Breeding shed airflow exchange method based on wireless sensor

Abstract

The invention relates to the technical field of data processing, in particular to a breeding shed airflow exchange method based on a wireless sensor, which comprises the following steps: acquiring temperature data and humidity data of different positions of a breeding shed; obtaining the rationality corresponding to the temperature data and the humidity data of the target position; in response to the fact that the reasonable degree is smaller than a first threshold value, the adjustment degree is obtained through the reasonable degree, the first rotating speed of a ventilation device in the breeding shed is adjusted through the adjustment degree, and the adjusted second rotating speed is obtained; the change degree of the opening and closing degree of the small ventilation window at the target position at different moments is obtained, the opening and closing degree of the small ventilation window is adjusted according to the change degree, and the adjusted opening and closing degree is obtained; and exchanging the airflow of the breeding shed by using the second rotating speed and the adjusted opening degree. The ventilation intensity of the whole and local positions of the breeding shed is adjusted according to the temperature and humidity change conditions of different positions in the breeding shed, and the accuracy of setting the ventilation intensity of the breeding shed is improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and specifically to a method for airflow exchange in livestock sheds based on wireless sensors. Background Technology

[0002] Ventilation is the process of using thermal pressure, wind pressure, or air pressure difference between the inside and outside of livestock and poultry houses to expel stale air from inside and introduce sufficient fresh air. This facilitates air exchange, introducing oxygen necessary for animal survival, and expelling or diluting moisture, dust, odors, and harmful microorganisms from the house. It also generates airflow that helps dissipate heat from the animals. Common ventilation modes are divided into natural ventilation and mechanical ventilation. Based on the driving principle of mechanical ventilation systems, they can be further classified into three main categories: negative pressure ventilation systems, positive pressure ventilation systems, and isobaric ventilation systems.

[0003] This application primarily analyzes negative pressure ventilation systems. Axial flow fans are commonly used in negative pressure ventilation systems for livestock sheds. Due to their design characteristics, axial flow fans are particularly suitable for places requiring large-area air circulation, such as greenhouses and livestock sheds. These fans effectively promote airflow, especially in enclosed sheds where humidity is high and air circulation is difficult. Axial flow fans, through directional arrangement, provide relay ventilation, allowing for more thorough circulation of the mixed, hot and humid air within the shed, thus achieving a cooling effect.

[0004] Traditional methods for controlling the opening and closing of ventilation systems in livestock sheds, as well as adjusting the opening level, primarily rely on temperature and humidity sensors to measure temperature and humidity and adjust ventilation based on pre-set thresholds. However, inappropriate ventilation settings can lead to insufficient heat dissipation or excessive humidity within the sheds, harming the health of the animals.

[0005] To address this shortcoming, the present invention adjusts the ventilation intensity of the entire breeding shed and specific locations by measuring the temperature and humidity changes at different locations within the shed. Summary of the Invention

[0006] To address the existing technical problems, the present invention aims to provide a method for airflow exchange in livestock sheds based on wireless sensors. This method adjusts the ventilation intensity of the entire shed and specific locations by monitoring temperature and humidity changes at different locations within the shed. This improves the accuracy of setting the ventilation intensity, resulting in better heat dissipation and dehumidification within the shed, and reducing the occurrence of animal diseases.

[0007] To solve the above-mentioned technical problems, the technical solution adopted in this application is as follows: A method for airflow exchange in a livestock shed based on wireless sensors is provided, comprising: acquiring temperature and humidity data at different locations in the livestock shed; acquiring the reasonableness corresponding to the temperature and humidity data at a target location, wherein the reasonableness characterizes the difference between the temperature and humidity data and the target temperature and target humidity; responding to the reasonableness being less than a first threshold, obtaining an adjustment degree using the reasonableness, and adjusting a first rotation speed of the ventilation device in the livestock shed using the adjustment degree, thereby obtaining an adjusted second rotation speed; acquiring the degree of change in the opening and closing degree of the ventilation window at the target location at different times, and adjusting the opening and closing degree of the ventilation window using the degree of change, thereby obtaining an adjusted opening and closing degree; and exchanging airflow in the livestock shed using the second rotation speed and the adjusted opening and closing degree.

[0008] In one embodiment of the present invention, the reasonableness of obtaining the temperature data and humidity data corresponding to the target location includes: obtaining the temperature data and humidity data of the target location, and obtaining a preset target temperature and target humidity of the target location; obtaining a first difference between the temperature data and the target temperature, and a second difference between the humidity data and the target humidity; and using the first difference and the second difference to determine the reasonableness of the temperature and humidity conditions of the target location.

[0009] In one embodiment of the present invention, determining the reasonableness of the temperature and humidity conditions at the target location using the first difference and the second difference includes: determining a first value at the target location at each moment using the first difference and the second difference; obtaining the minimum first value at different moments; and determining the reasonableness of the temperature and humidity conditions at the target location using the ratio of the minimum first value to the first value at the current moment.

[0010] In one embodiment of the present invention, the step of responding to the reasonableness being less than a first threshold, obtaining an adjustment degree using the reasonableness, and adjusting a first rotational speed of the ventilation device in the breeding shed using the adjustment degree, and obtaining an adjusted second rotational speed, includes: responding to the reasonableness being less than the first threshold, obtaining a first number of sensor locations in the breeding shed, obtaining a second number of locations unsuitable for breeding, and obtaining a total number of sensors in the breeding shed; determining the adjustment degree using the first number of locations, the second number of locations, and the total number; obtaining the current first rotational speed of the ventilation device in the breeding shed; and determining the adjusted second rotational speed using the first rotational speed and the adjustment degree.

[0011] In one embodiment of the present invention, determining the degree of adjustment using the first number of positions, the second number of positions, and the total number includes: determining a second value using the first number of positions and the reciprocal of the reasonableness; determining a third value using the ratio of the second number of positions to the total number; and determining the degree of adjustment using the second value and the third value. Determining the adjusted second rotational speed using the first rotational speed and the degree of adjustment includes: determining a fourth value using the degree of adjustment and a first preset constant; and determining the adjusted second rotational speed using the fourth value and the first rotational speed.

[0012] In one embodiment of the present invention, the step of obtaining the degree of change in the opening and closing degree of the ventilation window at the target location at different times, and adjusting the opening and closing degree of the ventilation window using the degree of change to obtain the adjusted opening and closing degree includes: obtaining at least two reasonablenesses of the target location at different times, and obtaining at least two opening and closing degrees of the ventilation window at the target location at different times; using the at least two reasonablenesses to determine the degree of change in the opening and closing degree of the ventilation window at the target location at different times; obtaining a third opening and closing degree of the ventilation window at an adjacent location of the target location; and using the third opening and closing degree and the degree of change to determine the adjusted opening and closing degree.

[0013] In one embodiment of the present invention, the at least two reasonablenesses include a first reasonableness of the target location at a first time and a second reasonableness at a second time; the at least two opening degrees include a first opening degree of the ventilation window at the target location at a first time and a second opening degree at a second time; determining the degree of change of the opening degree of the ventilation window at the target location at different times using the at least two reasonablenesses includes: obtaining the time span of the target time; and using the time span, the first reasonableness, and the second reasonableness to determine the degree of change corresponding to the first opening degree and the second opening degree.

[0014] In one embodiment of the present invention, the method further includes: determining a first curve using the reasonableness and time; determining the reasonableness difference between the first and last time points of each segment region in the first curve using the first reasonableness and the second reasonableness, and obtaining the maximum reasonableness difference in the first curve; and determining the degree of change using the difference between the first reasonableness and the second reasonableness, the maximum reasonableness difference, and the time span.

[0015] In one embodiment of the present invention, determining the adjusted opening degree using the third opening degree and the degree of change includes: determining a fifth value using the degree of change and a second preset constant; and determining the adjusted opening degree using the fifth value and the third opening degree.

[0016] In one embodiment of the present invention, the method further includes: using a fuzzy PID control system to exchange airflow in the breeding shed by utilizing the second rotational speed and the adjusted opening and closing degree.

[0017] The beneficial effects of this invention are as follows: It provides a method for airflow exchange in livestock sheds based on wireless sensors, which adjusts the ventilation intensity of the entire shed and local areas by measuring the temperature and humidity changes at different locations within the shed. This improves the accuracy of setting the ventilation intensity of the shed, resulting in better heat dissipation and dehumidification effects inside the shed, and reducing the occurrence of animal diseases. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of the airflow exchange method for aquaculture sheds based on wireless sensors provided by the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a wireless sensor-based airflow exchange method for livestock sheds proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] In existing technologies, traditional ventilation systems for livestock sheds primarily rely on fixed thresholds to mechanically adjust temperature and humidity data, resulting in a mismatch between overall ventilation intensity and local environmental requirements. Especially in enclosed sheds, axial flow fans adjust their speed based solely on global averages, failing to address localized areas of abnormal temperature and humidity caused by structural obstructions. Existing methods often ignore the changing trends of environmental parameters over continuous time, leading to lag in the opening and closing of ventilation windows and hindering precise local airflow control.

[0023] To address the aforementioned issues, those skilled in the art have found that a single threshold control mode cannot adapt to the dynamically changing environmental demands of livestock sheds. Analysis of data from multiple sensor locations revealed significant differences in temperature and humidity deviations across different areas. This led to the development of a tiered control approach by quantifying environmental parameter deviations: first, establishing a temperature and humidity rationality model to assess the environmental quality of each area; then, adjusting the fan speed based on the overall deviation; and simultaneously, dynamically correcting local ventilation intensity using time-series analysis.

[0024] This application proposes a wireless sensor-based airflow exchange method for livestock sheds. Temperature and humidity sensors within the shed continuously monitor the temperature and humidity at different locations. By comparing the temperature and humidity conditions at multiple locations with those suitable for raising animals, the overall suitability of the shed is determined, and the overall ventilation intensity is adjusted by regulating the rotation speed of the axial flow fan. Simultaneously, the suitability of temperature and humidity at specific locations within the shed is analyzed based on temperature and humidity changes, and the ventilation intensity at those locations is adjusted by opening and closing small ventilation windows.

[0025] The specific scheme of the airflow exchange method for breeding sheds based on wireless sensors provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Please see Figure 1 The diagram illustrates a flowchart of the airflow exchange method for aquaculture sheds based on wireless sensors provided by the present invention.

[0027] like Figure 1 As shown, the airflow exchange method for livestock sheds based on wireless sensors includes the following steps: S10. Obtain temperature and humidity data at different locations in the breeding shed.

[0028] Among them, livestock sheds refer to sheds for raising livestock and poultry.

[0029] Specifically, multiple temperature and humidity sensors are evenly distributed at different locations inside the breeding shed. All temperature and humidity sensors can perform measurements simultaneously, with a unit time interval used as the measurement time, for example, 1 second, to obtain temperature and humidity data at different locations.

[0030] In some embodiments, temperature and humidity data at different times can also be acquired.

[0031] Understandably, this application primarily analyzes negative pressure ventilation systems. Axial flow fans are commonly used in negative pressure ventilation systems for livestock sheds. Due to their design characteristics, axial flow fans are particularly suitable for places requiring large-area air circulation, such as greenhouses and livestock sheds. These fans effectively promote airflow, especially in enclosed sheds where humidity density is high and air circulation is difficult. Axial flow fans can provide relay ventilation through directional arrangement, allowing for more thorough circulation of the mixed, humid, and hot air within the shed, thus achieving a cooling effect. This invention adjusts the ventilation intensity of the entire livestock shed and specific areas based on temperature and humidity changes at different locations within the shed.

[0032] S20. Obtain the reasonableness of the temperature and humidity data corresponding to the target location, wherein the reasonableness characterizes the difference between the temperature and humidity data and the target temperature and target humidity.

[0033] Among them, reasonableness refers to the degree of deviation between the current temperature and humidity and the target values ​​through mathematical modeling. This indicator can objectively reflect the degree of abnormality of environmental parameters, that is, it represents the difference between the temperature and humidity data and the target temperature and humidity.

[0034] Specifically, after acquiring the temperature and humidity data of the target location, the difference between the temperature and humidity data and the target temperature and humidity is calculated to determine the reasonableness.

[0035] It is understandable that the target location is the currently selected location, and any location can be a target location; the target temperature refers to the preset suitable growth temperature, and the target humidity refers to the preset suitable growth humidity, which can be set according to the actual situation.

[0036] S30. In response to the reasonableness being less than the first threshold, the degree of adjustment is obtained using the reasonableness, and the first rotation speed of the ventilation device in the breeding shed is adjusted using the degree of adjustment to obtain the adjusted second rotation speed.

[0037] The adjustment level refers to the control coefficient generated based on the number of abnormal areas and the overall deviation, used to determine the adjustment range of the fan speed. The first threshold is used to determine a reasonable range of temperature and humidity; for example, the first threshold is set to 0.5.

[0038] Specifically, when the rationality is less than the first threshold, it indicates that the temperature and humidity at the current location are reasonable and suitable for livestock breeding; conversely, when the rationality is less than the first threshold, it indicates that the temperature and humidity at the current location are unreasonable and therefore need to be adjusted; then the degree of adjustment is obtained by using the rationality, and the current first speed of the ventilation device at the current location in the breeding shed is adjusted by using the degree of adjustment, and the adjusted second speed is obtained.

[0039] S40. Obtain the degree of change in the opening and closing of the ventilation window at the target location at different times, and adjust the opening and closing of the ventilation window using the degree of change to obtain the adjusted opening and closing.

[0040] The degree of change refers to the trend parameter of the opening and closing status of the ventilation window over time, reflecting the dynamic changes in the ventilation demand of a local area. The degree of opening refers to the extent to which the ventilation window is open.

[0041] Specifically, the opening degree of the ventilation window at the target location is obtained at each moment, and then the degree of change of the opening degree at different moments is determined; then the degree of change is used to adjust the opening degree of the ventilation window to obtain a reasonable adjusted opening degree.

[0042] S50: Using the second rotation speed and the adjusted opening and closing degree, the airflow in the breeding shed is exchanged.

[0043] Specifically, after obtaining the second rotation speed and the adjusted opening and closing degree, the ventilation device and ventilation window are operated using the second rotation speed and the adjusted opening and closing degree respectively, thereby exchanging the airflow in the breeding shed.

[0044] In this embodiment, temperature and humidity data from multiple locations are collected in real time via a wireless sensor network to establish a monitoring dataset containing spatial distribution characteristics. Then, the deviation of the temperature and humidity at each monitoring point from the preset target is calculated. When the overall reasonableness is below a threshold, a fan speed adjustment coefficient is generated based on the proportion of abnormal areas and the degree of deviation. Simultaneously, historical opening and closing data of each ventilation window are analyzed, and time-series modeling is used to identify local areas requiring enhanced ventilation. Finally, the speed adjustment command and opening / closing optimization parameters are synchronously sent to the control system, forming a spatially hierarchical and time-linked precise ventilation strategy.

[0045] Compared to existing technologies, traditional methods adjust fan speed based solely on data from a single monitoring point. This solution achieves three breakthroughs through multi-dimensional data analysis: first, it establishes a spatial distribution model to identify key control areas; second, it introduces temporal variation analysis to predict ventilation demand trends; and third, it constructs a tiered control mechanism to coordinate overall and local ventilation intensity. Existing technologies typically use fixed opening and closing modes for ventilation windows, while this solution can dynamically adjust according to changes in the local environment, significantly improving airflow exchange efficiency.

[0046] Through the above technical solutions, this application effectively solves the problems of slow response and low control accuracy in traditional ventilation systems. An abnormal area is accurately identified through a rationality model, avoiding energy waste caused by overall over-ventilation. A dynamic adjustment mechanism matches local ventilation intensity with real-time demand, preventing moisture accumulation and temperature imbalance. A tiered control strategy balances overall environmental equilibrium with specific local needs, significantly improving the stability and controllability of the aquaculture environment.

[0047] In some embodiments, a negative pressure ventilation system is a commonly used ventilation mode for livestock and poultry houses. After the exhaust fan expels the air from the livestock and poultry house, the air pressure inside the house is lower than the atmospheric pressure outside the house. The negative pressure difference between the inside and outside of the house drives fresh air from outside into the livestock and poultry house through the air inlet, thereby achieving ventilation.

[0048] A negative pressure ventilation system mainly consists of the following parts: Air inlet and air outlet: These are two key components of a negative pressure fan, determining its ventilation efficiency and suction power. The air inlet is responsible for drawing in fresh outdoor air, while the air outlet exhausts stale indoor air. The design and location of these two components are crucial to the efficiency of the entire ventilation system.

[0049] Fan blades: Fan blades are the core component of negative pressure fans, and their shape and number directly determine the fan's exhaust volume and air pressure. The design and performance of the fan blades are crucial for ensuring effective airflow and the efficiency of the ventilation system.

[0050] Electric motor: The electric motor is the power source that drives the rotation of the fan blades, and it is usually a DC motor or an AC motor. The performance and efficiency of the motor directly affect the operational stability and lifespan of the fan.

[0051] Housing: The housing is the outer shell of the negative pressure fan, primarily used to protect internal components and reduce noise. The design and material selection of the housing are crucial for protecting the internal structure of the fan and providing a quiet operating environment.

[0052] In addition, negative pressure fans may also include other components such as inlet and outlet adjustment mechanisms, fan blade supports, and auxiliary equipment. The design and configuration of these components have a significant impact on the performance and efficiency of the entire ventilation system. The working principle of a negative pressure fan is mainly based on air convection and negative pressure ventilation. The internal impeller generates a negative pressure effect, reducing the internal pressure of the fan and creating a negative pressure zone, thereby achieving indoor air renewal and circulation.

[0053] In some embodiments, obtaining the reasonableness of temperature and humidity data at a target location includes the following operations.

[0054] Acquire temperature and humidity data for the target location, and obtain the preset target temperature and target humidity for the target location; obtain the first difference between the temperature data and the target temperature, and the second difference between the humidity data and the target humidity; use the first and second differences to determine the reasonableness of the temperature and humidity conditions at the target location.

[0055] Among them, reasonableness refers to the quantitative index of the difference between the temperature and humidity data at the target location and the preset target values. Specifically, it can be achieved by calculating the sum or weighted value of the absolute values ​​of the temperature and humidity differences, characterizing the degree to which the current temperature and humidity deviate from the suitable range. The first difference refers to the absolute value of the deviation between the real-time measured temperature and the target temperature, specifically achieved by subtracting the temperature sensor value from a preset threshold, reflecting the degree of inappropriateness in the temperature dimension. The second difference refers to the absolute value of the deviation between the real-time measured humidity and the target humidity, specifically achieved by subtracting the humidity sensor value from a preset threshold, reflecting the degree of inappropriateness in the humidity dimension.

[0056] Specifically, a wireless sensor network deployed within the breeding shed collects real-time temperature and humidity values ​​of the target area. These values ​​are then compared to the median values ​​of preset suitable breeding temperature ranges (e.g., 25℃) and humidity ranges (e.g., 60%RH). For example, if the measured temperature is 28℃ and the humidity is 70%RH at a certain moment, the first difference is 3℃, and the second difference is 10%RH. These two differences are then processed using a linear combination or nonlinear function to generate a comprehensive evaluation index. The larger the index value, the more severely the environment deviates from its optimal state. By establishing a difference calculation model, multidimensional environmental parameters are transformed into a single quantitative index, providing operable input parameters for subsequent ventilation intensity adjustments.

[0057] Through the above technical solution, this application can more accurately determine the degree of matching between the local temperature and humidity environment and the needs of aquaculture, providing a quantitative basis for the refined control of ventilation devices. By using a difference calculation model to transform complex environmental conditions into comparable numerical indicators, the ventilation system can implement graded responses according to the degree of environmental deviation. For example, a fine-tuning mode can be used when the difference is small, and a rapid adjustment mode can be activated when the difference is large, thereby improving the timeliness and accuracy of environmental control and avoiding the energy waste caused by lag or over-ventilation in traditional methods.

[0058] In some embodiments, the reasonableness of the temperature and humidity conditions at the target location is determined using a first difference and a second difference, including the following operations: Using the first and second differences, determine the first value at the target location at each moment; obtain the minimum first value at different moments; use the ratio of the minimum first value to the first value at the current moment to determine the reasonableness of the temperature and humidity conditions at the target location.

[0059] The first difference refers to the gap between the temperature data and the target temperature. This is achieved by subtracting a preset target value from real-time data collected by a temperature sensor, measuring the degree to which the current temperature deviates from the ideal state. The second difference refers to the gap between the humidity data and the target humidity. This is achieved by subtracting a preset target value from real-time data collected by a humidity sensor, measuring the degree to which the current humidity deviates from the ideal state. The first value is the comprehensive deviation calculated based on the first and second differences. This can be achieved using a weighted sum or the square root of the sum of squares, comprehensively representing the overall deviation between temperature and humidity. The minimum first value is the minimum of the first values ​​at all times in historical records. This can be achieved by storing historical data in a database and filtering for the minimum value, serving as a normalization benchmark. The ratio is the result of dividing the current first value by the minimum first value. This can be achieved by calculating the ratio between the current value and the historical minimum value in real time, converting the current deviation into a relative reasonableness index.

[0060] Specifically, after temperature and humidity sensors are deployed at the target location to collect data in real time, the temperature data is subtracted from the preset target temperature to obtain the first difference, and the humidity data is subtracted from the preset target humidity to obtain the second difference. The two differences are then substituted into a preset formula to calculate the first value, for example, by using the square root of the sum of squares, to obtain an index comprehensively reflecting the degree of temperature and humidity deviation. Subsequently, the minimum value of the first value at all times is extracted from historical data as a benchmark value. The ratio of the current first value to this minimum value is calculated, thus converting the current deviation into a reasonableness relative to the historically ideal state. For example, when the current first value is 5 and the historical minimum is 2, the ratio is 2.5, indicating that the current temperature and humidity deviation is 2.5 times the historical minimum. Further, the comparison values ​​are processed using a normalization function, such as using the sigmoid function to map them to the range of 0 to 1, ultimately obtaining a quantitative index characterizing the reasonableness of the temperature and humidity.

[0061] For example, if we set the i-th time as the target time and the j-th location as the target location, the reasonableness of the temperature and humidity conditions at the j-th location at the i-th time is... for: In the above formula, , These are the temperature and humidity data at the j-th location at the i-th time, respectively. The larger the value, the less the temperature and humidity data at the j-th position at the i-th time point meet the breeding requirements. The first difference, This is the second difference. For all sensors in the historical record at different times The minimum value, that is, the smallest first value. for The reciprocal of, when The smaller the value, the closer it is to 1, indicating that the current temperature and humidity conditions are more reasonable. It should be noted that when... or At that time, the reasonableness of the temperature and humidity conditions is directly set to 1.

[0062] Furthermore, it is also possible to... Normalization is performed using the maximum-minimum normalization function to obtain... Its range is (0, 1). When A value greater than 0.5 indicates that the temperature and humidity at the current location are relatively reasonable and suitable for livestock farming. The rest indicate that the location is unsuitable for livestock farming. Here, 0.5 is the first threshold. It should be noted that the maximum and minimum values ​​in the maximum-minimum normalization function are the maximum and minimum values ​​of reasonableness in the historical dataset.

[0063] Through the above technical solution, this application can dynamically assess the rationality of temperature and humidity at the target location, improving the accuracy and adaptability of rationality calculation based on the comparison of historical and real-time data. This allows for more precise triggering of ventilation device adjustment logic, preventing the overall aquaculture environment from being affected by undetected localized temperature and humidity anomalies. For example, even if the temperature and humidity deviation at a certain moment does not exceed a fixed threshold, but has significantly deteriorated compared to the historical optimal state, this solution can still adjust the ventilation intensity in a timely manner based on changes in rationality, thereby maintaining the stability of the aquaculture environment.

[0064] In some embodiments, in response to a reasonableness level being less than a first threshold, an adjustment degree is obtained using the reasonableness level, and a first rotational speed of the ventilation device in the breeding shed is adjusted using the adjustment degree to obtain an adjusted second rotational speed, including: In response to a reasonableness level being less than a first threshold, the system obtains the number of first locations where sensors are installed in the breeding shed, the number of second locations where breeding is unsuitable, and the total number of sensors in the breeding shed; using the number of first locations, the number of second locations, and the total number, the system determines the degree of adjustment; the system obtains the current first rotational speed of the ventilation device in the breeding shed; and using the first rotational speed and the degree of adjustment, the system determines the adjusted second rotational speed.

[0065] Reasonableness refers to the difference between the temperature and humidity data and the target temperature and humidity. See the aforementioned section on reasonableness for details. The calculation process quantifies the degree to which the environment deviates from suitable aquaculture conditions. The first number of locations refers to the number of areas with sensors installed and where temperature and humidity meet requirements. This can be obtained through real-time statistics from the sensor network, reflecting the distribution range of suitable environments within the aquaculture shed. The second number of locations refers to the number of areas where temperature and humidity do not meet aquaculture needs. This can be determined and counted using a reasonableness threshold, used to identify areas of localized environmental abnormalities. The total number refers to the total number of all installed sensors, which can be obtained through system configuration parameters and serves as a benchmark parameter for calculating the degree of adjustment. The degree of adjustment refers to the extent to which the ventilation device's rotation speed needs to be changed. This can be calculated using the mathematical relationship between the first number of locations, the second number of locations, and the total number, used to dynamically adjust the overall ventilation intensity.

[0066] Specifically, when the system detects that the reasonableness level is lower than a preset threshold, it first counts the number of areas that meet the breeding conditions and the number of abnormal areas, and calculates the proportion of abnormal areas based on the total number of sensors. For example, when the proportion of abnormal areas exceeds a dynamic threshold, the system generates an adjustment degree parameter. This parameter is used to perform linear or nonlinear calculations with the current fan speed, and the adjusted target speed is output. Furthermore, the calculation of the adjustment degree can be processed using a normalization function, such as mapping the sensor quantity ratio to a preset adjustment range, to avoid sudden changes in speed affecting system stability. Thus, the speed adjustment of the ventilation device can be dynamically optimized based on the distribution density of abnormal areas, achieving precise control of the overall ventilation intensity.

[0067] Through the above technical solution, this application can dynamically adjust the ventilation intensity according to the distribution density of abnormal areas within the breeding shed, effectively solving the problem of low overall ventilation efficiency caused by the failure to identify local environmental anomalies in traditional methods. For example, when multiple adjacent areas simultaneously exceed temperature and humidity standards, the system automatically increases the fan speed to enhance the air exchange rate; when abnormal areas are scattered and few in number, the system maintains a lower speed to reduce energy consumption. This dynamic adjustment mechanism based on spatial distribution characteristics enables simultaneous optimization of the ventilation system's response accuracy and energy utilization rate.

[0068] In some embodiments, determining the degree of adjustment using the first number of positions, the second number of positions, and the total number includes: determining a second value using the first number of positions and the reciprocal of the reasonableness; determining a third value using the ratio of the second number of positions to the total number; and determining the degree of adjustment using the second value and the third value. Determining the adjusted second speed using the first speed and the adjustment degree includes: determining a fourth value using the adjustment degree and a first preset constant; and determining the adjusted second speed using the fourth value and the first speed.

[0069] The first number of locations refers to the total number of areas where temperature and humidity sensors are deployed. This can be achieved by counting all working sensors within the breeding shed, reflecting the coverage and data acquisition capabilities of the monitoring network. The second number of locations refers to the number of areas where temperature and humidity parameters exceed suitable ranges. This can be obtained by comparing real-time data with preset thresholds, quantifying the distribution of abnormal environmental areas. The total number serves as the denominator in the adjustment degree calculation, eliminating the influence of sensor quantity differences on the calculation results and making the adjustment strategy universal. The adjustment degree is calculated by multiplying the first number of locations by the reciprocal of the reasonableness, combined with the ratio of the second number of locations to the total number. This parameter dynamically reflects the matching relationship between current ventilation needs and equipment status. The first preset constant serves as a proportionality coefficient, specifically an empirical value set based on the fan performance curve, used to convert the adjustment degree into actual speed change.

[0070] Specifically, when the detected reasonableness level falls below a threshold, the system first counts the number of currently active sensors and identifies the number of areas with abnormal temperature and humidity. By combining the ratio of the number of abnormal areas to the total number of sensors with a weighted calculation based on the reciprocal of the reasonableness level, the adjustment level representing the intensity of ventilation demand is obtained. Further, the adjustment level is multiplied by a preset constant to obtain a speed correction, which is then added to the current speed to form a new control command. For example, when the proportion of abnormal areas increases, the adjustment level increases accordingly. Through the proportional amplification effect of the preset constant, the increase in fan speed is positively correlated with the degree of environmental deterioration. This process transforms discrete sensor data into continuous control parameters through mathematical calculations, avoiding the control lag problem caused by relying solely on threshold triggering in traditional methods.

[0071] For example, the degree of adjustment of the axial flow fan at time i. for: In the above formula, The reasonableness of the temperature and humidity at the j-th location at the i-th time. n is the number of sensor locations in the breeding shed, i.e., the number of first locations. The smaller the value, the less reasonable the temperature and humidity are in the entire breeding shed, and the overall ventilation intensity should be increased. This is the reciprocal of the reasonableness of all positions, i.e., the second value. This is the ratio of the number of items in the second position to the total number, which is the third value.

[0072] This refers to the number of locations where the temperature and humidity are unsuitable for raising livestock, i.e., the number of second locations. This refers to the number of sensors in the breeding shed, i.e., the total number of sensors. The higher the value, the lower the temperature and humidity level in the entire breeding shed, and the speed of the axial flow fan in the breeding shed should be increased.

[0073] Furthermore, it is also possible to... Normalization is performed using the maximum-minimum normalization function to obtain... Its range is (-1, 1). It should be noted that the maximum and minimum values ​​in the maximum and minimum value normalization function are the maximum and minimum values ​​of the adjustment degree in the corresponding historical dataset.

[0074] The second speed that should be adjusted to determine the rotational speed of the axial flow fan in the breeding shed at the i-th moment. for: In the above formula, Let be the rotational speed at the i-th moment, i.e., the first rotational speed. Rotational speed at time i The degree of adjustment is the result of normalization using the tanh function, corresponding to the degree of adjustment. 1 is the first preset constant. It is the fourth value.

[0075] Furthermore, the maximum and minimum speeds of the axial flow fan are set as follows: When the desired result is obtained When the speed is greater than the maximum speed, Adjustments should be made. When the desired result is obtained... When the speed is smaller than the minimum speed, Adjustments will be made.

[0076] Through the above technical solution, this application achieves adaptive speed control based on multi-dimensional environmental data, effectively solving the problem of inaccurate ventilation intensity adjustment caused by local data deviations or uneven sensor layout in traditional methods. By quantifying the correlation between the distribution characteristics of abnormal areas and equipment status, energy consumption can be reduced while ensuring ventilation effect, and equipment wear or airflow disturbance caused by excessive adjustment can be avoided.

[0077] In some embodiments, the degree of change in the opening and closing degree of the ventilation window at the target location at different times is obtained, and the opening and closing degree of the ventilation window is adjusted using the degree of change to obtain the adjusted opening and closing degree, including: Obtain at least two reasonableness values ​​of the target location at different times, and obtain at least two opening / closing degrees of the ventilation window at the target location at different times; use the at least two reasonableness values ​​to determine the degree of change in the opening / closing degree of the ventilation window at the target location at different times; obtain the third opening / closing degree of the ventilation window at the adjacent location of the target location; use the third opening / closing degree and the degree of change to determine the adjusted opening / closing degree.

[0078] Among them, "reasonableness" refers to the degree of difference between the temperature and humidity data at the target location and the preset target values. This can be achieved by calculating the normalized results of the temperature and humidity differences, used to quantify the deviation of the current temperature and humidity state from ideal aquaculture conditions. "Degree of change" refers to the trend of the opening and closing degree of the ventilation windows over time. This can be calculated by combining the difference in reasonableness at different times with the time span, used to reflect the dynamic evolution of temperature and humidity issues in a local area. "Third opening degree" refers to the opening and closing state of adjacent ventilation windows at the current moment. This can be obtained in real time through sensor data from adjacent areas, used to achieve coordinated adjustment of ventilation intensity between areas.

[0079] Specifically, by continuously monitoring the temperature and humidity levels at target locations at multiple time points, time-series data is established to analyze its changing patterns. For example, when the difference in reasonableness between two adjacent time points exceeds a preset threshold, it indicates a significant change in the temperature and humidity status of the area. Further, by combining the duration of this change, the required adjustment range for the ventilation window opening is calculated. Simultaneously, the real-time opening and closing status of ventilation windows in adjacent areas is acquired, and the adjustment requirements of both are weighted and fused to ultimately generate a new opening and closing parameter that can respond to local changes while maintaining regional coordination. This process can employ a sliding time window mechanism, for example, selecting data points from the most recent 5 minutes for trend analysis, and converting parameters of different dimensions into a unified adjustment coefficient through normalization.

[0080] Through the above technical solution, this application can achieve precise dynamic control of local ventilation intensity, effectively solving the ventilation lag problem caused by abnormal local temperature and humidity while maintaining airflow balance between areas. For example, when a continuous downward trend in the reasonableness of a certain area is detected, the system can intervene in advance to adjust the opening degree of the ventilation windows to prevent the problem from worsening; at the same time, by associating the ventilation status of adjacent areas, it ensures that the adjustment operation will not disrupt the overall airflow organization, significantly improving the response efficiency and resource utilization of the ventilation system.

[0081] In some embodiments, at least two degrees of reasonableness include a first degree of reasonableness of the target location at a first time and a second degree of reasonableness at a second time; at least two degrees of opening include a first degree of opening of the ventilation window at the target location at a first time and a second degree of opening of the ventilation window at a second time.

[0082] Using at least two rationality measures, determine the degree of change in the opening and closing degree of the ventilation window at the target location at different times, including: Obtain the time span of the target moment; using the time span, the first degree of reasonableness, and the second degree of reasonableness, determine the degree of change corresponding to the first degree of opening and closing and the second degree of opening and closing.

[0083] The time span refers to the interval between two moments, which can be calculated using timestamp differences to measure the duration of changes in reasonableness. Reasonableness difference refers to the difference in reasonableness at different times for the same location, which can be calculated through subtraction or proportion, reflecting the dynamic trend of temperature and humidity deviating from the suitable range. The degree of change refers to the adjustment range of the opening and closing of ventilation windows, which can be calculated through normalization or weighted averages, quantifying the adjustment needs of local ventilation intensity.

[0084] Specifically, the difference in the reasonableness of temperature and humidity at the target location between the first and second moments is extracted. Combined with the time span between the two moments, the degree of change in the opening and closing of the ventilation window is calculated. For example, when the reasonableness difference is large and the time span is short, it indicates that the temperature and humidity at that location have rapidly deviated from the suitable range in a short period. In this case, the degree of change is set to a higher value, and the opening and closing of the ventilation window needs significant adjustment. Conversely, when the reasonableness difference is small and the time span is long, the degree of change is set to a lower value, requiring only minor adjustments to the opening and closing. By combining the reasonableness change trend with the time factor, local ventilation needs can be dynamically assessed, avoiding adjustment lags or overreactions caused by relying solely on data from a single moment.

[0085] Furthermore, it also includes the following operations.

[0086] Using reasonableness and time, determine the first curve; using the first and second reasonableness, determine the reasonableness difference between the first and last time points of each segment region in the first curve, and obtain the maximum reasonableness difference in the first curve; using the difference between the first and second reasonableness, the maximum reasonableness difference, and the time span, determine the degree of change.

[0087] Among them, reasonableness refers to the quantitative indicator of the difference between the temperature and humidity data and the target temperature and humidity. Specifically, it can be achieved by calculating the normalized difference between the current temperature and humidity and the target values, reflecting the degree to which the local environment deviates from suitable breeding conditions. Time span refers to the time interval between two adjacent reasonableness measurement points, which can be achieved by using the interval of periodic data collection by sensors or a custom time window, used to measure the persistence of environmental changes. Reasonableness difference refers to the reasonableness difference value at different times at the same location, which can be achieved by extracting the extreme points of historical data segments and calculating the differences, used to identify the trend and magnitude of local environmental changes. Maximum reasonableness difference refers to the maximum reasonableness change across all segmented intervals in historical data, which can be achieved by traversing the reasonableness extreme value differences across all time windows, used to establish a benchmark reference value for dynamic adjustment.

[0088] Specifically, a time-series curve is constructed by continuously collecting reasonableness data of the target location. The curve is then segmented to identify significant change intervals. Within each segment, the reasonableness difference between the first and last moments is calculated as the current change, while the historical maximum change is used as an adjustment benchmark. Combining the ratio between the current change and the historical maximum change, and the time span of that segment, the degree of opening and closing of the ventilation windows is dynamically calculated. When the current change is close to the historical maximum and the time span is long, it is determined that there is continuous environmental deterioration at that location, requiring a significant increase in ventilation intensity; otherwise, adjustments are made appropriately based on the proportional relationship.

[0089] Through the above technical solution, this application can adaptively adjust the opening degree of the ventilation windows according to the dynamic characteristics of temperature and humidity changes at the target location. For example, when humidity continues to rise in a local area of ​​the breeding shed due to feed fermentation, the system can quickly identify this trend and increase the opening degree of the ventilation windows to promptly remove moisture; while when temperature and humidity fluctuate briefly due to short-term door openings, the system can suppress unnecessary adjustments. This adjustment strategy based on changes in time span and rationality effectively improves the precision of local ventilation control, while reducing energy consumption caused by frequent equipment start-ups and shutdowns. Furthermore, it can dynamically match the adjustment intensity of the ventilation windows according to the duration and magnitude of local environmental changes, maintaining overall ventilation efficiency while precisely eliminating abnormal temperature and humidity accumulation in local areas, thus preventing overall breeding conditions from becoming unbalanced due to the spread of local environmental deterioration.

[0090] In some embodiments, determining the adjusted opening degree using a third opening degree and a degree of change includes: The fifth value is determined by using the degree of change and the second preset constant; the adjusted opening degree is determined by using the fifth value and the third opening degree.

[0091] The degree of change refers to the dynamic change in the opening and closing degree of the ventilation window at the target location between adjacent moments. Specifically, it can be calculated as the ratio of the difference in opening and closing degree between adjacent moments to the time span, reflecting the changing trend of ventilation demand in a local area. The second preset constant is an empirical parameter used to control the adjustment range, determined through historical data fitting or experimental calibration, balancing adjustment speed and stability. The fifth value refers to the normalized adjustment range, achieved by multiplying the degree of change by the second preset constant, quantifying the correction amount for the ventilation window opening and closing degree. The third opening degree refers to the current opening and closing state of ventilation windows in adjacent areas of the target location, measured in real time by an angle sensor, preventing mutual interference of airflow between adjacent areas.

[0092] Specifically, when the temperature and humidity at the target location continuously deviate from the preset range, the system first calculates the trend of the opening degree of the ventilation windows at that location within a continuous time window, generating a representation of the degree of change in dynamic demand. Further, by multiplying the degree of change by a preset adjustment coefficient, a fifth value is obtained to eliminate the influence of dimensions. Finally, combining the current opening state of ventilation windows in adjacent areas, the fifth value is superimposed on the third opening degree, forming an adjustment scheme that balances local demand and regional coordination. For example, when the ventilation demand in a certain area increases sharply, the system increases the opening degree of the windows in that area while simultaneously decreasing the opening degree of adjacent areas to avoid airflow collision.

[0093] In some specific implementations, the second preset constant can be set to an empirical value within the range of 0.5-1.2 to match the spatial structural characteristics of different breeding sheds. The calculation process for the fifth value can incorporate amplitude limiting processing, constraining the adjustment range within ±30% to prevent airflow turbulence caused by sudden adjustments. The acquisition of the third opening / closing degree can be achieved based on a wireless sensor network, synchronizing the opening / closing status data of adjacent areas every 5 seconds.

[0094] For example: Using the reasonableness value W of the temperature and humidity at the j-th location at consecutive time points as the vertical axis and the corresponding time point as the horizontal axis, a Cartesian coordinate system is established to obtain the reasonableness-time curve of the temperature and humidity at the j-th location, i.e., the first curve. The Douglas-Puk algorithm is then used to obtain a smoothed reasonableness-time curve of the temperature and humidity at the j-th location. The smoothed curve retains the trend of the original curve. Multiple extreme points of the smoothed curve are obtained, and these extreme points are used as segmentation points to divide the reasonableness-time curve of the temperature and humidity at the j-th location into multiple segmented intervals. This segmentation operation is performed on all locations unsuitable for aquaculture.

[0095] The degree of change in the opening and closing degree of the ventilation window near the j-th position at the i-th time was obtained. for: In the above formula, The W value is the first time value within the segmented interval where the j-th position is located at the i-th time, which is the first reasonableness. The W value is the last time step in the segmented interval where the j-th position is located at the i-th time step, which is the second rationality. This represents the difference in reasonableness. The maximum reasonableness difference is the maximum difference among the differences between the last and first times of the segmented intervals of the curve obtained at position j. The larger the value, the more insufficient the ventilation intensity at the current j-th position is, and the more it should be increased.

[0096] This represents the time span of the segment interval containing the i-th time point, i.e., the time span. The larger the value, the longer the current high ventilation intensity will last, and the more the opening degree of the ventilation window should be increased.

[0097] right Normalization is performed using a maximum-minimum normalization function to obtain... Its range is (-1, 1). It should be noted that the maximum and minimum values ​​in the maximum-minimum normalization function are the maximum and minimum values ​​of the degree of change in the corresponding historical dataset.

[0098] The calculated opening degree of the ventilation window near the j-th position in the breeding shed at time i should be adjusted accordingly. for: In the above formula, Let represent the opening degree of the ventilation window at the j-th position at the i-th moment, i.e., the third opening degree. This is the normalized result of the change in the opening degree of the ventilation window near the j-th position at the i-th time. The second preset constant is 1. It is the fifth value.

[0099] Through the above technical solution, this application can achieve precise control of the opening and closing degree of ventilation windows, eliminate local temperature and humidity abnormalities, avoid environmental deterioration in adjacent areas caused by excessive adjustment of a single area, and significantly improve the coordination and stability of multi-point ventilation in breeding sheds.

[0100] In some embodiments, the following operations are also included.

[0101] The airflow in the breeding shed is exchanged by using the second rotation speed and the adjusted opening and closing degree through the fuzzy PID control system.

[0102] The fuzzy PID control system refers to a closed-loop control system combining fuzzy logic and proportional-integral-derivative control algorithms. Specifically, it uses a fuzzy rule base to dynamically adjust the error signal, achieving nonlinear control. This feature is used to dynamically optimize the opening and closing rate of the ventilation window based on real-time environmental parameters, avoiding mechanical shock. The second speed refers to the adjusted operating speed of the axial flow fan, which can be achieved by adjusting the motor input frequency through a frequency converter. Its function is to match the overall ventilation needs of the livestock shed. The adjusted opening angle refers to the target opening angle of the ventilation window after calculation of the degree of change. This can be achieved using a stepper motor-driven gear mechanism for adjusting the ventilation intensity in localized areas.

[0103] Specifically, when the axial fan operates at its second rotational speed and the ventilation window reaches the adjusted opening angle, the fuzzy PID control system collects the operating status parameters of the ventilation device in real time. The system uses the deviation between the target opening angle and the actual angle as input, converting it into a membership function through a fuzzification interface. Based on 49 preset control rules, the inference engine outputs correction values ​​for proportional, integral, and derivative coefficients. After defuzzification, a precise control quantity is generated, driving the actuator to gradually adjust the opening angle of the ventilation window. For example, when a large deviation in the opening angle is detected, the system automatically increases the proportional coefficient to accelerate the response; when approaching the target angle, it enhances the integral action to eliminate steady-state error. Simultaneously, the axial fan speed and the ventilation window angle form a coordinated control, prioritizing the resolution of abnormal temperature and humidity issues in the area through local opening angle adjustments while maintaining overall ventilation efficiency.

[0104] For example, a fuzzy PID control system is used to control the opening and closing rate of ventilation windows. The main control steps are: 1. Input the current ventilation window angle and the desired ventilation window angle into the fuzzy PID control system; 2. Obtain the opening and closing size arrangement at different time intervals output by the fuzzy PID control system; 3. Send a command to the ventilation window opening and closing controller, requiring it to start opening and closing operations according to the output of the PID control system. The overall ventilation intensity of the livestock shed is pre-tested, and the speed of the axial flow fan is adjusted accordingly. Then, the opening and closing degree of nearby ventilation windows is adjusted for less suitable livestock housing locations. This makes the temperature and humidity in the livestock shed, both overall and in specific areas, suitable for livestock housing.

[0105] Through the above technical solution, this application effectively reduces mechanical vibration during the operation of the ventilation window and prevents airflow turbulence caused by sudden angle changes. Simultaneously, the coordinated control of the axial fan speed and the opening / closing degree of the ventilation window improves overall ventilation efficiency by approximately 22%, and reduces the response time for temperature and humidity regulation in local hotspot areas to 65% of the original level. Under stable operating conditions, the system reduces motor energy consumption by approximately 18% compared to traditional control methods.

[0106] In this application, temperature and humidity data at different locations in the breeding shed are measured using temperature and humidity sensors. The measured data is transmitted to a central processing unit, where relevant reference data is retrieved from a database for analysis. The resulting axial fan speed adjustment and the opening / closing degree of the ventilation windows are sent as signals to the axial fan motor controller (the motor speed adjustment should strictly follow the preset data) and the corresponding ventilation window controller. The current adjustment results and the obtained temperature and humidity data are then stored in the database.

[0107] In this embodiment, this application uses a wireless sensor network to collect temperature and humidity data from multiple locations in real time, establishing a monitoring dataset containing spatial distribution characteristics. Then, the deviation of the temperature and humidity at each monitoring point from the preset target is calculated. When the overall reasonableness is below a threshold, a fan speed adjustment coefficient is generated based on the proportion of abnormal areas and the degree of deviation. Simultaneously, historical opening and closing data of each ventilation window are analyzed, and time-series modeling is used to identify local areas requiring enhanced ventilation. Finally, the speed adjustment command and opening / closing optimization parameters are synchronously sent to the control system, forming a spatially hierarchical and time-linked precise ventilation strategy.

[0108] It effectively solves the problems of slow response and low control precision in traditional ventilation systems. An abnormal area is accurately identified through a rationality model, avoiding energy waste caused by overall over-ventilation. A dynamic adjustment mechanism matches local ventilation intensity with real-time demand, preventing moisture accumulation and temperature imbalance. A tiered control strategy balances overall environmental equilibrium with specific local needs, significantly improving the stability and controllability of the aquaculture environment.

[0109] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for airflow exchange in aquaculture sheds based on wireless sensors, characterized in that, include: Obtain temperature and humidity data at different locations in the breeding shed; The reasonableness of the temperature and humidity data at the target location is obtained, wherein the reasonableness characterizes the difference between the temperature and humidity data and the target temperature and target humidity; In response to the reasonableness being less than a first threshold, the degree of adjustment is obtained using the reasonableness, and the first rotation speed of the ventilation device in the breeding shed is adjusted using the degree of adjustment to obtain the adjusted second rotation speed; The degree of change in the opening and closing degree of the ventilation window at the target location at different times is obtained, and the opening and closing degree of the ventilation window is adjusted using the degree of change, and the adjusted opening and closing degree is obtained. The second rotation speed and the adjusted opening and closing degree are used to exchange airflow in the breeding shed.

2. The airflow exchange method for livestock sheds based on wireless sensors according to claim 1, characterized in that, The reasonableness of obtaining the temperature and humidity data at the target location includes: Acquire the temperature data and humidity data of the target location, and acquire the preset target temperature and target humidity of the target location; Obtain a first difference between the temperature data and the target temperature, and a second difference between the humidity data and the target humidity; The reasonableness of the temperature and humidity conditions at the target location is determined by using the first difference and the second difference.

3. The airflow exchange method for livestock sheds based on wireless sensors according to claim 2, characterized in that, The step of determining the reasonableness of the temperature and humidity conditions at the target location using the first difference and the second difference includes: Using the first difference and the second difference, a first value at the target position at each moment is determined; Get the minimum first value at different times; The reasonableness of the temperature and humidity conditions at the target location is determined by using the ratio of the minimum first value to the first value at the current time.

4. The airflow exchange method for livestock sheds based on wireless sensors according to claim 1, characterized in that, The step of responding to a reasonableness level being less than a first threshold, using the reasonableness level to obtain an adjustment degree, and using the adjustment degree to adjust the first rotation speed of the ventilation device in the breeding shed, and obtaining the adjusted second rotation speed, includes: In response to the reasonableness being less than a first threshold, the number of first locations where sensors are installed in the breeding shed is obtained, the number of second locations where breeding is not suitable is obtained, and the total number of sensors in the breeding shed is obtained. The degree of adjustment is determined using the number of the first position, the number of the second position, and the total number. Obtain the current first rotational speed of the ventilation system in the breeding shed; The adjusted second speed is determined using the first speed and the degree of adjustment.

5. The airflow exchange method for livestock sheds based on wireless sensors according to claim 4, characterized in that, Determining the degree of adjustment using the number of the first position, the number of the second position, and the total number includes: The second value is determined by using the first number of positions and the reciprocal of the reasonableness. The third value is determined using the ratio of the second number of positions to the total number. The degree of adjustment is determined using the second and third values; Determining the adjusted second speed using the first speed and the adjustment degree includes: The fourth value is determined using the adjustment level and the first preset constant; Using the fourth value and the first rotational speed, the adjusted second rotational speed is determined.

6. The airflow exchange method for livestock sheds based on wireless sensors according to claim 1, characterized in that, The process of acquiring the degree of change in the opening and closing degree of the ventilation window at the target location at different times, and adjusting the opening and closing degree of the ventilation window using the degree of change, to obtain the adjusted opening and closing degree includes: Obtain at least two reasonableness values ​​of the target location at different times, and obtain at least two opening / closing degrees of the ventilation window at the target location at different times; Using the aforementioned at least two rationality measures, determine the degree of change in the opening and closing degree of the ventilation window at the target location at different times; Obtain the third opening degree of the ventilation windows at adjacent positions of the target location; The adjusted opening degree is determined using the third opening degree and the degree of change.

7. The airflow exchange method for livestock sheds based on wireless sensors according to claim 6, characterized in that, The at least two degrees of reasonableness include a first degree of reasonableness of the target location at a first moment and a second degree of reasonableness at a second moment; the at least two degrees of opening and closing include a first degree of opening and closing of the ventilation window at the target location at a first moment and a second degree of opening and closing at a second moment. The determination of the degree of change in the opening and closing degree of the ventilation window at the target location at different times using the at least two reasonableness factors includes: Obtain the time span of the target moment; Using the time span, the first reasonableness, and the second reasonableness, the degree of change corresponding to the first opening degree and the second opening degree is determined.

8. The airflow exchange method for livestock sheds based on wireless sensors according to claim 7, characterized in that, Also includes: Using the aforementioned reasonableness and timing, determine the first curve; Using the first reasonableness and the second reasonableness, determine the reasonableness difference between the first and last moments of each segment region in the first curve, and obtain the maximum reasonableness difference in the first curve; The degree of change is determined by using the difference between the first degree of reasonableness and the second degree of reasonableness, the maximum degree of reasonableness difference, and the time span.

9. The airflow exchange method for livestock sheds based on wireless sensors according to claim 6, characterized in that, The step of determining the adjusted opening degree using the third opening degree and the degree of change includes: The fifth value is determined using the degree of change and the second preset constant; The adjusted opening degree is determined using the fifth value and the third opening degree.

10. The airflow exchange method for livestock sheds based on wireless sensors according to claim 1, characterized in that, Also includes: The airflow in the breeding shed is exchanged by using the second rotation speed and the adjusted opening and closing degree through the fuzzy PID control system.

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