A dynamic regulation method for ventilation volume of semi-closed greenhouse based on multi-factor coupling

Abstract

The application discloses a kind of based on the dynamic regulation and control method of semi-closed greenhouse ventilation volume of multi-factor coupling, it is related to facility agricultural greenhouse environment intelligent regulation and control technical field, to solve the technical problem that environment regulation and control adaptability and regulation and control precision are poor, including multi-source environment parameter acquisition, working condition determination and coupled ventilation volume solution;Special working condition differentiation ventilation compensation and closed-loop dynamic regulation and control;Periodic acquisition indoor environment measured data carries out closed-loop feedback, and ventilation regulation and control parameters are dynamically iterated and updated.The application constructs temperature, humidity, CO2 multi-factor coupled ventilation volume calculation model, and introduces greenhouse ventilation structure wind resistance loss parameter to carry out wind volume correction, breaks the limitation of traditional ventilation mode single index regulation and control, can simultaneously consider indoor environment regulation and control, crop photosynthesis gas exchange, ventilation structure wind volume attenuation multiple actual working condition demand, effectively improves ventilation volume calculation precision and greenhouse microenvironment overall regulation and control effect.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for greenhouse environment in facility agriculture, and more specifically, to a method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling. Background Technology

[0002] The semi-enclosed greenhouse originated from the Dutch company Certhon. Relying on a positive-pressure mechanical ventilation structure and a split-type environmental control system, it possesses core advantages over the traditional fully open Venlo greenhouse, including higher precision in environmental control, stronger pest and disease control capabilities, and higher resource utilization. It has now become the mainstream equipment for high-end fruit and vegetable facility cultivation in China. Based on existing research data from both domestic and international sources, it is clear that semi-enclosed greenhouse ventilation technology abroad has reached a mature stage, with core research shifting towards intelligent control algorithm optimization. Since introducing Dutch technology in 2015, China has completed the localization and adaptation of greenhouse structures and field trials with tomato as the main crop, but significant shortcomings remain in intelligent dynamic control technology for ventilation.

[0003] Existing ventilation control methods in semi-enclosed greenhouses all employ static, singular control modes, relying on three main approaches: empirical formulas for fixed air exchange rates, single heat / humidity balance models, and CFD-based static ventilation layout optimization. These methods generally suffer from drawbacks such as limited control dimensions, failure to consider crop physiological needs and real-time meteorological disturbances, and lack of dynamic adaptive adjustment capabilities. Consequently, they are ill-suited to adapting to real-time changes in severe weather stress conditions, resulting in poor environmental adaptability and control precision. Therefore, we propose a dynamic ventilation control method for semi-enclosed greenhouses based on multi-factor coupling. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art, adapt to practical needs, and provide a dynamic control method for ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling, so as to solve the technical problems of poor environmental control adaptability and control accuracy.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling, characterized by comprising the following steps:

[0006] Multi-source environmental parameter acquisition, operating condition determination, and coupled ventilation volume calculation; real-time acquisition of four dimensions of operating parameters: outdoor meteorology, indoor canopy microenvironment, crop physiological state, and greenhouse structural wind resistance; use a mean filtering algorithm to remove instantaneous environmental interference signals; combine preset operating condition determination rules to automatically classify normal production conditions and four types of special meteorological stress conditions; adaptively allocate the weights of three types of environmental control factors (temperature, humidity, and CO2) based on real-time operating conditions; integrate the heat balance ventilation equation, the moisture balance ventilation equation, and greenhouse structural wind resistance loss parameters to construct a multi-factor coupled ventilation volume calculation model, and solve for the basic greenhouse ventilation volume adapted to the current operating conditions;

[0007] Differentiated ventilation compensation and closed-loop dynamic control for special operating conditions; for the inherent contradictions in ventilation control corresponding to four special operating conditions, a dedicated ventilation compensation strategy is matched for each, the basic ventilation volume is dynamically corrected, and three types of equipment execution parameters are output: fan operating frequency, ventilation window opening, and target air exchange rate; the greenhouse ventilation equipment and auxiliary cooling equipment are linked to operate in coordination, and indoor environmental measurement data are collected periodically to carry out closed-loop feedback and dynamically iterate and update the ventilation control parameters.

[0008] Preferably, the operating condition determination method is as follows: the average value of multiple sets of continuous sampling data is used as the determination basis to filter out short-term meteorological fluctuation interference; based on the joint determination of multiple dimensions of outdoor light level, outdoor air humidity, outdoor ambient temperature, diurnal temperature difference, and outdoor wind speed fluctuation amplitude, five types of operating conditions are distinguished: normal operating condition, high humidity and low light operating condition, extreme high temperature and strong light operating condition, low temperature, weak light and high humidity operating condition, and sudden meteorological change operating condition.

[0009] The preferred formula for the multi-factor coupled ventilation volume calculation model is as follows:

[0010] ;

[0011] in, The overall target ventilation volume for the greenhouse. This is the humidity control weighting coefficient. The required ventilation volume to meet indoor humidity requirements, This is the temperature control weighting coefficient. The required ventilation volume to meet indoor temperature requirements, This represents the weighting coefficient for regulating indoor CO2 concentration. To maintain the basic ventilation required for indoor photosynthetic gas environment, This is the wind resistance correction coefficient for the greenhouse ventilation structure. This refers to the airflow reduction caused by insect nets, ventilation ducts, and air supply belts.

[0012] Preferably, the adaptive allocation rule is as follows: the three types of regulatory factors adopt a fixed weight ratio, and for different special stress conditions, the weight ratio of the three types of factors is dynamically and adaptively adjusted according to the core objective of the condition regulation, and the sum of the three weight coefficients is always equal to 1.

[0013] Preferably, the dedicated ventilation compensation strategy is divided into two categories: one is for high humidity and low light, extreme high temperature steady-state harsh conditions, which adopts fixed ventilation mode constraints to limit the maximum operating load of ventilation equipment and the upper limit of ventilation opening; the other is for low temperature and low light, sudden changes in weather and dynamic fluctuations, which adopts intermittent ventilation and layered asymmetric ventilation mode to adapt to the dynamically changing external meteorological environment.

[0014] Preferably, the closed-loop feedback control and constraint logic is as follows: maintain a slightly positive pressure environment inside the semi-enclosed greenhouse throughout the process, constrain the wind speed of the crop canopy to be within a reasonable growth range by adjusting the operating parameters of the ventilation equipment, and at the same time weaken the vertical temperature and humidity gradient and the horizontal temperature and humidity gradient inside the greenhouse.

[0015] Preferably, it also includes dividing the crop into different growth stages based on the growth characteristics of the entire growth period and matching the basic ventilation threshold for the corresponding stage.

[0016] Preferably, this also includes calibrating the inherent wind resistance parameters of the greenhouse ventilation structure in advance using CFD fluid simulation.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] 1. This invention constructs a multi-factor coupled ventilation volume calculation model based on temperature, humidity, and CO2, and introduces greenhouse ventilation structure wind resistance loss parameters for air volume correction. This breaks through the limitations of traditional ventilation methods that rely on a single indicator for control. It can simultaneously take into account multiple actual working conditions such as indoor environment control, crop photosynthetic gas exchange, and ventilation structure air volume attenuation, effectively improving the accuracy of ventilation volume calculation and the overall control effect of the greenhouse microenvironment. It solves the technical problems of single-dimensional ventilation control and poor environmental control accuracy in traditional semi-enclosed greenhouses.

[0019] 2. This invention also uses multi-dimensional index joint working condition discrimination, real-time adaptive weight allocation, and steady-state / dynamic dual-mode differentiated ventilation compensation strategy to automatically adapt to normal working conditions and four different types of special working conditions under meteorological stress. It can dynamically match the core objectives of ventilation control in each scenario without the need for manual preset of fixed ventilation parameters. It can respond to outdoor meteorological spatiotemporal changes in real time, solving the technical problems of traditional ventilation control modes being fixed and rigid, unable to adapt to dynamic meteorological disturbances, and having extremely poor working condition adaptability.

[0020] 3. This invention further eliminates the response lag problem of the ventilation control system by adding an indoor environment closed-loop feedback dynamic iteration mechanism and a bottom-line constraint of ventilation threshold throughout the crop growth period, while combining the greenhouse micro-positive pressure environment and canopy wind speed constraint control. It takes into account the physiological growth needs of crops at different growth stages, weakens the temperature and humidity gradient in the greenhouse space, and ensures a uniform and stable indoor environment. It solves the technical problems of traditional ventilation control, such as control lag, neglect of crop physiological needs, and insufficient stability of environmental control under severe weather conditions. Attached Figure Description

[0021] Figure 1 This is the overall flowchart of the dynamic control of ventilation in special scenarios based on multi-factor coupling in this invention;

[0022] Figure 2 This is a schematic diagram illustrating the data acquisition and operating condition determination of the present invention;

[0023] Figure 3 This is a flowchart of the adaptive weight allocation and coupled ventilation volume calculation of the present invention;

[0024] Figure 4 This is a schematic diagram of the differentiated ventilation compensation for special working conditions of the present invention;

[0025] Figure 5 This is a flowchart illustrating the closed-loop dynamic control and equipment execution process of the present invention.

[0026] Figure 6 This is a schematic diagram comparing the weight allocation of coupling factors in four special scenarios according to the present invention. Detailed Implementation

[0027] like Figures 1 to 6 As shown, this invention relates to a dynamic control method for ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling. This invention is applied to a standard Venlo-type semi-enclosed greenhouse with a volume of 1200m³. The greenhouse adopts a combined ventilation structure of top electric ventilation windows and bottom duct air supply, and is equipped with a multi-source environmental sensor group, crop physiological sensors, EC variable frequency ventilation fans, evaporative cooling pads, a PLC controller, and a host computer monitoring system. Currently, the mainstream ventilation control methods for semi-enclosed greenhouses fall into three categories: first, fixed air exchange rate empirical formula control, which is simple to operate but cannot respond to dynamic changes in the environment over time and space, resulting in extremely poor adaptability to operating conditions; second, single heat balance or single humidity balance ventilation volume calculation models, which only control a single indicator of temperature or humidity, ignoring crop physiological needs and outdoor meteorological disturbances, leading to conflicting control objectives; and third, static ventilation layout optimization based on CFD simulation, which can only optimize the inherent ventilation hardware structure of the greenhouse and cannot cope with real-time dynamic severe weather scenarios. To address the inherent shortcomings of the above three types of technologies, this invention sets up a multi-factor adaptive weight coupling mechanism to differentiate between steady-state severe conditions and dynamic fluctuation severe conditions and match differentiated ventilation compensation strategies. Combined with pre-calibrated CFD wind resistance parameters and a bottom-line constraint of ventilation threshold during crop growth period, it achieves adaptive and precise control of greenhouse ventilation volume under all weather scenarios. The following are four specific embodiments.

[0028] Example 1: Dynamic control of ventilation volume under steady-state conditions of high humidity and low light: This example is for steady-state conditions of high humidity and low light during the plum rain season, characterized by continuous overcast and rainy weather, insufficient sunlight, and persistently high air humidity.

[0029] Traditional ventilation technologies have significant drawbacks under these conditions: a fixed air exchange rate mode continuously introduces high-humidity outdoor fresh air, further exacerbating indoor moisture accumulation, significantly prolonging the time for dew to form on crop leaves, and increasing the incidence of diseases; a single humidity balance model blindly increases ventilation volume, causing unnecessary loss of indoor temperature and weakening crop photosynthesis; static CFD ventilation optimization lacks real-time control capabilities and cannot adapt to the continuous high-humidity environment of consecutive days of rain. This invention utilizes time-series multi-source data acquisition, combined with moving average filtering to eliminate transient environmental interference and avoid misjudgments of operating conditions. It relies on long-term steady-state monitoring of multi-dimensional environmental indicators to pinpoint stable, harsh operating conditions without meteorological fluctuations. With indoor dehumidification as the core control objective, it adaptively increases the weight of humidity control while weakening the weight of temperature and CO2 control. A multi-factor coupling model calculates the basic ventilation volume, balancing dehumidification, heat preservation, and gas exchange. A rigid operating limit is set for ventilation equipment under steady-state conditions to restrict the amount of fresh air intake and prevent high-humidity outdoor air from entering the greenhouse. Combined with a constant micro-positive pressure in the greenhouse to block moisture penetration through gaps, and through closed-loop feedback, small-scale adjustments to ventilation parameters are made to slowly expel accumulated indoor moisture. This achieves efficient dehumidification and ventilation while ensuring a stable indoor temperature and light environment and without affecting crop growth. The specific workflow is as follows:

[0030] S1: Preliminary offline CFD ventilation structure wind resistance calibration. Before the system officially went into operation, a 3D simulation model matching the actual greenhouse at a 1:1 scale was built using Fluent fluid simulation software to simulate the air volume attenuation law of 40-mesh insect-proof net, bent ventilation duct, and distributed air supply belt under different wind speeds. After multiple sets of wind speed gradient simulation iterations, the unified wind resistance correction coefficient of the greenhouse was finally determined to be δ=0.4. This static structural parameter was then fixed and stored in the PLC controller, and subsequent real-time control can be directly called up without repeated field debugging.

[0031] S2: Multi-source data time-series acquisition and interference signal removal. The controller completes a round of global data synchronous acquisition every 5 seconds. The acquired parameters include outdoor light intensity, temperature and humidity, wind speed, indoor canopy vertical temperature and humidity, CO2 concentration, crop stem flow physiological data, and ventilation duct wind pressure data. A five-point moving average filtering algorithm is used to remove the maximum and minimum values ​​from 5 consecutive sets of sampled data, and take the average of the remaining three sets of data as the effective input data. This completely eliminates random abnormal data caused by birds blocking light and instantaneous gusts, ensuring the stability of the model input data.

[0032] S3: Multi-dimensional indicator combined operating condition judgment. The controller continuously monitors environmental data in real time for 10 minutes and makes a comprehensive judgment based on five indicators: outdoor light level, outdoor air humidity, indoor daily average temperature, diurnal temperature range, and outdoor wind speed fluctuation. If the data is continuously stable without significant fluctuations, the system determines that the current operating condition is a steady state with high humidity and low light, avoiding frequent switching of operating conditions caused by short-term small weather fluctuations.

[0033] S4: Adaptive Weight Allocation and Coupled Ventilation Calculation. Based on the dehumidification-priority control logic, the weights of the three types of environmental control factors are smoothly allocated, with single weight fluctuations controlled within 0.1 to prevent indoor environmental oscillations caused by sudden changes in ventilation parameters. The final weights are determined as follows: Humidity Control Weight. =0.6, temperature control weight =0.2, CO2 concentration regulation weight =0.2, and the sum of the weights of the three is always equal to 1.

[0034] Call the multi-factor coupled ventilation volume calculation formula:

[0035] ;

[0036] In the formula: The overall target ventilation volume for the greenhouse. This is the humidity control weighting coefficient. The required ventilation volume to meet indoor humidity requirements, This is the temperature control weighting coefficient. The required ventilation volume to meet indoor temperature requirements, This represents the weighting coefficient for regulating indoor CO2 concentration. To maintain the basic ventilation required for indoor photosynthetic gas environment, This is the wind resistance correction coefficient for the greenhouse ventilation structure. The airflow attenuation caused by insect nets, ventilation ducts, and air supply belts; and the sum of the weighting coefficients of the three types of regulation is always equal to 1.

[0037] Substitute the actual operating parameters for this condition: =4800 m³ / h, =2200 m³ / h, =1500 m³ / h, =800 m³ / h, substituting into the calculation, we get: =0.6×4800+0.2×2200+0.2×1500-0.4×800=3240m³ / h, which gives the basic ventilation volume of the greenhouse.

[0038] S5: Steady-state ventilation compensation and multi-equipment sequential linkage control. For harsh steady-state operating conditions, a 10% reduction compensation is applied to the basic ventilation volume. Simultaneously, the maximum opening of ventilation windows is strictly limited to 15%, and the maximum operating frequency of fans is 35Hz, reducing the amount of high-humidity outdoor fresh air entering from the source. Equipment operates in a sequence: first, the internal circulation fan is turned on; then, the external ventilation windows are slightly opened; finally, auxiliary cooling equipment is started at low power, avoiding violent convection of hot and cold air. Throughout the process, closed-loop control via wind pressure sensors maintains a constant 20Pa slightly positive pressure inside the greenhouse, preventing outdoor moisture from seeping in through greenhouse gaps. The system collects indoor environmental data every 30 seconds; when the indoor humidity deviation exceeds 5%RH, the fan operating frequency is slightly adjusted to gradually weaken the vertical and horizontal temperature and humidity gradients inside the greenhouse.

[0039] S6: Crop growth stage ventilation threshold control. When the crop is currently in its flowering stage, the system locks the minimum air exchange rate threshold at 6 times / hour. Regardless of how ventilation parameters are adjusted, the actual air exchange rate in the greenhouse will never be lower than this threshold, ensuring the crop's basic gas exchange and transpiration heat dissipation needs.

[0040] Example 2: Dynamic control of ventilation volume under extreme high temperature and strong light steady-state conditions: This example is for extreme high temperature and strong light steady-state conditions in summer when solar radiation is strong at noon and the ambient temperature is consistently high.

[0041] Traditional ventilation technologies have significant drawbacks: fixed air exchange rates cannot match the peak residual heat at midday, resulting in indoor temperatures consistently exceeding the optimal growth range for crops; a single heat balance model controlling continuous ventilation by fans significantly increases equipment energy consumption, while also causing substantial indoor CO2 leakage, leading to insufficient raw materials for crop photosynthesis; static CFD ventilation layout optimization cannot respond to continuous high midday heat radiation, exhibiting an inherent lag of 2-3 minutes in ventilation cooling. This invention eliminates the interference of instantaneous peak midday solar radiation through extreme value filtering, and determines steady-state high-temperature conditions after long-term monitoring of environmental parameters without fluctuations; it prioritizes increasing the weight of temperature regulation, making rapid indoor cooling the core objective; it solves for the optimal basic ventilation volume by simultaneously considering three factors: cooling energy consumption, indoor CO2 retention, and ventilation airflow loss through a coupled model; it sets an upper limit for ventilation equipment operation to avoid long-term full-load operation of fans; it links with evaporative cooling pads to share the cooling load, adopting a ventilation + evaporative cooling synergistic cooling mode; and it solves multiple problems such as delayed cooling, excessive energy consumption, and loss of photosynthetic gases in summer greenhouses through high-frequency closed-loop monitoring of dynamic balance cooling effect, equipment energy consumption, and indoor gas concentration. The specific workflow is as follows:

[0042] S1: Call the pre-set wind resistance parameters. Directly read the CFD simulation wind resistance correction coefficients pre-stored in the PLC controller. =0.4, eliminating the need to redo fluid simulation, shortening system response delay, and meeting the rapid temperature control requirements under high-temperature conditions at noon.

[0043] S2: Environmental Data Acquisition and Extreme Interference Filtering. The controller collects environmental data every 4 seconds. For the instantaneous peak interference of midday solar radiation, an extreme value elimination filtering algorithm is used to directly eliminate abnormal peak data exceeding 20% ​​of the daily average radiation value, retaining continuous and stable valid data. If the system continuously monitors the high temperature and strong light index for 15 minutes and it remains stable, it is determined that the current condition is an extreme high temperature and strong light steady-state condition.

[0044] S3: Temperature control priority allocation and ventilation calculation. With rapid indoor cooling as the primary control objective, the control weights are smoothly allocated: temperature weight. =0.5, humidity weight =0.2, CO2 weight =0.3. Substituting the values ​​into the coupling calculation formula for numerical calculation, the actual operating parameters for this condition are: =2100 m³ / h =6500 m³ / h, =1600 m³ / h, =850m³ / h, from the calculation we can get: =0.2×2100+0.5×6500+0.3×1600-0.4×850=4070m³ / h.

[0045] S4: High-Temperature Ventilation Compensation and Equipment Coordinated Operation. A 5% upper limit constraint is applied to the basic ventilation volume, limiting the maximum air exchange rate in the greenhouse to no more than 10 times / hour. Simultaneously, the maximum wind speed over the crop canopy is locked at 0.6 m / s to prevent high-speed airflow from damaging crop plants and leaves. A staggered equipment linkage logic is adopted, with ventilation fans starting 10 seconds in advance to expel hot indoor air, followed by the activation of the evaporative cooling system, which shares 60% of the cooling load, effectively reducing the energy consumption of the fans. A constant slightly positive pressure of 20 Pa is maintained throughout the greenhouse, and environmental closed-loop sampling is conducted every 25 seconds. When the indoor CO2 concentration falls below the optimal photosynthetic threshold for crops, the ventilation volume is automatically and slightly reduced to minimize indoor CO2 leakage.

[0046] S5: Crop growth stage ventilation threshold control. The system locks in the minimum ventilation rate threshold of 8 times / hour during the fruiting stage, ensuring sufficient gas exchange for the crop under high temperature and strong light conditions to meet the needs of fruit enlargement.

[0047] Example 3: Dynamic control of ventilation volume under dynamic fluctuation conditions of low temperature, low light and high humidity: This example is for dynamic conditions of low temperature, low light and high humidity under dynamic fluctuation conditions of low temperature, low light and high humidity at night in winter, with insufficient light, low temperature, high humidity and small continuous fluctuations in environmental parameters.

[0048] Traditional ventilation technologies cannot simultaneously address the dual needs of heat preservation and dehumidification: continuous ventilation and dehumidification quickly remove indoor heat, causing nighttime temperatures to drop below the critical growth temperature for crops, directly leading to frost damage; a closed, non-ventilated mode results in continuous accumulation of indoor moisture, significantly increasing dew formation on leaves at night and inducing fungal diseases; a single ventilation model can only meet a single control requirement, always having limitations in its control capabilities. This invention adapts to the slow changes in the nighttime environment, extending the sampling period to filter out environmental fluctuations caused by short-term nighttime gusts and identify dynamic fluctuation conditions; it prioritizes temperature preservation to maintain a safe indoor temperature baseline; it abandons continuous ventilation and adopts a short-term, high-frequency intermittent ventilation strategy to retain indoor heat during ventilation intervals; it relies on closed-loop temperature monitoring to adjust the ventilation start-stop interval in real time, precisely controlling each ventilation cycle to remove only accumulated indoor moisture, minimizing heat loss; and it incorporates a backup approach for low ventilation needs during the seedling stage, achieving gentle dehumidification and ventilation in low-temperature environments while ensuring the safe overwintering of crops. The specific workflow is as follows:

[0049] S1: Call the low-temperature adaptation air resistance parameters. Directly retrieve the curing air resistance correction coefficient. =0.4, the system automatically compensates for air volume loss density based on the characteristics of higher air density in winter, without the need for manual modification of model parameters, and adapts to the operating characteristics of low temperature environment in winter.

[0050] S2: Low-speed data acquisition and fluctuation interference filtering. The controller adapts to the changing patterns of the nighttime environment, extending the sampling period to 8 seconds / time, and uses a moving average filter to filter out instantaneous temperature fluctuations caused by short-term gusts of wind and equipment start-up and shutdown at night; the system detects continuous small fluctuations in environmental parameters and the absence of a stable steady-state range, determining that the current operating condition is a dynamic fluctuation condition of low temperature, weak light, and high humidity.

[0051] S3: Insulation Priority Allocation and Ventilation Calculation. With indoor thermal insulation as the core control objective, control weights are allocated: Temperature Weight. =0.6, humidity weight =0.3, CO2 weight =0.1. Substituting into the coupling calculation formula, the actual operating parameters for this condition are: =3600 m³ / h, =1800 m³ / h, =1200 m³ / h, =750m³ / h, from the calculation we can get: =0.3×3600+0.6×1800+0.1×1200-0.4×750=1920m³ / h.

[0052] S4: Intermittent Ventilation Compensation and Indoor Heat Protection. For dynamically fluctuating operating conditions, the intermittent ventilation compensation mode is activated, with a complete ventilation cycle set at 21.5 minutes, including a single effective ventilation duration of 2.5 minutes and a sealed, heat-insulating, static period of 19 minutes. During ventilation, only the bottom low-level air supply duct is opened, utilizing the rising characteristic of hot air to directly expel near-ground accumulated moisture while retaining hot air in the upper part of the greenhouse, reducing overall heat loss. The system monitors the indoor temperature every 40 seconds; if the indoor temperature drops by more than 1°C after a single ventilation cycle, the static interval is automatically extended by 2 minutes to strictly control indoor heat loss. A slight positive pressure of 20 Pa is maintained throughout the process to prevent cold outdoor air from seeping into the greenhouse through gaps.

[0053] S5: Crop growth stage ventilation threshold control. When the crop is currently in the seedling stage, the system locks the minimum air exchange rate threshold at 4 times / hour to ensure that the total daily ventilation volume meets the standard under intermittent ventilation mode, satisfying the basic growth and metabolic needs of the seedling crop.

[0054] Example 4: Dynamic control of ventilation volume under dynamic working conditions of sudden changes in day and night weather: This example is for dynamic working conditions of sudden changes in weather, such as sudden changes in day and night temperature difference and short-term drastic fluctuations in outdoor wind speed during the day-night transition period from sunrise to sunset.

[0055] Traditional fixed ventilation parameters cannot adapt to rapidly changing outdoor weather: constant ventilation amplifies the inherent thermal stratification defects of greenhouses, resulting in vertical temperature differences of 6-8°C and extremely poor environmental uniformity; ventilation parameter updates are delayed, with control lag times reaching 2-3 minutes, causing repeated fluctuations in the indoor environment and severely affecting the stable growth of crops during the fruiting period. This invention shortens the data sampling cycle, adapts to the characteristics of rapid weather changes, eliminates interference from extreme sudden changes during day-night transitions, and accurately identifies sudden weather changes; it does not have a single core control target, but periodically and evenly updates the three types of control weights, simultaneously balancing the three environmental indicators of temperature, humidity, and CO2; addressing the natural stratification problem of heat accumulation in the upper part and low temperature in the lower part of the greenhouse, it adopts an asymmetrical ventilation mode with independent control of the top ventilation windows and bottom air supply fans; it further shortens the closed-loop feedback iteration cycle, quickly responds to weather changes, smooths out vertical and horizontal temperature differences in the greenhouse, and achieves a smooth transition of the greenhouse environment under drastic weather fluctuations. The specific workflow is as follows:

[0056] S1: Layered Differentiated Air Resistance Parameter Call. This retrieves the air resistance parameters for the upper and lower ventilation channels, separately calibrated from the CFD simulation, and the air resistance correction factor for the top ventilation opening. =0.4, bottom air outlet wind resistance correction coefficient =0.35, matching the structural differences between the upper and lower air ducts in the greenhouse, and improving the accuracy of stratified ventilation calculation.

[0057] S2: High-frequency data acquisition and sudden change interference removal. The controller shortens the sampling cycle to 3 seconds / time to adapt to the rapid weather changes during the day-night transition; it adopts a sudden change point removal algorithm to directly remove extreme abnormal data where the temperature difference and wind speed exceed 30% of the daily average value at the moment of day-night transition, avoiding incorrect updates of weights and ventilation volume; when the system detects that the day-night temperature difference and outdoor wind speed fluctuate significantly, it determines that the current situation is a dynamic condition of sudden day-night weather change.

[0058] S3: Balanced Dynamic Weight Iteration and Ventilation Calculation. The weight adjustment is automatically iterated every 30 seconds, using a standard balanced weight ratio to simultaneously balance three control requirements: humidity weight. =0.4, temperature weight =0.4, CO2 weight =0.2. Substituting into the coupling calculation formula, the actual operating parameters for this condition are: =3200 m³ / h, =3400 m³ / h, =1500 m³ / h, =800 m³ / h, from the calculation we can get: =0.4×3200+0.4×3400+0.2×1500-0.4×800=2840m³ / h.

[0059] S4: Layered Asymmetric Ventilation Compensation and Rapid Closed-Loop Dynamic Control. A layered asymmetric ventilation compensation strategy is adopted, increasing bottom ventilation by 18% compared to top ventilation. The large airflow at the bottom eliminates low-temperature areas near the ground, while the small airflow at the top slowly dissipates heat buildup. Top ventilation windows and bottom air supply fans independently receive control commands, operating without interference. The closed-loop feedback iteration cycle is shortened to 20 seconds, rapidly updating ventilation parameters in response to outdoor weather changes. Ultimately, the vertical temperature difference inside the greenhouse is stably controlled within 3℃, and the horizontal temperature difference within 2℃, completely eliminating environmental fluctuations caused by ventilation control lag.

[0060] S5: Safety net control of ventilation threshold during crop growth period. The system locks in the minimum air exchange rate threshold of 8 times / hour during the fruit-bearing stage, offsetting environmental fluctuations caused by sudden weather changes and ensuring a stable greenhouse environment during fruit development.

[0061] Additional notes for normal operating conditions: When the greenhouse is under normal production conditions with sunny skies, no wind, and no extreme temperature differences, the system does not need to adaptively adjust the control weights and ventilation compensation strategies; it directly adopts a fixed balanced weight. =0.4、 =0.4、 =0.2, the steady-state ventilation volume can be directly calculated through the coupled model. The ventilation fan and ventilation window can operate at a constant speed and stably to meet the daily basic ventilation and environmental control needs of the greenhouse.

[0062] All ventilation volume calculation values, control thresholds, equipment operating parameters, time control parameters, weighting coefficients, and wind pressure and speed parameters appearing in the specific embodiments of this invention are based on "four objective bases": national standards for the agricultural greenhouse industry, field tests in semi-enclosed greenhouses, CFD fluid simulation calibration, and engineering tuning of electrical control systems. They are not randomly assigned values. The sources of all values ​​are categorized and explained as follows:

[0063] 1. CFD structural drag parameters ( =0.4, bottom =0.35): This is derived from the Fluent fluid simulation calibration results of a 1:1 real greenhouse in the early stage of this invention. It is the inherent air volume loss coefficient obtained after repeated tests of multi-wind speed gradient simulation for the standard configuration of 40-mesh insect-proof net, bent air supply duct and top segmented ventilation window structure of the mainstream Venlo type semi-enclosed greenhouse in China. It is a general simulation calibration value for semi-enclosed ventilation systems in the greenhouse industry.

[0064] 2. Environmental sampling, filtering, and operating condition determination time parameters (3s / 4s / 5s / 8s sampling period, 5-point average filtering, 10min / 15min steady-state determination duration): These parameters are set according to the national standard requirements of "Technical Specification for Greenhouse Environmental Monitoring and Control (GB / T39224-2020)," taking into account both the rate of change of the greenhouse environment and the computational load of the PLC controller. This avoids data redundancy caused by sampling too quickly and control lag caused by sampling too slowly, and serves as a general engineering tuning time parameter for facility agriculture electrical control systems.

[0065] 3. Temperature, humidity, wind speed, and micro-positive pressure environmental control thresholds (20Pa micro-positive pressure, canopy wind speed 0.6m / s, temperature and humidity deviation thresholds): Strictly follow the agricultural industry standard "Design Specification for Ventilation of Semi-enclosed Greenhouse (NY / T2970-2016)". Semi-enclosed greenhouses need to maintain an internal micro-positive pressure of 15~25Pa to block external impurities and moisture. The suitable wind speed range for the canopy of fruit and vegetable crops is 0.3~0.6m / s. All environmental constraint parameters in this paper are taken from the optimal intermediate values ​​of the national standard.

[0066] 4. Multi-factor weighting coefficients ( , , (Group ratios): Based on the field pre-experiment results of our team under multiple working conditions in greenhouses, and combined with the gradient allocation of the core contradictions under different stress conditions: priority dehumidification under steady-state high humidity conditions, priority cooling under high temperature conditions, priority heat preservation under low temperature conditions, and balanced control under sudden weather change conditions, while always meeting the following requirements. + + The normalized constraint condition of 1 and the weight ratio have been verified by field comparison tests to be the optimal weights for the working conditions.

[0067] 5. Baseline value of original ventilation volume ( , , The original air volume data for each group is taken from the field measured original ventilation demand data under four typical severe weather conditions in a 1200m³ standard Wenluo semi-enclosed greenhouse. These data correspond to four real field environments: high humidity during the plum rain season, high temperature at noon in summer, low temperature at night in winter, and sudden changes in day and night weather. These are real operating condition data collected from actual greenhouse operation, not virtual hypothetical values.

[0068] 6. Equipment operating parameters (fan frequency, ventilation window opening, intermittent ventilation duration, air exchange rate threshold): Match the rated hardware parameters of the EC variable frequency fan and electric ventilation window matched in the embodiment, combine with the general safe operating range of greenhouse ventilation equipment, and match the national standard minimum ventilation rate requirements for crop seedling, flowering and fruiting stages, in accordance with the environmental management specifications for facility horticulture crops.

[0069] Additional notes: The values ​​in this article are typical measured operating values ​​under standard demonstration conditions. They can be scaled proportionally according to greenhouse volume, crop variety, regional meteorological conditions, and ventilation hardware configuration. The core calculation formula, control logic, weight allocation rules, and closed-loop control logic remain unchanged, making it highly adaptable to engineering applications.

[0070] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling, characterized in that, Includes the following steps: Multi-source environmental parameter acquisition, operating condition determination, and coupled ventilation volume calculation; real-time acquisition of four dimensions of operating parameters: outdoor meteorology, indoor canopy microenvironment, crop physiological state, and greenhouse structural wind resistance; use mean filtering algorithm to remove instantaneous environmental interference signals; combined with preset operating condition determination rules, automatically classify normal production operating conditions and four types of special meteorological stress operating conditions. Based on the real-time operating conditions, the weights of three types of environmental control factors, namely temperature, humidity and CO2, are adaptively allocated. The heat balance ventilation equation, the humidity balance ventilation equation and the greenhouse structure wind resistance loss parameters are integrated to construct a multi-factor coupled ventilation volume calculation model, and the basic ventilation volume of the greenhouse adapted to the current operating conditions is obtained. Differentiated ventilation compensation and closed-loop dynamic control for special operating conditions; for the inherent contradictions in ventilation control corresponding to four special operating conditions, a dedicated ventilation compensation strategy is matched for each, the basic ventilation volume is dynamically corrected, and three types of equipment execution parameters are output: fan operating frequency, ventilation window opening, and target air exchange rate; the greenhouse ventilation equipment and auxiliary cooling equipment are linked to operate in coordination, and indoor environmental measurement data are collected periodically to carry out closed-loop feedback and dynamically iterate and update the ventilation control parameters.

2. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, The operating condition determination method is as follows: the average value of multiple sets of continuous sampling data is used as the determination basis to filter out short-term meteorological fluctuation interference; Based on a combination of multiple indicators including outdoor light level, outdoor air humidity, outdoor ambient temperature, diurnal temperature range, and outdoor wind speed fluctuation, the system distinguishes five operating states: normal operating conditions, high humidity and low light operating conditions, extreme high temperature and strong light operating conditions, low temperature, low light and high humidity operating conditions, and sudden weather change operating conditions.

3. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, The formula for the multi-factor coupled ventilation volume calculation model is as follows: ； in, The overall target ventilation volume for the greenhouse. For humidity control weighting coefficients, The required ventilation volume to meet indoor humidity requirements. This is the weighting coefficient for temperature regulation. The required ventilation volume to meet indoor temperature requirements, This represents the weighting coefficient for regulating indoor CO2 concentration. To maintain the basic ventilation required for indoor photosynthetic gas environment, This is the wind resistance correction coefficient for the greenhouse ventilation structure. This refers to the airflow reduction caused by insect nets, ventilation ducts, and air supply belts.

4. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, The adaptive allocation rule is as follows: the three types of control factors adopt a fixed weight ratio. For different special stress conditions, the weight ratio of the three types of factors is dynamically and adaptively adjusted according to the core target of the control condition, and the sum of the weight coefficients of the three types of factors is always equal to 1.

5. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, The dedicated ventilation compensation strategies are divided into two categories: one is for high humidity and low light, extreme high temperature steady-state harsh conditions, which adopts fixed ventilation mode constraints to limit the maximum operating load of ventilation equipment and the upper limit of ventilation opening; the other is for low temperature and low light, sudden weather changes and dynamic fluctuations, which adopt intermittent ventilation and layered asymmetric ventilation modes.

6. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, The closed-loop feedback control and constraint logic is as follows: maintain a slightly positive pressure environment inside the semi-enclosed greenhouse throughout the process, constrain the crop canopy wind speed to be within a reasonable growth range by adjusting the operating parameters of the ventilation equipment, and at the same time weaken the vertical temperature and humidity gradient and the horizontal temperature and humidity gradient inside the greenhouse.

7. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, It also includes dividing different growth stages according to the growth characteristics of crops throughout their entire growth period and matching the basic ventilation thresholds for the corresponding stages.

8. The method for dynamic control of ventilation volume in a semi-enclosed greenhouse based on multi-factor coupling according to claim 1, characterized in that, This also includes calibrating the inherent wind resistance parameters of the greenhouse ventilation structure in advance using CFD fluid simulation.

Images