CFD-IL Coupling Model-Based Method for Regulating Microclimate in Glass-Enclosed Space

Abstract

The present invention relates to the technical field of building environment control and intelligent regulation, and discloses a method for regulating the microclimate in a glass-enclosed space based on a CFD-IL coupling model. The method first establishes a CFD-IL coupling model including a three-dimensional flow field simulation module and a dynamic parameter optimization module, real-time collects multi-dimensional environmental parameters such as temperature, humidity, air velocity, and light intensity in the glass-enclosed space, inputs them into the model to predict the future distribution of microclimate parameters, generates a multi-objective regulation instruction set based on this, and dynamically adjusts the parameters of environmental control devices such as air conditioning systems and ventilation devices. At the same time, based on the feedback of environmental parameters, the weight coefficients and boundary conditions of the model are optimized, and the boundary conditions of the flow field simulation can also be corrected according to external meteorological data. This method can accurately regulate the microclimate, save energy and reduce consumption, and improve the system's adaptive ability, and has broad application prospects in enclosed spaces such as glass greenhouses, exhibition halls, and office buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of building environment control and intelligent regulation, and in particular to a microclimate regulation method for a glass-enclosed space based on a CFD-IL coupling model. Background Art

[0002] In the field of modern architecture, glass-enclosed spaces are widely used due to their unique lighting and aesthetic advantages, such as various greenhouses for plant cultivation, exhibition halls for exhibits, and office buildings for creating comfortable office environments. However, the special structure of glass-enclosed spaces makes it difficult to control the internal microclimate.

[0003] On the one hand, although the high light transmittance of glass is conducive to lighting, the intensity of solar radiation varies significantly in different seasons and time periods, which can cause drastic fluctuations in indoor temperature. In summer, strong solar radiation can quickly heat up the room. If the heat cannot be effectively dissipated, the excessively high temperature will not only affect human comfort, but will also be extremely detrimental to the growth of plants in the greenhouse and the preservation of exhibits in the exhibition hall, and may cause plants to wither and exhibits to be damaged. In winter, indoor heat is easily lost through the glass, resulting in too low an indoor temperature and increased heating energy consumption.

[0004] On the other hand, it is difficult to evenly distribute air circulation and humidity in glass-enclosed spaces. Since the space is relatively closed and air circulation is poor, local areas are prone to being stuffy or humid. In greenhouses, poor air circulation can affect plant photosynthesis and respiration, hindering plant growth; in offices, poor air environment can reduce staff work efficiency and even cause health problems. Traditional ventilation and humidification equipment often cannot accurately control the air flow rate and humidity in different areas, making it difficult to meet the diverse needs of complex spaces.

[0005] Most of the existing microclimate control methods have limitations. Some simple temperature control systems only control the start and stop of the air conditioner based on the feedback from the temperature sensor, without comprehensively considering other environmental factors such as humidity and light, and the control effect is single and rough. Although some complex control systems take multi-parameter control into consideration, the models and algorithms used lack adaptive capabilities and cannot optimize the control strategy in real time according to environmental changes in the space. For example, in a greenhouse environment, the requirements for environmental parameters at different stages of plant growth vary greatly, and traditional control methods are difficult to respond to these changes flexibly, resulting in waste of resources and poor control effects. Moreover, when dealing with complex spatial structures and dynamic environmental changes, the existing technology is difficult to balance the calculation accuracy and efficiency of the model, and cannot meet the requirements for precise control and rapid response in practical applications.

[0006] Therefore, it is of great practical significance to develop a small climate control method for glass-enclosed spaces that can comprehensively consider various environmental factors, adaptively optimize control strategies, and improve control accuracy and efficiency. This can not only enhance the usage experience and application value of glass-enclosed spaces, but also reduce energy consumption and achieve sustainable development. Summary of the Invention

[0007] The purpose of the present invention is to provide a small climate control method for glass-enclosed spaces based on the CFD-IL coupling model to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A small climate control method for glass-enclosed spaces based on the CFD-IL coupling model, where the full name of CFD is Computational Fluid Dynamics, representing Computational Fluid Dynamics, and the full name of IL is Iterative Learning, representing Iterative Learning. The coupling model emphasizes the deep integration of the flow field simulation ability of CFD and the dynamic optimization ability of IL. The method specifically includes:

[0009] Establish a CFD-IL coupling model for the glass-enclosed space, which includes a three-dimensional flow field simulation module based on computational fluid dynamics and a dynamic parameter optimization module based on the iterative learning algorithm;

[0010] Real-time collect multi-dimensional environmental parameters inside the glass-enclosed space, including temperature, humidity, air velocity, and light intensity;

[0011] Input the multi-dimensional environmental parameters into the CFD-IL coupling model, and predict the distribution of small climate parameters within a preset future time period through flow field simulation and iterative learning;

[0012] Generate a multi-objective control instruction set according to the prediction results, and the instruction set includes the operating mode, power, and action range of the environmental control equipment;

[0013] Dynamically adjust the parameters of the environmental control equipment, including the coordinated control strategies of the air conditioning system, ventilation device, humidifier, and sunshade mechanism;

[0014] Based on the updated environmental parameter feedback, optimize the weight coefficients and boundary conditions of the CFD-IL coupling model through the iterative learning algorithm.

[0015] Preferably, the steps of establishing the CFD-IL coupling model include:

[0016] Divide the grid topology structure of the glass-enclosed space and set the initial hydrodynamics boundary conditions;

[0017] Construct the objective function of the iterative learning algorithm, where the objective function takes the uniformity of environmental parameters, energy consumption minimization, and equipment response speed as optimization variables;

[0018] Train the CFD-IL coupling model with historical environmental data to dynamically correlate the flow field simulation results with the parameter correction amount of iterative learning.

[0019] Preferably, the acquisition steps of the multi-dimensional environmental parameters include:

[0020] Arrange a distributed sensor network in the glass enclosed space to obtain the gradient distribution data of temperature and humidity in real time;

[0021] Collect the spatial vector of air velocity through a laser velocimeter;

[0022] Use a light sensor to record the light intensity and incident angle in different regions.

[0023] Preferably, the steps of generating the multi-objective regulation instruction set include:

[0024] Compare the predicted microclimate parameter distribution with the preset comfort interval to generate a deviation matrix;

[0025] Based on the deviation matrix, solve the optimal parameter combination of the environmental control equipment through a multi-objective particle swarm optimization algorithm;

[0026] Convert the optimal parameter combination into an instruction sequence executable by the equipment and assign priority weights.

[0027] Preferably, the steps of dynamically adjusting the environmental control equipment include:

[0028] According to the instruction sequence, adjust the supply air temperature, air velocity, and direction of the air conditioning system;

[0029] Control the start-stop frequency and opening angle of the ventilation device;

[0030] Synchronously adjust the atomization particle concentration of the humidifier and the unfolding area of the sunshade mechanism.

[0031] Preferably, the steps of optimizing the CFD-IL coupling model include:

[0032] Calculate the residual between the actual environmental parameters and the predicted values, and construct a residual matrix;

[0033] Update the gain coefficient of the iterative learning algorithm through an adaptive Kalman filter;

[0034] Correct the turbulence equation coefficient of the CFD-IL coupling model and the boundary conditions of the locally refined mesh area.

[0035] Preferably, the method further includes:

[0036] Within a preset time interval, re - divide the grid density of the CFD - IL coupled model, and dynamically adjust the simulation step size according to the change rate of environmental parameters.

[0037] Preferably, the iterative learning algorithm includes:

[0038] Construct a recurrent neural network based on time series, input historical regulation instructions and corresponding environmental parameter feedbacks;

[0039] Optimize the weights of the hidden - layer nodes of the neural network through the back - propagation algorithm;

[0040] Output the parameter correction amount for the next time step, and fuse it into the initial conditions of the CFD - IL coupled model.

[0041] Preferably, the method further includes:

[0042] When it is detected that the environmental parameters in a local area exceed the preset threshold, start the emergency regulation mode, and preferentially adjust the environmental control equipment corresponding to this area;

[0043] In the emergency regulation mode, pause the global optimization algorithm and adopt a rule - based control strategy for rapid response.

[0044] Preferably, the method further includes:

[0045] Connect external meteorological data to the CFD - IL coupled model, including outdoor temperature, wind speed and solar radiation intensity;

[0046] Through the coupled analysis of external data and internal parameters, dynamically correct the external boundary conditions of the flow - field simulation.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] In terms of precisely regulating the microclimate, by collecting multi - dimensional environmental parameters such as temperature, humidity, air velocity and light intensity in real time, and using the CFD - IL coupled model for flow - field simulation and iterative learning prediction, it is possible to accurately grasp the distribution of microclimate parameters in the glass - enclosed space within a preset future time period. Taking a glass greenhouse as an example, under different seasons and weather conditions, this method can accurately predict the temperature change trend in each area of the greenhouse, and adjust environmental control equipment such as air - conditioning systems, ventilation devices, humidifiers and sun - shading mechanisms in advance. When it is predicted that the temperature in a certain area is about to rise, start the sun - shading mechanism to block sunlight in advance, and at the same time adjust the supply air temperature and wind speed of the air - conditioning system to precisely maintain the suitable temperature range required for plant growth, avoiding adverse effects on plant growth caused by too high or too low temperature. For an exhibition hall, it can accurately control the environmental parameters of the exhibition area, ensure that the exhibits are in the best preservation environment, and reduce the risk of damage to the exhibits caused by environmental factors.

[0049] In terms of energy conservation and consumption reduction, the objective function of the iterative learning algorithm constructed by the present invention takes the uniformity of environmental parameters, energy consumption minimization, and equipment response speed as optimization variables. By training the model with historical environmental data, dynamically correlating the results of the flow field simulation with the parameter correction amount of the iterative learning, intelligent collaborative control of environmental control equipment is achieved. For example, in an office space, when there are fewer people, according to the indoor environmental parameters and prediction results, the power of the air conditioning system is automatically reduced, and at the same time, the start-stop frequency and opening angle of the ventilation device are reasonably adjusted. On the premise of ensuring indoor comfort, energy consumption is minimized to the greatest extent. Compared with the traditional regulation method, it can effectively reduce energy consumption and save operating costs, meeting the concepts of green buildings and sustainable development.

[0050] From the perspective of the system's adaptability, based on the feedback of the updated environmental parameters, the weight coefficients and boundary conditions of the CFD-IL coupling model are optimized through the iterative learning algorithm. As time goes by and the environment changes, the model can continuously self-adjust and optimize. When alternating between different seasons, the outdoor meteorological conditions change significantly. The model can dynamically correct the external boundary conditions of the flow field simulation through the coupled analysis of external meteorological data (such as outdoor temperature, wind speed, and solar radiation intensity) and internal parameters. At the same time, the gain coefficient of the iterative learning algorithm is updated using an adaptive Kalman filter, and the turbulent equation coefficients of the CFD model and the boundary conditions of the locally refined mesh area are corrected, enabling the model to always maintain high prediction accuracy and regulation performance and adapt to the complex and changeable glass-enclosed space environment.

[0051] In response to emergencies, when it is detected that the environmental parameters in a local area exceed the preset threshold, the emergency regulation mode is activated. In a greenhouse, if the temperature in a certain area suddenly becomes too high, the system will first adjust the environmental control equipment corresponding to that area, such as increasing the cooling power of the air conditioner in that area to quickly reduce the temperature and ensure the normal growth of plants. In the emergency regulation mode, the global optimization algorithm is suspended, and a rule-based control strategy is adopted for rapid response. This mechanism ensures that effective measures can be taken quickly in case of emergencies to avoid serious damage to people, items, or plants in the space caused by environmental deterioration.

[0052] In addition, re-dividing the grid density of the CFD model at preset time intervals and dynamically adjusting the simulation step size according to the change rate of environmental parameters can improve the calculation efficiency while ensuring the calculation accuracy. The grid is encrypted in areas where environmental parameters change violently to improve the simulation accuracy of the model for local complex flow fields; the grid size is appropriately increased in areas with gentle changes to reduce the amount of calculation. This dynamic adjustment strategy enables the regulation method of the present invention to operate efficiently in glass-enclosed spaces of different scales and complexities, having wide applicability. Description of the Drawings

[0053] Figure 1 This is the working principle diagram of the small climate regulation method for the glass enclosed space based on the CFD-IL coupling model of the present invention;

[0054] Figure 2 This is the diagram of the multi-dimensional environmental parameter acquisition step;

[0055] Figure 3 This is the diagram of the multi-objective regulation instruction set generation step;

[0056] Figure 4 This is the diagram of the CFD-IL coupling model optimization step. Specific implementation mode

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to Figures 1-4 , the present invention provides a technical solution: a small climate regulation method for a glass enclosed space based on a CFD-IL coupling model, the method includes:

[0059] Establish a CFD-IL coupling model: The constructed model includes a three-dimensional flow field simulation module based on computational fluid dynamics and a dynamic parameter optimization module based on an iterative learning algorithm. By analyzing the glass enclosed space, its grid topology structure is divided, and the initial hydrodynamic boundary conditions are set to provide a basic framework for subsequent flow field simulation. At the same time, the objective function of the iterative learning algorithm is constructed, with the uniformity of environmental parameters, energy consumption minimization, and equipment response speed as optimization variables, so that the model can be continuously optimized according to actual needs.

[0060] Real-time collection of multi-dimensional environmental parameters: Corresponding sensors are arranged in the glass enclosed space to collect multi-dimensional environmental parameters such as temperature, humidity, air velocity, and light intensity in real time. These parameters are the basic data for the model to make predictions and regulations, and their accuracy and real-time nature are crucial.

[0061] Predict the distribution of small climate parameters: Input the collected multi-dimensional environmental parameters into the CFD-IL coupling model, and the model uses the methods of flow field simulation and iterative learning to predict the distribution of small climate parameters in the glass enclosed space within a preset future time period. This step provides a basis for the subsequent generation of regulation instructions.

[0062] Generate a multi-objective regulation instruction set: According to the predicted distribution results of microclimate parameters, generate a multi-objective regulation instruction set that includes the operating modes, powers, and action ranges of environmental control devices. This instruction set is the key to realizing microclimate regulation. By reasonably setting device parameters, the microclimate in the glass-enclosed space can reach an ideal state.

[0063] Dynamically adjust the parameters of environmental control devices: According to the generated regulation instruction set, dynamically adjust the parameters of environmental control devices such as air conditioning systems, ventilation devices, humidifiers, and sunshade mechanisms. By coordinating the control of these devices, precise regulation of the microclimate in the glass-enclosed space is achieved.

[0064] Optimize the CFD-IL coupling model: Based on the updated feedback of environmental parameters, use the iterative learning algorithm to optimize the weight coefficients and boundary conditions of the CFD-IL coupling model. As the environment changes, continuously adjust the model parameters to improve the prediction accuracy and regulation effect of the model.

[0065] The present invention will be further described below in conjunction with Embodiments 1 to 6:

[0066] Embodiment 1:

[0067] When establishing the CFD-IL coupling model, first, mesh generation needs to be carried out on the glass-enclosed space. Taking a large glass greenhouse as an example, the greenhouse is 50 meters long, 30 meters wide, and 8 meters high. Using professional mesh generation software, according to the internal structure of the greenhouse and the expected flow field changes, a non-uniform mesh generation method is adopted. In areas with large flow field changes, such as near heat sources (such as heating equipment), ventilation openings, and plant planting areas, the mesh is refined so that the mesh size reaches 0.2 m × 0.2 m × 0.2 m; while in the middle area of the space with relatively small flow field changes, the mesh size is set to 0.5 m × 0.5 m × 0.5 m, which not only ensures the calculation accuracy but also controls the calculation amount. After the division is completed, set the initial hydrodynamic boundary conditions. For example, at the ventilation opening, according to the design parameters of the ventilation equipment, set the wind speed to 2 m / s, the temperature to 25 °C, and the relative humidity to 60%; at the wall boundary, set it to a no-slip boundary condition, that is, the velocity at the contact between the fluid and the wall is 0.

[0068] Construct the objective function of the iterative learning algorithm, with the uniformity of environmental parameters, energy consumption minimization, and equipment response speed as optimization variables. Assume that the uniformity of environmental parameters is measured by the temperature standard deviation , humidity standard deviation , energy consumption is represented by the total energy consumption , and the equipment response speed is represented by the response time . The objective function can be expressed as: , where , , , is a weight coefficient, which is set according to actual needs and importance. For example, during the plant growth stage, more attention is paid to the uniformity of temperature and humidity. and can be set to larger values, such as . During the energy shortage period, the value of can be increased, such as ; when it is necessary to quickly respond to sudden environmental changes, the value of can be correspondingly increased, such as .

[0069] In terms of collecting multi-dimensional environmental parameters, a distributed sensor network is arranged inside the greenhouse. Along the length direction of the greenhouse, a temperature and humidity sensor node is set every 5 meters. Each node is equipped with a temperature sensor and a humidity sensor, with a total of 10 nodes. These sensors can obtain the gradient distribution data of temperature and humidity in real time. For example, at noon in summer, the sensor near the top detects a temperature of 32 °C and a relative humidity of 50%, while the sensor near the ground planting area detects a temperature of 28 °C and a relative humidity of 65%.

[0070] Use a laser velocimeter to collect the spatial vector of air velocity. The laser velocimeter is installed at the center of the greenhouse. By emitting laser beams, the air velocity in different directions is measured. For example, when the ventilation equipment is turned on, it is measured that in the horizontal direction, the air velocity near the ventilation opening is 1.5 m / s, and in the vertical direction, the air velocity 2 meters above the ground is 0.8 m / s.

[0071] Use light sensors to record the light intensity and incident angle in different areas. In different planting areas of the greenhouse, light sensors are installed respectively. For example, 3 light sensors are installed in the light-loving plant planting area and the shade-tolerant plant planting area respectively. At 10 am on a sunny day, the light sensors in the light-loving plant planting area detect a light intensity of 8000 lux and an incident angle of 45°; the light intensity in the shade-tolerant plant planting area is 3000 lux and the incident angle is 30°.

[0072] Example 2:

[0073] When generating a multi-objective regulation instruction set, it is based on the comparison between the predicted microclimate parameter distribution and the preset comfort interval. For example, for temperature, the preset comfort interval is 22 °C - 28 °C; for humidity, the preset comfort interval is 50% - 70%. Suppose the predicted temperature in a certain area of the greenhouse in the next 1 hour is 30 °C and the humidity is 45%. After comparing with the preset comfort interval, a deviation matrix is generated. The temperature deviation is , and the humidity deviation is .

[0074] Based on this deviation matrix, the optimal parameter combination of the environmental control equipment is solved by the multi-objective particle swarm optimization algorithm. In the multi-objective particle swarm optimization algorithm, each particle represents a parameter combination of a group of environmental control equipment, such as the supply air temperature of the air conditioning system , wind speed , the start-stop frequency of the ventilation device , opening and closing angle , the atomization particle concentration of the humidifier and the deployed area of the sunshade mechanism . The particles continuously update their positions and velocities in the search space to find the optimal solution.

[0075] Suppose that after multiple iterative calculations, a set of optimal parameter combinations are obtained: the supply air temperature of the air conditioning system , wind speed m / s; the start-stop frequency of the ventilation device times / hour, opening and closing angle ; the atomization particle concentration of the humidifier g / m³; the deployed area of the sunshade mechanism m².

[0076] Convert this set of optimal parameter combinations into an instruction sequence executable by the equipment and assign priority weights. For example, since the temperature deviation has a greater impact on plant growth, the instruction priority for adjusting the air conditioning system is set to high; the humidity deviation is relatively small, and the instruction priority for adjusting the humidifier is set to medium; the adjustment instruction priorities for the ventilation device and the sunshade mechanism are set to low. According to the priority order, first send instructions to the air conditioning system to adjust the supply air temperature and wind speed so that it operates according to the set parameters; then send instructions to the ventilation device to control the start-stop frequency and opening and closing angle; then send instructions to the humidifier to adjust the atomization particle concentration; finally, send instructions to the sunshade mechanism to adjust the deployed area.

[0077] When dynamically adjusting the environmental control equipment, operate according to the above instruction sequence. For the air conditioning system, by controlling the internal temperature adjustment device and the fan, adjust the supply air temperature to 24°C and the wind speed to 1.2 m / s. For the ventilation device, control the opening and closing of the ventilation equipment at a start-stop frequency of 10 times / hour through the controller and adjust the opening and closing angle of the ventilation opening to 60° to achieve ventilation and air change. The humidifier increases the air humidity by adjusting the spraying device to increase the atomization particle concentration to 50 g / m³. The sunshade mechanism expands the deployed area to 80 m² through motor drive to block sunlight and reduce the indoor temperature.

[0078] Example 3:

[0079] When optimizing the CFD-IL coupling model, calculating the residuals between the actual environmental parameters and the predicted values is a crucial step. For example, at a certain moment, the actual measured temperature at a point in a glass-closed space is 26°C, while the model predicts the temperature at this point to be 25.5°C, then the temperature residual is ; the actual measured humidity is 62%, the predicted humidity is 60%, and the humidity residual is . And so on, the residuals of each environmental parameter are constructed into a residual matrix.

[0080] Update the gain coefficient of the iterative learning algorithm through an adaptive Kalman filter. The adaptive Kalman filter dynamically adjusts the gain coefficient according to information such as the residual matrix and the system noise covariance matrix . Assume that the system noise covariance matrix is , the measurement noise covariance matrix is , the predicted error covariance matrix is , then the calculation formula for the gain coefficient is: , where is the observation matrix, which is used to describe the relationship between the actual measured value and the predicted value. In this embodiment, is determined according to the corresponding relationship between the actually measured environmental parameters and the model predicted parameters. By continuously updating the gain coefficient , the iterative learning algorithm can more accurately adjust the model parameters.

[0081] Modify the coefficients of the turbulence equation and the boundary conditions of the locally refined mesh region of the CFD-IL coupling model. For example, in the turbulence model commonly used in the CFD-IL coupling model, according to the residual analysis and the actual flow field situation, the coefficients , , in the turbulence equation are adjusted. Assume that the original , after optimization, it is adjusted to . For the boundary conditions of the locally refined mesh region, such as in the mesh refinement region near the heat source, the originally set heat flux boundary condition is watts per square meter, and according to the actual measurement and the model optimization requirements, it is adjusted to watts per square meter to more accurately simulate the changes in the flow field and temperature field.

[0082] Example 4:

[0083] Within a preset time interval, re - divide the grid density of the CFD - IL coupling model and dynamically adjust the simulation step size according to the change rate of environmental parameters, which is of great significance for improving the accuracy and computational efficiency of the model. For example, set the preset time interval to every 6 hours. During the day, with the change of solar radiation intensity, environmental parameters such as temperature and air velocity in the glass - enclosed space change relatively fast. Taking an exhibition greenhouse as an example, the grid is re - divided every 6 hours from 10 a.m. to 4 p.m. According to the monitoring data in the morning, it is found that the temperature and air velocity change violently in the areas near the glass curtain wall and the plant - dense areas. Therefore, when re - dividing the grid, the grids in these areas are further refined, and the grid size is reduced from the original 0.3 m×0.3 m×0.3 m to 0.1 m×0.1 m×0.1 m, while in other areas with relatively small changes, the grid size remains unchanged.

[0084] Dynamically adjust the simulation step size according to the change rate of environmental parameters. The change rate of environmental parameters is obtained by calculating the ratio of the difference between environmental parameters at adjacent times to the time interval. For example, within a certain 1 - hour period, if the temperature rises from 25°C to 27°C, then the temperature change rate is . When the temperature change rate is greater than 1°C / hour, shorten the simulation step size from the original 0.05 s to 0.02 s; when the temperature change rate is less than 0.5°C / hour, extend the simulation step size to 0.1 s. In this way, when the environmental parameters change violently, a smaller simulation step size is adopted to improve the accuracy of the model; when the environmental parameters change relatively little, a larger simulation step size is adopted to reduce the amount of calculation and improve the computational efficiency.

[0085] Example 5:

[0086] In this embodiment, the iterative learning algorithm is implemented by constructing a recurrent neural network based on time series. Taking a flower cultivation greenhouse as an example, collect the control instructions every hour in the past week, including the temperature setting value of the air - conditioning system, the start - stop state of the ventilation device, the working intensity of the humidifier, and the opening - closing degree of the sun - shading mechanism, etc., and at the same time collect the corresponding environmental parameter feedback, such as temperature, humidity, light intensity, etc. Arrange these historical data in chronological order as the input of the recurrent neural network.

[0087] The recurrent neural network includes an input layer, a hidden layer, and an output layer. The input layer receives historical control instructions and environmental parameter feedback data. The hidden layer processes the input data through a recurrent structure to learn the time - series features in the data. During the training process, optimize the weights of the hidden - layer nodes of the neural network through the back - propagation algorithm. Assume that the hidden layer has nodes, and the weight between node and node is , the gradient of the error with respect to the weights is calculated by the backpropagation algorithm, and the weight values are continuously adjusted to minimize the error between the predicted output and the actual output of the network.

[0088] After multiple trainings, the recurrent neural network can output the parameter correction amounts for the next time step. For example, when the network predicts that the temperature set value of the air conditioning system needs to be reduced by 1°C, the start-stop frequency of the ventilation device needs to be increased by 2 times / hour, and the working intensity of the humidifier needs to be increased by 10% in the next hour. These parameter correction amounts are incorporated into the initial conditions of the CFD-IL coupling model. For example, the adjusted temperature set value is used as the initial value of the temperature boundary condition in the CFD-IL coupling model, the start-stop frequency adjustment information of the ventilation device is used to update the flow boundary condition of the ventilation opening in the CFD-IL coupling model, and the adjustment of the working intensity of the humidifier is used to correct the parameters of the humidity source term in the CFD-IL coupling model, thereby realizing the dynamic optimization of the CFD-IL coupling model and improving the accuracy of model prediction.

[0089] Example 6:

[0090] During the operation of the glass enclosed space, when it is detected that the environmental parameters in a local area exceed the preset threshold, the emergency regulation mode is activated. For example, in a glass exhibition hall, the temperature threshold is set at 30°C and the humidity threshold is set at 80% in the exhibition area. At a certain moment, the temperature sensor at a corner of the exhibition area detects that the temperature reaches 32°C, exceeding the preset threshold. At this time, the system immediately activates the emergency regulation mode.

[0091] In the emergency regulation mode, the environmental control equipment corresponding to this area is preferentially adjusted. Due to the high temperature, first start the air conditioning system near this area, adjust its cooling power to the maximum, and at the same time increase the air supply volume to quickly reduce the temperature of this area. At the same time, suspend the global optimization algorithm and adopt a rule-based control strategy for rapid response. The rule-based control strategy is set as follows: when the temperature is higher than 30°C, for every 1°C increase, the cooling power of the air conditioning system increases by 10% and the air supply volume increases by 15%; when the humidity is higher than 80%, the humidifier stops working and the opening frequency of the ventilation device increases by 5 times / hour.

[0092] Connecting external meteorological data to the CFD-IL coupling model is crucial for improving the regulation effect of the model. Taking a glass solar greenhouse as an example, external meteorological data such as outdoor temperature, wind speed, and solar radiation intensity are connected. The outdoor temperature is monitored in real time through sensors , wind speed and solar radiation intensity . In winter, when the outdoor temperature is relatively low, such as when it reaches -5°C, through the coupled analysis of external data and internal parameters, the external boundary conditions of the flow field simulation are dynamically corrected. Due to the influence of outdoor cold air, in the CFD-IL coupling model, the heat transfer coefficient of the glass greenhouse wall is appropriately increased. Assume that the original heat transfer coefficient is W / (m·K), and it is adjusted to W / (m·K) to more accurately simulate the heat exchange process between indoors and outdoors. When the solar radiation intensity is strong, such as reaching 1000 W / m² at noon in summer, according to the solar radiation intensity and the indoor light distribution in the greenhouse, the deployment area of the sunshade mechanism is adjusted, and at the same time, the parameters of the light source term in the CFD-IL coupling model are corrected to effectively control the indoor light intensity and temperature.

[0093] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0094] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for regulating the microclimate in a glass enclosed space based on a CFD-IL coupling model, characterized in that, It includes the following steps: Establish a CFD-IL coupling model for the glass enclosed space, which model includes a three-dimensional flow field simulation module based on computational fluid dynamics and a dynamic parameter optimization module based on iterative learning algorithm; Collect multi-dimensional environmental parameters in the glass enclosed space in real time, including temperature, humidity, air velocity and light intensity; Input the multi-dimensional environmental parameters into the CFD-IL coupling model, and predict the distribution of microclimate parameters within a preset future time period through flow field simulation and iterative learning; Generate a multi-objective regulation instruction set according to the prediction results, and the instruction set includes the operation mode, power and action range of environmental control devices; Dynamically adjust the parameters of the environmental control devices, including the coordinated control strategies of air conditioning systems, ventilation devices, humidifiers and shading mechanisms; Based on the updated environmental parameter feedback, optimize the weight coefficients and boundary conditions of the CFD-IL coupling model through the iterative learning algorithm; It also includes: Within a preset time interval, re-divide the grid density of the CFD-IL coupling model and dynamically adjust the simulation step size according to the environmental parameter change rate; The steps to optimize the CFD-IL coupling model include: Calculate the residuals between the actual environmental parameters and the predicted values, and construct a residual matrix; Update the gain coefficient of the iterative learning algorithm through an adaptive Kalman filter; Correct the coefficients of the turbulence equation of the CFD-IL coupling model and the boundary conditions of the locally refined grid area; The iterative learning algorithm includes: Construct a recurrent neural network based on time series, and input historical regulation instructions and corresponding environmental parameter feedback; Optimize the weights of the hidden layer nodes of the neural network through the backpropagation algorithm; Output the parameter correction amount for the next time step and fuse it into the initial conditions of the CFD-IL coupling model.

2. The method for regulating the microclimate in a glass enclosed space according to claim 1, characterized in that, The steps to establish the CFD-IL coupling model include: Divide the grid topology of the glass enclosed space and set the initial hydrodynamics boundary conditions; Construct an objective function for the iterative learning algorithm, and the objective function takes the uniformity of environmental parameters, energy consumption minimization and equipment response speed as optimization variables; Train the CFD-IL coupling model through historical environmental data, and dynamically associate the flow field simulation results with the parameter correction amount of iterative learning.

3. The method for regulating the microclimate in a glass enclosed space according to claim 2, characterized in that, The steps to collect the multi-dimensional environmental parameters include: Arrange a distributed sensor network in the glass enclosed space to obtain the gradient distribution data of temperature and humidity in real time; Collect the spatial vectors of air velocity through a laser velocimeter; Use light sensors to record the light intensity and incident angles in different areas.

4. The method for regulating the microclimate in a glass-enclosed space according to claim 1, wherein, The steps to generate the multi-objective regulation instruction set include: Compare the predicted microclimate parameter distribution with the preset comfort interval to generate a deviation matrix; Based on the deviation matrix, solve the optimal parameter combination of environmental control devices through a multi-objective particle swarm optimization algorithm; Convert the optimal parameter combination into an instruction sequence executable by the device and assign priority weights.

5. The method for regulating the microclimate in a glass enclosed space according to claim 4, wherein, The steps to dynamically adjust the environmental control devices include: According to the instruction sequence, adjust the supply air temperature, velocity and direction of the air conditioning system; Control the start-stop frequency and opening angle of the ventilation device; Synchronously adjust the atomization particle concentration of the humidifier and the unfolding area of the sunshade mechanism.

6. The method for regulating the microclimate in a glass enclosed space according to claim 1, characterized in that, It further includes: When it is detected that the local area environmental parameters exceed the preset threshold, start the emergency regulation mode and preferentially adjust the environmental control equipment corresponding to this area; In the emergency regulation mode, suspend the global optimization algorithm and adopt a rule-based control strategy for rapid response.

7. The method for regulating the microclimate in a glass enclosed space according to claim 1, wherein, The method further includes: Connect external meteorological data to the CFD-IL coupling model, including outdoor temperature, wind speed, and solar radiation intensity; Through the coupling analysis of external data and internal parameters, dynamically correct the external boundary conditions of the flow field simulation.

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