An adaptive intelligent pig house environment collaborative regulation system and method

Abstract

The application provides a self-adaptive intelligent pig house environment cooperative regulation system and method, and relates to the technical field of automatic control.The system comprises a sensing module, a control module and an execution module.The sensing module collects the NH3 concentration, temperature and humidity of the environment.The control module receives the information collected by the sensing module and outputs control parameters by using the control algorithm integrated in the control module.The execution module receives the control parameters and adjusts the working state of each component according to the control parameters.The technical scheme of the application overcomes the problem in the prior art that the pig house environment cannot be optimized by combining multi-target control.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic control, in particular to a self-adaptive intelligent pig house environment collaborative regulation system and method. BACKGROUND

[0002] The pig house environment control system and method in the prior art have the following defects:

[0003] The traditional PID control is difficult to handle the strong coupling characteristics of temperature, humidity and ammonia concentration, resulting in large overshoot (>12%) and long regulation time (>4 hours).

[0004] The predictive control method based on accurate mathematical model has poor adaptability in the time-varying environment of pig house, and the model relies on manual debugging.

[0005] The existing system lacks control quantity constraint mechanism, the fan daily average start-stop number is more than 2000 times, the equipment life is less than 2 years, resulting in actuator loss.

[0006] The local area ammonia concentration exceeds the standard rate by more than 30%, resulting in imbalance of pig house environment.

[0007] Therefore, there is a need for a self-adaptive intelligent pig house environment collaborative regulation system and method that combines multi-objective optimization control to achieve collaborative regulation of temperature, humidity and ammonia concentration. SUMMARY

[0008] The main purpose of the present application is to provide a self-adaptive intelligent pig house environment collaborative regulation system and method to solve the problem that the existing technology cannot combine multi-objective control to optimize the pig house environment.

[0009] To achieve the above purpose, the present application provides a self-adaptive intelligent pig house environment collaborative regulation system, comprising a perception module, a control module and an execution module, the perception module collects NH3 concentration, temperature and humidity of the environment, the control module receives the information collected by the perception module, and outputs control parameters using the control algorithm integrated in the control module, and the execution module receives the control parameters and adjusts the working state of each component according to the control parameters.

[0010] Further, the perception module comprises an NH3 sensor and a temperature and humidity sensor.

[0011] Further, the components include a variable frequency permanent magnet fresh air fan, an adjustable spray humidifying device and an intelligent air deflector.

[0012] The present application also provides a self-adaptive intelligent pig house environment collaborative regulation method, which specifically comprises the following steps:

[0013] S1, initialize the system.

[0014] S2, pre-process the data obtained by the NH3 sensor and the temperature and humidity sensor.

[0015] S3, dynamically decouple the temperature, humidity, and NH3 concentration, and update the gradient matrix.

[0016] S4, optimize the control law in combination with the convergence rate, control amount fluctuation, and energy consumption.

[0017] S5, control the angle of the intelligent air deflector based on the NH3 concentration and the wind speed.

[0018] S6, update the historical data, wait for the next control period, and repeat steps S1-S5 to control the pig house environment in real time.

[0019] Further, step S2 specifically includes the following steps:

[0020] S2.1, perform sliding average filtering on the data.

[0021] S2.2, remove singular values.

[0022] Further, step S3 specifically includes the following steps:

[0023] S3.1, calculate the coupling strength factor :

[0024] ;

[0025] wherein, is a weight coefficient, is a coupling strength adjustment coefficient, is a natural exponential function, is a bias value, respectively correspond to temperature, humidity, and ammonia, is the deviation value of the real-time temperature at this moment and the previous time, is the deviation value of the real-time humidity at this moment and the previous time, is the deviation value of the real-time ammonia at this moment and the previous time.

[0026] S3.2, perform a weighted pseudo-gradient update with constraints:

[0027] ;

[0028] wherein, respectively correspond to temperature, humidity, and ammonia, respectively correspond to the variable-frequency permanent-magnet fresh air fan and the adjustable spray humidifying device, is a gradient value, is a limit, is a convergence speed of the gradient update, The deviation value output by the variable frequency permanent magnet new fan or the adjustable spray humidifying device.

[0029] S3.3, The range of the deviation value is [-2, 2].

[0030] Further, the multi-objective optimization control law calculation formula in step S4 is:

[0031] ;

[0032] Wherein, is the convergence speed of the control law, is the control amount fluctuation suppression parameter, is the energy consumption reduction parameter, is the deviation value of the target and the actual value.

[0033] Further, the angle of the intelligent air deflector in step S5 is The calculation formula is:

[0034] ;

[0035] Wherein, is the wind speed, = 10ppm, is the ammonia concentration.

[0036] The present application has the following beneficial effects:

[0037] The present application adjusts the multi-parameter coupling strength in real time through the dynamic decoupling factor Combining the multi-objective optimization control, the temperature, humidity and ammonia concentration are cooperatively regulated. The experiment shows that the system shortens the adjustment time by 45%, reduces the energy consumption by 27.5%, and prolongs the equipment life by 2.3 times. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor. In the drawings:

[0039] Figure 1 The flow chart of the adaptive intelligent pig house environment cooperative regulation method of the present application is shown. DETAILED DESCRIPTION

[0040] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0041] An adaptive intelligent pig house environment collaborative control system comprises a perception module, a control module and an execution module, the perception module collects NH3 concentration, temperature and humidity of the environment, the control module receives information collected by the perception module and outputs control parameters by using a control algorithm integrated in the control module, and the execution module receives the control parameters and adjusts the working state of each component according to the control parameters. The control module is an industrial programmable controller supporting Modbus-TCP / Profinet communication.

[0042] Specifically, the perception module comprises an NH3 sensor and a temperature and humidity sensor. The temperature and humidity sensor has an accuracy of ±0.5℃.

[0043] Specifically, the components comprise a variable-frequency permanent-magnet fresh air fan, an adjustable spray humidifying device and an intelligent air deflector. The variable-frequency permanent-magnet fresh air fan has a speed of 200-1000 rpm and stepless speed regulation. The adjustable spray humidifying device has a particle size of <10 μm. The adjustable angle range of the intelligent air deflector is 0-90° and is adjusted by electricity.

[0044] As shown in Figure 1 The present application also provides an adaptive intelligent pig house environment collaborative control method, which specifically comprises the following steps:

[0045] S1, initializing the system. Load parameters and reset historical data.

[0046] S2, pre-processing data obtained by the NH3 sensor and the temperature and humidity sensor.

[0047] S3, dynamically decoupling temperature, humidity and NH3 concentration and updating gradient matrix.

[0048] S4, optimizing control law in combination with convergence rate, control amount fluctuation and energy consumption.

[0049] S5, controlling the angle of the intelligent air deflector in combination with NH3 concentration and wind speed.

[0050] S6, updating historical data, waiting for the next control period and repeating steps S1-S5 to control the pig house environment in real time.

[0051] Specifically, step S2 specifically comprises the following steps:

[0052] S2.1, the data is filtered by moving average.

[0053] S2.2, the singular values are removed.

[0054] Specifically, step S3 specifically includes the following steps:

[0055] S3.1, the coupling strength factor is calculated :

[0056] ;

[0057] wherein, is a weight coefficient, =[0.5,0.3,0.2], is a coupling strength adjustment coefficient, , is a natural exponential function, is a bias value, respectively corresponding to temperature, humidity, ammonia, is the real-time temperature at this moment and the previous bias value, is the real-time humidity at this moment and the previous bias value, is the real-time ammonia at this moment and the previous bias value.

[0058] S3.2, the weighted pseudo-gradient update with constraints is performed:

[0059] ;

[0060] wherein, respectively corresponding to temperature, humidity, ammonia, respectively corresponding to the variable frequency permanent magnet fresh air fan and the adjustable spray humidifying device, is a gradient value, is a limit, is a convergence speed of gradient update, is a bias value of the output of the variable frequency permanent magnet fresh air fan or the adjustable spray humidifying device.

[0061] S3.3, the range of is [-2, 2], preventing the control from diverging.

[0062] Specifically, the multi-objective optimization control law calculation formula in step S4 is:

[0063] ;

[0064] wherein, is a convergence speed of the control law, =0.85, is a control amount fluctuation suppression parameter, =0.1, To reduce the energy consumption parameter, = 0.01, The target and actual deviation value.

[0065] Specifically, the angle of the intelligent air deflector in step S5 The calculation formula is:

[0066] ;

[0067] Wherein, The wind speed, = 10 ppm, The ammonia concentration.

[0068] As shown in Table 1, in the control comparison test on 1000 fattening pens, the present application dynamically decouples the factors Real-time adjustment of the coupling strength of multiple parameters, combined with multi-objective optimization control, realizes the coordinated regulation of temperature, humidity and ammonia concentration. Experiments show that the temperature regulation time of the system controlled by the method provided by the present application is shortened by 45%, the comprehensive energy consumption is reduced by 27.5%, and the equipment life is increased by 2.3 times.

[0069] Table 1 Control comparison test

[0070]

[0071] Of course, the above description is not a limitation of the present application, and the present application is not limited to the above examples. Changes, modifications, additions or replacements made by those skilled in the art within the scope of the present application should also be within the protection scope of the present application.

Claims

1. An adaptive intelligent pigsty environment collaborative control method, characterized in that, Specifically, the steps include the following: S1, initialize the system; S2, preprocess the data acquired by the NH3 sensor and the temperature and humidity sensor; S3 dynamically decouples temperature, humidity, and NH3 concentration, and updates the pseudo-gradient matrix. S4, combining convergence rate, control quantity fluctuation and energy consumption to optimize the control law; S5, based on NH3 concentration and wind speed, coordinates the angle of the intelligent air guide plate; S6, update the historical data, wait for the next control cycle, and repeat steps S1-S5 to control the pig house environment in real time. An adaptive intelligent pigsty environment collaborative control system is provided, which implements an adaptive intelligent pigsty environment collaborative control method. The system includes a sensing module, a control module, and an execution module. The sensing module collects the NH3 concentration, temperature, and humidity of the environment. The control module receives the information collected by the sensing module and outputs control parameters using a control algorithm integrated within the control module. The execution module receives the control parameters and adjusts the working state of each component according to the control parameters. S3.1, Calculate the coupling strength factor : ； in, These are the weighting coefficients. This is the coupling strength adjustment coefficient. It is a natural exponential function. This is the deviation value. These correspond to temperature, humidity, and ammonia, respectively. This represents the deviation of the current real-time temperature from the previous value. This represents the deviation of the current humidity level from the previous value. This represents the deviation of the current ammonia gas reading from the previous reading. S3.2, perform constrained weighted pseudo-gradient update: ； in, These correspond to temperature, humidity, and ammonia, respectively. These are respectively a strain frequency permanent magnet fresh air unit and an adjustable spray humidifier. The gradient value, In order to reach the limit, This represents the convergence speed of gradient updates. This refers to the deviation value of the output of the variable frequency permanent magnet fresh air unit or the output of the adjustable spray humidifier; The formula for calculating the multi-objective optimization control law in step S4 is: ； in, The convergence rate of the control law. To suppress control variable fluctuations, To reduce energy consumption parameters, This represents the deviation between the target and the actual value.

2. The adaptive intelligent pigsty environment collaborative control method according to claim 1, characterized in that, The sensing module includes an NH3 sensor and a temperature and humidity sensor.

3. The adaptive intelligent pigsty environment collaborative control method according to claim 1, characterized in that, Components include: Variable frequency permanent magnet fresh air unit, adjustable spray humidification device and intelligent air guide plate.

4. The adaptive intelligent pigsty environment collaborative control method according to claim 1, characterized in that, Step S2 specifically includes the following steps: S2.1, Perform a moving average filter on the data; S2.2, remove singular values.

5. The adaptive intelligent pigsty environment collaborative control method according to claim 1, characterized in that, In step S3 The range is [-2, 2].

6. The adaptive intelligent pigsty environment collaborative control method according to claim 1, characterized in that, The angle of the intelligent air guide plate in step S5 The calculation formula is: ； in, For wind speed, =10ppm, This represents the concentration of ammonia gas.

Images