Cold region greenhouse environment regulation and control method and system

Abstract

The invention provides a cold region greenhouse environment regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting real-time environment parameters in a greenhouse; the environmental parameters comprise temperature, humidity, O2 concentration and CO2 concentration; based on the real-time environment parameters, a combined gas dynamic model coupling a temperature field, a humidity field, an O2 concentration field and a CO2 concentration field is constructed and used for describing the interaction change rule of all the environment parameters under control variables and external disturbance; taking an environment parameter set value as a target, taking an equipment control instruction as an optimization variable, performing rolling optimization based on the combined gas dynamic model, and generating a control instruction for cooperatively controlling each execution layer equipment; issuing the cooperative control instruction to execution layer equipment, and controlling the execution layer equipment to act so as to maintain a target environment in the greenhouse; the multi-environment parameter control precision and stability are improved, the energy consumption and the operation cost are reduced, and the equipment collaboration and the control flexibility are enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology, and in particular to methods and systems for environmental regulation in cold-region greenhouses. Background Technology

[0002] Greenhouse agriculture, as an important method to overcome unfavorable natural conditions and achieve high-yield and high-quality crop production, is widely used globally. Cold regions, due to their harsh climatic conditions such as consistently low temperatures, short daylight cycles, and large diurnal temperature variations, place far more stringent demands on the temperature and humidity control of greenhouses than ordinary greenhouses. Maintaining a stable and suitable internal environment for crop growth is crucial for the success of greenhouse production in cold regions.

[0003] Existing environmental control technologies for cold-region greenhouses mostly focus on the independent control of single or a few parameters such as temperature, humidity, and CO2 concentration. For example, PID controllers are used to adjust heaters or ventilation windows based on temperature deviations, to start or stop dehumidifiers or humidifiers based on humidity deviations, or to control the release of CO2 fertilizer based on CO2 concentration sensors. However, these methods have significant limitations: First, the internal environment of a greenhouse is a complex system with multiple variables, strong coupling, and large time lags, and there are complex interactions between parameters such as temperature, humidity, and gas concentration. For example, turning on the ventilation system (fresh air system) lowers the temperature, but also leads to a decrease in humidity and CO2 concentration; injecting nitrogen (N2) for controlled atmosphere preservation inevitably dilutes the concentration of O2 and CO2 inside the greenhouse; and in high humidity environments, the adsorption efficiency of purification equipment for CO2 drops sharply. This strong coupling effect between parameters often causes traditional single-loop control methods to fail to address all aspects, resulting in system oscillations, low control accuracy, and high energy consumption.

[0004] Secondly, existing technologies are insufficient to address the unique energy-saving and high-disturbance issues present in cold-region greenhouses. The winter insulation requirements of cold-region greenhouses are enormous, and coordinating the operation of conflicting energy-consuming equipment such as heating and ventilation to reduce total energy consumption is a key challenge. Simultaneously, drastic changes in the outdoor environment (such as sudden strong winds, blizzards, and short-term intense radiation common in cold regions) act as unpredictable disturbances, severely disrupting the greenhouse environment. This necessitates control systems with extremely strong anti-interference capabilities and rapid response capabilities, which traditional control strategies struggle to meet. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for environmental control in cold-region greenhouses. By establishing multi-field coupling relationships within the cold-region greenhouse and enabling efficient environmental control of continuous and discrete motion equipment, it improves the accuracy and stability of multi-environmental parameter control, reduces energy consumption and operating costs, enhances equipment synergy and control flexibility, and simultaneously improves system reliability and adaptability.

[0006] In a first aspect, this application provides a method for controlling the environment in a cold-region greenhouse, the method comprising:

[0007] Real-time environmental parameters inside the greenhouse are collected; these parameters include temperature, humidity, O2 concentration, and CO2 concentration.

[0008] Based on the real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed to describe the interactive change law of each environmental parameter under control variables and external disturbances.

[0009] With environmental parameter settings as the objective and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate control commands for coordinated control of devices at each execution layer.

[0010] The coordinated control commands are sent to the execution layer devices to control their actions in order to maintain the target environment inside the greenhouse.

[0011] Preferably, the joint gas dynamics model is constructed through a set of partial differential equations, which include:

[0012] Convection-diffusion equations describing changes in O2 and CO2 gas concentrations;

[0013] The energy conservation equation describing temperature change;

[0014] The mass conservation equation describing humidity changes;

[0015] In this system of equations, the source terms, sink terms, or coefficients are coupled together.

[0016] Preferably, the coupling relationship of the system of equations includes at least one of the following:

[0017] Temperature changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop respiration and photosynthetic rates.

[0018] Humidity changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop transpiration and gas adsorption efficiency.

[0019] O2 and CO2 concentrations affect crop physiological activities, thus influencing the heat exchange terms in the temperature and humidity equations in a coupled manner.

[0020] The velocity field generated by ventilation equipment simultaneously couples with and affects the distribution and transport of temperature, humidity, O2 concentration, and CO2 concentration.

[0021] Preferably, the joint gas dynamics model is discretized in time and space to obtain its state-space approximate model, expressed as:

[0022] X(k+1)=A·X(k)+B·U(k)+G·W(k)

[0023] Where X(k)=[T(k),H(k),O2(k),CO2(k)]^T is the state vector at time k, U(k) is the control input vector, W(k) is the external disturbance vector, A is the state transition matrix, B is the control input matrix, and G is the disturbance input matrix.

[0024] Preferably, the control input vector includes: nitrogen injection flow rate, dehumidification / humidification power and fan speed of the constant humidity purification unit, CO2 replenishment rate, and damper opening of the fresh air system.

[0025] Preferably, the rolling optimization is based on the dynamic model to predict the future dynamics of the system, and is achieved by solving a problem that minimizes the following objective function:

[0026]

[0027] Where J is the objective function, Y is the system output variable, Y_set is the output setpoint, Q is the output error weight matrix, and R is the control increment weight matrix, used to suppress frequent device actions.

[0028] Preferably, decoupling control is achieved by dynamically configuring the weight matrices Q and R:

[0029] When the control command includes starting the nitrogen generator, the weight of CO2 concentration in the Q matrix and the weight of CO2 replenishment device in the R matrix are automatically increased to suppress the increase in CO2 concentration caused by nitrogen replacement.

[0030] When the control command includes activating the dehumidification function of the constant humidity purification unit, the weight of CO2 concentration in the Q matrix is ​​automatically increased to compensate for the decrease in activated carbon adsorption efficiency under high humidity conditions.

[0031] Preferably, the objective function further includes an energy consumption cost term. Where S is the energy consumption weight matrix, which is dynamically adjusted according to the indoor and outdoor temperature difference to achieve optimal energy consumption control.

[0032] Preferably, the process of minimizing the objective function in the rolling optimization includes:

[0033] The variables representing device switching and multi-speed in the control input vector U(k) are defined as integer variables, and the remaining continuously adjusted control quantities are defined as continuous variables, thus constructing the optimization problem as a mixed integer quadratic programming problem.

[0034] In each control cycle k, the mixed integer quadratic programming problem is solved to obtain the optimal control sequence that contains both integer solutions and continuous solutions.

[0035] Based on the sampling period of the control system, a maximum solution time threshold is set for the solution algorithm; if the optimal solution is not found within the threshold, the best feasible solution found at the moment is adopted as the control command and output.

[0036] Secondly, this application provides a cold-region greenhouse environment control system for implementing the cold-region greenhouse environment control method described in the embodiments of this application. The system includes:

[0037] A sensor array, deployed in different areas of the greenhouse, is used to collect real-time environmental parameters within the greenhouse, including at least temperature, humidity, O2 concentration, and CO2 concentration.

[0038] An edge computing device is communicatively connected to the sensor array to receive and preprocess the real-time environmental parameters and complete data fusion.

[0039] The central controller, which is communicatively connected to the edge computing device, is configured to execute:

[0040] Based on the preprocessed real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed;

[0041] With environmental parameter settings as the objective and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate a command sequence for coordinated control of devices at each execution layer.

[0042] The actuator group is communicatively connected to the central controller, receives the instruction sequence, and executes the corresponding actions.

[0043] Compared with existing technologies, the beneficial effects of this invention include at least the following: By constructing a joint gas dynamics model coupling temperature, humidity, O2 concentration, and CO2 concentration fields, the interaction patterns between various parameters are accurately described; by combining rolling optimization and dynamic weight configuration, multi-parameter collaborative control is achieved, effectively suppressing parameter fluctuations and ensuring a stable environment in cold-region greenhouses that meets crop growth requirements. An energy consumption cost term is introduced into the objective function, and the energy consumption weight matrix is ​​dynamically adjusted based on the indoor and outdoor temperature difference, prioritizing low-energy-consumption control schemes; simultaneously, by using mixed-integer quadratic programming, frequent equipment operation is avoided, further reducing energy consumption and significantly lowering greenhouse operating costs. Decoupling control is achieved through dynamic configuration of Q and R matrices (e.g., suppressing CO2 concentration rise during nitrogen injection), solving the interference problem of different equipment control commands; and mixed optimization of integer variables (equipment on / off, gear position) and continuous variables (adjustment rate) is supported, adapting to different types of execution equipment and meeting diverse control needs. Setting a maximum solution time threshold ensures that feasible control commands can be output even under complex operating conditions, avoiding system stagnation; the model considers external disturbances (such as changes in external temperature and humidity), and the sensor array covers different areas of the greenhouse to achieve data fusion, enabling the system to adapt to the control needs of different crops and different environmental conditions, with a wide range of applications. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a cold-region greenhouse regulation method according to an embodiment of the present invention. Detailed Implementation

[0045] In view of the shortcomings of the prior art, the applicant has proposed the technical solution of this application through long-term research and extensive practice. The technical solution, its implementation process and principle will be further explained below with reference to the accompanying drawings and specific implementation examples.

[0046] It should be noted that the embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, this application covers any substitutions, modifications, equivalent methods and solutions made within the spirit, principles and scope of this application as defined by the claims. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Furthermore, to provide the public with a better understanding of this application, certain specific details are described in detail in the following description of the application. However, those skilled in the art can fully understand this application even without these detailed descriptions.

[0048] Example 1: Refer to Appendix Figure 1This application provides a method for controlling the environment of a cold-region greenhouse, the method comprising:

[0049] Real-time environmental parameters inside the greenhouse are collected; these parameters are then uploaded to an edge device for preprocessing and multimodal fusion; the environmental parameters include temperature, humidity, O2 concentration, and CO2 concentration.

[0050] Based on the real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed to describe the interactive change law of each environmental parameter under control variables and external disturbances.

[0051] With environmental parameter settings as the target and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate control commands for coordinated control of each execution layer device. The execution layer devices include a nitrogen generator, a constant humidity purification unit, a CO2 replenishment device, and a fresh air system.

[0052] The coordinated control commands are sent to the execution layer devices to control their actions in order to maintain the target environment inside the greenhouse.

[0053] The working principle of the above technical solution is as follows:

[0054] By deploying sensors in different areas of the greenhouse (such as the crop canopy, soil surface, and near ventilation openings), four core environmental parameters—temperature, humidity, O2 concentration, and CO2 concentration—are collected in real time to ensure data coverage of key monitoring points in the greenhouse and provide comprehensive raw data for subsequent analysis.

[0055] The collected real-time environmental parameters are uploaded to the edge device. After preprocessing to remove data noise and fill in missing values, multimodal fusion technology is used to integrate the spatiotemporal and data type features of different parameters to generate high-quality, highly correlated unified data samples, laying the foundation for model input.

[0056] Based on preprocessed and fused real-time environmental parameters, a joint gas dynamics model coupling temperature, humidity, O2 concentration, and CO2 concentration fields is constructed. This model accurately depicts the interactive changes of various environmental parameters, enabling prediction of the greenhouse's environmental conditions over a future period.

[0057] Using preset environmental parameters for cold-region greenhouses (such as the suitable temperature range for crop growth and CO2 concentration threshold) as the target, and control commands for nitrogen generators, constant humidity and purification units, CO2 replenishment devices, and fresh air systems (such as nitrogen injection flow rate, dehumidification / humidification power, replenishment rate, and damper opening) as optimization variables, and combining the prediction results of the joint gas dynamics model with the current real-time environmental parameters, the system performs rolling optimization by solving a problem that minimizes the objective function, which includes output error (deviation between environmental parameters and setpoints), control increment (suppressing frequent equipment actions), and energy consumption cost (dynamically adapting to indoor and outdoor temperature differences). This generates commands that can coordinately control each executing device.

[0058] The generated collaborative control commands are sent to the execution layer devices. Each device adjusts its operating status according to the commands (such as adjusting the injection flow of the nitrogen generator and switching the dehumidification / humidification mode of the constant humidity purification unit), and intervenes in the cold greenhouse environment in real time, ultimately maintaining the environmental parameters inside the greenhouse within the target range.

[0059] The effects of the above technical solution are as follows:

[0060] By acquiring parameters in real time, accurately modeling, and continuously optimizing, the system can dynamically respond to changes in the cold-region greenhouse environment, reducing fluctuations in temperature, humidity, and gas concentration. This makes environmental parameters more aligned with the growth needs of crops and improves control precision. The collaborative control strategy avoids frequent start-ups and shutdowns or full-load operation of individual devices. For example, when temperature and humidity simultaneously deviate from set values, the system coordinates the actions of the fresh air system and the integrated humidity and air purification unit, rather than relying solely on one device, reducing overall energy consumption. Simultaneously, the constraints on control increments during continuous optimization reduce the frequency of device actions and extend equipment lifespan. Localized data processing by edge devices reduces data transmission latency and enables rapid response to sudden environmental changes in the greenhouse (such as sudden high temperatures or sharp drops in CO2 concentration). Furthermore, the model dynamically adjusts based on real-time parameters, adapting to the environmental needs of different crops (such as thermophilic and cool-climate crops) without requiring large-scale hardware modifications.

[0061] In one possible implementation, the joint gas dynamics model is constructed through a set of partial differential equations, which include:

[0062] Convection-diffusion equations describing changes in O2 and CO2 gas concentrations;

[0063] The energy conservation equation describing temperature change;

[0064] The mass conservation equation describing humidity changes;

[0065] In this system of equations, the source terms, sink terms, or coefficients are coupled together.

[0066] The convection-diffusion equation is used to describe the changes in O2 and CO2 gas concentrations. The equation includes the gas velocity field generated by ventilation and air mixing equipment on the convective transport of gas, the diffusion of gas molecules, and a net gas source term coupled with temperature and humidity. The net gas source term is the difference between the gas generation source term and the gas consumption sink term. The gas generation source term includes gas production from crop respiration, which increases with increasing temperature; gas production from CO2 supplementation devices, which is related to the supplementation rate; and secondary gas production such as soil microbial respiration, which is determined by soil characteristics. The gas consumption sink term includes gas consumption from crop photosynthesis, which increases first and then decreases with temperature and is inhibited by humidity; gas consumption from adsorption by the constant humidity purification unit, which decreases in efficiency with increasing humidity; and gas consumption from nitrogen injection replacement, which varies with the nitrogen injection rate.

[0067] The energy conservation equation is used to describe temperature changes, including changes in the thermodynamic energy per unit volume of air, heat convection transport caused by air flow, heat conduction transport of air molecules, and heat source and sink terms consisting of solar radiation heat gain, latent heat exchange coupled with humidity, sensible heat exchange, and heat sources coupled with gas concentration.

[0068] The mass conservation equation is used to describe humidity changes, including the mass change of water vapor per unit volume of air, the convective transport of water vapor caused by air flow, the diffusion transport of water vapor molecules, and the water vapor mass balance consisting of crop transpiration water production, water vapor condensation water consumption, and the action of humidification or dehumidification equipment. The water vapor concentration is determined by relative humidity, saturated vapor pressure at the current temperature, water vapor gas constant, and indoor absolute temperature.

[0069] The convection-diffusion equation comprehensively covers the net gas source terms of gas convection transport (driven by ventilation / mixing equipment), molecular diffusion, and temperature and humidity coupling. The dynamic setting of the gas generation source terms (crop respiration, CO2 supplementation, soil microbial respiration) and consumption sink terms (crop photosynthesis, activated carbon adsorption, nitrogen replacement) can match the changes in O2 and CO2 concentrations with the environment and equipment operation in real time, avoid the gas concentration from deviating from the suitable range for crop growth, and provide stable gas conditions for crop photosynthesis and respiration.

[0070] The energy conservation equation integrates thermodynamic energy changes, heat convection, heat conduction, and multi-dimensional heat input and output terms (solar radiation, latent heat coupled with temperature and humidity, gas concentration coupled with heat sources, etc.). It can accurately capture the influence of external environment (such as solar radiation), internal equipment (such as ventilation), and crop physiological activities (such as photosynthetic heat production) on greenhouse temperature, realize dynamic control of temperature changes, reduce the amplitude of temperature fluctuations, and meet the stringent requirements of constant temperature in cold-region greenhouses.

[0071] The mass conservation equation, through calculations involving changes in water vapor mass, convective transport, molecular diffusion, and mass balance (crop transpiration, condensation, and humidification / dehumidification equipment), combined with the correlation between water vapor concentration and temperature, humidity, and gas constant, can accurately reflect the mechanism by which humidity is affected by the environment and equipment. This helps avoid diseases caused by high humidity or crop water loss caused by low humidity, ensuring that the humidity in cold greenhouses remains stable within the suitable range for crops.

[0072] The three equations address gas concentration, temperature, and humidity respectively. They are designed to simultaneously influence the convection term through the flow velocity field, couple the gas source and sink terms with temperature and humidity, and couple the heat source term with gas concentration, thereby achieving a linkage between the three. This avoids imbalances in other parameters caused by the control of a single parameter, making the environmental control of cold-region greenhouses more holistic and coordinated, and improving the accuracy and efficiency of constant temperature and humidity control in cold-region greenhouses.

[0073] The gas generation source terms (such as CO2 replenishment rate), consumption sink terms (such as nitrogen injection rate), and heat / mass balance terms (such as the function of humidification / dehumidification equipment) in the equation are all related to the equipment control commands. At the same time, they cover external disturbance factors such as solar radiation and soil characteristics. The system can actively adjust the environment through equipment commands and dynamically respond to external disturbances, making the control scheme more adaptable to the complex and changeable environmental conditions of cold greenhouses and enhancing control flexibility and anti-interference ability.

[0074] In one possible implementation, the coupling relationships of the system of equations include at least one of the following:

[0075] Temperature changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop respiration and photosynthetic rates.

[0076] Humidity changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop transpiration and gas adsorption efficiency.

[0077] O2 and CO2 concentrations affect crop physiological activities, thus influencing the heat exchange terms in the temperature and humidity equations; the flow field generated by ventilation equipment simultaneously influences the distribution and transport of temperature, humidity, O2 concentration, and CO2 concentration.

[0078] The specific ways to achieve the coupling relationship of the system of equations include:

[0079] Temperature and gas concentration coupling: By experimentally measuring the rates of crop respiration and photosynthesis at different temperatures, the rate law is fitted and substituted into the net gas source term of the convection-diffusion equation, so that the net gas source term changes dynamically with temperature, thereby realizing the coupled effect of temperature on O2 and CO2 concentrations.

[0080] Humidity and gas concentration coupling: By experimentally measuring the amount of CO2 adsorbed by activated carbon in the constant humidity purification unit under different humidity conditions, the adsorption efficiency law was obtained by fitting. The adsorption gas consumption term in the convection-diffusion equation was defined as a humidity-related form, so that the adsorption gas consumption term changes dynamically with humidity, thereby reflecting the coupling of humidity with O2 and CO2 concentration.

[0081] Velocity field coupling with multiple parameters: The velocity field distribution under different ventilation equipment states (damper opening, fan speed) is measured by wind speed sensor. The mapping relationship between equipment control commands and velocity field is established. The velocity field is simultaneously substituted into the energy conservation equation, mass conservation equation and convection-diffusion equation, so that the velocity field synchronously affects the transport process of temperature, humidity, O2 concentration and CO2 concentration, thereby realizing multi-parameter coupling.

[0082] The relationship between the flow velocity field and the fan speed of the ventilation equipment was investigated by arranging an array of wind speed sensors in the greenhouse to measure the flow field distribution under different control commands, establishing a command-flow velocity mapping table and storing it in the controller.

[0083] Key coefficients in functions such as respiration, photosynthesis, and transpiration are measured and identified through specially designed crop physiological experiments (as described above).

[0084] Environmental disturbance parameters, such as solar radiation, are provided in real time by a total radiation sensor installed on the top of the greenhouse.

[0085] By achieving coordinated adjustment of temperature, humidity, gas concentration, and flow field through multi-dimensional coupling, the imbalance of other parameters caused by single parameter control is avoided (such as adjusting temperature without considering CO2 concentration, which leads to a decrease in photosynthetic efficiency). This ensures that all environmental parameters are always aligned with the suitable growth range of crops (such as the constant temperature of 18-22℃ and CO2 concentration of 800-1200ppm required for cold-region crops), reducing parameter fluctuations and improving crop yield and quality.

[0086] Key parameters (such as flow field, crop physiological coefficients, and solar radiation) are obtained through field measurements and experiments and can be dynamically updated (e.g., re-measuring physiological parameters when changing crops, adjusting the weight of radiation sensor data when seasonal changes occur). This allows the model to accurately cope with complex environments such as low external temperatures and large fluctuations in solar radiation in cold greenhouses, avoiding the disconnect between theoretical models and actual environments and improving the feasibility of control schemes.

[0087] The coupling design of the flow field and multiple parameters enables the coordinated operation of ventilation, humidity control, and gas replenishment equipment (such as adjusting the damper to simultaneously adapt to temperature, humidity and gas concentration, eliminating the need for repeated start-ups and shutdowns of multiple devices); at the same time, parameter input based on actual measurement patterns (such as dynamically adjusting adsorption efficiency with humidity to avoid excessive adsorption and power consumption by the equipment) reduces ineffective operation of the equipment and lowers the energy consumption cost of maintaining constant temperature and humidity in cold-region greenhouses.

[0088] By collecting external disturbance parameters such as solar radiation in real time and embedding them into the equation to dynamically adjust the calculation terms (such as reducing the power of heating equipment and reducing heat convection when solar radiation increases), the system can quickly respond to sudden changes in the external environment (such as a sudden increase in midday radiation on a sunny day in cold regions and the invasion of low temperatures at night), avoid large fluctuations in environmental parameters, and ensure the long-term stability of the greenhouse environment in cold regions.

[0089] In one possible implementation, the joint gas dynamics model is discretized in space and time to obtain its state-space approximation model, expressed as:

[0090] X(k+1)=A·X(k)+B·U(k)+G·W(k)

[0091] Where X(k)=[T(k),H(k),O2(k),CO2(k)]^T is the state vector at time k, U(k) is the control input vector, W(k) is the external disturbance vector, A is the state transition matrix, B is the control input matrix, and G is the disturbance input matrix.

[0092] In one possible implementation, the control input vector includes: nitrogen injection flow rate, dehumidification / humidification power and fan speed of the constant humidity purification unit, CO2 replenishment rate, and damper opening of the fresh air system.

[0093] The spatiotemporal discretization results are used to extract key environmental parameters within the greenhouse at time k, such as temperature T(k), humidity H(k), O2 concentration O2(k), and CO2 concentration CO2(k), forming a state vector X(k) = [T(k), H(k), O2(k), CO2(k)]^T, which is used to quantify the environmental state of the cold-region greenhouse at the current moment. Controllable equipment operation parameters affecting the cold-region greenhouse environment (nitrogen injection flow rate, dehumidification / humidification power and fan speed of the constant humidity purification unit, CO2 replenishment rate, and damper opening of the fresh air system) are integrated into a control input vector U(k), serving as variables for actively adjusting the environment. Uncontrollable external influencing factors are also considered. An external disturbance vector W(k) is constructed. Through mathematical derivation, the variation law of the discretized unit environmental parameters is transformed into a linear relationship, resulting in a state-space approximation model X(k+1) = A·X(k) + B·U(k) + G·W(k). Matrix A characterizes the influence of the state vector X(k) at time k on the state vector X(k+1) at time k+1 (i.e., the state transition law), matrix B quantifies the regulating effect of the control input vector U(k) on the state at the next time step, and matrix G characterizes the degree of interference of the external disturbance vector W(k) on the state change. Ultimately, a concise linear model is used to describe the dynamic variation law of the cold-region greenhouse environment with time, control actions, and external disturbances. An example of an external disturbance is solar radiation.

[0094] By extracting the state vector X(k) composed of temperature, humidity, O2 concentration, and CO2 concentration, the complex environmental state of the cold greenhouse is transformed into a quantifiable mathematical vector, which intuitively reflects whether the current environment meets the growth needs of the organisms (such as the suitable constant temperature range and CO2 concentration threshold for cold crops). This avoids the control deviation caused by the vague description of environmental parameters and provides accurate data support for determining the target direction for subsequent rolling optimization.

[0095] The controllable equipment actions, such as nitrogen injection flow rate and the operating parameters of the constant humidity and purification unit, are integrated into a control input vector U(k). The adjustment effect of this vector on the environmental state at the next moment is quantified by matrix B. The control weight and correlation of each device are clearly defined (such as the degree of influence of CO2 replenishment rate on CO2 concentration and the adjustment range of fresh air damper opening on temperature). This avoids conflicts or redundancy in the actions of multiple devices and realizes the coordinated linkage of equipment such as nitrogen generator and constant humidity and purification unit, which is suitable for the multi-parameter synchronous control needs of cold greenhouses.

[0096] By incorporating typical uncontrollable factors in cold regions, such as outdoor temperature fluctuations and changes in solar radiation, into the external disturbance vector W(k) and matrix G, the degree of their interference with the indoor environment is quantified. This enables the state-space model to respond in real time to changes in the external environment (such as the invasion of low outdoor temperatures at night in cold regions and the sudden increase in solar radiation on sunny days), providing a basis for disturbance compensation for rolling optimization, reducing fluctuations in temperature, humidity, and gas concentration caused by external disturbances, and ensuring that the greenhouse environment in cold regions remains stable within the target range.

[0097] By mathematically deriving the variation law of the discretized unit environmental parameters, the model is transformed into a linear state-space model, which greatly reduces the model complexity and avoids the lag in solving complex partial differential equations. The concise linear form can quickly complete iterative calculations, ensuring that the controller can quickly generate control commands when the cold greenhouse environment changes abruptly, meet the requirements of real-time regulation, and avoid the impact of calculation delay on the regulation effect.

[0098] Matrix A, B, and G clearly characterize the quantitative relationships of state transition, equipment adjustment, and disturbance effects, making the model logic clear and easy to understand (e.g., matrix A allows for intuitive analysis of the continued impact of the current temperature on the temperature at the next moment), facilitating understanding, debugging, and maintenance by technical personnel. Compared to complex nonlinear models, the parameter adjustment of the linear state-space model (e.g., adjusting the equipment adjustment coefficient in matrix B to adapt to different cold-region crops) is simpler, lowering the threshold for the application and promotion of cold-region greenhouse control technology.

[0099] In one possible implementation, the step of spatiotemporally discretizing the joint gas dynamics model includes:

[0100] Based on preset dimensions and combined with crop planting areas and equipment layout, the space inside the cold-region greenhouse is divided into several independent finite units, covering the crop canopy area, soil surface area, and area near ventilation openings. The unit size is adapted to the sensor sampling accuracy and computing efficiency.

[0101] Temperature, humidity, O2 concentration, and CO2 concentration are set to be uniformly distributed within each grid cell, and the average state of the cell is used to characterize the overall environment of the cell.

[0102] The finite volume method is used to process the convection-diffusion equation, energy conservation equation, and mass conservation equation separately. By calculating the amount of matter and energy exchanged at the unit boundary, the spatial variation term is transformed into a calculation term based on the average state of the unit, and the ordinary differential equations of the environmental parameters corresponding to each unit are obtained.

[0103] By setting a fixed time sampling interval, discretizing the ordinary differential equations of each unit, calculating the average state change of the unit within each interval, and iteratively simulating the dynamic changes of environmental parameters over time.

[0104] The working principle of the above technical solution is as follows:

[0105] The space inside the cold-region greenhouse is divided into several independent finite units (grids) according to a preset size (such as based on crop planting areas and equipment layout). Each unit is designed to cover key areas of the greenhouse (such as the crop canopy area, soil surface area, and area near the ventilation openings), and the unit size must be adapted to the sensor sampling accuracy and computing efficiency.

[0106] The temperature, humidity, O2 concentration, and CO2 concentration within each grid cell are set to be uniform and consistent. That is, the above environmental parameters at any point within the same cell have no spatial differences, and only the average state of the cell needs to be calculated to represent the overall environment of the cell.

[0107] Partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs): The finite volume method is used to process the convection-diffusion equations describing gas concentration, the energy conservation equations describing temperature, and the mass conservation equations describing humidity separately. For each grid cell, the spatially varying terms (such as concentration gradients and temperature gradients) in the original PDEs (by calculating the exchange of matter (gas, water vapor) or energy (heat) at the cell boundary) are transformed into calculation terms based on the average state of the cell. Finally, the average state change rate equation (ODE) for the corresponding environmental parameters (T, H, O2, CO2) of each cell is obtained. For example, the CO2 concentration change rate of a certain grid cell is determined only by the CO2 production / consumption of that cell and the amount of CO2 exchanged with adjacent cells.

[0108] Time dimension discretization: Set a fixed time sampling interval (e.g., 1 minute / time), and discretize the ODE of each unit in the time dimension. That is, calculate the change in the average state of the unit within each time interval. Through step-by-step iteration, the simulation and calculation of the dynamic changes of environmental parameters of cold greenhouse over time can be realized.

[0109] The effects of the above technical solution are as follows:

[0110] The system is divided into finite units based on key areas such as crop planting areas, soil surface areas, and areas near ventilation openings. The unit size is adapted to the sensor sampling accuracy, which can specifically capture the environmental differences in different areas of cold greenhouses (such as large temperature fluctuations near ventilation openings and concentrated CO2 consumption in the crop canopy area). This avoids the distortion of local parameters caused by overall modeling and provides more realistic basic data for subsequent precise control.

[0111] The environmental parameters within the unit are set to be uniformly distributed, and the unit environment is characterized by the average state, eliminating the need to calculate the parameter changes at every point within the unit. At the same time, the complex partial differential equations are transformed into ordinary differential equations through the finite volume method, which greatly simplifies the calculation process. This ensures effective simulation of environmental changes in cold greenhouses and is compatible with the real-time computing capabilities of the controller, avoiding the impact of calculation lag on the control response speed.

[0112] By discretizing the ODE at fixed time sampling intervals and iteratively calculating, the dynamic changes of the cold greenhouse environment over time can be captured in real time (such as temperature fluctuations caused by diurnal temperature differences and sudden drops in CO2 concentration during crop photosynthesis). This allows for timely updates of environmental status data for each unit, providing support for rolling optimization to generate real-time control commands and avoiding the problem that static modeling cannot cope with the temporal environmental changes in cold greenhouses.

[0113] When dividing the unit, the crop planting area and equipment layout are taken into account. The relationship between the calculation process and the specific crop type and equipment model is reflected by the material / energy exchange amount at the unit boundary. When changing crop varieties or adjusting equipment layout in the future, only the calculation parameters of the corresponding unit (such as the CO2 consumption coefficient of the crop canopy area) need to be adjusted, without reconstructing the entire model, thus improving the adaptability and reusability of the model in different cold greenhouse scenarios.

[0114] In one possible implementation, the spatiotemporal discretization of the joint gas dynamics model includes a step of simplifying the joint gas dynamics model:

[0115] The entire cold-region greenhouse or functionally divided independent areas are regarded as a completely mixed reactor, assuming that the air, temperature, humidity, O2 concentration, and CO2 concentration within the area can be instantly and uniformly mixed without spatial distribution differences.

[0116] Based on the assumption of complete mixing, the gradient terms representing spatial changes in the partial differential equations of the original joint gas dynamics model are eliminated, and only the terms related to time changes are retained, thus simplifying the computational dimension of the model.

[0117] Combining the method of dividing into finite units and assuming uniform state within the unit in the spatiotemporal discretization process, the states of multiple units are further integrated into the average state of the region as a whole. The final state-space approximation model is: X(k+1)=A·X(k)+B·U(k)+G·W(k), which can directly characterize the environmental parameter state changes of the greenhouse or the region as a whole without the need for additional calculation of spatial exchange terms between units, thus simplifying the model.

[0118] The working principle of the above technical solution is as follows:

[0119] Fully mixed scenario setting: The entire cold-region greenhouse or multiple independent areas (such as planting area and equipment operation area) are regarded as a "fully mixed reactor". It is assumed that the air and environmental parameters in the greenhouse or area can be instantly and uniformly mixed, with no spatial distribution differences. That is, the temperature, humidity, O2 concentration and CO2 concentration at any location are exactly the same.

[0120] Spatial gradient term elimination: Based on the assumption of complete mixing, the spatial gradient term (representing spatial variation) in the partial differential equations (PDEs) of the original joint gas dynamics model is eliminated. Terms such as the gradient describing spatial differences in concentration and the gradient describing spatial differences in temperature are simplified to 0, meaning that the influence of spatial distribution differences of environmental parameters on model calculations is no longer considered.

[0121] Simplified into an overall state equation: For greenhouses or areas considered as completely mixed reactors, the original discretized multi-unit ODEs are integrated into ordinary differential equations (ODEs) describing the overall average state. Only the average rate of change of temperature, humidity, O2 concentration, and CO2 concentration of the greenhouse or area as a whole is calculated, and the rate of change is only related to the overall environmental parameters generated / consumed (e.g., CO2 produced by crop respiration and gas supply to equipment) and external disturbances (e.g., solar radiation and outdoor temperature and humidity), without considering spatial exchanges between units.

[0122] The effects of the above technical solution are as follows:

[0123] The model is simplified and integrated into the spatiotemporal discretization process, eliminating the need for additional independent simplification steps and reducing computational steps. At the same time, the spatial gradient terms of the partial differential equation (PDE) are eliminated by the "completely mixed reactor" assumption, retaining only the time-varying terms, which greatly reduces the computational dimensionality and complexity of the model. This adapts to the rapid response requirements of cold-region greenhouses to sudden environmental changes (such as low temperature attacks and sudden drops in CO2 concentrations), avoiding inaccurate regulation caused by computational lag.

[0124] The simplification process divides the model into finite units and assumes uniform basic settings within each unit. This approach retains the targeted characterization of the environmental conditions in key areas of the cold-region greenhouse (such as the planting area and equipment operation area), while avoiding redundant calculations of spatial exchange terms between units by integrating multiple units into the overall average state of the region. This approach avoids the accuracy loss caused by ignoring regional differences in overall modeling and solves the problem of excessive computational load in fine-grid modeling, achieving a balance between accuracy and efficiency and ensuring that the model can operate stably in the actual control system of the cold-region greenhouse.

[0125] The simplified state-space approximation model can directly characterize the changing patterns of environmental parameters (temperature, humidity, O2 concentration, CO2 concentration) in the greenhouse or region. It is highly compatible with the rolling optimization requirements that use equipment control commands as optimization variables and environmental parameter setpoints as targets. Without further transformation or modification of the model, it can provide clear and concise state inputs for the optimization algorithm, helping to quickly generate commands for the collaborative control of equipment such as nitrogen generators and constant humidity purification units, thereby improving the accuracy of constant temperature and humidity control in cold-region greenhouses.

[0126] The simplified design, which divides the greenhouse into independent zones based on function, is adaptable to different layouts of cold-region greenhouses (such as single-zone planting greenhouses and multi-zone functional greenhouses). Only the zone division method and the overall average state calculation logic need to be adjusted, without the need to reconstruct the core structure of the model. At the same time, the simplified model reduces the hardware computing power requirements of the controller and can be deployed without additional hardware upgrades, thus lowering the threshold and cost of applying this control method to cold-region greenhouses, especially small and medium-sized greenhouses.

[0127] In one possible implementation, the rolling optimization is based on the dynamic model to predict the future dynamics of the system, and is achieved by solving a problem that minimizes the following objective function:

[0128]

[0129] Where J is the objective function, Y is the system output variable, Y_set is the output setpoint, Q is the output error weight matrix, and R is the control increment weight matrix, which is used to suppress frequent device actions. The deviation between the system output variable Y and the preset output setting value at various future times; Let ΔU be the control increment of the equipment at each future time. The objective function J is obtained by normalizing the output error and control increment respectively, and then summing them by weighting matrices Q and R. k is the current time of optimization calculation, and i represents the "future prediction step size", that is, the i-th control cycle (or time interval) predicted from the current time k. The value of i is usually 0, 1, 2, ..., N (N is the preset maximum number of prediction steps, such as N=5 representing the prediction of 5 future cycles).

[0130] Frequent adjustments within a preset time period exceeding the preset number of adjustments specifically refer to the number of changes in the equipment control commands within adjacent control cycles. For example, the opening of the fresh air damper is repeatedly adjusted from 20% to 80% to 30% within 1 minute, or the constant humidity purification unit switches between dehumidification / humidification modes every 30 seconds. Such actions will lead to unstable equipment operation and accelerate component aging.

[0131] The objective function incorporates a Q-matrix weighted environmental parameter deviation term, allowing for flexible adjustment of the importance of each parameter based on crop growth needs (e.g., prioritizing temperature stability during the seedling stage increases the weight of temperature deviation in the Q-matrix). This makes the optimization process more focused on key parameters, reducing the deviation of environmental parameters from set values ​​and ensuring the greenhouse environment consistently meets crop growth requirements. The introduction of the incremental weight matrix R effectively constrains abrupt changes in equipment control commands, reducing mechanical wear and electrical losses, significantly extending the lifespan of actuators such as nitrogen generators and humidity-controlled purification units, and lowering equipment maintenance costs. Based on a dynamic model, real-time prediction of future environmental dynamics and rolling optimization can proactively address potential environmental fluctuations (e.g., predicting increased solar radiation leading to temperature increases and reducing heating equipment power in advance), avoiding parameter overshoot caused by passive responses. Simultaneously, each cycle is re-optimized based on new real-time data, dynamically offsetting the effects of external disturbances (e.g., outdoor low temperatures and changes in crop photosynthetic CO2 consumption rates), ensuring long-term stability of the cold-region greenhouse environment.

[0132] In one possible implementation, decoupling control is achieved by dynamically configuring the weight matrices Q and R:

[0133] When the control command includes starting the nitrogen generator, the weight of CO2 concentration in the Q matrix and the weight of the CO2 replenishment device in the R matrix are automatically increased to suppress the rise in CO2 concentration caused by nitrogen injection replacement. The CO2 concentration error weight Q_CO2 increases functionally according to the nitrogen injection flow rate, and the control increment weight R_CO2 of the CO2 replenishment device increases functionally according to the rate of change of the nitrogen injection flow rate to proactively suppress CO2 concentration fluctuations caused by nitrogen injection replacement.

[0134] When the control command includes activating the dehumidification function of the constant humidity purification unit, the weight of CO2 concentration in the Q matrix is ​​automatically increased to compensate for the decrease in activated carbon adsorption efficiency under high humidity conditions. The CO2 concentration error weight Q_CO2 is increased functionally based on the product of the current ambient humidity value and the dehumidification power to compensate for the decrease in activated carbon adsorption efficiency under high humidity conditions in real time.

[0135] The functional enhancement includes, but is not limited to, linear, piecewise linear, or nonlinear mapping relationships based on expert rules, the parameters of which are determined through experimental data identification or simulation optimization.

[0136] By dynamically adjusting weights for specific operating conditions such as nitrogen generator startup and dehumidification function operation, the strong coupling problem of environmental parameters in cold-region greenhouses is directly solved:

[0137] The effects of the above technical solution are as follows:

[0138] During nitrogen injection, by increasing Q_CO2 with nitrogen injection flow rate and R_CO2 with nitrogen injection flow rate change rate, the control priority of CO2 concentration can be enhanced in real time and the frequent operation of the gas replenishment device can be suppressed. This avoids abnormal fluctuations in CO2 concentration (such as sudden rise or overshoot) caused by nitrogen injection replacement, and keeps the CO2 concentration stable within the set range.

[0139] During dehumidification operation, Q_CO2 increases with the product of humidity and dehumidification power, which can specifically compensate for the decrease in CO2 adsorption efficiency of activated carbon in high humidity environments. This ensures that the CO2 concentration does not deviate from the target value due to the weakening of adsorption capacity during dehumidification, thus achieving coordinated control of humidity regulation and CO2 stability.

[0140] The weighting adjustment is based on real-time or predictive parameters such as nitrogen injection flow rate, humidity, and dehumidification power product (e.g., the nitrogen injection flow rate change rate reflects future disturbance trends), giving the control strategy a forward-looking nature.

[0141] When the nitrogen generator is started and the flow rate changes rapidly, R_CO2 increases in advance with the rate of change, which can suppress the violent operation of the gas supply device before the CO2 concentration fluctuates significantly, and avoid overshoot and oscillation caused by passive adjustment after the disturbance occurs.

[0142] In high humidity environments, Q_CO2 is increased in real time by multiplying humidity and dehumidification power. This can strengthen the control weight of CO2 concentration in the early stage of the decline in activated carbon adsorption efficiency, offset potential concentration deviations in advance, and shorten the system response time.

[0143] The weight adjustment methods, which increase the functionality (linear, piecewise linear, and nonlinear mappings), can adapt to the parameter variation patterns under different greenhouse scenarios.

[0144] For example, a linear increase in Q_CO2 is used when the nitrogen injection flow rate is low (corresponding to small fluctuations in CO2 concentration), while a nonlinear steep increase is used when the flow rate is high (corresponding to the urgent suppression needs under strong disturbances). The parameters optimized through experiments / simulations ensure that the weight adjustment is accurately matched with the actual coupling strength.

[0145] For different crops (such as leafy vegetables with high CO2 requirements and fruit trees with low CO2 requirements) and different growth stages (such as seedling stage vs. fruiting stage), the environmental requirements can be flexibly adapted by adjusting the function mapping relationship, avoiding the control failure of fixed weights under complex working conditions.

[0146] The R matrix dynamically adjusts the weights of the CO2 replenishment device (e.g., R_CO2 increases during nitrogen injection), which can suppress CO2 concentration fluctuations while avoiding additional energy consumption caused by frequent start-ups or large adjustments of the replenishment device. At the same time, precise decoupling control reduces coordination conflicts between multiple devices (e.g., nitrogen generator and CO2 replenishment device, constant humidity purification unit and CO2-related equipment), reduces mechanical wear, and extends service life.

[0147] In one possible implementation, the objective function also includes an energy consumption cost term. Where S is the energy consumption weight matrix, which is dynamically adjusted according to the indoor and outdoor temperature difference to achieve optimal energy consumption control.

[0148] By quantifying the energy consumption weight of each device's control actions (such as nitrogen injection, dehumidification / humidification, and fresh air adjustment) using the S-matrix, high-energy-consumption redundant control (such as opening fresh air at a large angle when the outdoor temperature is low, or over-activating heating equipment when the indoor and outdoor temperature difference is small) is avoided in order to maintain environmental stability. This directly reduces the operating energy consumption cost of constant temperature and humidity in cold-region greenhouses while ensuring a suitable environment for crops.

[0149] The S-matrix dynamically adjusts according to the indoor and outdoor temperature difference, allowing for targeted matching of energy consumption optimization needs under different operating conditions:

[0150] When there is a large temperature difference between indoors and outdoors (e.g., 20℃ indoors and -10℃ outdoors in cold winters), increase the S weight of high-energy-consuming equipment (such as heating devices and fresh air systems) to strengthen the constraint on their energy consumption and avoid a surge in energy consumption due to the equipment operating at full load.

[0151] When the temperature difference between indoors and outdoors is small (e.g., 18℃ indoors and 15℃ outdoors in spring and autumn), the S weight should be appropriately reduced to allow the equipment to adjust the environment at a lower energy cost (e.g., by naturally adjusting the temperature through fresh air), thus balancing energy consumption and control response speed and adapting to the temperature difference variation patterns of different seasons and times in cold regions.

[0152] In one possible implementation, the process of minimizing the objective function in the rolling optimization includes:

[0153] The variables representing device switching and multi-speed in the control input vector U(k) are defined as integer variables, and the remaining continuously adjusted control quantities are defined as continuous variables, thus constructing the optimization problem as a mixed integer quadratic programming problem.

[0154] In each control cycle k, the mixed integer quadratic programming problem is solved to obtain the optimal control sequence that contains both integer solutions and continuous solutions.

[0155] Based on the sampling period of the control system, a maximum solution time threshold is set for the solution algorithm; if the optimal solution is not found within the threshold, the best feasible solution found at the moment is adopted as the control command and output.

[0156] In one possible implementation, the process of minimizing the objective function in the rolling optimization includes:

[0157] Step 1: Define the variable type of the control input vector U(k). Define the variables representing equipment switching and multi-speed as integer variables, and define the continuously adjusted control quantity as a continuous variable. Based on the sensitivity of the equipment to environmental parameters and energy consumption weight, the integer variables are dynamically divided into key equipment variables of priority 1 and auxiliary equipment variables of priority 2, and a mixed integer quadratic programming problem is constructed.

[0158] Step 2: In each control cycle k, a multi-stage solution is performed using the branch and bound algorithm framework, including:

[0159] Phase 1: Based on the historical best solution library, match the current working conditions to generate initial integer variable assumptions. After fixing these assumptions, use the gradient descent method to quickly solve the continuous variables and obtain the initial feasible solution.

[0160] In Phase 2, a depth-first search and constraint tightening strategy is used to prune integer variables of priority 1, and a neighborhood search constraint strategy is used to prune integer variables of priority 2 to optimize the objective function.

[0161] Phase 3 involves simulating and evaluating external disturbances to select the optimal solution that minimizes the objective function value and meets the anti-interference capability requirements.

[0162] Step 3: Dynamically set the maximum solution time threshold based on the deviation rate between the current environmental parameters and the target value. The larger the deviation rate, the larger the threshold.

[0163] Step 4: If no optimal solution is found within the threshold, adopt the current best feasible solution and perform local quadratic programming optimization on the continuous variables therein. At the same time, record the current working conditions and timeout solution characteristics to update the historical optimal solution library.

[0164] The priority of the integer variable is dynamically updated: when the environmental parameter associated with a device deviates from the target value by more than a preset threshold, the integer variable corresponding to the device is upgraded from priority 2 to priority 1.

[0165] In one possible implementation, the neighborhood search restriction strategy is to perform branch search only within the range of ±1 increments of the integer variable combinations of the current optimal solution.

[0166] In one possible implementation, the deviation rate is a weighted sum of the ratios by which each environmental parameter (temperature, humidity, O2 concentration, CO2 concentration) deviates from its corresponding target value.

[0167] In one possible implementation, the external disturbance simulation evaluation includes: assuming that the outdoor temperature and light intensity fluctuate by a preset amplitude within one control cycle in the future, calculating the objective function fluctuation value of the candidate optimal solution, and if the fluctuation value is less than or equal to a preset robust threshold, then it is determined that its anti-interference capability meets the standard.

[0168] Example 2: This application provides a method for controlling the cold-region greenhouse environment as described in Example 1. The system includes:

[0169] A sensor array, deployed in different areas of the greenhouse, is used to collect real-time environmental parameters within the greenhouse, including at least temperature, humidity, O2 concentration, and CO2 concentration.

[0170] An edge computing device is communicatively connected to the sensor array to receive and preprocess the real-time environmental parameters and complete data fusion.

[0171] The central controller, which is communicatively connected to the edge computing device, is configured to execute:

[0172] Based on the preprocessed real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed;

[0173] With environmental parameter settings as the objective and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate a command sequence for coordinated control of devices at each execution layer.

[0174] An actuator group, communicatively connected to the central controller, receives the instruction sequence and executes corresponding actions; the actuator group includes:

[0175] A nitrogen generator is used to adjust the nitrogen injection flow rate according to instructions.

[0176] The constant humidity and purification unit is used to switch between dehumidification / humidification modes and adjust the fan speed according to instructions;

[0177] The CO2 replenishment device is used to adjust the CO2 replenishment rate according to instructions.

[0178] The fresh air system is used to adjust the opening of the dampers according to instructions.

[0179] Optionally, the system may further include: an external disturbance monitoring unit connected to the central controller, used to acquire outdoor temperature and solar radiation intensity data in real time, and input them as external disturbance vectors into the joint gas dynamics model.

[0180] The working principle and effect of the above technical solution are the same as those in the method embodiments of this application, and will not be repeated here.

[0181] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described in this application.

[0182] This application also provides a computer-readable storage medium for storing a computer program. When the computer program is executed, it implements the steps of any of the methods described in this application. The specific implementation method is consistent with the implementation method and the technical effect achieved in the above method embodiments, and some contents will not be repeated.

[0183] In this application, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The program product can take the form of any combination of one or more readable media. A readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0184] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on a user computing device, partially on an associated device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0185] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the invention without departing from the principles and spirit of the invention, and all such changes should fall within the protection scope of the claims of the present invention.

Claims

1. A method for controlling the environment in a cold-region greenhouse, characterized in that, The method includes: Real-time environmental parameters inside the greenhouse are collected; these parameters include temperature, humidity, O2 concentration, and CO2 concentration. Based on the real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed to describe the interactive change law of each environmental parameter under control variables and external disturbances. With environmental parameter settings as the objective and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate control commands for coordinated control of devices at each execution layer. The coordinated control commands are sent to the execution layer devices to control their actions in order to maintain the target environment inside the greenhouse.

2. The method for controlling the environment of a cold-region greenhouse according to claim 1, characterized in that, The joint gas dynamics model is constructed using a set of partial differential equations, which include: Convection-diffusion equations describing changes in O2 and CO2 gas concentrations; The energy conservation equation describing temperature change; The mass conservation equation describing humidity changes; In this system of equations, the source terms, sink terms, or coefficients are coupled together.

3. The method for controlling the environment of a cold-region greenhouse according to claim 2, characterized in that, The coupling relationship of the system of equations includes at least one of the following: Temperature changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop respiration and photosynthetic rates. Humidity changes affect the source and sink terms of the O2 and CO2 concentration equations by influencing crop transpiration and gas adsorption efficiency. O2 and CO2 concentrations affect crop physiological activities, thus influencing the heat exchange terms in the temperature and humidity equations in a coupled manner. The velocity field generated by ventilation equipment simultaneously couples with and affects the distribution and transport of temperature, humidity, O2 concentration, and CO2 concentration.

4. The method for controlling the environment of a cold-region greenhouse according to claim 2, characterized in that, The joint gas dynamics model is discretized in time and space to obtain its state-space approximate model, which is expressed as follows: X(k+1)=A·X(k)+B·U(k)+G·W(k) Where X(k)=[T(k),H(k),O2(k),CO2(k)]^T is the state vector at time k, U(k) is the control input vector, W(k) is the external disturbance vector, A is the state transition matrix, B is the control input matrix, and G is the disturbance input matrix.

5. The method for controlling the environment of a cold-region greenhouse according to claim 3, characterized in that, The control input vectors include: nitrogen injection flow rate, dehumidification / humidification power and fan speed of the constant humidity purification unit, CO2 replenishment rate, and damper opening of the fresh air system.

6. The method for controlling the environment of a cold-region greenhouse according to claim 1, characterized in that, The rolling optimization predicts the future dynamics of the system based on the dynamic model, and is achieved by solving the minimization problem of the following objective function: Where J is the objective function, Y is the system output variable, Y_set is the output setpoint, Q is the output error weight matrix, and R is the control increment weight matrix, which is used to suppress frequent device actions.

7. The method for controlling the environment of a cold-region greenhouse according to claim 5, characterized in that, Decoupling control is achieved by dynamically configuring weight matrices Q and R: When the control command includes starting the nitrogen generator, the weight of CO2 concentration in the Q matrix and the weight of CO2 replenishment device in the R matrix are automatically increased to suppress the increase in CO2 concentration caused by nitrogen replacement. When the control command includes activating the dehumidification function of the constant humidity purification unit, the weight of CO2 concentration in the Q matrix is ​​automatically increased to compensate for the decrease in activated carbon adsorption efficiency under high humidity conditions.

8. The method for controlling the environment of a cold-region greenhouse according to claim 5, characterized in that, The objective function also includes an energy cost term. Where S is the energy consumption weight matrix, which is dynamically adjusted according to the indoor and outdoor temperature difference to achieve optimal energy consumption control.

9. The method for controlling the environment of a cold-region greenhouse according to claim 5, characterized in that, The process of minimizing the objective function in the rolling optimization includes: The variables representing device switching and multi-speed in the control input vector U(k) are defined as integer variables, and the remaining continuously adjusted control quantities are defined as continuous variables, thus constructing the optimization problem as a mixed integer quadratic programming problem. In each control cycle k, the mixed integer quadratic programming problem is solved to obtain the optimal control sequence that contains both integer solutions and continuous solutions. Based on the sampling period of the control system, a maximum solution time threshold is set for the solution algorithm; if the optimal solution is not found within the threshold, the best feasible solution found at the moment is adopted as the control command and output.

10. A cold-region greenhouse environment control system, used to implement the cold-region greenhouse environment control method according to any one of claims 1-9, characterized in that, The system includes: A sensor array, deployed in different areas of the greenhouse, is used to collect real-time environmental parameters within the greenhouse, including at least temperature, humidity, O2 concentration, and CO2 concentration. An edge computing device is communicatively connected to the sensor array to receive and preprocess the real-time environmental parameters and complete data fusion. The central controller, which is communicatively connected to the edge computing device, is configured to execute: Based on the preprocessed real-time environmental parameters, a joint gas dynamics model coupling temperature field, humidity field, O2 concentration field and CO2 concentration field is constructed; With environmental parameter settings as the objective and equipment control commands as the optimization variables, rolling optimization is performed based on the joint gas dynamics model to generate a command sequence for coordinated control of devices at each execution layer. The actuator group is communicatively connected to the central controller, receives the instruction sequence, and executes the corresponding actions.

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