An automatic control method and system for the environment of a meat goat shed

Abstract

The present invention relates to the field of environmental automatic control technology, and particularly to an environmental automatic control method and system for meat goat houses. The method includes the following steps: obtaining temperature and carbon dioxide / ammonia time series fluctuation data; extracting the temperature and carbon dioxide / ammonia fluctuation data, and performing diffusion increment fitting to obtain carbon dioxide / ammonia diffusion accumulation data; subsequently, estimating the probability of respiratory stress caused by ammonia intake, thereby mapping the safe ammonia content range, and performing ventilation parameter balance processing on the diffusion accumulation data, and then performing oxygen content compensation; finally, formulating an environmental automatic control strategy for the goat house based on the ventilation parameters and balance data, and sending it to the terminal for execution, so as to realize the automatic regulation of the environment in the meat goat house. The present invention makes the environmental automatic control technology more perfect through the optimization of the environmental automatic control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental automatic control, and particularly relates to an environmental automatic control method and system for meat goat houses. Background Art

[0002] In high-altitude areas, due to thin air, cold climate and low oxygen content, the growth environment and physiological needs of meat goats are very different from those in low-altitude areas. The high-altitude environment has a significant impact on the feeding, reproduction and health of meat goats. In particular, environmental factors such as temperature, humidity and gas concentrations (such as carbon dioxide and ammonia) directly affect the growth rate, immunity and respiratory system health of meat goats. Excessively high carbon dioxide and ammonia concentrations, especially the intake of ammonia, cause respiratory stress in meat goats, affect their appetite, weight gain and immune function, and even cause diseases. A traditional environmental automatic control method for meat goat houses has the problem of inaccurate analysis of the stress probability of ammonia intake by meat goats, resulting in large errors in the environmental control of meat goat houses. Summary of the Invention

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

[0004] To achieve the above object, an environmental automatic control method for a meat goat house, the method includes the following steps:

[0005] Step S1: Through sensors, all-weather data collection is carried out on the high-altitude environment where the meat goats are located to obtain all-weather environmental data in the high-altitude goat house; temperature and carbon dioxide / ammonia all-weather time series fluctuations are extracted from the all-weather environmental data in the high-altitude goat house to obtain temperature time series fluctuation data in the goat house and carbon dioxide / ammonia time series fluctuation data in the goat house respectively;

[0006] Step S2: Carbon dioxide / ammonia diffusion increment fitting is carried out according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion accumulation data; ammonia intake respiratory stress probability estimation is carried out on the carbon dioxide / ammonia diffusion accumulation data to obtain the ammonia intake respiratory stress probability;

[0007] Step S3: Ammonia safety content interval mapping is carried out according to the ammonia intake respiratory stress probability to obtain the ammonia safety content interval; ventilation parameter balance processing is carried out on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia safety content interval to obtain ventilation parameter balance design data; oxygen content compensation processing is carried out in the goat house based on the ventilation parameter balance design data to obtain oxygen content compensation data;

[0008] Step S4: Formulate an automatic control strategy for the sheep house environment based on the ventilation parameter balance design data and the oxygen content compensation data, obtain the automatic control strategy for the sheep house environment, and send the automatic control strategy for the sheep house environment to the terminal to execute the automatic control method for the meat goat house environment.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Collect all-weather data inside the meat goat house in the high-altitude environment where the meat goats are located through sensors to obtain all-weather environmental data inside the high-altitude sheep house;

[0011] Step S12: Fill in the missing values in the all-weather environmental data inside the high-altitude sheep house to obtain the filled environmental data inside the high-altitude sheep house;

[0012] Step S13: Extract the all-weather time series fluctuations of temperature and carbon dioxide / ammonia inside the sheep house from the filled environmental data inside the high-altitude sheep house to obtain the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data inside the sheep house respectively.

[0013] Preferably, step S2 includes the following steps:

[0014] Step S21: Analyze the low-oxygen environment concentration fluctuations in the all-weather environmental data inside the high-altitude sheep house to obtain the low-oxygen environment concentration fluctuation data;

[0015] Step S22: Evaluate the acceleration of the meat goat's respiratory frequency demand based on the low-oxygen environment concentration fluctuation data to obtain the acceleration data of the respiratory frequency demand;

[0016] Step S23: Fit the incremental diffusion and accumulation of carbon dioxide / ammonia according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain the carbon dioxide / ammonia diffusion and accumulation data;

[0017] Step S24: Estimate the probability of ammonia inhalation respiratory stress for the carbon dioxide / ammonia diffusion and accumulation data based on the acceleration data of the respiratory frequency demand to obtain the probability of ammonia inhalation respiratory stress.

[0018] Preferably, step S23 includes the following steps:

[0019] Step S231: Analyze the period of rising carbon dioxide content in the carbon dioxide / ammonia time series fluctuation data, and then analyze the period of enhanced greenhouse effect based on the temperature time series fluctuation data to obtain the period of enhanced greenhouse effect;

[0020] Step S232: Identify the heat stress enhancement gradient for the period of enhanced greenhouse effect to obtain the heat stress enhancement gradient data;

[0021] Step S233: Perform a thermal convection accumulation vertical orientation analysis on the carbon dioxide / ammonia time-series fluctuation data based on the heat pressure enhancement gradient data to obtain the carbon dioxide / ammonia accumulation vertical orientation data;

[0022] Step S234: Perform a fitting of the carbon dioxide / ammonia diffusion accumulation increment based on the heat pressure enhancement gradient data and the carbon dioxide / ammonia accumulation vertical orientation data to obtain the carbon dioxide / ammonia diffusion accumulation data.

[0023] Preferably, step S24 includes the following steps:

[0024] Step S241: Perform a numerical integration of the ammonia accumulation pressure on the carbon dioxide / ammonia diffusion accumulation data to obtain the ammonia accumulation pressure data;

[0025] Step S242: Perform a robust regression analysis of the accumulation distribution on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia accumulation pressure data to obtain the ammonia accumulation distribution pressure regression data;

[0026] Step S243: Perform a pressure logarithmic transformation on the ammonia accumulation distribution pressure regression data to obtain the ammonia accumulation pressure logarithmic transformation data;

[0027] Step S244: Obtain the body posture characteristic data of meat goats; perform a calculation of the gas inhalation volume of meat goats between different body postures per unit time on the ammonia accumulation pressure logarithmic transformation data based on the respiratory frequency demand acceleration data and the body posture characteristic data of meat goats to obtain the gas inhalation volume calculation data per unit time;

[0028] Step S245: Perform a simulation estimation of the ammonia respiration dissolution rate on the gas inhalation volume calculation data based on the body posture characteristic data of meat goats to generate the ammonia respiration dissolution rate estimation data;

[0029] Step S246: Perform an estimation of the probability of ammonia intake respiratory stress based on the ammonia respiration dissolution rate estimation data to obtain the probability of ammonia intake respiratory stress.

[0030] Preferably, step S245 includes the following steps:

[0031] Perform an assessment of the ammonia respiration cumulative exposure amount between different body postures on the gas inhalation volume calculation data based on the body posture characteristic data of meat goats to obtain the ammonia cumulative exposure data;

[0032] Calculate the ammonia absorption rate based on the ammonia cumulative exposure data and the respiratory frequency demand acceleration data to obtain the ammonia absorption rate;

[0033] Perform a correction calculation of the ammonia solubility parameter based on the ammonia cumulative exposure data and the ammonia absorption rate to obtain the ammonia respiration dissolution correction data;

[0034] Based on the ammonia respiration dissolution correction data, simulate and estimate the ammonia respiration dissolution rate to generate ammonia respiration dissolution rate estimation data.

[0035] Preferably, step S3 includes the following steps:

[0036] Step S31: Normalize the probability of ammonia intake respiratory stress to obtain normalized respiratory stress probability data;

[0037] Step S32: Map the ammonia safety content intervals between different respiratory frequencies for the respiratory frequency demand acceleration data according to the normalized respiratory stress probability data to obtain ammonia safety content intervals;

[0038] Step S33: Match the humidity content in the sheep house based on the ammonia safety content intervals to obtain humidity content matching data;

[0039] Step S34: Balance the ventilation parameters for the carbon dioxide / ammonia diffusion and accumulation data according to the humidity content matching data to obtain ventilation parameter balance design data;

[0040] Step S35: Compensate the oxygen content in the sheep house based on the humidity content matching data and the ventilation parameter balance design data to obtain oxygen content compensation data.

[0041] Preferably, step S34 includes the following steps:

[0042] Step S341: Match the ventilation humidity loss compensation according to the humidity content matching data to obtain ventilation humidity loss compensation data;

[0043] Step S342: Analyze the accumulation density difference of the carbon dioxide / ammonia diffusion and accumulation data to obtain carbon dioxide / ammonia accumulation density difference data;

[0044] Step S343: Match the ventilation air pressure in the sheep house based on the carbon dioxide / ammonia accumulation density difference data to obtain sheep house ventilation air pressure matching data;

[0045] Step S344: Design the ventilation structure parameters based on the carbon dioxide / ammonia accumulation density difference data and the sheep house ventilation air pressure matching data to obtain ventilation structure parameters;

[0046] Step S345: Balance the ventilation parameters according to the ventilation humidity loss compensation data and the ventilation structure parameters to obtain ventilation parameter balance design data.

[0047] Preferably, step S344 includes the following steps:

[0048] Obtain the sheep house structure design data; Extract the ventilation path from the sheep house structure design data to obtain sheep house ventilation path data;

[0049] Based on the data of the accumulation density difference of carbon dioxide / ammonia, analyze the carbon dioxide / ammonia accumulation pressure difference on the ventilation path data of the sheep house to obtain the carbon dioxide / ammonia accumulation pressure difference between ventilation paths;

[0050] According to the ventilation wind pressure matching data of the sheep house, perform wind speed and flow rate matching on the carbon dioxide / ammonia accumulation pressure difference to obtain wind speed and flow rate matching data;

[0051] Perform accumulation repeated behavior learning on the carbon dioxide / ammonia accumulation pressure difference to obtain accumulation pressure behavior learning data;

[0052] Based on the ventilation wind pressure matching data and wind speed and flow rate matching data of the sheep house, perform intelligent matching of the ventilation rate on the accumulation pressure behavior learning data to obtain intelligent ventilation rate matching data;

[0053] According to the wind speed and flow rate matching data, the ventilation wind pressure matching data of the sheep house, and the intelligent ventilation rate matching data, perform ventilation structure parameter design to obtain ventilation structure parameters.

[0054] Preferably, the present invention also provides an automatic control system for the environment of a meat goat house, which is used to execute the automatic control method for the environment of a meat goat house as described above. The automatic control system for the environment of a meat goat house includes:

[0055] An environmental data acquisition module, which is used to collect all-weather data in the meat goat house of the high-altitude environment where the meat goats are located through sensors to obtain all-weather environmental data in the high-altitude sheep house; extract the temperature and humidity and carbon dioxide / ammonia all-weather time series fluctuations from the all-weather environmental data in the high-altitude sheep house to obtain the temperature time series fluctuation data in the sheep house and the carbon dioxide / ammonia time series fluctuation data in the sheep house respectively;

[0056] A stress probability estimation module, which is used to perform carbon dioxide / ammonia diffusion increment fitting according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion and accumulation data; perform ammonia intake respiratory stress probability estimation on the carbon dioxide / ammonia diffusion and accumulation data to obtain ammonia intake respiratory stress probability;

[0057] A safety content interval mapping module, which is used to perform ammonia safety content interval mapping according to the ammonia intake respiratory stress probability to obtain an ammonia safety content interval; perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion and accumulation data based on the ammonia safety content interval to obtain ventilation parameter balance design data; perform oxygen content compensation processing in the sheep house based on the ventilation parameter balance design data to obtain oxygen content compensation data;

[0058] An automatic control strategy formulation module, which is used to formulate an automatic control strategy for the sheep house environment based on the ventilation parameter balance design data and the oxygen content compensation data, obtain the automatic control strategy for the sheep house environment, and send the automatic control strategy for the sheep house environment to the terminal to execute the automatic control method for the meat goat house environment.

[0059] The beneficial effects of the present invention are as follows: By using sensors to monitor the environmental data (such as temperature and carbon dioxide / ammonia concentration) in the high-altitude sheep house all-weather, the real-time environmental fluctuation information inside the sheep house can be comprehensively obtained. These data provide the basis for subsequent analysis and control. The extraction of the temperature time-series fluctuation and the carbon dioxide / ammonia time-series fluctuation helps to reveal the change trend of the sheep house environment, provides a scientific basis for accurately regulating environmental parameters, thereby avoiding the adverse effects caused by environmental fluctuations and ensuring the health of the flock. Through the fitting analysis of the carbon dioxide / ammonia diffusion increment, the accumulation situation of the gas in the sheep house can be effectively predicted, and the potential harmful gas accumulation trend can be identified. Estimating the respiratory stress probability of meat goats due to ammonia intake based on these data helps to evaluate the safety of the current sheep house environment. Timely identifying the change in ammonia concentration and evaluating its threat to the health of the flock can enable early intervention measures to be taken to avoid the health risks caused by excessive ammonia accumulation and improve the growth efficiency and survival rate of the flock. By mapping the respiratory stress probability of ammonia intake to the ammonia safety content interval, the ammonia concentration in the sheep house can be accurately controlled to ensure that it is within a safe range harmless to the health of meat goats. In addition, based on the carbon dioxide / ammonia diffusion and accumulation data, the ventilation parameter balance design is carried out, which helps to optimize the ventilation system and improve the air circulation efficiency. By optimizing the ventilation design, both the concentration of harmful gases can be reduced and the oxygen content can be increased, providing a more suitable growth environment for meat goats, thereby promoting their healthy development. Based on the ventilation parameter balance design data and the oxygen content compensation data, an efficient automatic control strategy can be formulated to adjust the environmental parameters such as temperature, humidity, and gas concentration in the sheep house in real time. This automatic control strategy can be automatically adjusted according to the changes in the sheep house environment, reducing the need for manual intervention and improving the intelligent level of sheep house management. By sending these strategies to the terminal for execution, the real-time control and optimization of the sheep house environment can be achieved. Therefore, the present invention is an optimization treatment for a traditional automatic control method for the meat goat house environment, solves the problem that the traditional automatic control method for the meat goat house environment has inaccurate analysis of the ammonia intake stress probability of meat goats, resulting in large errors in the control of the meat goat house environment, improves the accuracy of the analysis of the ammonia intake stress probability of meat goats, and reduces the error in the control of the meat goat house environment. Description of the Drawings

[0060] Figure 1 It is a schematic diagram of the step flow of an automatic control method for the meat goat house environment;

[0061] Figure 2For Figure 1 Schematic diagram of the detailed implementation steps of step S2 in

[0062] Figure 3 For Figure 1 Schematic diagram of the detailed implementation steps of step S3 in Detailed implementation method

[0063] Please refer to Figures 1 to 3 , an automatic control method for the environment of a meat goat shed, the method comprising the following steps:

[0064] Step S1: All-weather data collection of the high-altitude environment where the meat goats are located is carried out through sensors in the meat goat shed to obtain all-weather environment data in the high-altitude goat shed; temperature and carbon dioxide / ammonia all-weather time series fluctuations in the all-weather environment data in the high-altitude goat shed are extracted to respectively obtain temperature time series fluctuation data in the goat shed and carbon dioxide / ammonia time series fluctuation data in the goat shed;

[0065] Step S2: Carbon dioxide / ammonia diffusion increment fitting is carried out according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion accumulation data; ammonia intake respiratory stress probability estimation is carried out on the carbon dioxide / ammonia diffusion accumulation data to obtain the ammonia intake respiratory stress probability;

[0066] Step S3: Ammonia safety content interval mapping is carried out according to the ammonia intake respiratory stress probability to obtain the ammonia safety content interval; ventilation parameter balance processing is carried out on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia safety content interval to obtain ventilation parameter balance design data; oxygen content compensation processing is carried out in the goat shed based on the ventilation parameter balance design data to obtain oxygen content compensation data;

[0067] Step S4: An automatic control strategy for the environment of the goat shed is formulated based on the ventilation parameter balance design data and the oxygen content compensation data to obtain an automatic control strategy for the environment of the goat shed, and the automatic control strategy for the environment of the goat shed is sent to the terminal to execute the automatic control method for the environment of the meat goat shed.

[0068] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic diagram of the step flow of an automatic control method for the environment of a meat goat shed of the present invention. In this example, the automatic control method for the environment of the meat goat shed comprises the following steps:

[0069] Step S1: All-weather data collection of the high-altitude environment where the meat goats are located is carried out through sensors in the meat goat shed to obtain all-weather environment data in the high-altitude goat shed; temperature and carbon dioxide / ammonia all-weather time series fluctuations in the all-weather environment data in the high-altitude goat shed are extracted to respectively obtain temperature time series fluctuation data in the goat shed and carbon dioxide / ammonia time series fluctuation data in the goat shed;

[0070] In the embodiments of the present invention, all-weather environmental information is collected through a data acquisition system deployed in a meat goat shed at high altitude. The system consists of an integrated temperature and humidity sensor, an infrared carbon dioxide sensor, an electrochemical ammonia sensor, a barometric pressure sensor, and a wind speed and direction sensor. The data acquisition frequency is set to collect once every 30 seconds, and the acquisition period is not less than 30 consecutive days to ensure the continuity and periodicity of the data. The deployed temperature and humidity sensor has a measurement accuracy of ±0.1°C and ±1.5%RH; the carbon dioxide sensor has a measurement range of 400 ppm to 5000 ppm and an accuracy of ±(50 ppm + 3%); the ammonia sensor has a measurement range of 0 to 1000 ppm and an accuracy of ±5 ppm. During the acquisition process, the sensors are arranged at different spatial levels in the goat shed, including 0.5 meters above the ground surface, 1.2 meters at the height of the goat body, and 2 meters at the top, and 4 groups of sensor nodes are respectively arranged to obtain the distribution data of environmental parameters in the vertical space. The collected raw data is transmitted to the central controller through the RS485 bus and stored in the local database. Subsequently, preprocessing is performed on the raw data, including timestamp correction, outlier removal, and missing value interpolation. The interpolation method uses linear interpolation to fill in the data missing in a short time period (less than 5 minutes). After the preprocessing is completed, the time series fluctuation characteristics of the temperature data and the carbon dioxide and ammonia concentration data are extracted according to the time series order. The fluctuation extraction is performed in a sliding window manner, the window width is 30 minutes, and the sliding step is 5 minutes. The extraction indicators include the maximum value, the minimum value, the mean value, the standard deviation, and the change rate, forming the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data.

[0071] Step S2: Fit the carbon dioxide / ammonia diffusion increment according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain the carbon dioxide / ammonia diffusion accumulation data; estimate the probability of ammonia intake respiratory stress for the carbon dioxide / ammonia diffusion accumulation data to obtain the probability of ammonia intake respiratory stress;

[0072] In the embodiment of the present invention, in step S2, according to the extracted temperature time-series fluctuation data and carbon dioxide / ammonia time-series fluctuation data, the combined diffusion and accumulation analysis of the concentration changes of carbon dioxide and ammonia is first carried out. This process uses the time-series difference analysis method to calculate the change amount of gas concentration in each sliding window, and combines the temperature change rate in the corresponding time period to perform an incremental fitting operation. In the fitting process, no regression model is used, but a three-dimensional data table (time window, temperature change rate, gas concentration difference) is constructed for numerical interpolation, and the bilinear interpolation method is used to complete the diffusion increment complement between data, and the gas diffusion and accumulation value in each time window is obtained. After obtaining the carbon dioxide / ammonia diffusion and accumulation data, an ammonia inhalation risk assessment mechanism is introduced. This mechanism estimates the stress probability based on the relationship between the ammonia concentration in the sheep house and the standard breathing frequency of meat goats. The specific method is to establish ammonia concentration classification thresholds (for example: <10 ppm is low risk, 10 - 25 ppm is medium risk, >25 ppm is high risk), and combine the breathing frequency of meat goats under high-altitude conditions (based on field measurements, the average is 34 times per minute), and use the piecewise linear mapping method to quantify the inhalation amount corresponding to the ammonia concentration and the breathing frequency, and finally calculate the ammonia intake respiratory stress probability in each time period, and the output is a continuous value between 0 and 1.

[0073] Step S3: Map the ammonia safety content interval according to the ammonia intake respiratory stress probability to obtain the ammonia safety content interval; perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion and accumulation data based on the ammonia safety content interval to obtain ventilation parameter balance design data; perform oxygen content compensation processing in the sheep house based on the ventilation parameter balance design data to obtain oxygen content compensation data;

[0074] In the embodiment of the present invention, in step S3, according to the probability of ammonia intake respiratory stress estimated in the previous step, a mapping operation of the ammonia safe content range is performed. This operation is classified and mapped based on the established correspondence table between ammonia concentration thresholds and stress probabilities, and a suitable safe concentration range is divided, specifically: stress probability < 0.2 corresponds to ammonia concentration < 10 ppm, 0.2 < stress probability < 0.5 corresponds to 10 - 20 ppm, and stress probability > 0.5 corresponds to ammonia concentration > 20 ppm. After the mapping is completed, combined with the carbon dioxide / ammonia diffusion and accumulation data, parameter balancing processing is performed on the ventilation system of the sheep house. The ventilation parameter balancing processing includes three contents: wind pressure regulation, optimization of the opening angles of the inlet and outlet vents, and wind speed control. Among them, the wind pressure regulation is calculated inversely based on the measured wind speed data, and the goal is to increase the wind speed in the high-concentration accumulation area to more than 0.3 m / s; the angles of the inlet and outlet vents are adjusted in real time according to the layout of the sheep house structure, and the control range is between 15° and 45° to ensure that the convection path is unobstructed; the wind speed control realizes fine adjustment of the rotation speed of the centrifugal fan through a PWM speed controller, and the wind speed setting range is 0.2 - 0.8 m / s. After completing the ventilation parameter balance design, further compensation processing is performed on the oxygen content. The oxygen content compensation processing is adjusted according to the difference between the ventilation volume and the external air pressure. A high-precision oxygen sensor (such as Maxtec MaxO2+A) is used to monitor the oxygen concentration in the house in real time. When the oxygen concentration in the house is lower than 19.5%, the oxygen supply device is started and the ventilation frequency is increased to ensure that the oxygen concentration is maintained within the safe range of 20.5% ± 0.5%.

[0075] Step S4: Based on the ventilation parameter balance design data and the oxygen content compensation data, formulate an automatic control strategy for the sheep house environment, obtain the automatic control strategy for the sheep house environment, and send the automatic control strategy for the sheep house environment to the terminal to execute the automatic control method for the meat goat house environment.

[0076] In the embodiment of the present invention, in step S4, based on the ventilation parameter balance design data and the oxygen content compensation data obtained in step S3, an automatic control strategy for the sheep house environment is formulated. The formulation of the control strategy includes a ventilation strategy, an exhaust frequency strategy, a humidity control strategy, and a gas concentration warning linkage strategy. The ventilation strategy is based on the wind pressure-concentration distribution map to determine the start and stop time points of the fan and the set value of the wind speed; the exhaust frequency strategy automatically increases the exhaust frequency based on the peak time period fitted by the 24-hour fluctuation curve of carbon dioxide. The interval is set to exhaust once every 30 minutes from 10 am to 4 pm, and each time lasts for 5 minutes; the humidity control strategy sets the upper and lower limits according to the temperature-humidity coupling relationship to keep the relative humidity in the house between 55% and 65%; the gas concentration warning linkage strategy sets that when the ammonia concentration exceeds 25 ppm, forced exhaust is automatically started and an audible and visual alarm is issued. All control strategies are sent to the PLC control terminal through the RS485 data bus to achieve real-time linkage control of various actuators (fans, exhaust devices, oxygen supply systems, alarms). This control process performs closed-loop feedback in units of control cycles, refreshing the environmental data and control instructions every 5 minutes to ensure timely system response, accurate control, and stable environment.

[0077] Step S1 includes the following steps:

[0078] Step S11: Use sensors to collect all-weather data inside the meat goat house in the high-altitude environment where the meat goats are located to obtain all-weather environmental data inside the high-altitude sheep house;

[0079] Step S12: Fill in the missing values in the all-weather environmental data inside the high-altitude sheep house to obtain the environmental filled data inside the high-altitude sheep house;

[0080] Step S13: Extract the all-weather time series fluctuations of temperature, humidity, carbon dioxide / ammonia from the environmental filled data inside the high-altitude sheep house to obtain the temperature time series fluctuation data inside the sheep house and the carbon dioxide / ammonia time series fluctuation data inside the sheep house respectively.

[0081] In the embodiments of the present invention, a variety of sensors are used to collect all-weather data on the environment inside the meat goat shed. First, temperature sensors, humidity sensors, carbon dioxide sensors, and ammonia sensors are installed inside the goat shed. These sensors are all connected to the data acquisition module through the I2C or SPI interface. The data acquisition module is a Raspberry Pi 4, which has multi-channel data acquisition capabilities and powerful processing capabilities. The sampling frequency of the sensors is set to once per minute to ensure the continuity and accuracy of the data. The data acquisition module transmits the data to the cloud server in real time through a wireless network and stores it in a database. The database uses MySQL for subsequent data storage and query. During the data acquisition process, the sensors are calibrated to ensure the accuracy of the data. For example, the temperature sensor needs to be calibrated before installation to ensure the accuracy of its readings. The humidity sensor needs to be calibrated for humidity before installation to ensure the accuracy of its readings. The carbon dioxide sensor needs to be calibrated at zero before installation to ensure the accuracy of its readings. The ammonia sensor needs to be calibrated for ammonia concentration before installation to ensure the accuracy of its readings. Through the above steps, the acquisition of all-weather environmental data in the high-altitude goat shed is achieved. During the data acquisition process, due to network interruptions or sensor failures, data missing situations may occur. To ensure the integrity of the data, it is necessary to fill in the missing values. First, the linear interpolation method is used to fill in the missing values. For temperature and humidity data, the average value of the two adjacent data points is used for linear interpolation. For carbon dioxide and ammonia data, the average value of the two adjacent data points is used for linear interpolation. If the missing values continuously exceed a certain period of time, the mean filling method is used. For example, if the temperature data is continuously missing for more than 10 minutes, the average temperature during this period is used for filling. After filling the data, the filled data is stored back in the database through a database query statement. During the process of filling the data, the Python programming language is used, and the Pandas library is utilized for data processing. The Pandas library provides rich data processing functions, which can facilitate the operation of the data. Through the above steps, the filling of missing values in the environmental data in the high-altitude goat shed is achieved. After filling the data, the all-weather time series fluctuations of the environmental data in the high-altitude goat shed are extracted. First, the time series fluctuations of the temperature and humidity data are extracted. The moving average method is used to smooth the temperature and humidity data to remove the noise in the data. Then, the fluctuation amplitudes of the temperature and humidity data are calculated. For example, for the temperature data, the temperature difference between two adjacent data points is calculated to obtain the temperature fluctuation data. For the humidity data, the humidity difference between two adjacent data points is calculated to obtain the humidity fluctuation data. The same method is used for carbon dioxide and ammonia data. The moving average method is used to smooth the carbon dioxide and ammonia data, and then the fluctuation amplitudes of the carbon dioxide and ammonia data are calculated.For example, for carbon dioxide data, calculate the difference in carbon dioxide between two adjacent data points to obtain carbon dioxide fluctuation data. For ammonia data, calculate the difference in ammonia between two adjacent data points to obtain ammonia fluctuation data. Through the above steps, the all-weather time series fluctuations of temperature, humidity, carbon dioxide, and ammonia in the high-altitude sheep house environment data are extracted.

[0082] Step S2 includes the following steps:

[0083] Step S21: Analyze the low-oxygen environment concentration fluctuations of the all-weather environment data in the high-altitude sheep house to obtain low-oxygen environment concentration fluctuation data;

[0084] Step S22: Based on the low-oxygen environment concentration fluctuation data, evaluate the acceleration of the respiratory frequency demand of meat goats to obtain respiratory frequency demand acceleration data;

[0085] Step S23: Fit the carbon dioxide / ammonia diffusion accumulation increment according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion accumulation data;

[0086] Step S24: Estimate the probability of ammonia intake respiratory stress for the carbon dioxide / ammonia diffusion accumulation data based on the respiratory frequency demand acceleration data to obtain the probability of ammonia intake respiratory stress.

[0087] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0088] Step S21: Analyze the low-oxygen environment concentration fluctuations of the all-weather environment data in the high-altitude sheep house to obtain low-oxygen environment concentration fluctuation data;

[0089] In the embodiment of the present invention, first, an oxygen sensor (such as MX-ZRO2) is used to monitor the oxygen concentration in the sheep house all day long. The oxygen sensor measures the oxygen concentration through an electrode reaction, and its output signal is a voltage signal. The data acquisition module is a Raspberry Pi 4, which has multi-channel data acquisition capabilities and powerful processing capabilities. The output signal of the oxygen sensor is converted into a digital signal through an analog-to-digital converter (ADC) and connected to the data acquisition module through an I2C interface. The data acquisition module transmits the data to the cloud server in real time and stores it in a database. The database uses MySQL for easy subsequent data storage and query. During the data acquisition process, the sensor is calibrated to ensure the accuracy of the data. For example, the oxygen sensor needs to be calibrated before installation to ensure the accuracy of its readings. Through the above steps, the acquisition of all-day environmental data in the high-altitude sheep house is achieved. For the collected oxygen concentration data, the moving average method is used to smooth the data to remove the noise in the data. Then, the difference in oxygen concentration between two adjacent data points is calculated to obtain the low-oxygen environment concentration fluctuation data. For example, if the oxygen concentration drops at a certain moment, the difference in oxygen concentration between this moment and the previous moment is calculated to obtain the low-oxygen environment concentration fluctuation data. Through the above steps, the low-oxygen environment concentration fluctuation analysis of the environmental data in the high-altitude sheep house is achieved.

[0090] Step S22: Based on the low-oxygen environment concentration fluctuation data, conduct an accelerated assessment of the respiratory frequency demand of meat goats to obtain the accelerated data of the respiratory frequency demand;

[0091] In the embodiment of the present invention, in the data processing stage, a normal respiratory frequency benchmark database of meat goats is first established, and the respiratory frequency data of meat goats in areas below 500 meters above sea level are collected. The respiratory frequency distribution is obtained through long-term monitoring, and the sampling period is set to 10 seconds. The data is stored in a time series format, and data cleaning is performed to remove outliers. Basic statistical characteristics are calculated, including mean, median, standard deviation and distribution density function. The analysis results show that under a standard environment with an oxygen concentration of 20.9%, the normal respiratory frequency range of meat goats is 20-30 times per minute, the respiratory frequency is normally distributed, and the coefficient of variation is small. Based on the normal respiratory frequency data, the high altitude hypoxic environment simulation algorithm is used to calculate the high altitude hypoxic environment. The influence of environment on the respiratory rate of meat goats was studied. Oxygen partial pressure was selected as the main variable, and a regression model was established based on the functional relationship between oxygen partial pressure and respiratory rate. The Bayesian inference method was used to estimate the changes in respiratory rate under high altitude environment. The oxygen partial pressure was set to drop to three gradients of 80%, 70%, and 60% for simulation calculation. The results showed that when the oxygen partial pressure dropped to 80%, the average respiratory rate of meat goats increased to 32 times per minute. When the oxygen partial pressure further dropped to 70%, the respiratory rate increased to 38 times per minute. When the oxygen partial pressure dropped below 60%, the respiratory rate rose rapidly to more than 45 times per minute. The calculation results were stored in the database and used as the basic parameters for subsequent ventilation control and oxygen content compensation.

[0092] Step S23: performing carbon dioxide / ammonia diffusion accumulation increment fitting according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion accumulation data;

[0093] In the embodiments of the present invention, for the fitting of the diffusion accumulation increment of carbon dioxide / ammonia based on the temperature time-series fluctuation data and the carbon dioxide / ammonia time-series fluctuation data, first, a time-series data set is constructed to record the temperature, carbon dioxide concentration, and ammonia concentration at different time points in the high-altitude sheep house. The data sampling interval is set to 10 seconds. The sliding window method is used to segment the time-series data, and each window contains at least 600 data points to ensure the stability of the data and the accuracy of trend analysis. Then, the autoregressive integrated moving average (ARIMA, autoregressive moving average model) is used to fit the change trends of carbon dioxide and ammonia concentrations, and their growth rates are calculated respectively. A reference temperature range is set, and the diffusion rates of carbon dioxide and ammonia under different temperature conditions are calculated. The Kruskal-Wallis test is used to perform a significance analysis on the diffusion rate data under different temperature conditions to determine the influence degree of temperature on the diffusion trend. Subsequently, a multiple regression model is established, with the temperature fluctuation data as the independent variable and the change amounts of carbon dioxide and ammonia concentrations as the dependent variables, to calculate the diffusion accumulation increments of carbon dioxide and ammonia. After the model fitting is completed, the confidence interval of the prediction result of the model is evaluated by the Monte Carlo random sampling method to ensure the reliability of the data. Finally, the carbon dioxide / ammonia diffusion accumulation data is generated, which specifically refers to the diffusion process of carbon dioxide and ammonia in the sheep house and their cumulative phenomenon in specific areas. Based on the time-series fluctuation data of temperature, carbon dioxide, and ammonia, the diffusion trends and accumulation rates of carbon dioxide and ammonia are calculated, and their change increments are fitted to predict the future accumulation trend and store it in the database.

[0094] Step S24: Estimate the probability of ammonia intake respiratory stress for the carbon dioxide / ammonia diffusion accumulation data based on the respiratory frequency demand acceleration data to obtain the probability of ammonia intake respiratory stress.

[0095] In the embodiments of the present invention, according to the respiratory frequency demand acceleration data, the probability of ammonia intake respiratory stress is estimated for the carbon dioxide / ammonia diffusion and accumulation data. First, the respiratory frequency change data of meat goats in a high-altitude environment is interpolated to generate a continuously changing respiratory rate curve. The top 10% of the sample data with the highest respiratory frequency is selected as high-risk samples, and a probability density distribution is established. Then, the Lagrange Interpolation is used to calculate the respiratory frequency change rate under different oxygen partial pressures. Based on the diffusion and accumulation data, the ammonia concentration distribution at different spatial positions in the sheep house is calculated. The finite difference method is used to numerically simulate the ammonia diffusion process to solve the steady-state distribution of ammonia under different ventilation conditions. Subsequently, the ammonia concentration distribution data is matched with the respiratory frequency data of meat goats to calculate the ammonia intake per unit time. According to the toxicological cumulative exposure theory, the Weibull-Fisher Distribution is used to fit the relationship between the ammonia intake and the probability of respiratory stress, and a probability model of respiratory stress caused by ammonia intake is obtained. Finally, based on the quantile regression method, the probability of respiratory stress under different ammonia concentration thresholds is calculated and stored in the database.

[0096] Step S23 includes the following steps:

[0097] Step S231: Analyze the rising period of carbon dioxide content for the carbon dioxide / ammonia time-series fluctuation data, and then analyze the enhanced greenhouse effect period based on the temperature time-series fluctuation data to obtain the enhanced greenhouse effect period;

[0098] Step S232: Identify the heat pressure enhancement gradient for the enhanced greenhouse effect period to obtain the heat pressure enhancement gradient data;

[0099] Step S233: Based on the heat pressure enhancement gradient data, perform a vertical azimuth analysis of the carbon dioxide / ammonia diffusion and accumulation for the carbon dioxide / ammonia time-series fluctuation data to obtain the carbon dioxide / ammonia accumulation vertical azimuth data;

[0100] Step S234: Perform an increment fitting of the carbon dioxide / ammonia diffusion and accumulation based on the heat pressure enhancement gradient data and the carbon dioxide / ammonia accumulation vertical azimuth data to obtain the carbon dioxide / ammonia diffusion and accumulation data.

[0101] In the embodiments of the present invention, for the analysis of the rising period of carbon dioxide content in the time-series fluctuation data of carbon dioxide / ammonia, first, the time-series data is segmented. The 24 hours of a day are divided into 144 periods, each period lasting 10 minutes, so as to more finely capture the changing trend of the carbon dioxide concentration. The locally weighted regression (LOESS, locally weighted scatter smoothing) method is used to smooth the time-series data. After removing the high-frequency noise, the first derivative is calculated to identify the rising trend interval of the carbon dioxide concentration. The Markov Chain Monte Carlo (MCMC) method is used to estimate the transition probability of the rising carbon dioxide concentration, and the changing rules of the carbon dioxide concentration in different time periods are judged. Subsequently, based on the time-series fluctuation data of temperature, the wavelet transform is used to decompose the temperature signal, extract the temperature change characteristics of different frequency bands, calculate the high-frequency temperature fluctuation interval overlapping with the rising period of the carbon dioxide concentration, match the two to obtain the enhanced greenhouse effect period, and store it in the database. For the identification of the heat stress enhancement gradient in the enhanced greenhouse effect period, first, the temperature data is gridded, and the three-dimensional Kriging interpolation method is used to construct the temperature distribution model. The temperature changes at different heights in the sheep house are calculated at intervals of 0.5 meters in height. The area with a temperature gradient greater than the set threshold is selected as the high-temperature gradient area. The principal component analysis (PCA) method is used for dimensionality reduction to extract the main influencing factors. The time-series data of temperature, carbon dioxide concentration, and humidity are subjected to feature extraction, the covariance matrix of each variable is calculated, and the main component vector is obtained. According to the heat stress index (HSI), the heat stress level in the enhanced greenhouse effect area is calculated. The heat stress enhancement gradient data is normalized and stored in the database. Based on the heat stress enhancement gradient data, the vertical azimuth analysis of the heat convection accumulation of the carbon dioxide / ammonia time-series fluctuation data is carried out. The Laplace equation is used to solve the steady-state distribution of the heat field. With the heat stress enhancement gradient data as the input variable, a heat convection model is constructed. The heat flux density at different heights is calculated through finite element analysis. The heat convection mode of the air in the sheep house is simulated. The numerical integration method is used to calculate the carbon dioxide and ammonia concentration distributions in each height layer. Combining with the Lagrangian particle tracking method, the transmission paths of carbon dioxide and ammonia with the air flow are simulated, and finally, the carbon dioxide / ammonia accumulation vertical azimuth data is generated.Based on the heat pressure enhancement gradient data and the vertical orientation data of carbon dioxide / ammonia accumulation, perform fitting on the carbon dioxide / ammonia diffusion accumulation increment. First, establish a multiple regression model, with the temperature gradient, air humidity, carbon dioxide concentration, and ammonia concentration as independent variables, and the change in carbon dioxide and ammonia concentrations at each altitude layer as the dependent variable. Use the Ridge Regression method to optimize the model parameters to prevent overfitting. Subsequently, adopt the Monte Carlo random sampling method to evaluate the confidence interval of the model's prediction results to ensure the stability of the data. Finally, obtain the carbon dioxide / ammonia diffusion accumulation data and store it in the database.

[0102] Step S24 includes the following steps:

[0103] Step S241: Perform numerical integration of the ammonia accumulation pressure on the carbon dioxide / ammonia diffusion accumulation data to obtain the ammonia accumulation pressure data;

[0104] Step S242: Perform robust regression analysis of the accumulation distribution on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia accumulation pressure data to obtain the ammonia accumulation distribution pressure regression data;

[0105] Step S243: Perform logarithmic transformation of the pressure on the ammonia accumulation distribution pressure regression data to obtain the ammonia accumulation pressure logarithmic transformation data;

[0106] Step S244: Obtain the body shape characteristic data of meat goats; perform the calculation of the gas inhalation volume of meat goats between different body shapes per unit time on the ammonia accumulation pressure logarithmic transformation data according to the respiratory frequency demand acceleration data and the body shape characteristic data of meat goats to obtain the gas inhalation volume calculation data per unit time;

[0107] Step S245: Based on the body shape characteristic data of meat goats, simulate and estimate the ammonia respiratory dissolution rate on the gas inhalation volume calculation data to generate the ammonia respiratory dissolution rate estimation data;

[0108] Step S246: Estimate the probability of ammonia intake respiratory stress based on the ammonia respiratory dissolution rate estimation data to obtain the probability of ammonia intake respiratory stress.

[0109] In the embodiments of the present invention, when performing numerical integration of the ammonia accumulation pressure on the carbon dioxide / ammonia diffusion and accumulation data, first, the carbon dioxide / ammonia diffusion and accumulation data are discretized according to the spatial coordinates. The grid method (GridMethod) is used to divide the sheep house space. The side length of the grid is set to 1 meter. The carbon dioxide and ammonia concentrations within the grid are obtained using the measured data of gas sensors, and the weighted average of the gas concentrations is calculated for each grid cell to calculate the pressure of each small area. On this basis, the Gaussian integration method is used for numerical integration to ensure the integration accuracy. By optimizing the number of Gauss points, within a 1×1 square meter area, 9-point Gaussian integration is used to improve the calculation accuracy. Through this integration method, the ammonia diffusion pressure within the entire space is quantified, and finally the ammonia accumulation pressure data are obtained. The unit of this data is Pa·m², which represents the pressure accumulation situation of the gas per unit area. After integration, the ammonia accumulation pressure data for the overall space are obtained. At this time, the numerical range of the accumulation pressure is between 0 and 100 Pa·m², and the specific value changes according to the gas concentration and temperature conditions. The processed data are convenient for analyzing the subsequent gas diffusion trend. When performing robust regression analysis of the accumulation distribution on the carbon dioxide / ammonia diffusion and accumulation data based on the ammonia accumulation pressure data, a robust regression algorithm is adopted. The ammonia accumulation pressure data and the carbon dioxide / ammonia diffusion and accumulation data are combined for analysis. The goal is to fit the relationship between the carbon dioxide / ammonia concentration and the accumulation pressure. By using the Theil-Sen estimation method, the sensitivity of the traditional least squares regression method to outliers is avoided. The regression coefficient of this method is calculated based on the median, avoiding the influence of local outliers on the data fitting result. The iterative algorithm is calculated 500 times to optimize the coefficient. In the regression analysis, the standard error of the coefficient of each independent variable is less than 5%, and R² (goodness of fit) reaches above 0.95. Finally, the regression data of the carbon dioxide and ammonia accumulation pressure distributions are obtained. The regression data show that the relationship between the carbon dioxide concentration and the pressure is a linear relationship, while the relationship between the ammonia concentration and the pressure shows non-linear characteristics. When performing logarithmic transformation of the pressure on the regression data of the ammonia accumulation distribution pressure, first, the natural logarithm transformation (LnTransformation) is used to transform the pressure data obtained in the regression analysis. The transformation method is to take the natural logarithm of each pressure data point value. The transformed pressure data are used to remove the exponential growth part in the original data. In specific operations, for the pressure data P in the regression result, the logarithmic transformation is performed to obtain the logarithm value of the pressure data: ln(P). The pressure data are transferred from the linear space to the logarithmic space. The unit of the transformed pressure data remains Pa·m², and the range usually changes from the original pressure numerical range of 0 - 100 Pa·m² to the range of 0 to 4.61 (ln100). After the logarithmic transformation, the data become smoother, greatly reducing the influence of large pressure data and facilitating further analysis.When obtaining the body shape characteristic data of meat goats, first, high-precision measurement of the goat body is carried out through image processing technology. A video monitoring and image processing system combined with a deep convolutional neural network (CNN) is used to obtain the body shape data. During the processing, the video resolution is set to 1920×1080 pixels, and 30 frames of video are collected per second. The edge information of the goat body is extracted through preprocessing methods such as denoising, edge detection, and contour extraction. Then, using a pre-trained CNN model, body shape parameters such as the weight, body length, shoulder height, and chest circumference of each meat goat are extracted. In the experiment, the body length of meat goats is usually between 70 and 100 cm, the shoulder height is usually between 60 and 90 cm, the chest circumference is between 80 and 110 cm, and the weight is between 25 and 45 kg. After the image data is calibrated, the weight is accurate to 0.5 kg and the body shape data is accurate to 1 cm. When calculating the ammonia inhalation volume of meat goats between different body shape characteristics per unit time for the logarithmically transformed data of ammonia accumulation pressure based on the respiration frequency requirement acceleration data and the body shape characteristic data of meat goats, first, a respiration frequency model is established based on the body shape characteristic data, and the relationship between the respiration frequency and the body shape data is fitted through polynomial regression. Assuming that the respiration frequency requirement of meat goats in a high-altitude environment is 30% higher than the normal state, it is used as an acceleration factor in the calculation process. Taking a unit time (1 minute) as a cycle, the ammonia inhalation volume of each meat goat is calculated in combination with the gas concentration data. Assuming that in a high-concentration ammonia environment, each kilogram of body weight of meat goats inhales 0.5 mL of ammonia per minute, and in a low-concentration situation, it is 0.1 mL. Finally, the ammonia inhalation volume calculation data under different body shape characteristics is obtained through calculation, with the unit of mL / min / kg, and the obtained results are usually in the range of 0.1 mL / min / kg to 0.8 mL / min / kg, and the specific data depends on the body shape of the goat and the change of gas concentration. When simulating and estimating the ammonia respiration dissolution rate for the ammonia inhalation volume calculation data based on the body shape characteristic data of meat goats, a hydrodynamic model is used to model the dissolution process of ammonia. During the simulation process, the Navier-Stokes equation is used to accurately model the air flow, and factors such as air flow velocity, ammonia concentration, goat body shape, and respiration frequency are considered. Based on this model, the ammonia dissolution rate in the respiratory tract of meat goats is calculated. The simulation results show that under normal conditions, the ammonia dissolution rate of each kilogram of body weight of meat goats is about 0.25 mL / (kg·min). In a high-altitude state, considering the thin oxygen, the dissolution rate increases by 30% to 0.325 mL / (kg·min), and this rate is used for subsequent stress probability assessment.When estimating the probability of respiratory stress caused by ammonia intake based on the estimated data of ammonia respiratory dissolution rate, first, a Log-Normal Distribution model is used to describe the ammonia intake process, and Bayesian Inference is combined to deduce the relationship between the amount of ammonia intake and the stress probability. In the specific calculation process, the prior distribution of Bayesian Inference is set as a uniform distribution. Based on historical experimental data, the posterior distribution is gradually adjusted to simulate the probability of ammonia intake under different environmental conditions, and the stress probability of meat goats at different ammonia concentrations is obtained. The results show that when the ammonia concentration is higher than 50 ppm, the stress probability of meat goats begins to rise sharply. Usually, when the ammonia concentration is 80 ppm, the stress probability of meat goats reaches 80%. This data will be used in the environmental control system to formulate corresponding ventilation strategies and adjustment measures.

[0110] Step S245 includes the following steps:

[0111] Evaluate the cumulative ammonia exposure among different body postures for the gas inhalation volume calculation data based on the body posture characteristic data of meat goats to obtain the cumulative ammonia exposure data;

[0112] Calculate the ammonia absorption rate based on the cumulative ammonia exposure data and the respiratory frequency demand acceleration data to obtain the ammonia absorption rate;

[0113] Perform a correction calculation for the ammonia solubility parameter based on the cumulative ammonia exposure data and the ammonia absorption rate to obtain the corrected data for ammonia respiratory dissolution;

[0114] Simulate and estimate the ammonia respiratory dissolution rate based on the corrected data for ammonia respiratory dissolution to generate the estimated data for ammonia respiratory dissolution rate.

[0115] In the embodiments of the present invention, when evaluating the cumulative ammonia inhalation exposure amount among different body postures for the gas inhalation calculation data, first, a gas inhalation model is established based on the body posture data of meat goats. The specific data includes body weight (kg), shoulder height (cm), body length (cm), etc. Assuming standard conditions, there is a linear relationship between body weight and the gas inhalation amount per minute. For example, the ammonia inhalation amount per kilogram of body weight of a meat goat in a high-altitude environment can be set to 0.12 mL / kg·min, while in a low-altitude environment, it is 0.1 mL / kg·min. The breathing frequency of meat goats varies with the environment. Assuming that the breathing frequency increases by 30% in a high-altitude environment, that is, the breathing frequency of the meat goat per minute is 40 to 55 times (compared to 30 times in the normal state). According to these parameters, first calculate the gas inhalation amount of each meat goat per unit time, and calculate the ammonia exposure amount through the relationship with the gas concentration. Assuming that the exposure amount changes linearly with the increase in body weight per kilogram, for example, the gas inhalation amount per hour of a 30-kg sheep in a high-altitude environment is 30×0.12×55 = 198 mL. If the concentration of the inhaled gas per unit time is between 100 ppm (one in a million) and 500 ppm, then the gas exposure amount is between 10 mL and 200 mL. After evaluating the ammonia exposure amount data through this process, finally, the ammonia exposure amount data per unit time is obtained. On this basis, when calculating the ammonia absorption rate based on the ammonia cumulative exposure data and the breathing frequency demand acceleration data, first, according to the relationship between the inhaled gas amount per unit body weight and its absorption capacity, combined with the breathing frequency demand acceleration data to adjust the absorption rate. Under normal circumstances, assuming that the ammonia absorption rate of a meat goat per kilogram of body weight per unit time is 0.04 mL / (kg·min), while in a high-altitude environment, the ammonia absorption rate increases due to the increase in breathing frequency. Assuming that the absorption rate increases by 30%, that is, 0.04×1.3 = 0.052 mL / (kg·min). On this basis, use the relationship between gas concentration and inhalation amount to calculate the absorption amount per unit time. In the experiment, the gas concentration in different high-altitude environments is set to be between 50 ppm and 300 ppm. Assuming that the gas concentration fluctuates with time, it can be calibrated according to the gas inhalation amount and absorption rate model to calculate the change trend of the absorption rate under different gas concentrations. For example, if the gas concentration is 200 ppm, then the absorption amount per unit time can be jointly calculated by 0.052 mL / (kg·min) and the concentration data to obtain the specific absorption rate data. Finally, the absorption rate data obtained through this model will be between 0.05 and 0.1 mL / (kg·min), and the absorption rate increases with the acceleration of the breathing frequency.When calculating the ammonia solubility parameter correction based on ammonia cumulative exposure data and ammonia absorption rate, a gas solubility constant correction model is adopted, which is based on Henry's Law. This law states the relationship between the solubility of a gas in a solution, the pressure of the gas, and the solubility constant. Assuming the standard solubility constant of ammonia is 0.9 mol / L·atm, in a high-altitude environment, due to the decrease in air pressure, the solubility constant will decrease by 20%, that is, the solubility constant is 0.72 mol / L·atm. By combining the gas concentration and environmental conditions, the correction of the gas inhalation volume and the solubility constant is used to calculate the final ammonia solubility data. In this calculation, the solubility of the gas can be calculated by multiplying the unit gas inhalation volume by the solubility constant. Assuming the solubility constant of the gas is corrected to 0.72 mol / L·atm, the dissolved amount of ammonia per unit will decrease compared to the normal situation. During the calculation process, the final ammonia dissolution correction data is obtained by using the gas concentration change and the data after ammonia solubility correction, with a range of 0.05 to 0.8 mol / L·atm. When simulating and estimating the ammonia respiratory dissolution rate based on the ammonia respiratory dissolution correction data, a hydrodynamic model is used to simulate the gas dissolution process. First, it is assumed that within a unit time, the ammonia respiratory dissolution rate of meat goats is 0.1 mL / (min·kg), and the change in the dissolution rate is calculated by adjusting this rate. For example, when the gas concentration is 200 ppm and the temperature is 25 °C, the model calculates that the dissolution rate of meat goats is 0.12 mL / (min·kg). When the temperature rises to 30 °C, the dissolution rate increases by 15%, that is, 0.12×1.15 = 0.138 mL / (min·kg). In addition, changes in air humidity and air pressure also affect the dissolution rate. Assuming the humidity is 80%, the dissolution rate will increase by another 10%, resulting in a dissolution rate of 0.138×1.1 = 0.152 mL / (min·kg). Through this process, the ammonia respiratory dissolution rate can be simulated, and finally, the ammonia respiratory dissolution rate estimation data is generated.

[0116] Step S3 includes the following steps:

[0117] Step S31: Normalize the probability of ammonia intake respiratory stress to obtain the normalized data of the respiratory stress probability;

[0118] Step S32: Map the ammonia safety content interval between different respiratory frequencies for the ammonia safety content interval acceleration data according to the normalized data of the respiratory stress probability to obtain the ammonia safety content interval;

[0119] Step S33: Match the humidity content in the sheep house based on the ammonia safety content interval to obtain the humidity content matching data;

[0120] Step S34: Perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion and accumulation data according to the humidity content matching data to obtain ventilation parameter balance design data;

[0121] Step S35: Perform oxygen content compensation processing in the sheep house based on the humidity content matching data and the ventilation parameter balance design data to obtain oxygen content compensation data.

[0122] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0123] Step S31: Normalize the probability of ammonia intake respiratory stress to obtain normalized respiratory stress probability data;

[0124] In the embodiment of the present invention, when normalizing the probability of ammonia intake respiratory stress, first, all historical data needs to be extracted from the existing dataset of the probability of ammonia intake respiratory stress. Set a normalization standard and perform Min-Max Normalization on all data points. The calculation method is to set an ammonia intake probability range [0,1], map the minimum value of the historical data to 0, the maximum value to 1, and scale other values proportionally. Assume that the range of the probability of ammonia intake respiratory stress in the historical data is from 0.02 to 0.85, then the new data range after normalization is mapped to [0,1]. This processing method can make the change trend of the data more obvious and is beneficial for subsequent analysis.

[0125] Step S32: Map the respiratory frequency demand acceleration data between different respiratory frequencies according to the normalized respiratory stress probability data to obtain the ammonia safety content interval;

[0126] In the embodiments of the present invention, according to the normalized respiratory stress probability data, the ammonia safety content intervals are mapped for different respiratory frequency demand acceleration data. First, it is necessary to calculate the air intake per unit time according to different respiratory frequencies. Assuming that the basic respiratory frequency of meat goats is 30 times per minute, the maximum accelerated respiratory frequency is 90 times per minute, and the air intake per inhalation is 5 liters, then the air intake in the basic state is 150 liters per minute, and the air intake in the maximum respiratory acceleration state is 450 liters per minute. On this basis, the gas partial pressure law is used to calculate the ammonia safety content interval. Assuming that the highest ammonia concentration acceptable to meat goats is 20 ppm (parts per million), then at different respiratory frequencies, the ammonia intake is calculated, and the safety content interval is delimited accordingly. The safe ammonia concentration corresponding to the low respiratory frequency (30 times per minute) can be set to 15-20 ppm, the safe interval corresponding to the medium respiratory frequency (60 times per minute) is 10-15 ppm, and the safe interval corresponding to the high respiratory frequency (90 times per minute) needs to be reduced to 5-10 ppm.

[0127] Step S33: Based on the ammonia safety content interval, perform humidity content matching in the sheep house to obtain humidity content matching data;

[0128] In the embodiments of the present invention, the humidity content in the sheep house is matched based on the safe ammonia content range. First, high-precision temperature and humidity sensors (humidity sensors) are arranged at different positions in the sheep house to collect air humidity data in real time. The sensor spacing is set to 2 meters, and they are arranged at three different heights of 0.5 meters, 1.5 meters, and 2.5 meters from the ground to obtain humidity gradient data in the vertical direction. At the same time, a laser scattering dust sensor (laser scattering dust sensor) is used to measure the concentration of suspended particulate matter in the air, and the proportion of water-attached particles in the air is calculated to estimate the influence of humidity on gas diffusion. The sensor sampling frequency is set to once every 10 seconds, and the data is uploaded to the central control unit through a wireless communication module. After obtaining the real-time humidity data, according to the internal air flow model of the sheep house, the finite element mesh division method is used to divide the sheep house space into small units of 0.5 cubic meters, and the humidity values in each unit are interpolated. The 3D Kriging interpolation method is used to establish a humidity spatial distribution model to determine the humidity distribution state in each area of the sheep house. At the same time, combined with the ammonia diffusion simulation data, the influence of humidity on the ammonia dissolution rate is calculated. According to the solubility curve of ammonia in water, the ammonia concentration decay rate under different humidity conditions is determined. Assuming the initial ammonia concentration is 15 ppm, when the relative humidity is 50%, the ammonia concentration decay rate is 0.1 ppm / min, and when the relative humidity is 80%, the decay rate can be increased to 0.3 ppm / min. Therefore, by adjusting the humidity level, the ammonia concentration in the sheep house can be effectively controlled. According to the calculation results of the humidity spatial distribution model, the humidity adjustment device is controlled for fine adjustment. An ultrasonic humidifier (ultrasonic humidifier) is used for humidification, which is started when the humidity is 5% lower than the target value and stops running when the humidity reaches the target value. The fog output of the humidifier is set at 3 L / h to prevent the internal environment of the sheep house from being too humid due to excessive humidity. At the same time, a forced ventilation system (forced ventilation system) is used to diffuse the humidity so that the humidity reaches the target level within 5 minutes. If the humidity is more than 5% higher than the target value, a dehumidifier (dehumidifier) is enabled, and the condensation dehumidification method is adopted. The water vapor is condensed and discharged by lowering the air temperature. The dehumidification capacity per hour is set at 2 L / h and dynamically adjusted according to the ventilation volume of the sheep house to ensure the accuracy of humidity control.

[0129] Step S34: Perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion and accumulation data according to the humidity content matching data to obtain ventilation parameter balance design data;

[0130] In the embodiments of the present invention, according to the humidity matching data, ventilation parameter balance processing is performed on the carbon dioxide / ammonia diffusion and accumulation data. First, it is necessary to obtain the internal air flow data of the sheep house, including air velocity, air flow distribution, ventilation rate, etc. Assuming that the basic ventilation rate inside the sheep house is 6 times per hour, in the case of high ammonia accumulation, the ventilation rate can be increased to 12 times per hour. The ventilation wind speed inside the sheep house is calculated using a fluid dynamics model, and the dynamic pressure change of air flow is calculated according to Bernoulli's equation. The wind speed range is set at 0.3 - 1.2 m / s to ensure uniform air flow inside the sheep house. At the same time, it is necessary to evaluate the impact of different air flow rates on ammonia diffusion. If the ammonia concentration is high and accumulates at the top of the sheep house, the heights of the air inlet and outlet can be appropriately adjusted so that the outlet is set in the ammonia accumulation area to improve the discharge efficiency.

[0131] Step S35: Based on the humidity content matching data and the ventilation parameter balance design data, oxygen content compensation processing is performed inside the sheep house to obtain oxygen content compensation data.

[0132] In the embodiments of the present invention, based on the humidity content matching data and the ventilation parameter balance design data, oxygen content compensation processing is performed on the oxygen content inside the sheep house. First, it is necessary to monitor the oxygen concentration inside the sheep house. Assuming that the normal oxygen content is 20.9%, in high-altitude areas, due to the thin air, the oxygen content is reduced to 18.5% - 19.5%. At this time, oxygen content compensation is required. The compensation method can adopt gas mixing calculation. According to the inverse ratio relationship between the oxygen and carbon dioxide concentrations, the carbon dioxide concentration inside the sheep house is controlled not to exceed 0.08% to ensure sufficient oxygen supply. The method for calculating the oxygen compensation amount is to use the product relationship between the oxygen concentration and the air velocity. Assuming that the current oxygen content is 19.0% and the target oxygen content is 20.5%, then it is necessary to compensate for 1.5% of the oxygen concentration by increasing the ventilation rate or oxygen supplementation device. At the same time, monitor the impact of the oxygen compensation on the respiratory rate of the meat goats to ensure that the oxygen concentration adjustment will not cause respiratory acceleration and thus affect the health status of the meat goats.

[0133] Step S34 includes the following steps:

[0134] Step S341: According to the humidity content matching data, ventilation humidity loss compensation matching is performed to obtain ventilation humidity loss compensation data;

[0135] Step S342: Perform accumulation density difference analysis on the carbon dioxide / ammonia diffusion and accumulation data to obtain carbon dioxide / ammonia accumulation density difference data;

[0136] Step S343: Based on the carbon dioxide / ammonia accumulation density difference data, sheep house ventilation wind pressure matching is performed to obtain sheep house ventilation wind pressure matching data;

[0137] Step S344: Design ventilation structure parameters based on the carbon dioxide / ammonia accumulation density difference data and the sheep house ventilation wind pressure matching data to obtain the ventilation structure parameters;

[0138] Step S345: Perform ventilation parameter balance processing according to the ventilation humidity loss compensation data and the ventilation structure parameters to obtain the ventilation parameter balance design data.

[0139] In the embodiments of the present invention, for ventilation humidity loss compensation matching based on humidity content matching data, first, the differential method is used to calculate the loss amount of humidity carried away by the air flow during ventilation. Under the conditions of a ventilation wind speed of 2 m / s and a relative humidity of 60%, the loss rate of air water vapor content is set to 0.02 g / m³ / s. When the wind speed increases to 4 m / s, the loss rate increases to 0.05 g / m³ / s. To ensure the stability of humidity after ventilation, a spray humidification system is used for compensation. The humidification nozzles are arranged in the inlet area of the sheep house, the nozzle flow rate is set to 5 L / h, and real-time adjustment is carried out through a humidity feedback control system. The PID (Proportional-Integral-Derivative) control algorithm is used to calculate the humidity loss compensation requirement to ensure that the humidity inside the sheep house is maintained within the target range. If the humidity loss caused by ventilation exceeds 5% or more, the humidification amount is automatically increased. If the humidity is too high, the spray flow rate is reduced or the dehumidification system is enabled. For the analysis of the accumulation density difference of carbon dioxide / ammonia diffusion accumulation data, a gas sensor array is used to monitor the concentrations of carbon dioxide and ammonia in different areas of the sheep house in real time. The spacing between the sensors is 1.5 meters, and the acquisition frequency is 1 Hz. At the same time, three-dimensional computational fluid dynamics (CFD) modeling is used to analyze the flow characteristics of the gas in the sheep house. Based on the gas molecular diffusion equation, the accumulation density difference of carbon dioxide and ammonia in different areas is calculated. The safety threshold of carbon dioxide in the sheep house is set to 2000 ppm, and the safety threshold of ammonia is set to 10 ppm. If the concentration in a certain area is more than 20% higher than the average value, it is determined as a high accumulation area, the gas density in this area is recorded, and the concentration gradient with the low accumulation area is calculated to determine the demand for air flow transportation. For the ventilation air pressure matching of the sheep house based on the carbon dioxide / ammonia accumulation density difference data, a pressure sensor is used to measure the air pressure difference inside and outside the sheep house. The measurement accuracy of the sensor is set to ±0.1 Pa, and the data update frequency is 10 Hz. When the air pressure difference is less than 2 Pa, the opening degrees of the inlet and outlet vents are adjusted to increase the wind speed to enhance the air flow transportation ability. If the air pressure difference exceeds 5 Pa, the opening degree of the outlet vent is reduced to prevent temperature and humidity fluctuations caused by excessive ventilation. A variable frequency fan is used to control the air pressure, the adjustable range of the fan speed is set to 500 - 1500 rpm, and the fan power is dynamically adjusted according to the real-time air pressure data.Design the ventilation structure parameters based on the carbon dioxide / ammonia accumulation density difference data and the ventilation wind pressure matching data of the sheep house. First, calculate the air velocity requirements in different areas of the sheep house according to the gas diffusion simulation data. Add local ventilation openings in the high-accumulation areas and optimize the layout of the ventilation ducts to make the air flow evenly distributed. The size of the ventilation openings is set according to the fluid mechanics calculation to ensure that the air velocity is between 1.5 - 3.5 m / s. At the same time, use a streamlined deflector to adjust the air flow direction to prevent the air flow from short-circuiting and affecting the ventilation efficiency. Add an adjustable ventilation window on the top of the sheep house, and control the opening range from 10% to 100% to ensure smooth air convection. Perform ventilation parameter balance processing according to the ventilation humidity loss compensation data and the ventilation structure parameters. Use a dynamic ventilation control system to integrate multi-dimensional data such as humidity, wind pressure, and gas concentration, and use a fuzzy control algorithm to jointly adjust the fan speed, the opening of the ventilation opening, and the operating state of the humidification device. The goal is to keep the humidity in the sheep house at 55 - 65%, the carbon dioxide concentration below 1800 ppm, and the ammonia concentration below 8 ppm. The system updates the data every 5 seconds and makes prediction adjustments based on historical data to ensure the accuracy of the ventilation parameter balance design data.

[0140] Step S344 includes the following steps:

[0141] Obtain the sheep house structure design data; extract the ventilation paths from the sheep house structure design data to obtain the sheep house ventilation path data;

[0142] Analyze the carbon dioxide / ammonia accumulation pressure difference on the sheep house ventilation path data based on the carbon dioxide / ammonia accumulation density difference data to obtain the carbon dioxide / ammonia accumulation pressure difference between the ventilation paths;

[0143] Perform wind speed and flow rate matching on the carbon dioxide / ammonia accumulation pressure difference according to the sheep house ventilation wind pressure matching data to obtain the wind speed and flow rate matching data;

[0144] Conduct accumulation repeated behavior learning on the carbon dioxide / ammonia accumulation pressure difference to obtain the accumulation pressure behavior learning data;

[0145] Perform intelligent matching of the air change rate on the accumulation pressure behavior learning data based on the sheep house ventilation wind pressure matching data and the wind speed and flow rate matching data to obtain the intelligent air change rate matching data;

[0146] Design the ventilation structure parameters according to the wind speed-flow matching data, the ventilation wind pressure matching data of the sheep house and the intelligent matching data of the air change rate, and obtain the ventilation structure parameters.

[0147] In the embodiments of the present invention, to obtain the sheep house structure design data, first, a three-dimensional laser scanner (3D laser scanner) is used to scan the internal and external structures of the sheep house. The scanning accuracy is set to 1 mm to obtain complete geometric structure data. At the same time, combined with the architectural design drawings, key structure parameters such as walls, ceilings, windows, ventilation openings, and air ducts are extracted. Computer-aided design (CAD) software is used for data modeling to generate a complete three-dimensional sheep house structure model, which includes detailed information such as ventilation opening sizes, wall materials, and air duct layouts to ensure the accuracy of ventilation path calculation. For the ventilation path extraction of the sheep house structure design data, computational fluid dynamics (CFD) software is used to simulate the internal air flow of the sheep house. Initial boundary conditions are set, including an external wind speed of 3 m / s, an internal temperature of 25°C, and a relative humidity of 60%. Based on the air flow streamline analysis method, natural ventilation paths and forced ventilation paths are extracted. The natural ventilation paths are mainly calculated based on the shortest path of the air flow from the air inlet to the air outlet, and the forced ventilation paths are simulated and calculated based on the positions of the fans. At the same time, the velocity vector field is used to analyze the internal air flow situation of the sheep house, and the main air flow channels in different regions are extracted to generate sheep house ventilation path data. Based on the carbon dioxide / ammonia accumulation density difference data, the carbon dioxide / ammonia accumulation pressure difference analysis on the ventilation paths of the sheep house ventilation path data is carried out. First, carbon dioxide and ammonia sensors are arranged in different regions of the sheep house. The measurement accuracy of the sensors is set to 1 ppm, and the data acquisition frequency is 1 Hz. Through long-term data monitoring, the average concentrations of carbon dioxide / ammonia in each region are calculated, and the gas accumulation pressure differences on each ventilation path are calculated using the pressure gradient equation. If the carbon dioxide concentration on a certain path exceeds 2000 ppm and the ammonia concentration exceeds 10 ppm, then its pressure gradient is calculated and the reasons for gas accumulation are analyzed. At the same time, combined with the CFD simulation data, the air pressure differences between high and low concentration regions are compared, and the pressure distribution between the ventilation paths inside the sheep house is calculated to obtain the carbon dioxide / ammonia accumulation pressure difference data.Match the wind speed and flow rate of the carbon dioxide / ammonia accumulation pressure difference according to the ventilation wind pressure matching data of the sheep house. First, use an anemometer to measure the wind speed of each ventilation path in the sheep house. The wind speed measurement range is set to 0 - 10 m / s, and the accuracy is ±0.1 m / s. In areas where the wind speed is less than 2 m / s, calculate the required compensatory air volume. If the wind speed of a certain ventilation path is lower than 1 m / s, install deflectors on this path to change the air flow direction, and add axial fans at necessary positions. The fan flow rate is set to 500 - 1500 m³ / h, and the wind speed control range is 2 - 5 m / s. At the same time, use the real-time wind speed data to adjust the fan speed to ensure that the air flow meets the set target, and finally calculate the wind speed and flow rate matching data. Conduct accumulation repeated behavior learning on the carbon dioxide / ammonia accumulation pressure difference. Adopt the long short-term memory (LSTM) algorithm to analyze the gas accumulation trend in different areas of the sheep house. The input data includes the carbon dioxide / ammonia concentration change curve, wind speed and direction data, and ventilation opening and closing status in the past 30 days. Through deep learning model training, identify the high-incidence areas and time periods of gas accumulation. For example, when the temperature drops at night and ventilation weakens, the probability of carbon dioxide accumulation in the middle of the sheep house increases, then the model automatically marks this area as a key monitoring area and outputs the accumulation pressure behavior learning data. Conduct intelligent matching of the air change rate for the accumulation pressure behavior learning data based on the ventilation wind pressure matching data and wind speed and flow rate matching data of the sheep house. First, set the target air change rate inside the sheep house to 10 times / h. The air change rate is calculated based on the volume of the sheep house and the ventilation flow rate. Use an intelligent ventilation control system to dynamically adjust the operating status of the ventilation fans. When the air change rate is lower than 8 times / h, automatically increase the fan power and adjust the opening degrees of the air inlets and outlets to ensure that the air change rate is stable at the target value. At the same time, based on the accumulation pressure behavior learning data, predict the gas accumulation trend in advance. If the model predicts that the carbon dioxide concentration in a certain area will exceed 1800 ppm within the next 30 minutes, the system will increase the ventilation volume in advance to ensure that the air quality meets the standards, and finally output the intelligent matching data of the air change rate.Design the ventilation structure parameters according to the wind speed - flow matching data, the ventilation wind pressure - sheep house matching data, and the intelligent matching data of the air change rate, optimize the sizes of the air inlets and outlets, and ensure uniform wind speed distribution. For example, install adjustable louvers on both sides of the sheep house, with the opening range set at 10 - 100%, control the air inlet angle, and prevent the short - circuit air flow from affecting the ventilation effect. At the same time, install a mechanical exhaust system on the top of the sheep house, with the fan speed control range of 500 - 1200 rpm, and dynamically adjust according to the real - time air quality data to ensure the stability of the sheep house ventilation system, and finally generate the ventilation structure parameter data.

[0148] Preferably, the present invention also provides an automatic control system for the environment of a meat goat house, which is used to execute the automatic control method for the environment of a meat goat house as described above. The automatic control system for the environment of a meat goat house includes:

[0149] An environmental data acquisition module, which is used to collect all - weather data in the meat goat house of the high - altitude environment where the meat goats are located through sensors, and obtain all - weather environmental data in the high - altitude goat house; extract the all - weather time - series fluctuations of temperature, humidity, carbon dioxide / ammonia in the all - weather environmental data in the high - altitude goat house, and respectively obtain the temperature time - series fluctuation data and the carbon dioxide / ammonia time - series fluctuation data in the goat house.

[0150] A stress probability estimation module, which is used to fit the carbon dioxide / ammonia diffusion increment according to the temperature time - series fluctuation data and the carbon dioxide / ammonia time - series fluctuation data, and obtain the carbon dioxide / ammonia diffusion and accumulation data; estimate the probability of ammonia - induced respiratory stress for the carbon dioxide / ammonia diffusion and accumulation data, and obtain the probability of ammonia - induced respiratory stress.

[0151] A safety content interval mapping module, which is used to map the ammonia safety content interval according to the probability of ammonia - induced respiratory stress, and obtain the ammonia safety content interval; perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion and accumulation data based on the ammonia safety content interval, and obtain the ventilation parameter balance design data; perform oxygen content compensation processing in the goat house based on the ventilation parameter balance design data, and obtain the oxygen content compensation data.

[0152] An automatic control strategy formulation module, which is used to formulate an automatic control strategy for the environment of the goat house based on the ventilation parameter balance design data and the oxygen content compensation data, obtain the automatic control strategy for the environment of the goat house, and send the automatic control strategy for the environment of the goat house to the terminal to execute the automatic control method for the environment of the meat goat house.

[0153] The above are only specific embodiments of the present invention, enabling 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, and 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 rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. An automatic control method for the environment of a meat goat house, characterized in that, It includes the following steps: Step S1: Use sensors to collect all-weather data of the high-altitude environment where the meat goats are located in the meat goat shed to obtain all-weather environmental data in the high-altitude goat shed; extract the all-weather time series fluctuations of temperature and carbon dioxide / ammonia in the all-weather environmental data in the high-altitude goat shed to obtain the temperature time series fluctuation data in the goat shed and the carbon dioxide / ammonia time series fluctuation data in the goat shed respectively; Step S2: Fit the carbon dioxide / ammonia diffusion increment according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain the carbon dioxide / ammonia diffusion accumulation data; Estimate the probability of ammonia intake respiratory stress for the carbon dioxide / ammonia diffusion accumulation data to obtain the probability of ammonia intake respiratory stress; among them, Step S2 includes: Step S21: Analyze the low-oxygen environment concentration fluctuation of the all-weather environmental data in the high-altitude goat shed to obtain the low-oxygen environment concentration fluctuation data; Step S22: Evaluate the acceleration of the meat goat's respiratory frequency demand based on the low-oxygen environment concentration fluctuation data to obtain the respiratory frequency demand acceleration data; Step S23: Fit the carbon dioxide / ammonia diffusion accumulation increment according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain the carbon dioxide / ammonia diffusion accumulation data; Step S24: Estimate the probability of ammonia intake respiratory stress for the carbon dioxide / ammonia diffusion accumulation data according to the respiratory frequency demand acceleration data to obtain the probability of ammonia intake respiratory stress; among them, Step S24 includes: Step S241: Numerically integrate the ammonia accumulation pressure for the carbon dioxide / ammonia diffusion accumulation data to obtain the ammonia accumulation pressure data; Step S242: Perform a robust regression analysis of the aggregation distribution on the carbon dioxide / ammonia diffusion accumulation data according to the ammonia accumulation pressure data to obtain the ammonia aggregation distribution pressure regression data; Step S243: Perform a logarithmic conversion of the pressure on the ammonia aggregation distribution pressure regression data to obtain the ammonia accumulation pressure logarithmic conversion data; Step S244: Obtain the body shape characteristic data of the meat goats; calculate the gas inhalation volume of the meat goats between different body shape characteristics per unit time for the ammonia accumulation pressure logarithmic conversion data according to the respiratory frequency demand acceleration data and the body shape characteristic data of the meat goats to obtain the gas inhalation volume calculation data per unit time; Step S245: Simulate and estimate the ammonia respiration dissolution rate for the gas inhalation volume calculation data based on the body shape characteristic data of the meat goats to generate the ammonia respiration dissolution rate estimation data; Step S246: Estimate the probability of ammonia intake respiratory stress according to the ammonia respiration dissolution rate estimation data to obtain the probability of ammonia intake respiratory stress; Step S3: Map the ammonia safety content interval according to the probability of ammonia intake respiratory stress to obtain the ammonia safety content interval; perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia safety content interval to obtain the ventilation parameter balance design data; perform oxygen content compensation processing in the goat shed based on the ventilation parameter balance design data to obtain the oxygen content compensation data; Step S4: Formulate an automatic control strategy for the sheep house environment based on the ventilation parameter balance design data and the oxygen content compensation data, obtain the automatic control strategy for the sheep house environment, and send the automatic control strategy for the sheep house environment to the terminal to execute the automatic control method for the meat goat house environment.

2. The automatic control method for the environment of meat goat houses according to claim 1, wherein Step S1 includes the following steps: Step S11: Collect all-weather data inside the meat goat house in the high-altitude environment where the meat goats are located through sensors to obtain all-weather environmental data inside the high-altitude sheep house; Step S12: Fill in the missing values in the all-weather environmental data inside the high-altitude sheep house to obtain the filled environmental data inside the high-altitude sheep house; Step S13: Extract the all-weather time series fluctuations of temperature and carbon dioxide / ammonia in the filled environmental data inside the high-altitude sheep house to obtain the temperature time series fluctuation data inside the sheep house and the carbon dioxide / ammonia time series fluctuation data inside the sheep house respectively.

3. The automatic control method for the environment of meat goat houses according to claim 1, wherein Step S23 includes the following steps: Step S231: Analyze the rising period of carbon dioxide content in the carbon dioxide / ammonia time series fluctuation data, and then analyze the enhanced greenhouse effect period based on the temperature time series fluctuation data to obtain the enhanced greenhouse effect period; Step S232: Identify the heat stress enhancement gradient for the enhanced greenhouse effect period to obtain the heat stress enhancement gradient data; Step S233: Analyze the vertical direction of heat convection accumulation of the carbon dioxide / ammonia time series fluctuation data based on the heat stress enhancement gradient data to obtain the vertical direction data of carbon dioxide / ammonia accumulation; Step S234: Fit the carbon dioxide / ammonia diffusion and accumulation increment based on the heat stress enhancement gradient data and the vertical direction data of carbon dioxide / ammonia accumulation to obtain the carbon dioxide / ammonia diffusion and accumulation data.

4. The automatic control method for the environment of meat goat houses according to claim 1, wherein Step S245 includes the following steps: Evaluate the ammonia respiratory cumulative exposure amount among different body postures for the gas inhalation calculation data according to the meat goat body posture characteristic data to obtain the ammonia cumulative exposure data; Calculate the ammonia absorption rate based on the ammonia cumulative exposure data and the respiratory frequency demand acceleration data to obtain the ammonia absorption rate; Perform ammonia solubility parameter correction calculation according to the ammonia cumulative exposure data and the ammonia absorption rate to obtain the ammonia respiratory dissolution correction data; Simulate and estimate the ammonia respiratory dissolution rate according to the ammonia respiratory dissolution correction data to generate the ammonia respiratory dissolution rate estimation data.

5. The automatic control method for the environment of meat goat houses according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Normalize the probability of ammonia intake respiratory stress to obtain the normalized data of the respiratory stress probability; Step S32: Map the ammonia safety content interval among different respiratory frequencies for the respiratory frequency demand acceleration data according to the normalized data of the respiratory stress probability to obtain the ammonia safety content interval; Step S33: Match the humidity content inside the sheep house based on the ammonia safety content interval to obtain the humidity content matching data; Step S34: Balance the ventilation parameters for the carbon dioxide / ammonia diffusion and accumulation data according to the humidity content matching data to obtain the ventilation parameter balance design data; Step S35: Perform oxygen content compensation processing inside the sheep house based on the humidity content matching data and the ventilation parameter balance design data to obtain the oxygen content compensation data.

6. The automatic control method for the environment of meat goat houses according to claim 5, wherein, Step S34 includes the following steps: Step S341: Perform ventilation humidity loss compensation matching according to the humidity content matching data to obtain ventilation humidity loss compensation data; Step S342: Analyze the accumulation density difference of carbon dioxide / ammonia diffusion accumulation data to obtain carbon dioxide / ammonia accumulation density difference data; Step S343: Perform sheep house ventilation air pressure matching based on the carbon dioxide / ammonia accumulation density difference data to obtain sheep house ventilation air pressure matching data; Step S344: Design ventilation structure parameters based on the carbon dioxide / ammonia accumulation density difference data and the sheep house ventilation air pressure matching data to obtain ventilation structure parameters; Step S345: Perform ventilation parameter balance processing according to the ventilation humidity loss compensation data and the ventilation structure parameters to obtain ventilation parameter balance design data.

7. The automatic control method for the environment of a meat goat house according to claim 6, wherein Step S344 includes the following steps: Obtain the sheep house structure design data; extract the ventilation path from the sheep house structure design data to obtain the sheep house ventilation path data; Analyze the carbon dioxide / ammonia accumulation pressure difference on the path of the sheep house ventilation path data based on the carbon dioxide / ammonia accumulation density difference data to obtain the carbon dioxide / ammonia accumulation pressure difference between ventilation paths; Perform wind speed and flow rate matching on the carbon dioxide / ammonia accumulation pressure difference according to the sheep house ventilation air pressure matching data to obtain wind speed and flow rate matching data; Perform accumulation repeated behavior learning on the carbon dioxide / ammonia accumulation pressure difference to obtain accumulation pressure behavior learning data; Perform intelligent matching of the ventilation rate on the accumulation pressure behavior learning data based on the sheep house ventilation air pressure matching data and the wind speed and flow rate matching data to obtain intelligent ventilation rate matching data; Design ventilation structure parameters according to the wind speed and flow rate matching data, the sheep house ventilation air pressure matching data, and the intelligent ventilation rate matching data to obtain ventilation structure parameters.

8. An automatic control system for the environment of a meat goat house, characterized in that, For implementing the automatic control method for the environment of a meat goat house as described in claim 1, the automatic control system for the environment of a meat goat house includes: An environmental data acquisition module, which is used to collect all-weather data in the meat goat house of the high-altitude environment where the meat goats are located through sensors to obtain all-weather environmental data in the high-altitude goat house; extract the temperature and humidity and carbon dioxide / ammonia all-weather time series fluctuations from the all-weather environmental data in the high-altitude goat house to obtain the temperature time series fluctuation data in the goat house and the carbon dioxide / ammonia time series fluctuation data in the goat house respectively; A stress probability estimation module, which is used to perform carbon dioxide / ammonia diffusion increment fitting according to the temperature time series fluctuation data and the carbon dioxide / ammonia time series fluctuation data to obtain carbon dioxide / ammonia diffusion accumulation data; estimate the probability of ammonia intake respiratory stress on the carbon dioxide / ammonia diffusion accumulation data to obtain the probability of ammonia intake respiratory stress; A safety content interval mapping module, which is used to perform ammonia safety content interval mapping according to the probability of ammonia intake respiratory stress to obtain the ammonia safety content interval; perform ventilation parameter balance processing on the carbon dioxide / ammonia diffusion accumulation data based on the ammonia safety content interval to obtain ventilation parameter balance design data; perform oxygen content compensation processing in the sheep house based on the ventilation parameter balance design data to obtain oxygen content compensation data; An automatic control strategy formulation module, which is used to formulate an automatic control strategy for the sheep house environment based on the ventilation parameter balance design data and the oxygen content compensation data, obtain the automatic control strategy for the sheep house environment, and send the automatic control strategy for the sheep house environment to the terminal to execute the automatic control method for the meat goat house environment.

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