Intelligent control method and system for environmental parameters of closed hog house

Abstract

The invention relates to the technical field of control systems, in particular to an intelligent control method and system for environmental parameters of a closed hog house, and the method comprises the steps: setting an environmental condition model; acquiring multiple types of sensor data; on the basis of the environmental condition model, abnormal value detection is carried out on the multiple types of sensor data; when the sensor data is abnormal, optimal control parameter configuration is determined by taking temperature, humidity, gas concentration and energy consumption as optimization targets through a particle swarm optimization algorithm; dynamically adjusting the running state of the ventilation equipment according to the optimal control parameter configuration; and displaying the multi-sensor data and the operation state of the ventilation equipment through a visual interface. According to the invention, comprehensive regulation and control of multi-dimensional parameters can be realized, a suitable breeding environment is created, the disease risk is reduced, and the health and growth efficiency of pigs are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to an intelligent control method and system for environmental parameters of a closed pig house. Background Art

[0002] The traditional pig house in the farm is an open pig house, which relies on natural ventilation and natural light. The ventilation effect is greatly affected by external climate conditions and the environmental control ability is weak. The pig house environment that is heavily dependent on external conditions is difficult to provide the appropriate temperature required for the growth of pigs, which can easily lead to stress reactions in pigs, affecting their growth rate and health. In addition, humidity is also difficult to control. Too high humidity will increase the risk of pathogen reproduction, while too low humidity will cause dry respiratory tracts in pigs, making them susceptible to respiratory diseases. The accumulation of harmful gases further increases the incidence and mortality of diseases.

[0003] With the advancement of technology, closed pig houses can achieve refined control of the environment. Through advanced environmental control systems, farms can accurately adjust the temperature, humidity, light and ventilation conditions in the pig house. Closed pig houses can continuously keep the air fresh and flowing through mechanical ventilation systems. Fans and ventilation ducts can effectively remove harmful gases (such as ammonia and hydrogen sulfide) in the house, introduce fresh air, and maintain an appropriate oxygen concentration.

[0004] However, in current pig houses, a single or a small number of sensors cannot fully and real-time monitor the multi-dimensional environmental parameters inside the pig house, such as temperature, humidity, CO2, NH3, H2S, etc., resulting in delayed or inaccurate environmental control. At the same time, parameters such as temperature, humidity and gas concentration in the pig house environment affect each other, making it difficult to control. Traditional manual or simple automated control methods cannot achieve comprehensive control of multi-dimensional parameters, often resulting in unsatisfactory control effects. Furthermore, different environmental parameters have different effects on pigs, and the control process requires coordination of control strategies for multiple parameters (such as temperature, humidity and gas concentration). Traditional methods cannot achieve comprehensive control of multi-dimensional parameters. Summary of the invention

[0005] In order to solve the above-mentioned problems in the prior art, the present invention provides a closed pig house environmental parameter intelligent control method and system: A first aspect of the present invention provides a method for intelligently controlling environmental parameters of a closed pig house, comprising: S1: Setting the environmental condition model; S2: Acquire multiple types of sensor data; S3: Based on the environmental condition model, performing outlier detection on multiple types of sensor data respectively; S4: When there are abnormalities in the sensor data, the particle swarm optimization algorithm is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as the optimization targets; S5: dynamically adjusting the operating state of the ventilation equipment according to the optimal control parameter configuration; S6: Displaying the multi-sensor data and the operating status of the ventilation equipment through a visual interface.

[0006] The second aspect of the present invention provides a closed pig house environmental parameter intelligent control system, comprising: A setting module, used to set the environmental condition model; An acquisition module is used to acquire multiple types of sensor data; A detection module, used for performing outlier detection on multiple types of sensor data respectively based on the environmental condition model; The determination module is used to determine the optimal control parameter configuration by using the particle swarm optimization algorithm when there is an abnormality in the sensor data, taking temperature, humidity, gas concentration and energy consumption as the optimization targets; An adjustment module, configured to dynamically adjust the operating state of the ventilation equipment according to the optimal control parameter configuration; The display module is used to display the multi-sensor data and the operating status of the ventilation equipment through a visual interface.

[0007] The beneficial effects of the present invention are reflected in acquiring multiple types of sensor data, comprehensively and in real time monitoring the multi-dimensional environmental parameters inside the pig house, and determining the optimal control parameter configuration based on the sensor data through the particle swarm optimization algorithm, with temperature, humidity, gas concentration and energy consumption as optimization targets, to achieve comprehensive regulation of multi-dimensional parameters, create a suitable breeding environment, reduce disease risks, and improve pig health and growth efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a flow chart of an intelligent control method for environmental parameters of a closed pig house provided by the present invention; Figure 2 A flow chart of a control parameter configuration method provided by the present invention; Figure 3 A structural schematic diagram of a ventilation hardware system provided by the present invention; Figure 4 This is a schematic diagram of the structure of a closed pig house environmental parameter intelligent control system provided by the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0010] Example 1 Reference Manual Attached Figure 1 , showing a flow chart of a closed pig house environmental parameter intelligent control method provided by the present invention.

[0011] An embodiment of the present invention provides a closed pig house environmental parameter intelligent control method, comprising: S1: Set up the environmental condition model.

[0012] Optionally, the environmental condition model for each type of pig includes minimum and / or maximum values ​​for each type of environmental condition.

[0013] Optionally, the environmental condition model specifically includes: ; ; ; ; ; in, T min Indicates the minimum temperature. T max Indicates the maximum temperature, H min Indicates the minimum humidity value. H max Indicates the maximum humidity, CO 2max Indicates the maximum carbon dioxide concentration, NH 3max Indicates the maximum ammonia concentration, H 2 S max Indicates the maximum value of hydrogen sulfide concentration.

[0014] In the present invention, by setting an environmental condition model and clarifying the minimum and maximum values ​​of various environmental parameters (such as temperature, humidity, carbon dioxide, ammonia and hydrogen sulfide concentrations), a scientific and accurate environmental benchmark can be provided for different types of pigs (such as boars), ensuring that the pig house environment is always maintained within the optimal range for pig health and growth, effectively reducing the health risks caused by abnormal environmental parameters, and at the same time providing clear standards for the intelligent monitoring and control system, facilitating rapid detection of anomalies, triggering early warnings, and formulating precise control strategies, thereby improving breeding efficiency and reducing disease risks and management costs.

[0015] S2: Acquire multiple types of sensor data.

[0016] Optionally, the multiple sensor data include: temperature data, humidity data, CO 2 Concentration data, NH 3 Concentration data, H 2 S concentration data and wind speed data.

[0017] Specifically, a variety of sensors are placed at different locations in the pig house to collect temperature, humidity, CO 2 concentration, NH 3 Concentration, H 2 S concentration and wind speed data. Then, through data preprocessing (removal of outliers, calibration and time synchronization), the accuracy and reliability of the data are improved, providing a solid data foundation for subsequent intelligent control.

[0018] Temperature sensors can monitor temperature changes in different areas of the pig house.

[0019] Humidity sensors can measure the humidity level in the air and prevent it from being too high or too low.

[0020] Anemometer sensors record air velocity to ensure good ventilation.

[0021] CO 2 The sensor is based on the physical principle of infrared absorption spectrum. The infrared light source emits 1-20μm infrared light, which is absorbed by a certain length of air chamber and then passes through a 4.26μm narrow-band filter. The infrared sensor monitors the intensity of the infrared light with a wavelength of 4.26μm, which indicates the CO 2 The concentration of the gas.

[0022] NH 3 Sensors usually use electrochemical and semiconductor principles for detection. Sampling is done by pumping and diffusion. When ammonia in the environment diffuses or is inhaled to the sensor, the sensor converts the concentration of ammonia into a specific electronic signal, which is then displayed on the monitor. H 2The S sensor uses the electrochemical oxidation process of the gas to be measured on the working electrode in the electrolytic cell. The working electrode and the reference electrode of the electrolytic cell are kept constant at an appropriate potential through electronic circuits. At this potential, the electrochemical oxidation of the gas to be measured can occur. Therefore, the current generated by the electrochemical reaction of the gas to be measured is proportional to its concentration and follows Faraday's law. In this way, the concentration of the gas to be measured can be determined by measuring the magnitude of the current.

[0023] S3: Based on the environmental condition model, outlier detection is performed on multiple types of sensor data.

[0024] In a possible implementation, S3 specifically includes sub-steps S301 and S302: S301: Calculate the Z-score for each type of sensor data: ; ; ; ; ; in, Z T Represents the Z-score of the temperature data, T Represents temperature data, m T represents the average value of temperature data, s T represents the standard deviation of the temperature data, Z H Represents the Z-score of humidity data, H Represents humidity data, m H Represents the average value of humidity data, s H represents the standard deviation of humidity data, Represents the Z-score of the carbon dioxide concentration data, represents the carbon dioxide concentration data, represents the average value of carbon dioxide concentration data, represents the standard deviation of the carbon dioxide concentration data, Represents the Z-score of the ammonia concentration data, Indicates ammonia concentration data, represents the average value of ammonia concentration data, represents the standard deviation of the ammonia concentration data, Represents the Z-score of hydrogen sulfide concentration data, Represents hydrogen sulfide concentration data, represents the average value of hydrogen sulfide concentration data, Represents the standard deviation of the hydrogen sulfide concentration data.

[0025] S302: Determine whether the Z-score of each type of sensor data exceeds the constraints of the environmental condition model. If so, determine that the sensor data is abnormal and trigger an environmental condition abnormality alarm. The relevant sensor information enters the intelligent pig house control system for adjustment and enters the intelligent visualization interface module for export. Otherwise, return to S2 to continue monitoring.

[0026] In the present invention, by calculating the Z-score of multiple types of sensor data and judging whether it exceeds the constraints of the environmental condition model, abnormal values ​​in environmental parameters can be detected quickly and accurately, and environmental anomalies, such as excessive temperature, humidity or harmful gas concentrations, can be discovered in a timely manner, triggering early warnings and quickly transmitting relevant information to the intelligent control system for adjustment, thereby avoiding the negative impact of environmental deterioration on pig health. At the same time, the visual export of abnormal data facilitates management personnel to analyze the source of the problem and optimize management, thereby improving the reliability and efficiency of pig house environmental control.

[0027] S4: When there are abnormalities in the sensor data, the particle swarm optimization algorithm is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as the optimization targets.

[0028] Among them, Particle Swarm Optimization (PSO) is a global optimization algorithm based on swarm intelligence, which is inspired by the behavior of bird flocks or fish schools searching for food in nature. It assumes that individuals gradually approach the optimal solution by sharing information with surrounding individuals.

[0029] Reference Manual Attached Figure 2 , showing a flow chart of a control parameter configuration method provided by the present invention.

[0030] In a possible implementation, the particle swarm optimization algorithm in S4 is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as optimization targets, specifically including sub-steps S401 to S406: S401: Taking temperature, humidity, gas concentration and energy consumption as optimization targets, set the multi-objective optimization function of the particle swarm optimization algorithm: ; in, J represents the multi-objective optimization function, f T represents the temperature error, l 1 represents the weight of temperature error, f HRepresents the humidity error, l 2 represents the weight of humidity error, f C Indicates the gas concentration penalty, l 3 represents the weight of gas concentration penalty, f P Indicates energy consumption, l 4 Represents the weight of energy consumption.

[0031] In the present invention, by setting a multi-objective optimization function with temperature error, humidity error, gas concentration penalty and energy consumption as optimization targets, the particle swarm optimization algorithm can achieve dynamic trade-offs between multiple dimensions, comprehensively consider the accuracy of environmental control and energy-saving efficiency, and dynamically adjust the weight of each optimization target (such as giving priority to temperature and humidity or energy consumption according to environmental requirements and breeding stages), thereby generating the optimal control parameter configuration. This can not only provide the best growth environment for pigs, but also reduce energy consumption and improve the overall efficiency and economy of system operation.

[0032] It should be noted that in traditional PSO, the target weight of the fitness function is usually fixed. However, in pig house control, the priorities of temperature, humidity, gas concentration and energy consumption change with the season, time period and pig growth stage. Therefore, this method introduces dynamic weight adjustment: Optionally, the weights of the various objectives are: ; in, Indicates i The goal is t The dynamic weight at the iteration, I i Indicates i The relative importance of the goals, I j Indicates j The relative importance of the goals, Indicates i The goal is t The error at the iteration, Indicates that the current particle swarm is in the j The particle position that performs best on the target, M represents the total number of targets, M =4.

[0033] In the present invention, by introducing a dynamic weight adjustment mechanism, the target weights of temperature, humidity, gas concentration and energy consumption are dynamically adjusted according to the different needs of seasons, time periods and pig growth stages, so that the optimization algorithm can flexibly balance the priorities of various optimization goals in different situations, ensuring the rational allocation of resources and the accuracy of regulation. Compared with the traditional method of fixed weights, this dynamic adjustment can more effectively adapt to actual environmental changes, thereby improving the optimization effect, so that the system can not only meet the requirements of environmental parameters, but also minimize energy consumption, and achieve efficient and intelligent pig house environmental control.

[0034] S402: Initialize the particle swarm. Each dimension of the particle corresponds to a control parameter: fan frequency, wet curtain water pump opening, roller curtain opening, ceiling window opening, and diversion window opening.

[0035] S403: Calculate the fitness value of each particle according to the multi-objective optimization function.

[0036] S404: Determine the individual historical optimal value and the global optimal value of each particle.

[0037] S405: Update the speed and position of the particle according to the individual historical optimal value and the global optimal value: ; ; in, Indicates i The particle in k +1 iteration speed, oh represents the inertia weight factor, Indicates i The particle in k The speed at the iteration, P i Indicates i The individual historical optimal value of each particle, Indicates i The particle in k The position at the iteration, G represents the global optimal value, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 , r 2 represents a random number between [0,1], Denotes the position of the 𝑖th particle at the 𝑘+1th iteration.

[0038] S406: Determine whether the termination condition is met. If so, output the current global optimal value as the optimal control parameter configuration. Otherwise, return to S403 to continue iteration.

[0039] In the present invention, the particle swarm optimization algorithm is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as optimization targets, which can quickly respond to abnormal situations, achieve a balance between environmental quality and energy consumption through multi-objective optimization, and dynamically generate the optimal equipment control plan. This can not only effectively improve the abnormal environment in the pig house and restore suitable growth conditions, but also avoid resource waste caused by excessive operation or inefficient regulation of equipment, and improve the intelligence level and operation efficiency of the system.

[0040] S5: Dynamically adjust the operating status of the ventilation equipment according to the optimal control parameter configuration.

[0041] Optionally, the ventilation equipment includes: wet curtains, fans, small ceiling windows, guide windows and sliding plates.

[0042] It should be noted that the present invention can accurately control the temperature below the lower limit of the suitable temperature, the temperature within the suitable temperature range, and the temperature above the upper limit of the suitable temperature in the closed pig house. When the temperature is below the suitable range, the wind speed is limited to below 0.2m / s to prevent the pigs from being affected by excessive cold; under high temperature conditions, the wind speed gradually increases to 0.6m / s, which helps to reduce the occurrence of heat stress. When the temperature is between 28℃ and 32℃, higher wind speeds (1.5 to 1.7m / s) are allowed.

[0043] In a possible implementation manner, dynamically adjusting the operating state of the ventilation device in S5 specifically includes: In low temperature conditions, adjust the variable frequency fan, ceiling windows and guide windows to ensure that the wind speed and gas concentration at the outlet are within a safe range.

[0044] Optionally, in the case of low temperature, the ventilation volume is determined in the following manner: ; in, M C represents the low temperature air mass flow rate, ∑ C represents the mass flow rate of carbon dioxide produced by the pigs in the house and, Ci Indicates the amount of carbon dioxide allowed in the room. Co Indicates the carbon dioxide content in the air outside the house.

[0045] In the present invention, the minimum air mass flow rate that meets the carbon dioxide concentration control requirements in the pig house under low temperature environment can be accurately calculated, which can not only effectively discharge the excess carbon dioxide in the pig house and maintain the air quality, but also avoid the energy waste and cold stress problems of pigs caused by excessive ventilation. It provides a scientific basis for environmental control under low temperature conditions, balances ventilation requirements and energy-saving goals, and improves the efficiency of environmental regulation and the health protection of pigs.

[0046] Under suitable temperature conditions, maintain appropriate wind speed and gas concentration.

[0047] In high temperature conditions, increase the wind speed and turn on the wet curtain water pump and roller blinds.

[0048] Optionally, under high temperature conditions, the ventilation volume is determined in the following manner: ; in, M S represents the high temperature air mass flow rate, r Indicates the correction factor when sensible heat is converted into latent heat. It indicates the total heat generated by the pigs at their maximum design capacity. represents the total amount of sensible heat lost through the floor, ∆ i Indicates the calculated difference in temperature inside and outside the house. C pl Represents the specific heat capacity of dry air.

[0049] In the present invention, the required air flow in the pig house under high temperature environment can be accurately calculated to dissipate heat and control the temperature. According to the heat generated by the pigs and the temperature difference between the inside and outside of the house, combined with the dynamic adjustment mechanism of heat balance, it is ensured that the temperature in the house is efficiently reduced, heat stress is alleviated, and energy waste caused by excessive ventilation is avoided. It can more scientifically optimize ventilation management under high temperature conditions, maintain the comfort and health of pigs, and improve the operating efficiency of the ventilation system.

[0050] Reference Manual Attached Figure 3 , showing a structural schematic diagram of a ventilation hardware system provided by the present invention.

[0051] The hardware system of closed pig house ventilation includes wet curtain, sliding plate or guide window, ceiling window and fan. The wet curtain rolling curtain system is composed of motor, rolling curtain cloth, rolling curtain cloth, twisted rope, etc. It is closed in low temperature season to prevent cold wind from entering. In high temperature season, it is used to adjust the wind speed through the curtain. Guide window: change the direction of air inlet to maintain uniform ventilation in the house. Sliding plate: linked with the controller to maintain air inlet 4-5m / s. It is used when the ceiling window is below the suitable temperature limit to ensure the outlet wind speed of 1-2m / s. The fan adopts variable frequency motor.

[0052] Under low temperature conditions, set the minimum frequency of the variable frequency fan (24 or 18 inches) until the blinds can be blown open. If it is greater than the minimum ventilation volume at this time and the wind speed in the house is less than 0.3m / s, it can be determined as the minimum frequency. If the wind speed is greater than 0.3m / s, it means that the pig house is too small (the cross-sectional area is too small). If it is less than the minimum ventilation volume at this time, adjust the frequency that meets the minimum ventilation volume as the minimum frequency. If it is still less than the minimum ventilation volume at 50Hz, increase the power of the variable frequency fan. CO 2 concentration, NH 3 Concentration, H 2 When the S concentration is higher than the environmental setting value, close the side wall guide window, open the small ceiling window and adjust its opening angle to ensure the outlet wind speed is 1-2m / s for the minimum opening degree.

[0053] Under suitable temperature conditions, as the temperature gradually rises and the CO2 concentration, NH3 concentration, and H2S concentration are higher than the environmental setting values, the opening angle of the small ceiling window gradually increases to ensure that the outlet wind speed is always 1-2m / s and the wind speed in the house is kept below 0.3m / s.

[0054] Under high temperature conditions, when the temperature is 28°, the wind speed in the house is 0.3-0.6m / s, the perceived temperature is below 26℃, turn off the variable frequency fan (24:00 or 18:00), the fan frequency gradually increases at 36:00, the wet curtain roller curtain opens 100%, the guide window opens 30%, the sliding plate opens 20%, and the air inlet is 4-5m / s. When the temperature is 28°~30°, the wind speed in the house is 0.6-1.0m / s, the perceived temperature is: 26℃~27℃, at 51:00, the fan is turned on, the wet curtain water pump is started, the wet curtain roller curtain is opened about 60%, and the wind speed over the curtain is maintained at 1.5-1.8m / s. The guide window or sliding plate is opened at an angle (70%) to ensure that the air inlet is 4-5m / s. When the temperature is between 30° and 35°, the wind speed in the house is 1.0-1.5m / s, and the perceived temperature is 27℃~28.4℃. At 51 and 36 o'clock, the fan is turned on, the wet curtain water pump is started, the wet curtain roller curtain is opened to about 80-100%, and the wind speed over the curtain is maintained at 1.5-1.8m / s. The opening angle of the guide window or sliding plate (100%) ensures 4-5m / s at the air inlet.

[0055] S6: Display multi-sensor data and the operating status of ventilation equipment through a visual interface.

[0056] Specifically, the intelligent visual interface displays and exports the environmental parameters in the closed pig house and the information such as the fan, wet curtain pump, roller curtain opening, ceiling opening, diversion opening, power supply frequency, etc. that are controlled by the intelligent pig house precision control module. The backend provides a RESTful API to support the acquisition of real-time environmental parameters and equipment status data. WebSocket is used for real-time data push to ensure the instant update of front-end interface data. React.js is used to build the interface on the front end. Ant Design or Element UI is used for interface layout and component development in the UI component library. ECharts is used to display environmental parameters and equipment status data. React's Redux is used for state management of real-time data, combined with WebSocket push to achieve real-time display of data. Node.js is used to build API and data processing logic. MySQL database is used to store data. RabbitMQ is used to process large amounts of real-time data.

[0057] It should be noted that the sensor data and the output results of the intelligent control module are visualized through the intelligent visual interface, providing an intuitive interface for management personnel to monitor and operate.

[0058] The beneficial effects of the present invention are reflected in acquiring multiple types of sensor data, comprehensively and in real time monitoring the multi-dimensional environmental parameters inside the pig house, and determining the optimal control parameter configuration based on the sensor data through the particle swarm optimization algorithm, with temperature, humidity, gas concentration and energy consumption as optimization targets, to achieve comprehensive regulation of multi-dimensional parameters, create a suitable breeding environment, reduce disease risks, and improve pig health and growth efficiency.

[0059] Example 2 Reference Manual Attached Figure 4 , showing a structural schematic diagram of a closed pig house environmental parameter intelligent control system provided by the present invention.

[0060] An embodiment of the present invention provides a closed pig house environmental parameter intelligent control system 20, comprising: A setting module 201 is used to set an environmental condition model; An acquisition module 202, used to acquire multiple types of sensor data; A detection module 203, configured to perform outlier detection on multiple types of sensor data based on the environmental condition model; The determination module 204 is used to determine the optimal control parameter configuration by using a particle swarm optimization algorithm with temperature, humidity, gas concentration and energy consumption as optimization targets when there is an abnormality in the sensor data; An adjustment module 205, configured to dynamically adjust the operating state of the ventilation equipment according to the optimal control parameter configuration; The display module 206 is used to display the multi-sensor data and the operating status of the ventilation equipment through a visual interface.

[0061] In a possible implementation manner, the environmental condition model specifically includes: ; ; ; ; ; in, T min Indicates the minimum temperature. T max Indicates the maximum temperature, H min Indicates the minimum humidity value. H max Indicates the maximum humidity, CO 2max Indicates the maximum carbon dioxide concentration, NH 3max Indicates the maximum ammonia concentration, H 2 S max Indicates the maximum value of hydrogen sulfide concentration.

[0062] In a possible implementation, the multiple types of sensor data include: temperature data, humidity data, CO 2 Concentration data, NH 3 Concentration data, H 2 S concentration data and wind speed data.

[0063] In a possible implementation, the detection module 203 is specifically configured to: Calculate the Z-score for each of the multiple sensor data types: ; ; ; ; ; in, Z T Represents the Z-score of the temperature data, T Represents temperature data, m T represents the average value of temperature data, s T represents the standard deviation of the temperature data, Z HRepresents the Z-score of humidity data, H Represents humidity data, m H Represents the average value of humidity data, s H represents the standard deviation of humidity data, Represents the Z-score of the carbon dioxide concentration data, represents the carbon dioxide concentration data, represents the average value of carbon dioxide concentration data, represents the standard deviation of the carbon dioxide concentration data, Represents the Z-score of the ammonia concentration data, Indicates ammonia concentration data, represents the average value of ammonia concentration data, represents the standard deviation of the ammonia concentration data, Represents the Z-score of hydrogen sulfide concentration data, Represents hydrogen sulfide concentration data, represents the average value of hydrogen sulfide concentration data, Represents the standard deviation of hydrogen sulfide concentration data; Determine whether the Z-score of each type of sensor data exceeds the constraints of the environmental condition model; if so, determine that the sensor data is abnormal and trigger an environmental condition abnormality alarm; otherwise, call the acquisition module 202 to continue monitoring.

[0064] In a possible implementation manner, the ventilation equipment includes: a wet curtain, a fan, a small ceiling window, a guide window and a sliding plate.

[0065] In a possible implementation, the determination module 204 is specifically configured to: Taking temperature, humidity, gas concentration and energy consumption as optimization targets, set the multi-objective optimization function of the particle swarm optimization algorithm: ; in, J represents the multi-objective optimization function, f T represents the temperature error, l 1 represents the weight of temperature error, f H Represents the humidity error, l 2 represents the weight of humidity error, f C Indicates the gas concentration penalty, l 3 represents the weight of gas concentration penalty, f P Indicates energy consumption, l 4 represents the weight of energy consumption; Initialize particle swarm; Calculating the fitness value of each particle according to the multi-objective optimization function; Determine the individual historical optimal value and the global optimal value of each particle; According to the individual historical optimal value and the global optimal value, the speed and position of the particle are updated: ; ; in, Indicates i The particle in k +1 iteration speed, oh represents the inertia weight factor, Indicates i The particle in k The speed at the iteration, P i Indicates i The individual historical optimal value of each particle, Indicates i The particle in k The position at the iteration, G represents the global optimal value, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 , r 2 represents a random number between [0,1], represents the position of the 𝑖th particle at the 𝑘+1th iteration; Determine whether the termination condition is met; if so, output the current global optimal value as the optimal control parameter configuration; otherwise, return to continue iteration.

[0066] In a possible implementation, the weights of the various objectives are specifically: ; in, Indicates i The goal is t The dynamic weight at the iteration, I i Indicates i The relative importance of the goals, I j Indicates j The relative importance of the goals, Indicates i The goal is t The error at the iteration, Indicates that the current particle swarm is in the j The particle position that performs best on the target, M represents the total number of targets, M =4.

[0067] In a possible implementation, the adjustment module 205 is specifically configured to: In low temperature conditions, adjust the variable frequency fan, ceiling window and guide window to ensure that the wind speed and gas concentration at the air outlet are within a safe range; Under suitable temperature conditions, maintain appropriate wind speed and gas concentration; In high temperature conditions, increase the wind speed and turn on the wet curtain water pump and roller blinds.

[0068] In a possible implementation, under low temperature conditions, the ventilation rate is determined in the following manner: ; in, M C represents the low temperature air mass flow rate, ∑ C represents the mass flow rate of carbon dioxide produced by the pigs in the house and, Ci Indicates the amount of carbon dioxide allowed in the room. Co Indicates the carbon dioxide content of the air outside the house; In high temperature conditions, the ventilation rate is determined as follows: ; in, M S represents the high temperature air mass flow rate, r Indicates the correction factor when sensible heat is converted into latent heat. It indicates the total heat generated by the pigs at their maximum design capacity. represents the total amount of sensible heat lost through the floor, ∆ i Indicates the calculated difference in temperature inside and outside the house. C pl Represents the specific heat capacity of dry air.

[0069] An embodiment of the present invention provides an intelligent control system 20 for environmental parameters of a closed pig house, which can realize the steps and effects of the intelligent control method for environmental parameters of a closed pig house in Example 1. To avoid repetition, the present invention will not go into details.

[0070] The beneficial effects of the present invention are reflected in acquiring multiple types of sensor data, comprehensively and in real time monitoring the multi-dimensional environmental parameters inside the pig house, and determining the optimal control parameter configuration based on the sensor data through the particle swarm optimization algorithm, with temperature, humidity, gas concentration and energy consumption as optimization targets, to achieve comprehensive regulation of multi-dimensional parameters, create a suitable breeding environment, reduce disease risks, and improve pig health and growth efficiency.

[0071] In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "top", "bottom", "inside", "outside", "inner side", "outer side" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Among them, "inside" refers to an internal or enclosed area or space. "Periphery" refers to the area surrounding a specific component or a specific area.

[0072] In the description of the embodiments of the present invention, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0073] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", and "assemble" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] In the description of the embodiments of the present invention, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0075] In the description of the embodiments of the present invention, it should be understood that "-" and "~" represent the range between two values, and the range includes the endpoints. For example: "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0076] In the description of the embodiments of the present invention, the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0077] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A closed pig house environmental parameter intelligent control method, characterized in that: include: S1: Setting the environmental condition model; S2: Acquire multiple types of sensor data; S3: Based on the environmental condition model, performing outlier detection on multiple types of sensor data respectively; S4: When there are abnormalities in the sensor data, the particle swarm optimization algorithm is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as the optimization targets; S5: dynamically adjusting the operating state of the ventilation equipment according to the optimal control parameter configuration; S6: Displaying multiple types of sensor data and the operating status of the ventilation equipment through a visual interface.

2. The method for intelligently controlling environmental parameters of a closed pig house according to claim 1, characterized in that: The environmental condition model specifically includes: ； ； ； ； ； in, T min Indicates the minimum temperature. T max Indicates the maximum temperature, H min Indicates the minimum humidity value. H max Indicates the maximum humidity, CO 2max Indicates the maximum carbon dioxide concentration, NH 3max Indicates the maximum concentration of ammonia, H2S max Indicates the maximum value of hydrogen sulfide concentration.

3. The method for intelligently controlling environmental parameters of a closed pig house according to claim 1, characterized in that: The multiple types of sensor data include: temperature data, humidity data, CO2 concentration data, NH3 concentration data, H2S concentration data and wind speed data.

4. The method for intelligently controlling environmental parameters of a closed pig house according to claim 3 is characterized in that: S3 specifically includes: S301: Calculate Z-scores for the multiple types of sensor data respectively: ； ； ； ； ； in, Z T Represents the Z-score of the temperature data, T Represents temperature data, μ T represents the average value of temperature data, σ T represents the standard deviation of the temperature data, Z H Represents the Z-score of humidity data, H Represents humidity data, μ H Represents the average value of humidity data, σ H represents the standard deviation of humidity data, Represents the Z-score of the carbon dioxide concentration data, represents the carbon dioxide concentration data, represents the average value of carbon dioxide concentration data, represents the standard deviation of the carbon dioxide concentration data, Represents the Z-score of the ammonia concentration data, Indicates ammonia concentration data, represents the average value of ammonia concentration data, represents the standard deviation of the ammonia concentration data, Represents the Z-score of hydrogen sulfide concentration data, Represents hydrogen sulfide concentration data, represents the average value of hydrogen sulfide concentration data, Represents the standard deviation of hydrogen sulfide concentration data; S302: Determine whether the Z-score of each type of sensor data exceeds the constraints of the environmental condition model; if so, determine that the sensor data is abnormal and trigger an environmental condition abnormality alarm; otherwise, return to S2 to continue monitoring.

5. The method for intelligently controlling environmental parameters of a closed pig house according to claim 1, characterized in that: The ventilation equipment includes: a wet curtain, a fan, a small ceiling window, a guide window and a sliding plate.

6. The method for intelligently controlling environmental parameters of a closed pig house according to claim 1, characterized in that: In S4, the particle swarm optimization algorithm is used to determine the optimal control parameter configuration with temperature, humidity, gas concentration and energy consumption as optimization targets, which specifically includes: S401: Taking temperature, humidity, gas concentration and energy consumption as optimization targets, set the multi-objective optimization function of the particle swarm optimization algorithm: ； in, J represents the multi-objective optimization function, f T represents the temperature error, λ 1 represents the weight of temperature error, f H Represents the humidity error, λ 2 represents the weight of humidity error, f C Indicates the gas concentration penalty, λ 3 represents the weight of gas concentration penalty, f P Indicates energy consumption, λ 4 represents the weight of energy consumption; S402: Initialize particle swarm; S403: Calculating the fitness value of each particle according to the multi-objective optimization function; S404: Determine the individual historical optimal value and the global optimal value of each particle; S405: Update the speed and position of the particle according to the individual historical optimal value and the global optimal value: ； ； in, Indicates i The particle in k +1 iteration speed, ω represents the inertia weight factor, Indicates i The particle in k The speed at the iteration, P i Indicates i The individual historical optimal value of each particle, Indicates i The particle in k The position at the iteration, G represents the global optimal value, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1. r 2 represents a random number between [0,1], represents the position of the 𝑖th particle at the 𝑘+1th iteration; S406: Determine whether the termination condition is met; if so, output the current global optimal value as the optimal control parameter configuration; otherwise, return to S403 to continue iteration.

7. The method for intelligently controlling environmental parameters of a closed pig house according to claim 6, characterized in that: The weights of each goal are as follows: ； in, Indicates i The goal is t The dynamic weight at the iteration, I i Indicates i The relative importance of the goals, I j Indicates j The relative importance of the goals, Indicates i The goal is t The error at the iteration, Indicates that the current particle swarm is in the j The particle position that performs best on the target, M represents the total number of targets, M =4.

8. The method for intelligently controlling environmental parameters of a closed pig house according to claim 1, characterized in that: The dynamically adjusting the operating state of the ventilation equipment in S5 specifically includes: In low temperature conditions, adjust the variable frequency fan, ceiling window and guide window to ensure that the wind speed and gas concentration at the air outlet are within a safe range; Under suitable temperature conditions, maintain appropriate wind speed and gas concentration; In high temperature conditions, increase the wind speed and turn on the wet curtain water pump and roller blinds.

9. The method for intelligently controlling environmental parameters of a closed pig house according to claim 8, characterized in that: In low temperature conditions, the ventilation rate is determined as follows: ； in, M C represents the low temperature air mass flow rate, ∑ C represents the mass flow rate of carbon dioxide produced by the pigs in the house and, Ci Indicates the amount of carbon dioxide allowed in the room. Co Indicates the carbon dioxide content of the air outside the house; In high temperature conditions, the ventilation rate is determined as follows: ； in, M S represents the high temperature air mass flow rate, r Indicates the correction factor when sensible heat is converted into latent heat. It indicates the total heat generated by the pigs at their maximum design capacity. represents the total amount of sensible heat lost through the floor, ∆ θ Indicates the calculated difference in temperature inside and outside the house. C pl Represents the specific heat capacity of dry air.

10. An intelligent control system for environmental parameters of a closed pig house, characterized in that: include: A setting module, used to set the environmental condition model; An acquisition module is used to acquire multiple types of sensor data; A detection module, used for performing outlier detection on multiple types of sensor data respectively based on the environmental condition model; The determination module is used to determine the optimal control parameter configuration by using the particle swarm optimization algorithm when there is an abnormality in the sensor data, taking temperature, humidity, gas concentration and energy consumption as the optimization targets; An adjustment module, configured to dynamically adjust the operating state of the ventilation equipment according to the optimal control parameter configuration; The display module is used to display the multiple types of sensor data and the operating status of the ventilation equipment through a visual interface.

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