior. Step S312: Evaluate the temperature fluctuation tolerance of mulberry silkworms at different growth stages for the mulberry silkworm environmental requirement difference data according to the abnormal temperature fluctuation data of the agglomeration behavior to obtain the temperature fluctuation tolerance data of mulberry silkworms. Step S313: Simulate the air circulation of the agglomeration behavior during the mulberry silkworm feeding process according to the agglomeration behavior environment interaction effect data to obtain the air circulation data of the agglomeration behavior. Step S314: Analyze the air duct blockage effect of the agglomeration behavior during the mulberry silkworm feeding process on the air circulation data of the agglomeration behavior to obtain the air duct blockage effect data of the agglomeration behavior. Step S315: Pair the data of environmental demand differences of silkworms based on the data of the temperature fluctuation tolerance of silkworms and the data of the air duct blocking effect of the aggregation behavior to obtain the paired data of environmental demand differences; Step S316: Regulate the environmental demands of the behavioral activities according to the paired data of environmental demand differences to obtain the regulated data of environmental demands of the behavioral activities; among which the regulated data of environmental demands of the behavioral activities includes the regulated data of temperature of the behavioral activities and the regulated data of air flow of the behavioral activities.

[0017] According to the data of the environmental interaction effect of the aggregation behavior, the present invention analyzes the abnormal temperature fluctuations caused by the aggregation behavior of silkworms during the feeding process. By identifying and analyzing the abnormal temperature fluctuations, the influence degree of the aggregation behavior on the temperature stability can be revealed, providing basic data and reference for subsequent temperature regulation, ensuring that silkworms grow and reproduce in a stable temperature environment. Based on the data of abnormal temperature fluctuations of the aggregation behavior, the tolerance of silkworms at different growth stages to temperature fluctuations is evaluated, and the sensitivity of silkworms at different growth stages to temperature changes is determined, helping to formulate targeted temperature control strategies, improving the feeding success rate and production efficiency. Using the data of the environmental interaction effect of the aggregation behavior, the influence of the silkworm aggregation behavior on the air circulation is simulated, understanding the influence of the aggregation behavior on the air flow in the feeding environment, helping to evaluate the air quality and oxygen supply situation, optimizing the ventilation design and management of the feeding space, analyzing the influence of the aggregation behavior on the air duct blocking effect, that is, how the aggregation behavior affects the ventilation effect of the air duct, identifying and evaluating the air duct blocking effect caused by the aggregation behavior, so as to adjust the air duct design and layout to ensure the air circulation and the stability of the feeding environment. According to the data of the temperature fluctuation tolerance of silkworms and the data of the air duct blocking effect of the aggregation behavior, pair the data of environmental demand differences and regulate the environmental demands of the behavioral activities. Through data pairing and regulation, precise control of temperature and air flow is achieved to create the most suitable feeding environment. This precise regulation can improve the growth rate and quality of silkworms, reduce losses, and optimize the breeding benefits.

[0018] Preferably, step S34 includes the following steps: Step S341: Analyze the time-delay effect of the environmental impact of the adjacent activity area of the silkworm feeding aggregation behavior according to the data of the environmental impact degree of the azimuth distribution to obtain the time-delay effect data of the distribution environmental impact; Step S342: Extract the environmental temperature mutation and analyze the air flow circulation structure of the adjacent activity area of the silkworm feeding aggregation behavior according to the regulated data of temperature of the behavioral activities, the regulated data of air flow of the behavioral activities and the time-delay effect data of the distribution environmental impact to obtain the azimuth distribution environmental temperature mutation data and the azimuth distribution air flow circulation structure data; Step S343: Identify the progressive law of the spatial distribution mutation of the adjacent activity area of the silkworm feeding aggregation behavior for the azimuth distribution environmental temperature mutation data to obtain the progressive law data of the temperature distribution mutation; Step S344: Perform intelligent control optimization of the global temperature balance for silkworm rearing based on the temperature distribution mutation progression law data and the behavior activity environment requirement regulation data to obtain the intelligent control optimization data for temperature balance; Step S345: Conduct an analysis of the airflow stacking effect in the adjacent activity area of the silkworm rearing aggregation behavior on the azimuth distribution airflow circulation structure data to obtain the azimuth distribution airflow stacking effect data; Step S346: Perform intelligent control optimization of the global airflow balance for silkworm rearing based on the azimuth distribution airflow stacking effect data and the behavior activity environment requirement regulation data to obtain the intelligent control optimization data for airflow balance; Step S347: Perform intelligent control optimization of the global environment balance for silkworm rearing based on the intelligent control optimization data for temperature balance and the intelligent control optimization data for airflow balance to obtain the intelligent control optimization data for the environment.

[0019] Based on the data of the degree of influence of the azimuth distribution environment, this invention analyzes the time-delay effect of the environmental regulation on the environment of the adjacent activity area when regulating the environmental factors for the aggregation behavior of silkworm rearing. It reveals the time-delay effect of the environmental regulation on the surrounding environment during the aggregation behavior, helps to predict and adjust the impact of environmental changes on the growth of silkworms, and optimizes the dynamic regulation strategies of temperature and air flow. According to the temperature regulation data of behavioral activities, the air flow regulation data of behavioral activities, and the data of the time-delay effect of the distribution environment influence, it accurately extracts the mutation of temperature and the air flow structure in the environment of the adjacent activity area when analyzing the environmental regulation for the aggregation behavior of silkworm rearing, reveals the influencing mode of the environmental regulation during the aggregation behavior on the environmental temperature and air flow in the adjacent activity area, and provides detailed data support for further optimizing the environmental control strategy. Analyze the mutation data of the azimuth distribution environmental temperature, and identify the mutation progression law of the temperature distribution in the adjacent activity area caused by the aggregation behavior of silkworm rearing. By understanding the progression law of the temperature distribution, optimize the temperature regulation strategy to ensure the balance and stability of the temperature during the silkworm rearing process, and improve the rearing efficiency and quality. Based on the data of the mutation progression law of the temperature distribution and the data of the regulation of the environmental requirements of behavioral activities, conduct the intelligent control optimization of the global temperature balance for silkworm rearing. Use intelligent control technology to achieve the dynamic adjustment and balance of the temperature of the overall rearing environment, ensure that the temperature fluctuates within an appropriate range, and improve the growth rate and production efficiency of silkworms. Analyze the data of the air flow structure of the azimuth distribution, and evaluate the impact of the environmental regulation for the aggregation behavior of silkworm rearing on the air flow stacking effect in the adjacent activity area. Understand how the air flow stacking effect affects the air quality and oxygen supply in the rearing environment, provide a basis for optimizing the ventilation design and management, and ensure the air circulation and cleanliness in the rearing space. Use the intelligent control system to optimize the distribution and flow structure of the air flow, ensure the gas balance and stability in the rearing environment, and improve the health and production efficiency of silkworms. Combine the intelligent control optimization data of temperature and air flow to achieve the intelligent control optimization of the global environmental balance for silkworm rearing. By comprehensively regulating temperature and air flow, achieve the stability and balance of the overall environment, improve the growth quality and economic benefits of silkworms, and provide scientific and technological support and guarantee for the sustainable development of the aquaculture industry.

[0020] Preferably, the intelligent control optimization of the global air flow balance for the regulation of the environmental requirements of behavioral activities according to the data of the air flow stacking effect of the azimuth distribution includes the following steps: Simulate the air flow return path in the adjacent activity area of the aggregation behavior of silkworm rearing for the data of the air flow stacking effect of the azimuth distribution to obtain the air flow return path simulation data; Evaluate the difficulty of air flow exchange and diffusion for the data of the air flow stacking effect of the azimuth distribution according to the air flow return path simulation data to obtain the air flow exchange and diffusion difficulty data; Optimize the control of the air flow direction angle for the air flow exchange and diffusion difficulty data to obtain the air flow direction angle control data; According to the air flow direction angle control data, the air flow velocity control is adapted to the air flow exchange and diffusion difficulty data to obtain the air flow velocity control adaptation data; Based on the air flow direction angle control data, the air flow velocity control adaptation data, and the behavioral activity environment demand regulation data, the intelligent control optimization of the overall air balance for silkworm rearing is carried out to obtain the intelligent control optimization data of the air balance.

[0021] The present invention simulates the air flow return path in the adjacent activity area of the silkworm rearing aggregation behavior according to the azimuth distribution air flow stacking effect data. By simulating the air flow return path, understanding the movement trajectory of the air flow in the rearing environment helps to optimize the fluidity and uniformity of the air flow, ensuring good ventilation and gas exchange effects in the overall environment. Based on the air flow return path simulation data, the exchange and diffusion difficulty of the azimuth distribution air flow stacking effect is evaluated. Understanding the exchange difficulty of the air flow in different regions helps to determine the bottlenecks and obstacles of the air flow, providing a basis for subsequent flow direction control and flow velocity adjustment. According to the air flow exchange and diffusion difficulty data, the flow direction angle of the air flow is optimized. By optimizing the air flow direction angle, ensuring the effective flow of the air flow in the rearing environment, avoiding dead corners and air flow blockages, and improving the overall ventilation effect and air quality. Based on the air flow direction angle control data, the flow velocity control strategy of the air flow is adapted. According to the actual demand, the flow velocity of the air flow is adjusted to ensure the balance and stability of the air flow in each region, effectively avoiding the problem of environmental non-uniformity caused by too fast or too slow air flow. Combining the air flow direction angle control data, the air flow velocity control adaptation data, and the behavioral activity environment demand regulation data, the intelligent control optimization of the overall air balance for silkworm rearing is carried out. By comprehensively controlling the direction and speed of the air flow, the gas balance and fluidity in the rearing environment are realized, improving the comfort and growth efficiency of silkworms, thereby increasing the breeding yield and quality.

[0022] Preferably, the present invention also provides an intelligent environment control system for silkworm rearing, which is used to execute the intelligent environment control method for silkworm rearing as described above. The intelligent environment control system for silkworm rearing includes: A silkworm growth stage classification module, which is used to obtain silkworm growth cycle data; perform real-time monitoring on the silkworm rearing community to obtain a set of silkworm rearing community monitoring images; classify the set of silkworm rearing community monitoring images according to the silkworm growth cycle data to obtain the data of the growth stage where the silkworms are located; The environmental analysis module for the agglomeration behavior of mulberry silkworms is used to analyze the differences in environmental requirements among different mulberry silkworms based on the data of the growth stages of mulberry silkworms, so as to obtain the data on the differences in environmental requirements of mulberry silkworms; identify the agglomeration behavior of mulberry silkworms from the monitoring image set of the mulberry silkworm breeding community to obtain the data on the agglomeration behavior of mulberry silkworms; evaluate the interactive effect of the agglomeration behavior environment among different growth stages of mulberry silkworms based on the data of the agglomeration behavior of mulberry silkworms on the data of the differences in environmental requirements of mulberry silkworms, so as to obtain the data on the interactive effect of the agglomeration behavior environment. The overall control optimization module for mulberry silkworm breeding is used to regulate the environmental requirements of behavioral activities based on the data of the interactive effect of the agglomeration behavior environment on the data of the differences in environmental requirements of mulberry silkworms, so as to obtain the data on the regulation of environmental requirements of behavioral activities; perform intelligent control optimization of the overall environmental balance of mulberry silkworm breeding based on the data on the regulation of environmental requirements of behavioral activities, so as to obtain the data on intelligent control optimization of the environment. The control management strategy execution module is used to design the automatic control management strategy for the mulberry silkworm breeding environment based on the data on intelligent control optimization of the environment, obtain the control management strategy for the mulberry silkworm breeding environment, and send the control management strategy for the mulberry silkworm breeding environment to the cloud platform to execute the intelligent environmental control of mulberry silkworm breeding.

[0023] Therefore, the present invention is an optimized treatment of a traditional intelligent environmental control method for mulberry silkworm breeding, which solves the problems existing in the traditional intelligent environmental control method for mulberry silkworm breeding, that is, during the mulberry silkworm breeding process, it is impossible to accurately regulate the local area environment change caused by the agglomeration behavior of mulberry silkworms, and the effect of the overall area mulberry silkworm breeding environment balance control is poor, improves the accuracy of the local area environment regulation for the local area environment change caused by the agglomeration behavior of mulberry silkworms, and enhances the overall area mulberry silkworm breeding environment balance control ability. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the step flow of an intelligent environmental control method for mulberry silkworm breeding; Figure 2 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in Figure 3 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in

[0025] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0026] The following clearly and completely describes the technical method of the present invention in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0028] To achieve the above object, please refer to Figures 1 to 3 , an intelligent environmental control method for silkworm breeding, the method comprising the following steps: Step S1: Obtain silkworm growth cycle data; monitor the silkworm breeding community in real time to obtain a set of monitoring images of the silkworm breeding community; classify the set of monitoring images of the silkworm breeding community according to the silkworm growth cycle data to obtain data on the growth stage of the silkworms. Step S2: Analyze the environmental requirement differences among different silkworms based on the data on the growth stage of the silkworms to obtain data on the environmental requirement differences of the silkworms; identify the agglomeration behavior of the silkworms in the set of monitoring images of the silkworm breeding community to obtain data on the agglomeration behavior of the silkworms; evaluate the agglomeration behavior environmental interaction effect among different silkworm growth stages based on the data on the agglomeration behavior of the silkworms to obtain data on the agglomeration behavior environmental interaction effect. Step S3: Regulate the environmental requirements of the behavioral activities based on the data on the agglomeration behavior environmental interaction effect to obtain data on the regulation of the environmental requirements of the behavioral activities; optimize the intelligent control of the overall environmental balance of silkworm breeding based on the data on the regulation of the environmental requirements of the behavioral activities to obtain data on the intelligent control optimization of the environment. Step S4: Design an automated environmental control management strategy for silkworm breeding based on the data on the intelligent control optimization of the environment to obtain an environmental control management strategy for silkworm breeding, and send the environmental control management strategy for silkworm breeding to the cloud platform to perform intelligent environmental control of silkworm breeding.

[0029] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic flowchart of the steps of an intelligent environmental control method for silkworm breeding according to the present invention. In this example, the intelligent environmental control method for silkworm breeding comprises the following steps: Step S1: Obtain the data of the growth cycle of silkworms; conduct real-time monitoring on the silkworm breeding community to obtain a set of monitoring images of the silkworm breeding community; classify the set of monitoring images of the silkworm breeding community according to the data of the growth cycle of silkworms to obtain the data of the growth stage where the silkworms are located; In the embodiment of the present invention, first, by observing and recording the growth process of silkworms from eggs to adults, key data of each growth stage are collected. These data include the time lengths of the incubation period, larval stage, pupal stage, and adult stage, temperature and humidity requirements, feed requirements, and mulberry leaf consumption, etc. These data can be summarized and analyzed through laboratory observations, historical literature reviews, and data provided by agricultural research institutions. To ensure the accuracy and comprehensiveness of the data, high-precision sensors and recording devices are used to continuously monitor the growth environment of silkworms, and detailed data of each stage are recorded. During the silkworm breeding process, a high-resolution camera and an image acquisition system are used to continuously monitor the breeding community for 24 hours. These devices should be installed at different positions in the breeding room to ensure that the entire breeding area can be fully covered and images of silkworms at different angles can be captured. The image acquisition system needs to be equipped with an automatic shooting function to take high-definition images of the silkworm community regularly or as needed. These images will be used as a set of monitoring images and stored in a database for subsequent image processing and analysis. Machine learning and image processing techniques are used to analyze each frame of the silkworm monitoring image set. First, through image preprocessing techniques such as image enhancement and denoising, the quality and recognizability of the images are improved. Then, a pre-trained deep learning model is used to identify and classify the silkworms in the images. The model needs to be able to identify the appearance characteristics of silkworms at different growth stages and classify them into the corresponding growth stages according to the data of the silkworm growth cycle. To ensure the accuracy of classification, the training data set of the model should contain a large number of silkworm images at different growth stages and be accurately labeled and verified. After the classification process is completed, the data of the growth stage of the silkworms corresponding to each image are recorded in the database for subsequent environmental control and management.

[0030] Step S2: Analyze the differences in environmental requirements among different silkworms based on the data of the growth stage where the silkworms are located to obtain the data of the differences in environmental requirements of silkworms; identify the aggregation behavior of silkworms in the set of monitoring images of the silkworm breeding community to obtain the data of the aggregation behavior of silkworms; evaluate the interaction effect of the aggregation behavior and the environment among different growth stages of silkworms based on the data of the aggregation behavior of silkworms to obtain the data of the interaction effect of the aggregation behavior and the environment; In the embodiments of the present invention, according to the growth stage data of silkworms obtained in step S1, the environmental requirements of silkworms in different stages are first summarized and analyzed. These requirements include temperature, humidity, light, ventilation, as well as the type and quantity of feed. By comparing the requirements in different growth stages, the differences in environmental requirements between stages can be found. Statistical analysis software, such as SPSS or R, is used to perform statistical methods such as analysis of variance and cluster analysis on these data to quantify the differences in environmental requirements among different silkworms. The analysis results will generate data on the differences in environmental requirements of silkworms, which serve as the basic data for the environmental control system to ensure that silkworms in each growth stage can grow in the most suitable environment. Computer vision technology and behavior recognition algorithms are used to analyze the aggregation behavior of silkworms in the monitored image set. First, each silkworm in the image is identified through image segmentation and object detection techniques. Then, based on the position and movement trajectory of the silkworms, their aggregation behavior is analyzed, such as the aggregation density, aggregation time, and frequency of silkworms in the same area. Deep learning models, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are used to identify and classify the aggregation behavior of silkworms. These models need to be trained and verified with a large amount of labeled data to ensure the accuracy and robustness of the recognition results. The aggregation behavior data will be recorded in the database to provide basic data for subsequent analysis of the differences in environmental requirements and evaluation of the interaction effects between behavior and environment. Using the silkworm aggregation behavior data obtained in step S2 and the environmental requirement difference data in step S1, the interaction effects between the aggregation behavior and environmental requirements among different silkworm growth stages are evaluated. Through statistical methods such as multiple regression analysis and factor analysis, the aggregation behavior characteristics of silkworms in different growth stages under different environmental conditions are evaluated, as well as the impact of these behaviors on environmental requirements, mainly the impact of the aggregation behavior of silkworms in different growth stages on environmental requirements. Analyze the local environmental changes under the aggregation behavior of silkworms in different growth stages, such as temperature and humidity changes, gas concentration changes, etc. Then, combined with the environmental requirement difference data, evaluate the adaptability and impact of silkworms in different growth stages in these environmental changes. Multiple regression analysis and cluster analysis methods are used to quantify the aggregation behavior and environmental requirements of silkworms to obtain the specific performance of silkworms in different growth stages in the interaction effects between aggregation behavior and environment. For example, the change in the aggregation density of silkworms in the instar growth stage will increase the local temperature by 2°C, and this temperature change will cause discomfort in the environmental change of silkworms in the instar growth stage. Through this analysis, the quantitative relationship between the aggregation behavior and environmental requirements of silkworms in different growth stages can be obtained, that is, the data on the interaction effects between aggregation behavior and environment, which causes changes in the local environment (such as temperature, air circulation) where silkworms are in the larval stage, and the impact of the environmental changes caused by the aggregation behavior on their growth rate and health status.By quantifying these interaction effects, more precise and scientific environmental control strategies can be formulated to ensure that silkworms at each stage can grow in the optimal environment without being affected by local environmental changes caused by the agglomeration effect of silkworms at different stages. Finally, the data on the interaction effects between the agglomeration behavior and the environment are recorded in the system database to provide data support for intelligent environmental control.

[0031] Step S3: Adjust the environmental requirements for behavioral activities of the silkworm environmental requirement difference data according to the data on the interaction effects between the agglomeration behavior and the environment to obtain the adjusted data for the environmental requirements for behavioral activities; optimize the intelligent control of the overall environmental balance for silkworm rearing according to the adjusted data for the environmental requirements for behavioral activities to obtain the optimized data for intelligent environmental control; In the embodiment of the present invention, when regulating the environmental demand of silkworms according to the aggregated behavior-environment interaction effect data, it is indeed necessary to comprehensively consider the environmental demand of silkworms at various growth stages. In addition to considering silkworms of different ages (such as 1 to 5 years old), other key growth stages should also be considered, such as moths, eggs, pupae, etc. In specific implementation, more complex machine learning models, such as deep neural networks (DNN) or long short-term memory networks (LSTM), can be used to handle this multi-stage, multi-factor complex relationship. First, collect and organize the environmental demand data of each stage of the silkworm's life cycle, including but not limited to: egg stage: suitable temperature 18-25°C, relative humidity 75-80%. 1 to 5 years old silkworms: the optimum temperature and humidity range for each age. Before and after clustering: the temperature demand will change. Pupa stage: suitable temperature 25-27°C, relative humidity 70-75%. Moth stage: suitable temperature 23-25°C, relative humidity 70-75%. Then, these data are combined with the previously obtained aggregated behavior-environment interaction effect data and input into the machine learning model. The input parameters of the model include: current growth stage, aggregation behavior intensity, current environmental parameters (temperature, humidity, light, etc.), historical environmental data, etc. The output of the model is the adjusted optimal environmental parameters. For example, the model will find that: in the transition period from 2-instar silkworms to 3-instar silkworms, due to molting stress, the aggregation behavior is more obvious. At this time, the temperature needs to be slightly lowered by 0.5℃ to offset the local temperature increase caused by aggregation. In the late 4-instar silkworms, the appetite increases and the metabolism is vigorous. The aggregation behavior has a greater impact on the local temperature, and the temperature needs to be lowered by 1-1.5℃. In the early pupal stage, since the silkworm body has not yet completely hardened, it is more sensitive to environmental changes. At this time, even if aggregation occurs, relatively stable temperature and humidity should be maintained, and only minor adjustments should be made. After the model training is completed, the real-time monitored silkworm growth stage data and aggregation behavior data can be continuously input to obtain dynamically adjusted environmental demand parameters. These parameters will be continuously updated with the changes in the growth stage of the silkworm and the changes in the aggregation behavior, forming a dynamic behavioral activity environmental demand regulation data set. In addition, reinforcement learning algorithms, such as the deep Q network (DQN), can be introduced to allow the system to continuously optimize the control strategy through continuous environmental control and effect feedback. For example, the system can evaluate the effect of environmental control based on indicators such as the growth status and silk production of silkworms, and adjust future control strategies accordingly. Through this comprehensive, dynamic, and intelligent control method, it can ensure that the most suitable growth environment can be obtained at all stages of the silkworm's life cycle, thereby improving the growth quality and silk production of silkworms.

[0032] Step S4: Design an automated silkworm breeding environment control management strategy based on the environmental intelligent control optimization data, obtain the silkworm breeding environment control management strategy, and send the silkworm breeding environment control management strategy to the cloud platform to execute the silkworm breeding intelligent environment control.

[0033] In the embodiments of the present invention, environment intelligent control optimization data (especially temperature and air flow data considering the aggregation effect of mulberry silkworms) are imported into the management strategy design platform. The platform uses data analysis tools to analyze these optimization data in detail and identify the impacts of mulberry silkworm aggregation behavior on temperature and air flow. For example, when mulberry silkworms aggregate, the local temperature rises and the air flow decreases, and these changes will affect the growth of mulberry silkworms. According to these analysis results, the platform uses optimization algorithms (such as linear regression, decision tree, genetic algorithm, etc.) to design the best environment control management strategy. Specific strategies include: setting appropriate temperature ranges according to the aggregation behavior of mulberry silkworms at different growth stages. For example, mulberry silkworms need a higher temperature during the hatching period, and the local temperature should not exceed 28°C when they aggregate. For example, the 2nd instar silkworms have different temperature requirements during the aggregation behavior, so the system sets the temperature regulator to keep the temperature between 26 - 28°C in the mulberry silkworm aggregation area. Increase the ventilation frequency in the aggregation area to ensure smooth air flow and avoid local overheating. The system sets the ventilation equipment to automatically start when mulberry silkworms aggregate and adjusts the wind speed and direction according to the real-time monitoring data to ensure uniform air flow distribution. The management strategy design platform transmits the generated mulberry silkworm breeding environment control management strategy to the cloud platform through the network. The cloud platform, as the central control center, is responsible for receiving and storing these strategies and sending control instructions to each environment control device through Internet of Things (IoT) technology. The cloud platform communicates with sensors and control devices (such as temperature and humidity regulators, air flow control systems, etc.) in the breeding environment in real time to ensure that each device operates according to the requirements of the management strategy. For example, after receiving the strategy, the cloud platform instructs the temperature regulator to set the temperature to 26 - 28°C in the mulberry silkworm aggregation area and controls the air flow system to start when mulberry silkworms are detected to aggregate, increasing the ventilation frequency and wind speed to ensure smooth air flow. The cloud platform also provides remote monitoring and management functions. Breeders can view the temperature and air flow status in real time through mobile devices (such as smart phones, tablets) and adjust the control strategy at any time. For example, if breeders find that the temperature in the mulberry silkworm aggregation area is abnormally high through the cloud platform, they can immediately remotely lower the temperature setting and increase the operation duration of the ventilation equipment to ensure a suitable growth environment for mulberry silkworms. The cloud platform regularly collects and analyzes environmental data to generate environmental control reports and performance evaluation reports to help breeders optimize the management strategy. The reports include the change trends of temperature and air flow parameters, equipment operation efficiency, mulberry silkworm growth status, etc., providing comprehensive data support to help breeders make scientific decisions.

[0034] Preferably, step S1 includes the following steps: Step S11: Obtain mulberry silkworm growth cycle data; Step S12: Use an electronic monitoring device to conduct real-time monitoring on the mulberry silkworm breeding community to obtain a set of mulberry silkworm breeding community monitoring images; Step S13: Perform image sharpening processing on the mulberry silkworm breeding community monitoring image set to obtain a sharpened image set of the mulberry silkworm community monitoring; Step S14: Perform classification processing on the growth stage of the mulberry silkworms in the sharpened image set of the mulberry silkworm community monitoring according to the mulberry silkworm growth cycle data to obtain the growth stage data of the mulberry silkworms.

[0035] In the embodiments of the present invention, through experiments and literature research, specific environmental condition data required by silkworms at various growth stages (such as eggs, larvae, pupae, adults) are collected. These data include temperature, humidity, light intensity, feed requirements, etc. These data are entered into a database, and a timeline of the silkworm growth cycle is established, marking the start and end times of each stage. For example, the larval stage of silkworms usually requires 20 - 25 days, during which higher humidity and moderate temperature are needed. Through these data, the specific requirements of silkworms at different growth stages can be accurately grasped. High - resolution cameras and other electronic monitoring devices are installed in the silkworm rearing room for 24 - hour continuous real - time monitoring. The cameras are installed at various angles of the rearing racks to ensure that the entire rearing area can be fully covered and clear images can be obtained. The monitoring devices transmit the image data to the central control system through the network to form a set of monitoring images of the silkworm rearing community. These image sets record the activities and environmental states of silkworms at different time points, providing a large amount of data for subsequent analysis. The obtained set of monitoring images of the silkworm rearing community is imported into image - processing software such as OpenCV, MATLAB, etc. Image sharpening algorithms are used to process the images to enhance the edges and details of the images, improving the clarity and contrast of the images. The specific operation steps include: first, performing image pre - processing such as denoising and grayscale adjustment, then applying sharpening algorithms such as Laplacian sharpening or Unsharp Masking, and finally performing image post - processing to adjust brightness and contrast to obtain a set of sharpened monitoring images of the silkworm community. Through image sharpening processing, the morphological characteristics and activity states of silkworms can be more clearly identified, ensuring the accuracy of subsequent analysis. The silkworm growth cycle data and the set of sharpened monitoring images of the silkworm community are imported into an image - processing and analysis platform. The silkworm growth cycle data contains feature descriptions of silkworms at different stages. For example, in the egg stage: in the image, it shows as small and fixed eggs with a lighter color. In the larval stage: in the image, it shows as active larvae with their bodies gradually growing larger and their color becoming darker. In the pupal stage: in the image, it shows as immobile pupae with a fixed form and a further - darkened color. In the adult stage: in the image, it shows as winged adults capable of moving and flying. Then, using these growth cycle data to train an image classification model such as a convolutional neural network (CNN). During the training process, a large amount of labeled image data is provided so that the model can learn and identify the characteristics of silkworms at different growth stages. After training is completed, the sharpened monitoring image set is input into the classification model for classification processing. Determine the growth stage of the silkworms. For example, by identifying the body length, color, and activity frequency of the silkworms, determine whether the silkworms are in the larval stage or the pupal stage. After the classification processing is completed, record the growth stage data of each time point and each silkworm to generate a data set of the growth stages of the silkworms. Through these data, the growth process of silkworms can be tracked in real - time, providing a scientific basis for environmental control and feeding management.

[0036] Preferably, step S2 includes the following steps: Step S21: Analyze the environmental requirement differences among different growth stages of the mulberry silkworms based on the mulberry silkworm growth cycle data to obtain the mulberry silkworm environmental requirement difference data; Step S22: Quantify the phased clustering differences of the mulberry silkworm environmental requirement difference data to obtain the environmental requirement difference clustering quantification data; Step S23: Identify the aggregation behaviors of the mulberry silkworms in the mulberry silkworm breeding community monitoring image set to obtain the mulberry silkworm aggregation behavior data; Step S24: Evaluate the aggregation behavior environment interaction effects among different growth stages of the mulberry silkworms on the environmental requirement difference clustering quantification data based on the mulberry silkworm aggregation behavior data to obtain the aggregation behavior environment interaction effect data.

[0037] As an example of the present invention, refer to Figure 2 shown, in this example, the step S2 includes: Step S21: Analyze the environmental requirement differences among different growth stages of the mulberry silkworms based on the mulberry silkworm growth cycle data to obtain the mulberry silkworm environmental requirement difference data; In the embodiment of the present invention, first, according to the growth cycle data of the mulberry silkworms, the growth stages of the mulberry silkworms are divided into the egg stage, the larval stage (instars 1-5), and the pupal stage. The breeding of the mulberry silkworms is carried out in a greenhouse, and different areas in the greenhouse are provided with special areas for breeding mulberry silkworms in different growth stages. For example, an egg mulberry hatching area, a larval mulberry breeding area, a pupal mulberry cocoon room, and an adult mulberry mating area can be set up. Then, environmental monitoring equipment is used to collect environmental parameter data such as temperature, humidity, and light in each growth stage of different breeding areas. Next, statistical analysis software (such as SPSS or R) is used to perform variance analysis and multiple comparisons on the environmental parameters of different growth stages. For example, it can be found that the 1st instar larval stage requires a temperature of 25-28 °C and a relative humidity of 80-85%, while the 5th instar larval stage requires a temperature of 22-24 °C and a relative humidity of 70-75%. Through this analysis, specific difference data on environmental requirements among different growth stages can be obtained, providing a basis for subsequent environmental control.

[0038] Step S22: Quantify the phased clustering differences of the mulberry silkworm environmental requirement difference data to obtain the environmental requirement difference clustering quantification data; In the embodiments of the present invention, a clustering algorithm (such as K-means or hierarchical clustering) is used to perform clustering analysis on the data of the environmental requirement differences of mulberry silkworms. The environmental requirement parameters of mulberry silkworms at each growth stage are used as inputs, and the algorithm divides them into several clusters according to similarity. For example, the temperature requirements of mulberry silkworms at different growth stages are divided into three categories: high temperature, medium temperature, and low temperature, and the humidity requirements are divided into three categories: high humidity, medium humidity, and low humidity. Through clustering analysis, the environmental requirement differences between different growth stages are quantified, and the clustering quantification data of environmental requirement differences are obtained. These data can help identify which stages of mulberry silkworms require similar environmental conditions, thereby optimizing the environmental control strategy.

[0039] Step S23: Identify the aggregation behavior of mulberry silkworms in the monitoring image set of the mulberry silkworm breeding community to obtain the aggregation behavior data of mulberry silkworms; In the embodiments of the present invention, the monitoring image set of the mulberry silkworm breeding community is imported into a computer vision platform (such as OpenCV or TensorFlow). The aggregation behavior of mulberry silkworms is identified through an image recognition algorithm (such as YOLO or Mask R-CNN). The specific operation steps include: preprocessing the monitoring images, such as denoising and grayscale adjustment, using the trained aggregation behavior recognition model to detect and label the mulberry silkworms in the images, and identifying the aggregation area and aggregation degree of the mulberry silkworms. The recognition results are converted into structured data, and the aggregation behavior of mulberry silkworms in each image is recorded, including the position of the aggregation area, the number of aggregated mulberry silkworms, and the aggregation degree, etc. Through these steps, detailed aggregation behavior data of mulberry silkworms are obtained, providing data support for the subsequent evaluation of the environmental interaction effect.

[0040] Step S24: Evaluate the environmental interaction effect of the aggregation behavior between different mulberry silkworm growth stages on the clustering quantification data of environmental requirement differences according to the aggregation behavior data of mulberry silkworms to obtain the environmental interaction effect data of the aggregation behavior.

[0041] In the embodiments of the present invention, the aggregation behavior data of mulberry silkworms and the clustering quantification data of environmental requirement differences are imported into the environmental interaction effect evaluation platform. Through statistical methods such as multiple regression analysis or factor analysis, the impact of the aggregation behavior of mulberry silkworms on the environmental requirements at different growth stages is evaluated. For example, analyze the impact of the aggregation behavior on the changes in local temperature and humidity, and evaluate whether these changes have abnormal interaction effects on mulberry silkworms at different growth stages. If the aggregation behavior causes a local temperature increase, evaluate whether the mulberry silkworms in the larval stage can adapt to such environmental changes for subsequent environmental regulation of the aggregation behavior.

[0042] Preferably, step S24 includes the following steps: Step S241: Calculate the aggregation density of the aggregation behavior data of mulberry silkworms to obtain the aggregation density data of mulberry silkworms; calculate the aggregation peristaltic friction frequency of the aggregation behavior data of mulberry silkworms to obtain the aggregation peristaltic friction frequency data of mulberry silkworms; Step S242: Perform an aggregation temperature effect simulation based on the silkworm aggregation density data and the silkworm aggregation peristaltic friction frequency data to obtain aggregation temperature effect simulation data; Step S243: Calculate the temperature increment of the aggregation effect on the aggregation temperature effect simulation data to obtain the temperature increment data of the aggregation effect; Step S244: Perform a stress state simulation between different silkworm growth stages on the environmental demand difference clustering quantization data according to the temperature increment data of the aggregation effect to obtain silkworm stress state simulation data; Step S245: Calculate the difference in silkworm vitality reduction between different growth stages on the silkworm stress state simulation data to obtain the difference data of silkworm vitality reduction; Step S246: Evaluate the aggregation behavior environment interaction effect between different silkworm growth stages on the environmental demand difference clustering quantization data according to the temperature increment data of the aggregation effect, the silkworm stress state simulation data, and the difference data of silkworm vitality reduction to obtain the aggregation behavior environment interaction effect data.

[0043] In the embodiments of the present invention, first, the aggregation behavior data of silkworms are imported into the computing platform. An image processing tool (such as OpenCV) is used to analyze the monitoring images, and the aggregation density of silkworms in each image is calculated. The aggregation density can be represented by the number of silkworms per unit area, such as the number of silkworms per square centimeter. Then, a motion detection algorithm (such as the optical flow method) is used to analyze the wriggling behavior of silkworms, and the wriggling friction frequency of silkworms in the aggregation area is calculated. The wriggling friction frequency represents the number of frictions of silkworms per unit time and is calculated through the inter-frame differences of the image sequence. Through these steps, detailed silkworm aggregation density data and aggregation wriggling friction frequency data are obtained. Computational Fluid Dynamics (CFD) software (such as ANSYS Fluent) is used to simulate the aggregation temperature effect. First, the aggregation density data and the wriggling friction frequency data are input into the CFD model to establish a temperature field simulation of the silkworm aggregation area. The heat generated by friction is calculated according to the wriggling friction frequency of silkworms, and the temperature change in the aggregation area is simulated through the CFD model. During the simulation process, the initial temperature and humidity conditions of the environment are set to simulate the influence of silkworm aggregation on the local temperature, and the aggregation temperature effect simulation data are obtained. Using the CFD simulation results, the temperature increment of the silkworm aggregation area is calculated. The temperature increment represents the local temperature increase caused by silkworm aggregation and wriggling friction. The specific operations include extracting the temperature distribution of the aggregation area from the simulation data and calculating the average increment and maximum increment of the temperature. For example, if the initial temperature of a certain aggregation area is 25°C and the temperature rises to 28°C after simulation, the temperature increment is 3°C. Through these steps, the aggregation effect temperature increment data are obtained. The aggregation effect temperature increment data and the environmental demand difference clustering quantization data are imported into the stress state simulation platform. A physiological model (such as a metabolic model) is used to simulate the stress state of silkworms at different growth stages under the temperature change caused by the aggregation behavior. The stress state simulation includes the physiological reactions, metabolic changes, and behavioral changes of silkworms. For example, under the condition of a large temperature increment, the changes in the respiration rate, heart rate, and activity frequency of silkworms at different growth stages are simulated. Through these simulations, the stress state simulation data of silkworms at different growth stages are obtained. Using the stress state simulation data, the vitality reduction of silkworms at different growth stages is calculated. The vitality reduction can be measured by indicators such as the activity frequency, feed intake, and growth rate of silkworms. For example, under the stress state, the activity frequency of silkworms decreases, the feed intake decreases, and the weight gain is slow. By comparing these indicators of silkworms at different growth stages, the difference in the degree of vitality reduction is calculated, and the silkworm vitality reduction difference data are obtained. The aggregation effect temperature increment data, the silkworm stress state simulation data, and the silkworm vitality reduction difference data are integrated into the evaluation platform. Through multiple regression analysis or factor analysis, the environmental interaction effect of silkworms at different growth stages under the aggregation behavior is evaluated. For example, analyze the influence of the temperature increase caused by the silkworm aggregation behavior on the stress state and vitality reduction of silkworms at each stage, and evaluate its comprehensive effect.Through these evaluations, the data of the interaction effect between the agglomeration behavior and the environment are obtained.

[0044] Preferably, calculating the different variances of the vitality reduction between different growth stages of the silkworm stress state simulation data includes the following steps: Evaluating the slowing trend of the silkworm peristalsis frequency between different growth stages of the silkworm stress state simulation data to obtain the data of the slowing trend of the silkworm peristalsis frequency; Calculating the slowing trend fluctuation interval of the silkworm peristalsis frequency for different growth stages of the data of the slowing trend of the silkworm peristalsis frequency to obtain the slowing trend fluctuation interval; Performing a logarithmic transformation of the slowing trend fluctuation on the data of the slowing trend of the silkworm peristalsis frequency according to the slowing trend fluctuation interval to obtain the logarithmic transformation data of the slowing trend fluctuation; Analyzing the skewness of the slowing trend time series distribution of the logarithmic transformation data of the slowing trend fluctuation to obtain the skewness data of the slowing trend time series distribution; Calculating the approximate interval of the slowing trend value of the logarithmic transformation data of the slowing trend fluctuation according to the skewness data of the slowing trend time series distribution to obtain the approximate interval of the slowing trend value; Performing a non-linear slowing trend constraint analysis on the slowing trend fluctuation interval based on the grey relational analysis method and the approximate interval of the slowing trend value to obtain the relational degree data of the slowing trend of the silkworm peristalsis frequency; Calculating the difference in the vitality reduction of the silkworm between different growth stages according to the relational degree data of the slowing trend of the silkworm peristalsis frequency to obtain the difference data of the vitality reduction of the silkworm.

[0045] In the embodiments of the present invention, the simulated data of the stress state of silkworms is imported into a statistical analysis platform (such as MATLAB or R). The peristaltic frequencies of silkworms at different growth stages are evaluated. The specific operation is as follows: Using the time series analysis method, the trend analysis of the peristaltic frequency changes of silkworms at different growth stages in different growth stage regions is carried out, and the change rate of the peristaltic frequency is calculated. During the evaluation process, a linear regression model is used to fit the change trend of the peristaltic frequency over time to obtain the data of the slowdown trend of the silkworm peristaltic frequency. Using the data of the slowdown trend of the silkworm peristaltic frequency, the fluctuation interval of the peristaltic frequency of silkworms at different growth stages is calculated. The specific operation is as follows: Using a statistical analysis tool (such as the Pandas library in Python), the standard deviation and mean of the peristaltic frequency of each growth stage are calculated to form a fluctuation interval. For example, if the mean of the data of the slowdown trend of the peristaltic frequency in a certain growth stage is 5 times per minute and the standard deviation is 1 time per minute, then its fluctuation interval is 4 - 6 times per minute. Through these calculations, the data of the slowdown fluctuation interval of the peristaltic frequency is obtained. The data of the slowdown fluctuation interval of the peristaltic frequency is logarithmically transformed to reduce the data volatility and scale difference. The specific operation is as follows: Take the natural logarithm (log) of the fluctuation interval data. For example, if the fluctuation interval is 4 - 6 times per minute, it becomes ln(4) to ln(6) after taking the logarithm. Through these steps, the logarithmically transformed data of the slowdown fluctuation is obtained. The time series distribution skewness analysis is carried out using the logarithmically transformed data. The specific operation is as follows: Using a time series analysis tool (such as the statsmodels library in Python), the time series distribution skewness (such as skewness and kurtosis) of the data of the slowdown of the peristaltic frequency of each growth stage is calculated. For example, if the skewness of the data of the slowdown of the peristaltic frequency in a certain growth stage is calculated to be 0.5 and the kurtosis is 3.2, it represents the skewness degree of its time series distribution. Through these calculations, the data of the slowdown time series distribution skewness is obtained. According to the data of the slowdown time series distribution skewness, the numerical approximation interval calculation of the logarithmically transformed data of the slowdown fluctuation is carried out. The specific operation is as follows: Using a statistical tool (such as the NumPy library in Python), the numerical approximation interval (such as the 95% confidence interval) of the logarithmically transformed data of the slowdown fluctuation is calculated. For example, the 95% confidence interval of the logarithmically transformed data of the slowdown fluctuation in a certain growth stage is [1.3, 2.5], which represents the interval range of its numerical distribution. Through these calculations, the slowdown numerical approximation interval is obtained. The grey relational degree method is used to carry out the non-linear slowdown constraint analysis on the slowdown numerical approximation interval and the slowdown fluctuation interval of the peristaltic frequency. The specific operation is as follows: Using the grey system theory (such as the GRA algorithm), the correlation degree between the slowdown fluctuation interval and the numerical approximation interval is calculated. The correlation degree reflects the relationship strength between the two data sets. For example, if the correlation degree in a certain growth stage is 0.85, it indicates a strong relationship between its slowdown fluctuation interval and the numerical approximation interval. Through these calculations, the data of the correlation degree of the slowdown of the peristaltic frequency is obtained. Using the data of the correlation degree of the slowdown of the peristaltic frequency, the difference in the reduction of the vitality of silkworms at different growth stages is calculated.The specific operation is as follows: Compare the data of the correlation degree of the slowing-down peristaltic frequency at different growth stages, and evaluate the difference in vitality loss at each stage. For example, the correlation degree at a certain stage is 0.85, and at another stage it is 0.75. Calculate the difference value of vitality loss as 0.10. Through these comparisons, the difference data of the vitality loss of silkworms is obtained.

[0046] Preferably, step S3 includes the following steps: Step S31: According to the data of the interaction effect of the aggregation behavior environment, regulate the environmental demand data of the silkworm's environmental needs for behavioral activities to obtain the regulated data of the environmental demand for behavioral activities; wherein the regulated data of the environmental demand for behavioral activities includes the regulated data of the temperature of behavioral activities and the regulated data of the air flow of behavioral activities; Step S32: Calculate the influence degree of the adjacent activity area of the aggregation behavior of silkworm breeding according to the regulated data of the environmental demand for behavioral activities to obtain the environmental influence data of the adjacent activity area; Step S33: Identify the influence degree of the environmental distribution of the adjacent activity area of the aggregation behavior of silkworm breeding on the environmental influence data of the adjacent activity area to obtain the data of the influence degree of the environmental distribution; Step S34: According to the data of the influence degree of the environmental distribution and the regulated data of the environmental demand for behavioral activities, optimize the intelligent control of the overall environmental balance of silkworm breeding to obtain the optimized data of environmental intelligent control.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: According to the data of the interaction effect of the aggregation behavior environment, regulate the environmental demand data of the silkworm's environmental needs for behavioral activities to obtain the regulated data of the environmental demand for behavioral activities; wherein the regulated data of the environmental demand for behavioral activities includes the regulated data of the temperature of behavioral activities and the regulated data of the air flow of behavioral activities; In the embodiments of the present invention, the data of the interaction effect between the aggregation behavior and the environment is imported into an environmental regulation system (such as MATLAB or Python). Using the multiple regression analysis method, the environmental requirements for the behavioral activities of silkworms are regulated according to the data of the differences in the environmental requirements of silkworms. The specific operations include: analyzing the changes in the requirements of the aggregation behavior of silkworms at different growth stages for temperature and air flow. Through regression analysis, a mathematical model of the behavioral activities and environmental requirements is established to obtain the data for regulating the temperature of the behavioral activities and the data for regulating the air flow of the behavioral activities. For example, according to the analysis results, it is obtained that under the condition of high-density aggregation, the temperature requirement of silkworms is 28°C, and the air flow requirement is 2 meters per second. (For example, during the process of raising silkworms, the data of the interaction effect between the aggregation behavior of silkworms and the environment shows that during the fifth instar stage, the high-density aggregation of silkworms will cause the temperature to rise in the local area. Through regression analysis, it is determined that at this stage, the optimal growth temperature of silkworms during high-density aggregation is 28°C, and the optimal air flow speed is 2 meters per second. Therefore, the data for regulating the temperature of the behavioral activities is 28°C, and the data for regulating the air flow is 2 m / s.) Step S32: Calculate the influence degree of the aggregation behavior of silkworm rearing on the adjacent activity area according to the data for regulating the environmental requirements of the behavioral activities, and obtain the environmental influence data of the adjacent activity area; In the embodiments of the present invention, first, a spatial distribution map of the silkworm rearing area is drawn using Geographic Information System (GIS) software, and the specific locations where the aggregation behavior occurs are marked. Then, based on the data for regulating the environmental requirements of the behavioral activities obtained in step S31, the heat conduction and fluid dynamics models are used to simulate the influence of the aggregation behavior on the surrounding environment. Specifically, Computational Fluid Dynamics (CFD) software, such as ANSYS Fluent, can be used to build a three-dimensional model and perform numerical simulations. The simulation results will show the distribution changes of environmental factors such as temperature, humidity, and air flow in space. By comparing the simulation results with the original environmental data, the environmental influence on the adjacent activity area can be quantitatively calculated, thereby obtaining the environmental influence data of the adjacent activity area. (For example, using ANSYS Fluent software, the data for regulating the temperature of the behavioral activities 28°C and the data for regulating the air flow 2 m / s are input into the simulation model. The simulation results show that the temperature influence range of the silkworm aggregation area on the adjacent activity area is between 27°C and 29°C, and the air flow speed is between 1.8 and 2.2 m / s. Therefore, the environmental influence data of the adjacent activity area is a temperature of 27°C to 29°C and an air flow speed of 1.8 to 2.2 m / s.)

[0048] Step S33: Identify the influence degree of the environmental distribution in the azimuth of the adjacent activity area of the aggregation behavior of silkworm rearing on the adjacent activity area according to the environmental influence data of the adjacent activity area, and obtain the data of the influence degree of the environmental distribution in the azimuth; In the embodiments of the present invention, it is necessary to further analyze the environmental impact data of the adjacent activity area to identify the environmental impact degrees in different directions. First, import the environmental impact data obtained in step S32 into a professional spatial analysis software, such as ArcGIS. Utilize the spatial interpolation function of this software to generate a continuous distribution surface of the environmental impact. Then, with the occurrence point of the aggregation behavior as the center, divide the surrounding area into several azimuth sectors (such as eight directions: east, southeast, south, southwest, west, northwest, north, and northeast). Conduct statistical analysis on the environmental impact degree within each sector, and calculate indicators such as the average value, maximum value, and minimum value. In addition, a hot spot analysis tool can also be used to identify areas with particularly significant environmental impacts. Finally, integrate these analysis results to form a detailed data report on the environmental impact degree of the azimuth distribution, including the impact degree ranking, impact range, and impact intensity information of each azimuth. (For example, using the ArcGIS software, map the environmental impact data of the adjacent activity area onto the azimuth map of the mulberry silkworm breeding area. The analysis results show that the temperature change in the south azimuth is the most significant, with a temperature increment of 2°C and an air velocity increment of 0.5 m / s. Therefore, the data on the environmental impact degree of the azimuth distribution is a temperature increment of 2°C and an air velocity increment of 0.5 m / s in the south azimuth).

[0049] Step S34: Perform intelligent control optimization of the overall environmental balance for mulberry silkworm breeding according to the data on the environmental impact degree of the azimuth distribution and the data on the regulation of the environmental requirements for behavioral activities, and obtain the optimized data of environmental intelligent control.

[0050] In the embodiments of the present invention, input the data on the environmental impact degree of the azimuth distribution and the data on the regulation of the environmental requirements for behavioral activities into an intelligent control system (such as an AI-based control algorithm). The specific operation is as follows: Utilize an intelligent control algorithm (such as a reinforcement learning algorithm) to optimize the overall balance of the mulberry silkworm breeding environment. The system dynamically adjusts the temperature and air flow in the breeding environment according to the input data to achieve an optimal balance state in each azimuth. For example, the intelligent control system adjusts the temperature in the south azimuth to 28°C and keeps the air velocity at 2 m / s to ensure the best growth environment for mulberry silkworms at each growth stage. Through these optimization operations, obtain the optimized data of environmental intelligent control to guide the mulberry silkworm breeding. (For example, input the data of a temperature increment of 2°C and an air velocity increment of 0.5 m / s in the south azimuth into the intelligent control system. Through the reinforcement learning algorithm, the intelligent control system adjusts the temperature in the south azimuth to 28°C and keeps the air velocity at 2 m / s to ensure the best growth environment for mulberry silkworms at each growth stage. After optimization, the optimized data of environmental intelligent control is a temperature of 28°C and an air velocity of 2 m / s in the south azimuth).

[0051] Preferably, step S31 includes the following steps: Step S311: Analyze the abnormal temperature fluctuations in the aggregation behavior during the silkworm rearing process based on the aggregation behavior environment interaction effect data to obtain the abnormal temperature fluctuation data of the aggregation behavior; Step S312: Evaluate the temperature fluctuation tolerance of silkworms at different growth stages for the silkworm environmental demand difference data based on the abnormal temperature fluctuation data of the aggregation behavior to obtain the temperature fluctuation tolerance data of silkworms; Step S313: Simulate the air circulation in the aggregation behavior during the silkworm rearing process based on the aggregation behavior environment interaction effect data to obtain the air circulation data of the aggregation behavior; Step S314: Analyze the air duct blockage effect in the aggregation behavior during the silkworm rearing process for the air circulation data of the aggregation behavior to obtain the air duct blockage effect data of the aggregation behavior; Step S315: Pair the demand difference data for the silkworm environmental demand difference data based on the temperature fluctuation tolerance data of silkworms and the air duct blockage effect data of the aggregation behavior to obtain the paired environmental demand difference data; Step S316: Regulate the environmental demands of the behavior activities based on the paired environmental demand difference data to obtain the regulated data of the environmental demands of the behavior activities; among which the regulated data of the environmental demands of the behavior activities includes the regulated temperature data of the behavior activities and the regulated air flow data of the behavior activities.

[0052] In the embodiments of the present invention, data analysis software (such as the pandas library in Python) is used to process the data of the agglomeration behavior environment interaction effect, and a temperature sensor network and data analysis software are used to analyze the abnormal temperature fluctuations of the agglomeration behavior of silkworms in different growth stages during the silkworm rearing process. First, a plurality of high-precision temperature sensors (such as PT100) are arranged in the rearing area to ensure that the entire space is covered, especially the areas where silkworms are prone to agglomerate. These sensors record temperature data at a frequency of once per minute. A data processing script is written in Python. First, the average temperature of the entire area is calculated as a benchmark, and then the areas and time points where the temperature deviates significantly from the average value are identified. For example, if the temperature in a certain area exceeds the average temperature by more than 2°C and lasts for more than 30 minutes, it is marked as an abnormal temperature fluctuation event. The frequency, duration, and spatial distribution of these events are also analyzed, as well as the correlation with the agglomeration behavior of silkworms. Finally, a detailed report on the abnormal temperature fluctuations of the agglomeration behavior is generated, including information such as the time, location, amplitude, and duration of the fluctuations. To analyze the abnormal temperature fluctuations of the agglomeration behavior during the silkworm rearing process. Or, the temperature data is arranged in a time series, and then the moving average method is used to calculate the normal temperature change trend. Then, the difference between the actual temperature and the moving average value is calculated, and a threshold (such as ±1.5°C) is set to identify abnormal fluctuations. For example, if at a certain time point, the actual temperature is 2°C higher than the moving average value, it is marked as an abnormal high temperature event. Time series analysis methods (such as the ARIMA model) can also be used to predict the normal temperature change, and the predicted value is compared with the actual value to identify abnormalities. Finally, the frequency, duration, and amplitude of the abnormal events are statistically analyzed to form a report on the abnormal temperature fluctuations of the agglomeration behavior. According to the data of the abnormal temperature fluctuations of the agglomeration behavior obtained in S311, the temperature fluctuation tolerance of silkworms in different growth stages is evaluated. The growth cycle of silkworms is divided into 5 stages: the 1st instar, the 2nd instar, the 3rd instar, the 4th instar, and the 5th instar. For each stage, a series of temperature fluctuation experiments are designed. In the experiments, a precision temperature control box is used to simulate different degrees of temperature fluctuations (such as ±1°C, ±2°C, ±3°C), and the durations are 1 hour, 3 hours, and 6 hours respectively. In each experiment, the physiological indexes (such as survival rate, molting rate) and behavioral changes (such as food intake, activity frequency) of silkworms are observed and recorded. The statistical software SPSS is used for data analysis, and the tolerance index of each growth stage under different temperature fluctuation conditions is calculated. For example, it is found that the 4th instar silkworms have a higher tolerance to the temperature fluctuation of ±2°C, while the 1st instar silkworms are more sensitive to the same fluctuation. Finally, a detailed report on the temperature fluctuation tolerance of silkworms in different growth stages is obtained, providing an important basis for subsequent environmental control. During the silkworm rearing process, in order to understand the impact of the agglomeration behavior of silkworms on the air circulation, it is first necessary to collect the data of the agglomeration behavior environment interaction effect.These data are usually obtained through sensor networks and image processing techniques, including environmental parameters such as the aggregation density, peristalsis frequency, temperature, and humidity of silkworms. According to the actual size and layout of the silkworm rearing environment, a three-dimensional CFD model is established. In the model, the silkworm aggregation area and the air circulation path around it are defined. The data of the aggregation behavior-environment interaction effect are input into the CFD model, including parameters such as the density distribution and temperature change in the aggregation area. Boundary conditions are set in the model, such as the air flow velocity, temperature, and humidity at the inlet and outlet. The CFD simulation is run to analyze the air flow situation in the silkworm aggregation area. During the simulation process, data such as the air flow velocity, flow direction, and pressure distribution in the aggregation area are calculated. Key data are extracted from the CFD simulation results to obtain the air circulation data of the aggregation behavior. For example, the simulation results show that in the high-density aggregation area, the air flow velocity decreases significantly, and the air flow speed drops to 0.3 m / s, while in the low-density area, the air flow speed remains at 0.6 m / s. Based on the air circulation data of the aggregation behavior obtained in step S313, further analysis is carried out to evaluate the blocking effect of the silkworm aggregation behavior on the air duct of the rearing environment. The air circulation data obtained from the CFD simulation are processed to extract key parameters such as the air flow velocity, flow direction, and pressure distribution. These data are correlated with factors such as the aggregation density and peristalsis frequency of silkworms. By analyzing the air circulation data, the blocking effect of the silkworm aggregation area on the air duct is identified. For example, in the high-density aggregation area, the air flow velocity decreases significantly, indicating that this area has a blocking effect on the air duct. Quantify the blocking degree of the aggregation behavior on the air duct. The specific method includes calculating the change in air flow velocity at different aggregation densities. For example, in the high-density aggregation area, the air flow velocity decreases from 0.6 m / s to 0.3 m / s, and the blocking degree is 50%. Evaluate the impact of the air duct blocking effect of the aggregation behavior on the silkworm rearing environment. The analysis results show that the air duct blocking effect in the high-density aggregation area leads to poor air circulation, causing local temperature rise and humidity change, thus affecting the growth environment of silkworms. Collect the data of the temperature fluctuation tolerance of silkworms. These data include the tolerance range and response of silkworms to temperature changes at different growth stages. For example, the temperature fluctuation tolerance of the initial larval stage is relatively low, while that of the mature stage is relatively high. Integrate the air duct blocking effect data of the aggregation behavior obtained in the previous steps into the environmental demand analysis. These data include the blocking effect of different aggregation behaviors on the air duct and the corresponding air circulation changes. Use data analysis tools (such as the pandas library in R language or Python) to perform paired analysis on the temperature fluctuation tolerance data and the air duct blocking effect data. Through multiple regression analysis or machine learning algorithms, find the correlation between the two sets of data. For example, when the temperature fluctuation tolerance is relatively low and the air duct blocking effect is significant during a growth stage, special attention needs to be paid to the regulation of temperature and air flow. Generate paired data of environmental demand differences according to the results of the paired analysis.For example, for the initial larval stage, paired data shows that higher air flow rates and a more stable temperature environment are required, while for the mature stage, moderate air flow and temperature regulation are needed. Pair the data according to the differences in environmental requirements and set the regulation targets for each growth stage. For example, for the initial larval stage, the goal is to maintain the temperature at around 25°C and the air flow rate above 0.5 m / s. Select suitable environmental regulation equipment, including temperature regulation equipment (such as thermostats, heaters, cooling equipment) and air flow regulation equipment (such as fans, ventilation systems). Ensure that these devices can be adjusted in real time to meet the environmental requirements of different stages. Use a sensor network to monitor the temperature and air flow data in the silkworm rearing environment in real time. The sensors can be installed at different positions inside the silkworm rearing box to ensure the comprehensiveness and accuracy of the data. Based on the real-time monitoring data and the preset regulation targets, use a control system (such as a PLC or DCS system) to automatically adjust the temperature and air flow. For example, when the monitoring data shows that the temperature exceeds the target value, the control system automatically activates the cooling equipment; when the air flow rate is lower than the target value, the fan is automatically started. Record all the data during the regulation process and analyze it regularly to evaluate the regulation effect and the growth status of the silkworms. Through data analysis, the regulation strategy can be further optimized to ensure the best environmental conditions.

[0053] Preferably, step S34 includes the following steps: Step S341: Conduct an environmental impact time-delay effect analysis on the environmental impact of the adjacent activity area of the silkworm rearing aggregation behavior based on the azimuth distribution environmental impact degree data to obtain the distribution environmental impact time-delay effect data; Step S342: Extract the environmental temperature mutation and analyze the air flow circulation structure in the adjacent activity area of the silkworm rearing aggregation behavior based on the behavior activity temperature regulation data, behavior activity air flow regulation data, and distribution environmental impact time-delay effect data to obtain the azimuth distribution environmental temperature mutation data and the azimuth distribution air flow circulation structure data; Step S343: Identify the spatial distribution mutation progressive law of the adjacent activity area of the silkworm rearing aggregation behavior for the azimuth distribution environmental temperature mutation data to obtain the temperature distribution mutation progressive law data; Step S344: Optimize the global temperature balance intelligent control of silkworm rearing based on the temperature distribution mutation progressive law data and the behavior activity environmental requirement regulation data to obtain the temperature balance intelligent control optimization data; Step S345: Analyze the air flow stacking effect in the adjacent activity area of the silkworm rearing aggregation behavior for the azimuth distribution air flow circulation structure data to obtain the azimuth distribution air flow stacking effect data; Step S346: Optimize the global air flow balance intelligent control of silkworm rearing based on the azimuth distribution air flow stacking effect data and the behavior activity environmental requirement regulation data to obtain the air flow balance intelligent control optimization data; Step S347: Perform global environmental balance intelligent control optimization for silkworm rearing based on temperature balance intelligent control optimization data and airflow balance intelligent control optimization data to obtain environmental intelligent control optimization data.

[0054] In the embodiments of the present invention, first, data on the degree of environmental impact of the azimuth distribution is collected. These data include the changes in environmental parameters such as temperature, humidity, and air velocity at different positions inside the silkworm rearing box. A suitable time-delay effect analysis tool is selected, such as time series analysis software (e.g., the Time Series toolbox in MATLAB) or a dedicated environmental data analysis platform. The selected tool is used to perform a time-delay effect analysis on the environmental impact data. Through cross-correlation analysis, the relationship between the changes in environmental parameters in different azimuths and time is determined, and the time-delay phenomenon of the changes in environmental parameters is identified. For example, the change in temperature at a certain position affects the change in temperature at a neighboring position after 10 minutes. The analysis results are extracted to generate time-delay effect data on the distributed environmental impact. These data detail the changes in environmental parameters in each azimuth and their time-delay effects. For example, it is recorded that the time-delay of the temperature change at a certain azimuth affecting the temperature at a neighboring azimuth is 10 minutes, and the time-delay of the humidity change is 15 minutes. The temperature control data for behavioral activities, air flow control data, and time-delay effect data on the distributed environmental impact are integrated. Ensure the consistency and integrity of the data. Using a temperature monitoring sensor network, the temperature data at each azimuth inside the silkworm rearing box are monitored and recorded in real time. A statistical analysis tool (such as the changepoint package in R language) is used to perform a mutation analysis on the temperature data to identify the mutation points of the environmental temperature. For example, the temperature at a certain azimuth suddenly changes from 25°C to 30°C in a short period of time. A fluid dynamics simulation software (such as ANSYS Fluent) is used to analyze the air flow structure inside the silkworm rearing environment. The air flow control data and time-delay effect data are input into the simulation model to simulate the flow path and velocity distribution of the air flow in the rearing environment. Through the simulation, areas with poor air flow and potential air duct blockage points are identified. Based on the results of the mutation extraction and flow structure analysis, azimuth distribution environmental temperature mutation data and azimuth distribution air flow structure data are generated. For example, the mutation points and their mutation amplitudes of the temperature at specific azimuths inside the rearing box are recorded, and at the same time, the blockage points and their positions in the air flow path are recorded. The previously obtained azimuth distribution environmental temperature mutation data are integrated to ensure that the data cover all important areas inside the silkworm rearing box. A suitable progressive law analysis tool is selected, such as a geographic information system (GIS) analysis software or a time-space analysis tool (such as the Space-Time Cube). Using the selected analysis tool, the spatial distribution progressive law of the temperature mutation data is identified. By analyzing the spatial distribution and time change of the temperature mutation points, the progressive path and law of the temperature mutation are identified. For example, after analyzing the temperature mutation in a certain area, the time and amplitude of the temperature change in the neighboring area are analyzed to determine the propagation path of the temperature mutation. According to the analysis results, temperature distribution mutation progressive law data are generated. These data detail the time, spatial distribution, and propagation path of the temperature mutation. For example, it is recorded that after the temperature mutation in a certain area, the amplitude and direction of the temperature change in the neighboring area within 5 minutes.Integrate the previously obtained data on the progressive law of temperature distribution mutation and the data on the regulation of behavioral activity environmental requirements. Ensure the comprehensiveness and accuracy of the data, including the temperature changes in all directions and the temperature requirements of silkworms at different stages. Select a suitable optimization tool, such as the optimization toolbox of MATLAB or professional environmental control optimization software. Establish a global temperature balance model. Use the optimization tool to input the integrated data and simulate the temperature changes in the silkworm rearing environment. Adjust the model parameters to optimize the temperature control strategy to ensure uniform temperature in all directions and avoid local overheating or overcooling. For example, by adjusting the operating parameters of the ventilation system, heating system or cooling system, achieve the global temperature balance. Extract the optimized temperature control strategy data to generate intelligent control optimization data for temperature balance. These data detail each regulation parameter and its set value, such as the wind speed of the ventilation system, the temperature setting value of the heating system, etc. Collect data on the air flow circulation structure of the azimuth distribution to ensure that the air flow data of all important azimuths in the silkworm rearing environment are covered. Select a suitable analysis tool for the air flow stacking effect, such as fluid mechanics simulation software (such as ANSYS Fluent) or an air flow analysis platform, and use the selected tool to analyze the air flow circulation structure data to identify the air flow stacking effect. For example, by simulating the flow path of the air flow in the rearing environment, identify the air flow accumulation area and its influence range. Extract the analysis results to generate data on the air flow stacking effect of the azimuth distribution. These data detail the air flow stacking phenomenon and its influence in each azimuth, such as recording parameters such as the speed and pressure of the air flow accumulation in a certain azimuth. Integrate the data on the air flow stacking effect of the azimuth distribution and the data on the regulation of behavioral activity environmental requirements. Ensure the comprehensiveness and accuracy of the data, including the air flow conditions in all directions and the air flow requirements of silkworms at different stages. Select a suitable optimization tool, such as the optimization toolbox of MATLAB or professional environmental control optimization software. Establish a global air flow balance model. Use the optimization tool to input the integrated data and simulate the air flow changes in the silkworm rearing environment. Adjust the model parameters to optimize the air flow control strategy to ensure uniform air flow and avoid local air flow accumulation or air flow shortage. For example, by adjusting the operating parameters of the ventilation system, achieve the global air flow balance. Extract the optimized air flow control strategy data to generate intelligent control optimization data for air flow balance. These data detail each regulation parameter and its set value, such as the wind speed setting value of the ventilation system. Through these steps, ensure the air flow balance in the silkworm rearing environment, achieve intelligent air flow control, and provide the most suitable air flow conditions; Integrate the intelligent control optimization data for temperature balance and the intelligent control optimization data for air flow balance. Ensure the comprehensiveness and consistency of the data, including the optimization parameters of temperature and air flow. Select a suitable comprehensive optimization tool, such as the multi-objective optimization toolbox of MATLAB or professional environmental control optimization software. Establish a global environmental balance model. Use the optimization tool to input the integrated data and comprehensively simulate the temperature and air flow changes in the silkworm rearing environment.Adjust the model parameters to achieve the best control strategy for the global balance of the environment. For example, by jointly adjusting the operating parameters of the ventilation system, heating system, and cooling system, the global optimization of the environment is achieved. Extract the data of the environmental control strategy after comprehensive optimization to generate the optimized data for intelligent environmental control. These data detail each comprehensive regulation parameter and its set value, such as the comprehensive operating parameters of the ventilation system, heating system, and cooling system. Through these steps, ensure that the temperature and air flow in the silkworm rearing environment reach the best balance state, achieve global intelligent environmental control, and provide the most suitable growth conditions.

[0055] Preferably, the intelligent control optimization of the global air flow balance for the data of the environmental requirements for behavioral activities according to the data of the air flow stacking effect in the azimuth distribution includes the following steps: Conduct a simulation of the air flow return path in the adjacent activity area of the silkworm rearing aggregation behavior for the data of the air flow stacking effect in the azimuth distribution to obtain the air flow return path simulation data; Evaluate the difficulty of air flow exchange and diffusion for the data of the air flow stacking effect in the azimuth distribution according to the air flow return path simulation data to obtain the data of the difficulty of air flow exchange and diffusion; Optimize the air flow direction angle control for the data of the difficulty of air flow exchange and diffusion to obtain the air flow direction angle control data; Adapt the air flow velocity control for the data of the difficulty of air flow exchange and diffusion according to the air flow direction angle control data to obtain the air flow velocity control adaptation data; Conduct the intelligent control optimization of the global air flow balance for silkworm rearing according to the air flow direction angle control data, the air flow velocity control adaptation data, and the data of the environmental requirements for behavioral activities to obtain the optimized data for intelligent air flow balance control.

[0056] In the embodiments of the present invention, data on the air flow stacking effect in different directions in the silkworm rearing environment is collected, including air flow velocity, air flow direction, pressure distribution, etc. The collected data is analyzed using fluid mechanics simulation software (such as ANSYS Fluent), and a three-dimensional model of the rearing environment is established. The air flow stacking effect data is input into the software, and the air flow return path simulation is run. Through the simulation, observe the flow path of the air flow in the rearing environment, especially paying attention to the air flow return area and the air flow convergence point. Record and extract the air flow return path simulation data, which will show the return path of the air flow in each direction and its influence area. Integrate the air flow return path simulation data with the air flow stacking effect data to ensure the comprehensiveness and accuracy of the data. Use an air flow analysis platform or a self-written evaluation algorithm to evaluate the difficulty of air flow exchange and diffusion for the integrated data. Analyze the exchange and diffusion of the air flow in each return path, and evaluate the difficulty of air flow exchange in different regions. For example, evaluate whether the flow of the air flow in a certain area is affected by obstacles, resulting in difficult exchange. Generate air flow exchange and diffusion difficulty data, and record the air flow exchange difficulty levels in each direction. Select a suitable optimization tool, such as the optimization toolbox of MATLAB, or professional air flow control optimization software. Adjust the air flow angle to optimize the air flow direction, ensuring smoother exchange and diffusion of the air flow in each direction. For example, optimize the air flow direction by adjusting the angle and position of the ventilation openings. Generate air flow direction angle control data, and record the set values of the optimized air flow angles. Integrate the air flow direction angle control data with the air flow exchange and diffusion difficulty data to ensure the comprehensiveness and accuracy of the data. Select a suitable air flow velocity control adaptation tool, such as fluid dynamics optimization software or the control system toolbox of MATLAB. Use the adaptation tool to input the integrated data and establish an air flow velocity control adaptation model. Through simulation and calculation, find the optimal air flow velocity setting to meet the air flow exchange and diffusion requirements in different regions. Generate air flow velocity control adaptation data, which details the air flow velocity settings in each region. Through these steps, optimize the air flow velocity in the silkworm rearing environment, ensure smooth air flow, and improve the ventilation effect. Integrate the air flow direction angle control data, the air flow velocity control adaptation data with the data for regulating the requirements of the behavioral activity environment to ensure the comprehensiveness and consistency of the data. Establish an air flow global balance model. Use the optimization tool to input the integrated data and comprehensively simulate the air flow changes in the silkworm rearing environment. Adjust the model parameters to achieve the global balance and intelligent control optimization of the air flow. Generate air flow balance intelligent control optimization data, which details the comprehensive operating parameters of each system and their set values, such as the wind speed, wind direction, and operating duration of the ventilation system. Through these steps, ensure that the air flow in the silkworm rearing environment reaches the optimal balance state, achieve global intelligent air flow control, and provide the most suitable air flow conditions.

[0057] Preferably, the present invention further provides an intelligent environment control system for silkworm breeding, which is used to execute the intelligent environment control method for silkworm breeding as described above. The intelligent environment control system for silkworm breeding includes: A silkworm growth stage classification module, which is used to obtain silkworm growth cycle data; monitor the silkworm breeding community in real time to obtain a set of silkworm breeding community monitoring images; classify the set of silkworm breeding community monitoring images according to the silkworm growth cycle data to obtain the growth stage data of the silkworms. A silkworm aggregation behavior environment analysis module, which is used to analyze the environmental demand differences among different silkworms based on the growth stage data of the silkworms to obtain silkworm environmental demand difference data; identify the silkworm aggregation behavior in the set of silkworm breeding community monitoring images to obtain silkworm aggregation behavior data; evaluate the aggregation behavior environment interaction effect among different silkworm growth stages based on the silkworm aggregation behavior data to obtain aggregation behavior environment interaction effect data. A global control optimization module for silkworm breeding, which is used to regulate the environmental requirements of behavioral activities based on the aggregation behavior environment interaction effect data to obtain regulated data on environmental requirements for behavioral activities; perform intelligent control optimization of the overall environmental balance for silkworm breeding based on the regulated data on environmental requirements for behavioral activities to obtain optimized data on environmental intelligent control. A control management strategy execution module, which is used to design an automated silkworm breeding environment control management strategy based on the optimized data on environmental intelligent control to obtain a silkworm breeding environment control management strategy, and send the silkworm breeding environment control management strategy to the cloud platform to execute the intelligent environment control for silkworm breeding.

[0058] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0059] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent environment control method for silkworm rearing, characterized in that Including the following steps: Step S1: Obtain the growth cycle data of silkworms; conduct real-time monitoring on the silkworm breeding community to obtain a set of monitoring images of the silkworm breeding community; classify the growth stage of silkworms in the set of monitoring images of the silkworm breeding community according to the growth cycle data of silkworms to obtain the growth stage data of silkworms; Step S2: Analyze the environmental requirement differences among different silkworms based on the growth stage data of silkworms to obtain the environmental requirement difference data of silkworms; identify the aggregation behavior of silkworms in the set of monitoring images of the silkworm breeding community to obtain the aggregation behavior data of silkworms; evaluate the aggregation behavior environment interaction effect among different silkworm growth stages based on the aggregation behavior data of silkworms for the environmental requirement difference data of silkworms to obtain the aggregation behavior environment interaction effect data; Step S3: Regulate the environmental requirements of behavioral activities for the environmental requirement difference data of silkworms based on the aggregation behavior environment interaction effect data to obtain the regulated data of environmental requirements for behavioral activities; optimize the intelligent control of the overall environmental balance of silkworm breeding based on the regulated data of environmental requirements for behavioral activities to obtain the optimized data of environmental intelligent control; Step S4: Design an automated silkworm breeding environment control management strategy based on the optimized data of environmental intelligent control to obtain the silkworm breeding environment control management strategy, and send the silkworm breeding environment control management strategy to the cloud platform to execute the intelligent environmental control of silkworm breeding.

2. The intelligent environment control method for mulberry silkworm breeding according to claim 1, characterized in that Step S1 includes the following steps: Step S11: Obtain the growth cycle data of silkworms; Step S12: Conduct real-time monitoring on the silkworm breeding community through an electronic monitoring device to obtain a set of monitoring images of the silkworm breeding community; Step S13: Perform image sharpening processing on the set of monitoring images of the silkworm breeding community to obtain a set of sharpened images of the silkworm community monitoring; Step S14: Classify the growth stage of silkworms in the set of sharpened images of the silkworm community monitoring according to the growth cycle data of silkworms to obtain the growth stage data of silkworms.

3. The intelligent environment control method for mulberry silkworm breeding according to claim 1, wherein, Step S2 includes the following steps: Step S21: Analyze the environmental requirement differences among different silkworm growth stages based on the growth cycle data of silkworms to obtain the environmental requirement difference data of silkworms; Step S22: Quantify the phased clustering differences of the environmental requirement difference data of silkworms to obtain the quantified data of environmental requirement difference clustering; Step S23: Identify the aggregation behavior of silkworms in the set of monitoring images of the silkworm breeding community to obtain the aggregation behavior data of silkworms; Step S24: Evaluate the aggregation behavior environment interaction effect among different silkworm growth stages based on the aggregation behavior data of silkworms for the quantified data of environmental requirement difference clustering to obtain the aggregation behavior environment interaction effect data.

4. The intelligent environment control method for mulberry silkworm breeding according to claim 3, wherein, Step S24 includes the following steps: Step S241: Calculate the aggregation density of the aggregation behavior data of silkworms to obtain the aggregation density data of silkworms; calculate the aggregation peristaltic friction frequency of the aggregation behavior data of silkworms to obtain the aggregation peristaltic friction frequency data of silkworms; Step S242: Simulate the aggregation temperature effect based on the aggregation density data of silkworms and the aggregation peristaltic friction frequency data of silkworms to obtain the simulated data of the aggregation temperature effect; Step S243: Calculate the aggregation effect temperature increment for the simulated data of the aggregation temperature effect to obtain the aggregation effect temperature increment data; Step S244: Based on the aggregation effect temperature increment data, simulate the stress states among different silkworm growth stages for the clustered and quantified data of environmental demand differences to obtain the simulated silkworm stress state data; Step S245: Calculate the difference in silkworm vitality reduction among different growth stages for the simulated silkworm stress state data to obtain the difference in silkworm vitality reduction data; Step S246: Based on the aggregation effect temperature increment data, the simulated silkworm stress state data, and the difference in silkworm vitality reduction data, evaluate the aggregation behavior - environment interaction effect among different silkworm growth stages for the clustered and quantified data of environmental demand differences to obtain the aggregation behavior - environment interaction effect data.

5. The intelligent environment control method for mulberry silkworm breeding according to claim 4, characterized in that, Calculating the variance of the difference in vitality reduction among different silkworm growth stages for the simulated silkworm stress state data includes the following steps: Evaluate the slowing trend of the silkworm peristalsis frequency among different silkworm growth stages for the simulated silkworm stress state data to obtain the data of the slowing trend of the silkworm peristalsis frequency; Calculate the slowing - down fluctuation interval of the silkworm peristalsis frequency for different growth stages based on the data of the slowing trend of the silkworm peristalsis frequency to obtain the slowing - down fluctuation interval; Perform a logarithmic transformation of the slowing - down fluctuation for the data of the slowing trend of the silkworm peristalsis frequency according to the slowing - down fluctuation interval to obtain the logarithmically transformed slowing - down fluctuation data; Analyze the skewness of the slowing - down time - series distribution for the logarithmically transformed slowing - down fluctuation data to obtain the skewness data of the slowing - down time - series distribution; Calculate the approximate numerical interval of the slowing - down based on the skewness data of the slowing - down time - series distribution for the logarithmically transformed slowing - down fluctuation data to obtain the approximate numerical interval of the slowing - down; Based on the grey correlation degree method and the approximate numerical interval of the slowing - down, conduct a non - linear slowing - down constraint analysis on the slowing - down fluctuation interval to obtain the correlation degree data of the slowing - down of the silkworm peristalsis frequency; Calculate the difference in silkworm vitality reduction among different growth stages according to the correlation degree data of the slowing - down of the silkworm peristalsis frequency to obtain the difference in silkworm vitality reduction data.

6. The intelligent environment control method for mulberry silkworm breeding according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Based on the aggregation behavior - environment interaction effect data, regulate the environmental demands for behavioral activities of the silkworm environmental demand difference data to obtain the regulated data of environmental demands for behavioral activities; where the regulated data of environmental demands for behavioral activities includes the regulated data of temperature for behavioral activities and the regulated data of air flow for behavioral activities; Step S32: Calculate the influence degree on the adjacent activity area of the silkworm feeding aggregation behavior according to the regulated data of environmental demands for behavioral activities to obtain the environmental influence data of the adjacent activity area; Step S33: Identify the influence degree of the azimuth distribution environment of the adjacent activity area of the silkworm feeding aggregation behavior on the environmental influence data of the adjacent activity area to obtain the influence degree data of the azimuth distribution environment; Step S34: Based on the influence degree data of the azimuth distribution environment and the regulated data of environmental demands for behavioral activities, optimize the intelligent control for the overall environmental balance of silkworm feeding to obtain the optimized data of environmental intelligent control.

7. The intelligent environment control method for mulberry silkworm breeding according to claim 6, characterized in that Step S31 includes the following steps: Step S311: Analyze the abnormal temperature fluctuations of the aggregation behavior during the silkworm feeding process according to the aggregation behavior - environment interaction effect data to obtain the abnormal temperature fluctuation data of the aggregation behavior; Step S312: Evaluate the temperature fluctuation tolerance of silkworms in different growth stages for the silkworm environmental demand difference data based on the abnormal temperature fluctuation data of the aggregation behavior, and obtain the temperature fluctuation tolerance data of silkworms; Step S313: Simulate the air circulation of the aggregation behavior during the silkworm rearing process based on the aggregation behavior environment interaction effect data, and obtain the air circulation data of the aggregation behavior; Step S314: Analyze the air duct blockage effect of the aggregation behavior during the silkworm rearing process on the air circulation data of the aggregation behavior, and obtain the air duct blockage effect data of the aggregation behavior; Step S315: Pair the demand difference data of the silkworm environmental demand difference data according to the temperature fluctuation tolerance data of silkworms and the air duct blockage effect data of the aggregation behavior, and obtain the paired environmental demand difference data; Step S316: Regulate the environmental demand of the behavior activity according to the paired environmental demand difference data, and obtain the regulated data of the environmental demand of the behavior activity; wherein the regulated data of the environmental demand of the behavior activity includes the regulated temperature data of the behavior activity and the regulated air flow data of the behavior activity.

8. The intelligent environment control method for mulberry silkworm breeding according to claim 6, wherein Step S34 includes the following steps: Step S341: Analyze the time-delay effect of the environmental impact on the adjacent activity area of the silkworm rearing aggregation behavior based on the environmental impact degree data of the azimuth distribution, and obtain the time-delay effect data of the distribution environmental impact; Step S342: Extract the environmental temperature mutation and analyze the air flow circulation structure in the adjacent activity area of the silkworm rearing aggregation behavior based on the regulated temperature data of the behavior activity, the regulated air flow data of the behavior activity, and the time-delay effect data of the distribution environmental impact, and obtain the azimuth distribution environmental temperature mutation data and the azimuth distribution air flow circulation structure data; Step S343: Identify the progressive law of the spatial distribution mutation in the adjacent activity area of the silkworm rearing aggregation behavior for the azimuth distribution environmental temperature mutation data, and obtain the progressive law data of the temperature distribution mutation; Step S344: Optimize the intelligent control of the global temperature balance of the silkworm rearing based on the progressive law data of the temperature distribution mutation and the regulated data of the environmental demand of the behavior activity, and obtain the optimized data of the intelligent control of the temperature balance; Step S345: Analyze the air flow stacking effect in the adjacent activity area of the silkworm rearing aggregation behavior for the azimuth distribution air flow circulation structure data, and obtain the azimuth distribution air flow stacking effect data; Step S346: Optimize the intelligent control of the global air flow balance of the silkworm rearing based on the azimuth distribution air flow stacking effect data and the regulated data of the environmental demand of the behavior activity, and obtain the optimized data of the intelligent control of the air flow balance; Step S347: Optimize the intelligent control of the global environment of the silkworm rearing based on the optimized data of the intelligent control of the temperature balance and the optimized data of the intelligent control of the air flow balance, and obtain the optimized data of the intelligent control of the environment.

9. The intelligent environment control method for mulberry silkworm breeding according to claim 8, wherein The intelligent control optimization of the global air flow balance of the regulated data of the environmental demand of the behavior activity according to the azimuth distribution air flow stacking effect data includes the following steps: Simulate the air flow return path in the adjacent activity area of the silkworm rearing aggregation behavior for the azimuth distribution air flow stacking effect data, and obtain the simulated data of the air flow return path; Based on the simulated data of the air flow return path, evaluate the difficulty of air flow exchange and diffusion for the azimuth-distributed air flow stacking effect data to obtain the air flow exchange and diffusion difficulty data; Optimize the air flow direction angle control for the air flow exchange and diffusion difficulty data to obtain the air flow direction angle control data; Adapt the air flow velocity control for the air flow exchange and diffusion difficulty data according to the air flow direction angle control data to obtain the air flow velocity control adaptation data; Conduct intelligent control optimization for the global air flow balance in silkworm rearing based on the air flow direction angle control data, the air flow velocity control adaptation data, and the regulation data of the behavioral activity environment requirements to obtain the intelligent control optimization data for air flow balance.

10. An intelligent environment control system for silkworm rearing, characterized in that, For implementing the intelligent environment control method for silkworm rearing as described in claim 1, the intelligent environment control system for silkworm rearing includes: A silkworm growth stage classification module, which is used to obtain the silkworm growth cycle data; conduct real-time monitoring on the silkworm rearing community to obtain the silkworm rearing community monitoring image set; classify the silkworm rearing community monitoring image set according to the silkworm growth cycle data to obtain the data of the growth stage where the silkworms are located; A silkworm aggregation behavior environment analysis module, which is used to analyze the environmental requirement differences among different silkworms based on the data of the growth stage where the silkworms are located to obtain the silkworm environmental requirement difference data; identify the silkworm aggregation behavior from the silkworm rearing community monitoring image set to obtain the silkworm aggregation behavior data; evaluate the aggregation behavior environment interaction effect among different silkworm growth stages based on the silkworm aggregation behavior data for the silkworm environmental requirement difference data to obtain the aggregation behavior environment interaction effect data; A silkworm rearing global control optimization module, which is used to regulate the behavioral activity environment requirements based on the aggregation behavior environment interaction effect data for the silkworm environmental requirement difference data to obtain the regulation data of the behavioral activity environment requirements; conduct intelligent control optimization for the global environment balance in silkworm rearing based on the regulation data of the behavioral activity environment requirements to obtain the intelligent control optimization data for the environment; A control management strategy execution module, which is used to design an automated silkworm rearing environment control management strategy based on the intelligent control optimization data for the environment to obtain the silkworm rearing environment control management strategy, and send the silkworm rearing environment control management strategy to the cloud platform to execute the intelligent environment control for silkworm rearing.

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