A sunlight greenhouse environment control system and control method for preventing and treating cucumber downy mildew

Abstract

The application discloses a sunlight greenhouse environment control system and control method for preventing and treating cucumber downy mildew, relates to the technical field of agricultural systems, and mainly aims to provide a sunlight greenhouse environment control system for preventing and treating cucumber downy mildew, which can predict and control diseases and maintain the optimal temperature of crop production. The main technical scheme of the application is as follows: a sunlight greenhouse environment control system for preventing and treating cucumber downy mildew, comprising: a calculation module, a greenhouse environment prediction module receiving a 24-hour hourly weather forecast in the future, a risk determination module receiving environment prediction result data, an optimization module receiving environment prediction result data and disease risk prediction result data, and an execution module, wherein the optimization module calculates optimal control setting values in the current hour through iterative calculation and outputs the optimal control setting values; the execution module comprises a PLC controller, a temperature sensor and a film rolling executor, the temperature sensor is connected to the optimization module and the PLC controller, and the PLC controller is connected to the film rolling executor.

Description

Technical Field

[0001] This invention relates to the field of agricultural systems technology, and in particular to a solar greenhouse environmental control system and control method for the prevention and control of cucumber downy mildew. Background Technology

[0002] Cucumber downy mildew is a disease caused by infection with Pseudomonas columbarium. It mainly affects the leaves, but can also damage the stems and inflorescences. The disease can occur from the seedling stage to the mature plant stage, and is particularly severe during the cucumber harvesting period. Cucumber downy mildew is the most serious epidemic disease in greenhouse cucumber production. After infection, it can cause most of the cucumber leaves to wither and die within one or two weeks, turning the cucumber field into a withered and yellow landscape.

[0003] Existing methods for preventing cucumber downy mildew mainly involve artificially controlling cucumber growing conditions and temperature and humidity to create an environment unfavorable for pathogen growth and development. However, in practice, growers find it difficult to precisely control growing conditions. Existing technology discloses an intelligent greenhouse parameter control system for preventing cucumber downy mildew, which uses a temperature detection device to monitor greenhouse temperature parameters and then adjusts the temperature using cold and hot air blowers. However, the cold and hot air blowers have limitations in their range and duration of action. Furthermore, the greenhouse temperature and humidity are easily affected by the external environment, leading to delayed adjustments by the cold and hot air blowers. Additionally, the temperature detection device can only monitor the current temperature and humidity and lacks predictive capabilities. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a greenhouse environmental control system and control method for the prevention and control of cucumber downy mildew. The main purpose is to provide a greenhouse environmental control system for the prevention and control of cucumber downy mildew that can predict and control the disease and maintain the optimal temperature for crop production.

[0005] To achieve the above objectives, the present invention mainly provides the following technical solutions:

[0006] On one hand, embodiments of the present invention provide a greenhouse environmental control system for the prevention and control of cucumber downy mildew, the system comprising:

[0007] The calculation module includes a greenhouse environment prediction module, a risk assessment module, and an optimization module. The greenhouse environment prediction module receives hourly weather forecasts for the next 24 hours and outputs environmental prediction results data of temperature, humidity, and leaf wet development curves in the greenhouse for the next 24 hours. The risk assessment module receives the environmental prediction results data and outputs disease risk prediction results data for the next 24 hours in the greenhouse. The optimization module receives the environmental prediction results data and disease risk prediction results data, calculates the optimal control setpoint for the current hour through iterative calculation, and outputs it.

[0008] The execution module includes a PLC controller, a temperature sensor, and a film winding actuator. The temperature sensor is connected to the optimization module and the PLC controller to receive the optimal control setpoint for the current hour. The PLC controller is connected to the film winding actuator to control the opening degree of the air vent of the film winding actuator.

[0009] Furthermore, the optimization module includes a cost function, which is expressed as follows:

[0010]

[0011] Where J is the output of the cost function; j is a given discrete instantaneous time, in seconds; α and β are weighting factors; IFC is the number of positive infection reports in the prediction time domain; Np is the prediction time domain; and T is the number of positive infection reports in the prediction time domain. min and T max These represent the minimum and maximum allowable temperatures for the crop, in °C and T, respectively. OPT It is the optimal temperature setpoint °C, T sp A vector in °C representing how the future temperature setpoint changes over time.

[0012] Furthermore, the optimization module also includes an optimizer algorithm, which serves as a global solver for the time-consuming objective function, finding the optimal temperature setpoint per hour. The solver searches for the global minimum of the real-valued objective function in a multi-dimensional space.

[0013] Furthermore, the PLC controller is equipped with the following PID control algorithm:

[0014]

[0015] Where u is the air vent opening degree (0-100%); e is the control deviation, and e is the temperature setpoint T. sp The difference between the current monitored temperature and the temperature in °C; k p It is the proportional gain, T i It is the integration time.

[0016] Furthermore, the risk assessment module determines the risk using the following formula:

[0017]

[0018] IF is the infection factor, h°C, where LWD is the duration of leaf wetting in h; TLWD is the hourly average temperature in LWD units, °C.

[0019] On the other hand, embodiments of the present invention also provide a method for controlling the environment of a solar greenhouse for the prevention and control of cucumber downy mildew, the method comprising the following steps:

[0020] Data analysis and prediction: Based on the environmental prediction results of temperature and humidity forecasts and leaf moisture development curves, the risk of disease is judged, and the optimal control setpoints are output through iterative calculation.

[0021] During the execution process, the PLC controller calculates and adjusts the air outlet opening of the film winding actuator based on the optimal control setpoint, the temperature detected by the temperature sensor, and the deviation between the setpoint and the temperature.

[0022] Compared with the prior art, the present invention has the following technical effects:

[0023] First, the present invention receives weather forecast data once an hour and predicts the temperature, humidity and leaf wet development curve for the next 24 hours through the greenhouse environment prediction module. The risk assessment module assesses the disease risk for the next 24 hours and generates disease risk prediction data, thereby improving the accuracy of disease risk prediction.

[0024] Secondly, this invention inputs environmental prediction data and disease risk prediction data into an optimization module for iterative calculation and generates the optimal control setpoint for the current hour. Then, it transmits the setpoint to the PLC controller through a communication gateway. The PLC controller calculates the vent opening that the film-rolling actuator should perform based on the deviation between the current monitored temperature and the set temperature, and controls the film-rolling actuator to adjust the opening, thereby achieving the function of controlling and adjusting the greenhouse environment temperature, and thus achieving the technical effect of maintaining the optimal temperature for crop production. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of a solar greenhouse environmental control system for the prevention and control of cucumber downy mildew, provided in an embodiment of the present invention.

[0026] Figure 2 A chart named "envir-crop.xlsx" is provided for an embodiment of the present invention;

[0027] Figure 3 A chart with a file named "control.xlsx" is provided for an embodiment of the present invention;

[0028] Figure 4 A chart with the file name "gsize.xlsx" provided for an embodiment of the present invention;

[0029] Figure 5 A flowchart of a PID control algorithm provided in an embodiment of the present invention;

[0030] Figure 6A flowchart illustrating a greenhouse environmental control method for preventing and controlling cucumber downy mildew, provided as an embodiment of the present invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0032] like Figures 1 to 5 As shown, this embodiment of the invention provides a greenhouse environmental control system for the prevention and control of cucumber downy mildew. The system includes:

[0033] The calculation module includes a greenhouse environment prediction module, a risk assessment module, and an optimization module. The greenhouse environment prediction module receives hourly weather forecasts for the next 24 hours and outputs environmental prediction results data of temperature, humidity, and leaf wet development curves in the greenhouse for the next 24 hours. The risk assessment module receives the environmental prediction results data and outputs disease risk prediction results data for the next 24 hours in the greenhouse. The optimization module receives the environmental prediction results data and disease risk prediction results data, calculates the optimal control setpoint for the current hour through iterative calculation, and outputs it.

[0034] The execution module includes a PLC controller, a temperature sensor, and a film winding actuator. The temperature sensor is connected to the optimization module and the PLC controller to receive the optimal control setpoint for the current hour. The PLC controller is connected to the film winding actuator to control the opening degree of the air vent of the film winding actuator.

[0035] Compared with the prior art, the present invention has the following technical effects:

[0036] First, the present invention receives weather forecast data once an hour and predicts the temperature, humidity and leaf wet development curve for the next 24 hours through the greenhouse environment prediction module. The risk assessment module assesses the disease risk for the next 24 hours and generates disease risk prediction data, thereby improving the accuracy of disease risk prediction.

[0037] Secondly, this invention inputs environmental prediction data and disease risk prediction data into an optimization module for iterative calculation and generates the optimal control setpoint for the current hour. Then, it transmits the setpoint to the PLC controller through a communication gateway. The PLC controller calculates the vent opening that the film-rolling actuator should perform based on the deviation between the current monitored temperature and the set temperature, and controls the film-rolling actuator to adjust the opening, thereby achieving the function of controlling and adjusting the greenhouse environment temperature, and thus achieving the technical effect of maintaining the optimal temperature for crop production.

[0038] The greenhouse environment prediction module described above uses a greenhouse environment prediction model. The model code is translated and run using the Python 3.12 interpreter. The model code is as follows:

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[0063] Each time the model runs, it calls three Excel files stored in the current working directory, generating one Excel file named "output.xlsx", and calling the first file named "envir-crop.xlsx". Figure 2 As shown, Figure 2 The data in the table includes outdoor weather forecasts, initial indoor temperature and humidity, and crop parameters. Columns A, B, and C represent the date and time, respectively; B represents outdoor temperature (°C); C represents outdoor relative humidity (%); and D represents outdoor solar radiation (W / m²). 2 E. Outdoor wind speed (m / s), F. Outdoor wind direction, G. Outdoor air pressure (pa), H. Initial indoor temperature (°C), I. Initial outdoor temperature (°C), J. Initial indoor soil temperature (°C), K. Initial indoor relative humidity (%), L. Initial outdoor relative humidity (%), M. Initial indoor soil moisture content, N. Initial outdoor solar radiation (W / s) m2 O Initial indoor CO2 concentration (PPM), P Initial outdoor wind speed (m / s), Q Initial outdoor wind direction, R Initial outdoor air pressure (pa), S Crop name, T Growth stage, U Leaf area index, V Crop canopy height (m), W Leaf characteristic length (m), X Ratio of crop area to total greenhouse area, Y Number of plants.

[0064] like Figure 3 As shown, the file being called is named "control.xlsx". Figure 3 The data in the middle represents the status parameters of the greenhouse environmental control equipment. Columns A, B, and C represent the date and time, respectively; B represents the opening degree of the upwind vent (0-100%); C represents the opening degree of the downwind vent (0-100%); DE represents the opening and closing of the shading net (0, 1); and FG represents the opening and closing of the insulation blanket (0, 1).

[0065] like Figure 4 As shown, the file being called is named "gsize.xlsx". Figure 4 The data in the middle section represents the dimensions and material properties of the solar greenhouse. The variable names are explained below:

[0066] 'l' Greenhouse length, 's' Greenhouse width, 'h_w' Rear wall height, 'h_r' Roof ridge height, 'h_v' Upwind vent height, 'w_r1' Upper roof span, 'w_r2' Front roof span, 'w_u' Fully open width of upwind vent, 'w_l' Fully open width of downwind vent, 'th_w1' Rear wall thickness, 'th_w2' Thermal storage wall thickness, 'th_w3' Gable wall thickness, 'th_r1' Front roof thickness, 'th_r2' Rear roof thickness, 'th_b' Insulation blanket thickness, 'th_s' Soil thickness (depth measured by soil temperature sensor);

[0067] 'sink' sink height, 'a_s' gable area, 'x_rw' front roof angle to rear wall, 'x_wr' rear wall angle to front roof, 'x_gr' ground angle to front roof, 'a_g' indoor ground area, 'a_r1' upper roof area, 'a_r2' front roof area, 'a_w1' indoor rear wall area; 'a_w2' outdoor rear wall area, 'a_w3' gable area, 'pe' rear wall perimeter, 'v' greenhouse volume, 'v_w' rear wall volume, 'v_g' soil volume, 'c_w1' rear wall conductivity, 'c_w2' rear wall heat storage layer conductivity, 'c_w3' gable conductivity, 'c_r1' front roof conductivity; 'c_r2' rear roof conductivity, 'c_b' insulation blanket conductivity. 'c_g' Soil conductivity, 'cp_w1' Rear wall specific heat capacity, 'cp_w2' Rear wall heat storage layer specific heat capacity, 'cp_w3' Gable wall specific heat capacity, 'cp_r1' Front roof specific heat capacity, 'cp_r2' Rear roof specific heat capacity, 'cp_g' Soil specific heat capacity; 'd_w1' Rear wall material density, 'd_w2' Rear wall heat storage layer material density, 'd_w3' Gable wall material density, 'd_r1' Front roof material density, 'd_r2' Rear roof material density, 'd_g' Soil density, 'rf' Rear wall surface convective heat transfer parameters, 'e_w' Rear wall emissivity, 'e_g' Soil emissivity, 'e_r' Roof emissivity, 'e_b' Insulation blanket emissivity; 'e_wr' Long-wave radiation exchange rate between rear wall and front roof. 'e_wb' Long-wave radiation exchange rate between the rear wall and the insulation blanket, 'e_rw' Long-wave radiation exchange rate between the front roof and the rear wall, 'e_bw' Long-wave radiation exchange rate between the insulation blanket and the rear wall, 'e_gr' Long-wave radiation exchange rate between the ground and the roof, 'as_w' Solar radiation absorption rate of the rear wall, 'as_r' Solar radiation absorption rate of the front roof, 'as_g' Solar radiation absorption rate of the ground, 'tau' Solar radiation transmittance of the front roof, 'tau_l' Long-wave radiation transmittance of the front roof; 'd_air' Air density, 'cp_air' Air specific heat capacity, 'cp_vap' Water vapor specific heat capacity, 'E' Wind pressure ventilation coefficient, 'M' Thermal pressure ventilation coefficient, 'E_i' Wind pressure coefficient of the cold air infiltration model, 'M_i' Thermal pressure coefficient of the cold air infiltration model, 'z' Depreciation coefficient of the front roof in the cold air infiltration model, 'g' Gravity acceleration. 'sgma' Stefan-Boltzmann constant, 'hs' Thermodynamic constant of moist air, 'kc' Canopy extinction coefficient, 'longi' Accuracy, 'lati' Latitude.

[0068] Furthermore, the risk assessment module determines the risk using the following formula:

[0069]

[0070] Wherein, IF is the infection factor, h°C; LWD is the duration of leaf wetting, h; TLWD is the hourly average temperature in LWD units, °C; and the number of positive infection reports (IFC) represents the number of consecutive periods that meet the environmental criteria for infection risk assessment.

[0071] Furthermore, the optimization module includes a cost function, which is expressed as follows:

[0072]

[0073] Where J is the output of the cost function; j is a given discrete instantaneous time, in seconds; α and β are weighting factors; IFC is the number of positive infection reports in the prediction time domain; Np is the prediction time domain; and T is the number of positive infection reports in the prediction time domain. min and T max These represent the minimum and maximum allowable temperatures for the crop, in °C and T, respectively. OPT It is the optimal temperature setpoint °C, T sp This represents a vector in °C representing the future temperature setpoint as a function of time. In this embodiment, by adjusting the greenhouse temperature setpoint, the number of positive reports of downy mildew in greenhouse cucumbers is minimized. When the setpoint temperature deviates from the recommended optimal growth temperature for the crop, a slack variable φ is used as a penalty. From an optimization perspective, this is considered in conjunction with the predicted rolling time domain N. p This problem can be described as follows:

[0074]

[0075] To determine the optimal temperature setpoint for each hour, the optimization module also includes an optimizer algorithm. This algorithm acts as a global solver for the time-consuming objective function, searching for the global minimum of the real-valued objective function in a multi-dimensional space. The recommended diurnal temperature range for cucumber production is 18 to 30°C. In the cost function, the optimal temperature setpoint for production is considered to be 28°C. The lower and upper limits of the tolerance temperature for cucumber growth are T0 and T1, respectively. min = 15 °C and T max = 40 °C.

[0076] Furthermore, such as Figure 4 As shown, the PID control algorithm set in the PLC controller is as follows:

[0077]

[0078] Where u is the air vent opening degree (0-100%); e is the control deviation, and e is the temperature setpoint T. sp The difference between the current monitored temperature and the temperature in °C; k p It is the proportional gain, Ti This refers to the integral time. In this embodiment, a temperature control algorithm is provided. The input of the temperature control algorithm is the set temperature value, and the output is the opening degree of the upper and lower air vents. After calculating the result, the PLC controller adjusts the opening degree of the film winding machine according to the forward and reverse winding time of the film winding machine, thereby achieving the technical effect of controlling the opening degree.

[0079] On the other hand, embodiments of the present invention also provide a method for controlling the environment of a solar greenhouse for the prevention and control of cucumber downy mildew, the method comprising the following steps:

[0080] Data analysis and prediction: Based on the environmental prediction results of temperature and humidity forecasts and leaf moisture development curves, the risk of disease is judged, and the optimal control setpoints are output through iterative calculation.

[0081] During the execution process, the PLC controller calculates and adjusts the air outlet opening of the film winding actuator based on the optimal control setpoint, the temperature detected by the temperature sensor, and the deviation between the setpoint and the temperature.

[0082] Compared with the prior art, the present invention has the following technical effects:

[0083] First, the present invention receives weather forecast data once an hour and predicts the temperature, humidity and leaf wet development curve for the next 24 hours through the greenhouse environment prediction module. The risk assessment module assesses the disease risk for the next 24 hours and generates disease risk prediction data, thereby improving the accuracy of disease risk prediction.

[0084] Secondly, this invention inputs environmental prediction data and disease risk prediction data into an optimization module for iterative calculation and generates the optimal control setpoint for the current hour. Then, it transmits the setpoint to the PLC controller through a communication gateway. The PLC controller calculates the vent opening that the film-rolling actuator should perform based on the deviation between the current monitored temperature and the set temperature, and controls the film-rolling actuator to adjust the opening, thereby achieving the function of controlling and adjusting the greenhouse environment temperature, and thus achieving the technical effect of maintaining the optimal temperature for crop production.

[0085] Example 1

[0086] like Figures 1 to 6 As shown in the figure, this embodiment of the invention also provides a method for controlling the environment of a solar greenhouse for the prevention and control of cucumber downy mildew. The method includes the following steps:

[0087] 101. Data analysis and prediction: Based on the environmental prediction results of temperature and humidity forecasts and leaf moisture development curves, assess disease risk, and output the optimal control setpoints through iterative calculation.

[0088] The system updates and receives 24-hour weather forecast data hourly and inputs it into the greenhouse environment prediction module. This module predicts and outputs environmental prediction data for the temperature, humidity, and leaf moisture development curves within the greenhouse for the next 24 hours. This environmental prediction data is then input into the risk assessment module. The risk assessment module predicts the disease risk within the greenhouse for the next 24 hours and outputs the disease risk prediction data. Whether cucumber downy mildew is present is determined by the following formula:

[0089]

[0090] Wherein, IF is the infection factor, h°C; LWD is the duration of leaf wetting, h; TLWD is the hourly average temperature in LWD units, °C; and the number of positive infection reports (IFC) represents the number of consecutive periods that meet the environmental criteria for infection risk assessment.

[0091] Then, the environmental prediction data and disease risk prediction data are input into the optimization module. The optimization module iteratively calculates and outputs the optimal control setpoint for the current hour through a 4G communication gateway. To determine the optimal temperature setpoint for each hour, the optimization module also includes an optimizer algorithm. This algorithm acts as a global solver for the time-consuming objective function, finding the optimal temperature setpoint for each hour. The solver searches for the global minimum of the real-valued objective function in a multi-dimensional space. The recommended daytime and nighttime temperature for cucumber production is 18 to 30°C. In the cost function, the optimal temperature setpoint for production is considered to be 28°C. The lower and upper limits of the tolerance temperature for cucumber growth are T, respectively. min = 15 °C and T max = 40 °C.

[0092] 102. Execution Processing: The PLC controller calculates and adjusts the air outlet opening of the film winding actuator based on the optimal control setpoint, the temperature detected by the temperature sensor, and the deviation between the setpoint and the setpoint temperature.

[0093] The PLC controller receives the optimal control setpoint for the current hour, and then calculates the appropriate vent opening for the film-winding actuator based on the deviation between the temperature detected by the temperature sensor and the setpoint. The temperature control algorithm within the PLC controller takes the temperature setpoint as input and outputs the upper and lower vent openings. The algorithm is as follows:

[0094]

[0095] After calculating the results, the PLC controller adjusts the opening of the film winding machine according to the forward and reverse winding times, thereby achieving the technical effect of controlling the opening.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A sunlight greenhouse environment control system for the prevention of cucumber downy mildew, characterized in that, include: The calculation module includes a greenhouse environment prediction module, a risk assessment module, and an optimization module. The greenhouse environment prediction module receives hourly weather forecasts for the next 24 hours and outputs environmental prediction results data of temperature, humidity, and leaf wet development curves in the greenhouse for the next 24 hours. The risk assessment module receives the environmental prediction results data and outputs disease risk prediction results data for the next 24 hours in the greenhouse. The optimization module receives the environmental prediction results data and disease risk prediction results data, calculates the optimal control setpoint for the current hour through iterative calculation, and outputs it. The execution module includes a PLC controller, a temperature sensor, and a film winding actuator. The temperature sensor is connected to the optimization module and the PLC controller to receive the optimal control setpoint for the current hour. The PLC controller is connected to the film winding actuator to control the opening degree of the air vent of the film winding actuator.

2. A greenhouse environmental control system for controlling cucumber downy mildew according to claim 1, characterized in that, The optimization module includes a cost function, which is described as follows: Where J is the output of the cost function; j is a given discrete instantaneous time, in seconds; α and β are weighting factors; IFC is the number of positive infection reports in the prediction time domain; Np is the prediction time domain; and T is the number of positive infection reports in the prediction time domain. min and T max These represent the minimum and maximum allowable temperatures for the crop, in °C and T, respectively. OPT It is the optimal temperature setpoint °C, T sp A vector in °C representing how the future temperature setpoint changes over time.

3. A greenhouse environmental control system for the prevention and control of cucumber downy mildew according to claim 2, characterized in that, The optimization module also includes an optimizer algorithm, which acts as a global solver for the time-consuming objective function to find the optimal temperature setpoint per hour. The solver searches for the global minimum of the real-valued objective function in a multi-dimensional space.

4. A greenhouse environmental control system for the prevention and control of cucumber downy mildew according to claim 1, characterized in that, The PLC controller is equipped with the following PID control algorithm: Where u is the air vent opening degree (0-100%); e is the control deviation, and e is the temperature setpoint T. sp The difference between the current monitored temperature and the temperature in °C; k p It is the proportional gain, T i It is the integration time.

5. A greenhouse environmental control system for controlling cucumber downy mildew according to claim 1, characterized in that, The risk assessment module determines the risk using the following formula: Where IF is the infection factor, h°C; LWD is the duration of leaf wetting, h; TLWD is the hourly average temperature in LWD units, °C.

6. A method for controlling the environment of a solarium oriented to the control of downy mildew of cucumber, characterized in that, Includes the following steps: Data analysis and prediction: Based on the environmental prediction results of temperature and humidity forecasts and leaf moisture development curves, the risk of disease is judged, and the optimal control setpoints are output through iterative calculation. During the execution process, the PLC controller calculates and adjusts the air outlet opening of the film winding actuator based on the optimal control setpoint, the temperature detected by the temperature sensor, and the deviation between the setpoint and the temperature.

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