Method for optimizing the construction parameters of a solar greenhouse and related equipment

Abstract

The application provides a sunlight greenhouse building parameter optimization method and a related device, and the method comprises the following steps: designing building parameters of a sunlight greenhouse based on an orthogonal test method, determining at least one building parameter optimization scheme; obtaining a three-dimensional transient fluid mechanics CFD model corresponding to the building parameter optimization scheme, the three-dimensional transient CFD model being created according to sunlight greenhouse modeling data in a hot and humid environment; obtaining an initialization result and a boundary condition of the hot and humid environment; inputting the boundary condition and the initialization result into the three-dimensional transient CFD model, calculating a comprehensive score of a hot and humid environment evaluation index corresponding to each building parameter optimization scheme, and determining an optimal building parameter scheme with the highest comprehensive score. That is, the CFD technology is used to more accurately calculate the indoor environment distribution of the sunlight greenhouse, the indoor temperature and humidity distribution heterogeneity is fully considered, the indoor complex fluid mass transfer and heat transfer can be accurately calculated, and the actual situation of the greenhouse can be truly reflected.

Description

Technical Field

[0001] This invention relates to the field of agricultural building technology, and in particular to a method for optimizing the building parameters of a solar greenhouse and related equipment. Background Technology

[0002] Solar greenhouses are unique agricultural production facilities with excellent heat preservation properties, allowing for winter production without additional heating. By the end of 2021, the area of ​​solar greenhouses in my country exceeded 550,000 hectares, accounting for 30% of the total implemented area. A solar greenhouse consists of a rear roof, a north wall (rear wall), gable walls, and a front roof. The rear roof prevents heat loss and provides insulation; the north wall serves as an important heat storage and release medium, absorbing heat during the day and releasing it at night through convection to meet the greenhouse's heat requirements; the front roof is a crucial light-receiving surface, with insulation rolled up during the day and lowered at night for insulation.

[0003] The optimal values ​​of building parameters for solar greenhouses directly affect the indoor lighting and thermal and humidity environment, which in turn affects crop growth and development. Selecting reasonable building parameters for solar greenhouses can directly improve the environmental performance inside the greenhouse.

[0004] Greenhouse microclimates are characterized by nonlinearity, strong coupling, and dynamic parameter changes. Traditional environmental control and prediction methods are difficult to solve the complexity and variability of the solar greenhouse environment, and cannot truly reflect the actual situation of the greenhouse or provide reasonable building parameters. Summary of the Invention

[0005] This invention provides a method and related equipment for optimizing the building parameters of a solar greenhouse, which addresses the shortcomings of existing methods for optimizing building parameters of solar greenhouses, which cannot accurately reflect the actual situation of the greenhouse and result in unsatisfactory optimization effects. The invention achieves more accurate calculation of the indoor environment distribution through CFD technology, fully considers the heterogeneity of indoor temperature and humidity distribution, and combines relative humidity as the optimization target, so as to truly reflect the actual situation of the greenhouse and improve the optimization effect of building parameters of solar greenhouses.

[0006] This invention provides a method for optimizing the building parameters of a solar greenhouse, the method comprising:

[0007] The building parameters of the solar greenhouse are designed based on the orthogonal experimental method, and at least one optimized building parameter scheme is determined.

[0008] Obtain the three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters.

[0009] Obtain the initialization results and boundary conditions of the thermal and humid environment;

[0010] The boundary conditions and initialization results are input into the three-dimensional transient CFD model to calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and the optimal building parameter scheme with the highest comprehensive score is determined.

[0011] According to a method for optimizing building parameters of a solar greenhouse provided by the present invention, the step of obtaining the initialization results and boundary conditions of the thermal and humid environment includes:

[0012] First measured data of the initialization area is obtained, and the first measured data is fitted to obtain an initial binary fitting function. The initialization area includes an air area, a back wall area, and a soil area.

[0013] The initialization binary fitting function is loaded into the three-dimensional transient CFD model, and the initialization region is initialized to obtain the initialization result.

[0014] Second measured data of the boundary region is obtained, and the second measured data is fitted to obtain a binary fitting function for the boundary conditions. The boundary region includes the outer surface region of the rear wall, the south side region of the soil, and the north side region of the soil.

[0015] The boundary condition binary fitting function is loaded into the three-dimensional transient CFD model, and boundary conditions are set for the boundary region to obtain the boundary conditions.

[0016] According to the method for optimizing the building parameters of a solar greenhouse provided by the present invention, the initialization result of the thermal and humid environment includes temperature initialization result and humidity initialization result, wherein the first measured data includes temperature and humidity data of the initialization area;

[0017] The boundary conditions of the thermal and humid environment include temperature boundary conditions and humidity boundary conditions, wherein the second measured data includes temperature and humidity data of the boundary region.

[0018] According to the present invention, a method for optimizing building parameters of a solar greenhouse includes the following steps: inputting the boundary conditions and the initialization results into a three-dimensional transient CFD model, calculating the comprehensive score of the thermal and humidity environment evaluation index corresponding to each optimized building parameter scheme, and determining the optimal building parameter scheme with the highest comprehensive score.

[0019] The boundary conditions and initialization results are input into a three-dimensional transient CFD model, which performs numerical simulations under unsteady conditions to obtain simulation results.

[0020] Based on the simulation results, the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is calculated, and the building parameter optimization scheme corresponding to the highest comprehensive score is taken as the optimal building parameter scheme.

[0021] According to the method for optimizing building parameters of a solar greenhouse provided by the present invention, the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each optimized building parameter scheme based on the simulation results includes:

[0022] Based on the simulation results, calculate the index values ​​corresponding to each thermal and humid environment evaluation index of each of the building parameter optimization schemes;

[0023] Based on the aforementioned index values, the index weight of each of the aforementioned thermal and humid environment evaluation indicators is calculated using the entropy weight method.

[0024] Based on the index values ​​and index weights, a comprehensive score for the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is determined.

[0025] According to a method for optimizing building parameters of a solar greenhouse provided by the present invention, before the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each optimized building parameter scheme based on the simulation results, the method further includes:

[0026] Obtain the measured results of the solar greenhouse;

[0027] The effectiveness of the three-dimensional transient CFD model is verified based on the difference between the simulation results and the measured results.

[0028] If so, then the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results is performed.

[0029] The present invention also provides a device for optimizing the building parameters of a solar greenhouse, comprising:

[0030] The scheme determination module is used to design the building parameters of the solar greenhouse based on the orthogonal experimental method and determine at least one optimized scheme for the building parameters.

[0031] The model acquisition module is used to acquire the three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters.

[0032] The condition acquisition module is used to acquire the initialization results and boundary conditions of the thermal and humid environment;

[0033] The scheme optimization module is used to input the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and determine the optimal building parameter scheme with the highest comprehensive score.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the solar greenhouse building parameter optimization method as described above.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the building parameters of a solar greenhouse as described above.

[0036] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for optimizing the building parameters of a solar greenhouse as described above.

[0037] The present invention provides a method and related equipment for optimizing the building parameters of a solar greenhouse. Based on orthogonal experimental design, the method designs the building parameters of a solar greenhouse and determines at least one optimized building parameter scheme. It then acquires a three-dimensional transient fluid dynamics (CFD) model corresponding to the optimized building parameter scheme. This CFD model is created based on solar greenhouse modeling data under thermal and humid conditions, including the building parameters. The method obtains the initialization results and boundary conditions of the thermal and humid environment. These boundary conditions and initialization results are input into the three-dimensional transient CFD model to calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each optimized building parameter scheme. The optimal building parameter scheme with the highest comprehensive score is then determined. In other words, CFD technology is used to more accurately calculate the indoor environmental distribution of the solar greenhouse, fully considering the heterogeneity of indoor temperature and humidity distribution, and accurately calculating complex fluid mass and heat transfer, thus truly reflecting the actual situation of the greenhouse. Furthermore, the combination of orthogonal experimental design and numerical simulation using the CFD model improves the calculation efficiency of the building parameters. By introducing the comprehensive evaluation index of the thermal and humid environment as the final optimization target, the optimal building parameters can be obtained, providing a theoretical basis for the design and production guidance of solar greenhouses. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 This is one of the flowcharts illustrating the method for optimizing the building parameters of a solar greenhouse provided by the present invention;

[0040] Figure 2 This is a diagram of the solar greenhouse building parameter optimization system provided by the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0043] The following is combined with Figures 1-3 The method for optimizing the building parameters of a solar greenhouse according to the present invention is described with reference to... Figure 1 The method for optimizing the building parameters of the solar greenhouse includes:

[0044] Step S100: Design the building parameters of the solar greenhouse based on the orthogonal experimental method, and determine at least one optimized building parameter scheme;

[0045] Step S200: Obtain the three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters.

[0046] Step S300: Obtain the initialization results and boundary conditions of the thermal and humid environment;

[0047] Step S400: Input the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and determine the optimal building parameter scheme with the highest comprehensive score.

[0048] This embodiment aims to: utilize CFD technology to more accurately calculate the indoor environmental distribution of a solar greenhouse, fully considering the heterogeneity of indoor temperature and humidity distribution, accurately calculate complex fluid mass and heat transfer within the greenhouse, and truly reflect the actual conditions of the greenhouse. Furthermore, by combining orthogonal experiments with numerical simulations using CFD models, the calculation efficiency of solar greenhouse building parameters is improved. By introducing a comprehensive evaluation index of the thermal and humidity environment as the final optimization objective, optimal building parameters can be obtained, providing a theoretical basis for solar greenhouse design and production guidance.

[0049] In this embodiment, the specific application scenario is:

[0050] The optimal values ​​of building parameters for solar greenhouses directly affect the indoor lighting and thermal and humidity environment, which in turn affects crop growth and development. Selecting reasonable building parameters for solar greenhouses can directly improve the environmental performance inside the greenhouse.

[0051] Greenhouse microclimates are characterized by nonlinearity, strong coupling, and dynamic parameter changes. Traditional environmental control and prediction methods are unable to address the complexity and variability of the solar greenhouse environment, and cannot accurately reflect the actual situation of the greenhouse or provide reasonable building parameters.

[0052] As an example, the method for optimizing the building parameters of a solar greenhouse can be applied to a solar greenhouse building parameter optimization system, which is applied to a solar greenhouse building parameter optimization device.

[0053] The specific steps are as follows:

[0054] Step S100: Design the building parameters of the solar greenhouse based on the orthogonal experimental method, and determine at least one optimized building parameter scheme.

[0055] As an example, the architectural parameters of a solar greenhouse include the greenhouse span, ridge height, rear roof length, rear wall height, and front roof shape parameters.

[0056] As an example, select building parameters for a solar greenhouse. Each building parameter corresponds to multiple levels. The number of levels for the same building parameter is within a reasonable range. Establish a multi-factor, multi-level orthogonal experimental scheme based on the selected building parameters and their corresponding levels. Select an appropriate orthogonal experimental table based on the selected number of factors and levels to determine at least one optimized building parameter scheme for a solar greenhouse.

[0057] It should be noted that the number of factors in a multi-factor, multi-level orthogonal experimental scheme is more than two. That is, two or more building parameters are randomly selected from the above-mentioned solar greenhouse building parameters to create a multi-factor, multi-level orthogonal experimental scheme.

[0058] As an example, if three building parameters for a solar greenhouse are selected, namely ridge height, rear wall height, and horizontal projection of the rear roof, and four horizontal values ​​are set for each factor, the specific parameters are shown in Table 1 below.

[0059] Table 1: Factors and Levels

[0060]

[0061] Based on the selected ridge height, rear wall height, and horizontal projection of the rear roof, and their corresponding multiple horizontal values, a multi-factor, multi-level orthogonal experimental table is established to determine at least one optimized architectural parameter scheme for the greenhouse, such as architectural parameter optimization scheme A1B1C1, where the ridge height is 4.52m, the rear wall height is 3.2m, and the horizontal projection of the rear roof is 0.9m. Other architectural parameter optimization schemes A... a B b C c The design methods for a, b, c ∈ [1, 4] are basically the same, and will not be repeated here.

[0062] Step S200: Obtain the three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters.

[0063] As an example, the modeling data for solar greenhouses includes the building parameters of the solar greenhouse; it also includes crop row data, crop physiological data, indoor and outdoor environmental data, physical property parameters of solar greenhouse materials, and greenhouse geographic information.

[0064] The crop row data includes: number of crop rows, spacing, and crop canopy geometry (length, width, and height).

[0065] Crop physiological data include: crop transpiration rate, leaf area, water vapor concentration, atmospheric temperature, leaf temperature, transpiration rate, stomatal conductance, etc.

[0066] Indoor and outdoor environmental parameters include indoor and outdoor solar radiation intensity, indoor and outdoor air temperature and relative humidity, soil temperature, and back wall temperature.

[0067] The physical properties of materials used in solar greenhouses include: density, thermal conductivity, and thermal resistance of insulation blankets, back walls, soil, crops, and films;

[0068] The geographical location information of the greenhouse includes: latitude, longitude, altitude, etc. of the greenhouse construction site.

[0069] Computational Fluid Dynamics (CFD) is an effective method for numerically analyzing and studying fluid flow, mass transfer, and heat transfer processes through computer numerical simulation and visualization. Compared with traditional energy balance equations, CFD technology can more accurately calculate the distribution of indoor environments, fully considering the heterogeneity of indoor temperature and humidity distribution, rather than assuming that the soil, back wall, and air temperatures are uniformly distributed. Furthermore, greenhouse microclimates are characterized by nonlinearity, strong coupling, and dynamic parameter changes. Traditional environmental control and prediction methods are insufficient to address the complexity and variability of the solar greenhouse environment. CFD technology can simulate and predict airflow, temperature, and relative humidity under different environmental conditions and the effects of greenhouse control facilities. Therefore, in this invention, the optimization of solar greenhouse building parameters based on the indoor thermal and humidity environment and CFD technology is cost-effective, has a short research cycle, and can accurately calculate complex fluid mass and heat transfer within the greenhouse, providing a theoretical basis for greenhouse design and production guidance.

[0070] As an example, using the above-mentioned greenhouse modeling data, a three-dimensional physical model of the greenhouse is established using three-dimensional modeling software. This three-dimensional physical model includes: the rear roof area, the rear wall area, the soil area, the front roof foundation area, the front roof area, the crop area, and the indoor air flow area.

[0071] Different building parameter optimization schemes have different corresponding parameters in the three-dimensional physical model of the solar greenhouse. Therefore, when optimizing building parameters for different building parameter optimization schemes, the parameters corresponding to the building parameter optimization schemes are changed to create a three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The comprehensive score of the thermal and humid environment evaluation index is calculated through this three-dimensional transient fluid dynamics CFD model, which lays the foundation for determining the optimal building parameter scheme in the future.

[0072] As an example, 3D modeling software could be Solidworks and Ansys SCDM (SpaceClaimDirect Modeler).

[0073] A three-dimensional physical model of the greenhouse was built using Solidworks and Ansys SCDM (SpaceClaim Direct Modeler). Meshing software was used to mesh the physical model of the greenhouse, select appropriate mesh size and mesh type, check mesh quality, and output mesh file. This process can be achieved by conventional techniques and no specific restrictions are imposed here.

[0074] As an example, in the process of creating a three-dimensional transient fluid dynamics CFD model, the rear wall area is a composite wall structure. The thickness of each wall layer is determined, and each wall layer is modeled independently. Compared with modeling a single wall, this is more realistic and improves the modeling accuracy, so that the model calculation results are more accurate.

[0075] As an example, in the process of creating a three-dimensional transient fluid dynamics CFD model, the soil region considers the indoor soil and the soil on the north and south sides. The soil on the south side edge and the soil on the north side edge are modeled independently to take into account the influence of the edge soil on the indoor thermal and humid environment and improve the model accuracy.

[0076] Step S300: Obtain the initialization results and boundary conditions of the thermal and humid environment.

[0077] As an example, the mesh file is imported into Fluent for numerical simulation. The standard k-ε turbulence model is selected, and the initialization results and boundary conditions are set.

[0078] When setting boundary conditions and initialization, the dynamic changes in indoor and outdoor environments and the heterogeneity of distribution are considered, and a binary fitting function is used to improve the calculation accuracy of the model.

[0079] As an example, the steps for obtaining the initialization results and boundary conditions of the thermal and humid environment include:

[0080] Step S310: Obtain the first measured data of the initialization area, perform fitting processing on the first measured data to obtain the initial binary fitting function, wherein the initialization area includes the air area, the back wall area, and the soil area.

[0081] Step S320: Load the initialization binary fitting function into the three-dimensional transient CFD model, initialize the initialization region, and obtain the initialization result;

[0082] Step S330: Obtain the second measured data of the boundary region, perform fitting processing on the second measured data to obtain the boundary condition binary fitting function, wherein the boundary region includes the outer surface region of the rear wall, the south side region of the soil, and the north side region of the soil.

[0083] Step S340: Load the boundary condition binary fitting function into the three-dimensional transient CFD model, set the boundary conditions for the boundary region, and obtain the boundary conditions.

[0084] As an example, the first measured data of multiple measurement points in the initialization area are obtained, and the multiple first measured data are fitted to obtain the initial binary fitting function.

[0085] The general form of the basic bivariate fitting function is as follows:

[0086] F = Ax 2 y 2 +Bx 2 y+Cxy 2 +Dx 2 +Ey 2 +Fxy+Gx+Hy+I

[0087] Where A, B...I are the function coefficients.

[0088] During initialization, span and height coordinates are used as independent variables, and temperature is used as the dependent variable to account for the heterogeneity of environmental parameters.

[0089] The initialized binary fitting function is loaded into the three-dimensional transient CFD model via UDF (Universal Disc Format) to initialize the region and obtain the initialization result of the region.

[0090] As an example, before inputting the initialization results into the three-dimensional transient CFD model for numerical simulation, the initialization results are compared with the actual measured results. When the initialization results and the actual measured results are within the allowable error range or the error is minimized, the initialization results are valid, thereby ensuring the accuracy of the fitting function.

[0091] As an example, the initialization region includes the air region, the back wall region, and the soil region. Specifically, the values ​​of each parameter of the basic binary fitting function are determined using measured environmental data (soil temperature, air temperature, and back wall temperature) from the greenhouse. Let's take air temperature initialization as an example. Measured air temperature data from each measuring point in the greenhouse at time t1 are collected and converted into x (span direction coordinates), y (height direction coordinates), and F (air temperature) data types. These are then substituted into the basic binary fitting function to obtain the values ​​of each parameter while ensuring fitting accuracy, thus obtaining the initial binary fitting function for the specific parameters. This is then loaded into the indoor air region using a UDF (User-Defined Function) to obtain the initial air temperature distribution results. The implementation methods for other greenhouse environmental parameters are basically the same and will not be elaborated further here.

[0092] As an example, second measured data from multiple measurement points in the boundary region are obtained, and the multiple second measured data are fitted to obtain a binary fitting function for the boundary conditions.

[0093] The general form of the basic binary fitting function in the boundary condition settings is the same as that in the initialization settings. The second measured data of multiple measurement points in the boundary region are input into the binary fitting function for fitting to obtain the boundary condition binary fitting function. The specific implementation process is the same as that of the initialization binary fitting function, and will not be repeated here.

[0094] When setting the boundaries, time and altitude values ​​are used as independent variables, and the dynamic changes in environmental parameters are considered, with temperature used as the dependent variable.

[0095] The fitted boundary condition binary fitting function is loaded into the three-dimensional transient CFD model via UDF, and boundary conditions are set for the boundary region to obtain the boundary conditions.

[0096] As an example, the boundary area includes the outer surface of the rear wall and the north and south sides of the soil. The boundary setting process is basically the same as the initialization implementation method, and will not be described again here.

[0097] As an example, the initialization results of the thermal and humid environment include temperature initialization results and humidity initialization results, wherein the first measured data includes temperature and humidity data of the initialized area; the boundary conditions of the thermal and humid environment include temperature boundary conditions and humidity boundary conditions, wherein the second measured data includes temperature and humidity data of the boundary area.

[0098] Relative humidity is a crucial environmental parameter for crop growth; excessively high or low humidity can cause crop stagnation or even death. Maintaining a suitable relative humidity range is beneficial for healthy crop growth. Currently, most methods for optimizing building parameters are based on temperature as the optimization objective, neglecting the impact of humidity distribution. Therefore, this paper incorporates relative humidity into the optimization objective while still using temperature as the primary objective. By comprehensively considering the coupling factors of thermal and humid environments, this approach better meets the actual needs of agricultural production and provides a more comprehensive approach.

[0099] As an example, humidity can be initialized using the average indoor humidity mass fraction, or it can be initialized using the same method as temperature for fitting a bivariate function. It should be noted that humidity initialization is limited to the air and soil regions.

[0100] As an example, after initializing and setting the boundaries of the three-dimensional transient CFD model, the solar radiation model, crop model, and component transport model are loaded and set.

[0101] The solar radiation model setup steps include importing the solar radiation intensity from indoor and outdoor data and the latitude and longitude from the greenhouse's geographical location information into the solar radiation model, specifically into the Fluent software's solar tracking model.

[0102] The crop model setup steps include establishing a physical model of the crop rows based on crop row data, importing crop physiological data into the crop row model, and simulating crop transpiration and heat production. (Implementation Example) For ease of understanding, a complete physical model of the solar greenhouse can be obtained based on crop row data and greenhouse building parameters. The transpiration rate in the crop row physiological data is converted into a mass source term and loaded onto the crop row region. The latent heat of transpiration and the sensible heat exchange are converted into an energy source term and loaded onto the crop row region using the following formula.

[0103] The sensible heat flux between the canopy and the air is expressed as follows:

[0104]

[0105] In the formula, C p The specific heat of air under constant pressure is expressed in J / kg·K, r. a For the aerodynamic drag of the blade, s / m, T l and T a These are the temperatures of the blades and the air, respectively, in °C.

[0106] Latent heat flux, which is the energy required for crop transpiration, is expressed as follows:

[0107]

[0108] In the formula, L w The latent heat absorbed by water vapor is taken as 2450 kJ / kg, w l and w a The absolute humidity of the leaves and the air. r s The stomatal resistance of the blade is expressed in s / m.

[0109] When setting up the component transport model, indoor air is considered as a mixture consisting of only two components: dry air and water vapor.

[0110] Step S400: Input the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and determine the optimal building parameter scheme with the highest comprehensive score.

[0111] As an example, thermal and humid environment evaluation indicators are selected based on the optimization results of greenhouse building parameters and agronomic needs. In this invention, relative humidity is incorporated into the optimization objective while using temperature as the optimization target. By comprehensively considering the coupling factors of thermal and humid environment, this approach better meets the actual needs of agricultural production and provides a more comprehensive consideration.

[0112] As an example, the rate of temperature rise, the coefficient of temperature and humidity non-uniformity, and the average temperature and humidity are selected as evaluation indicators for thermal and humid environments.

[0113] In this embodiment, the combination of orthogonal experiments and numerical simulation greatly improves the computational efficiency, and a comprehensive evaluation index of thermal and humid environment is introduced as the final optimization target, which can quantitatively reflect the quality of building structural parameters.

[0114] As an example, the step of inputting the boundary conditions and the initialization results into a three-dimensional transient CFD model, calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the optimized building parameter schemes, and determining the optimal building parameter scheme with the highest comprehensive score includes:

[0115] Step S410: Input the boundary conditions and the initialization results into the three-dimensional transient CFD model. The three-dimensional transient CFD model performs numerical simulation under unsteady conditions to obtain simulation results.

[0116] Step S420: Calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results. The building parameter optimization scheme corresponding to the highest comprehensive score is taken as the optimal building parameter scheme.

[0117] As an example, after inputting the boundary conditions and initialization results into the three-dimensional transient CFD model, the simulation duration is set, and the three-dimensional transient CFD model runs under unsteady conditions. At each time step, the boundary conditions of the solar greenhouse building structure are updated in real time and used as the starting conditions for the next time step for iterative calculation to obtain the final simulation results, namely the values ​​of the thermal and humid environment evaluation indexes corresponding to each measuring point.

[0118] Based on the simulation results and the calculation methods of each thermal and humid environment evaluation index, the index values ​​of each thermal and humid environment evaluation index are calculated. Based on the preset comprehensive evaluation index expression for thermal and humid environment and the index values ​​of each thermal and humid environment evaluation index, the comprehensive score of each building parameter optimization scheme is calculated. The schemes are sorted according to the comprehensive evaluation scores, the optimal combination of building parameters and dimensions are determined, and the optimization results are output.

[0119] As an example, the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results includes:

[0120] Step S421: Calculate the index value corresponding to each thermal and humid environment evaluation index of the building parameter optimization scheme based on the simulation results;

[0121] Step S422: Based on the index values, calculate the index weight of each of the thermal and humid environment evaluation indicators using the entropy weight method;

[0122] Step S423: Based on the index values ​​and index weights, determine the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes.

[0123] As an example, the rate of temperature rise, the coefficient of temperature and humidity non-uniformity, and the average temperature and humidity were selected as evaluation indicators for the thermal and humidity environment. A combination of numerical simulation and orthogonal experiments was used to obtain the magnitudes of each indicator under different building parameters. The entropy weight method was used to calculate the weight coefficient of each evaluation indicator, and these coefficients were linearly added to obtain the comprehensive score for the greenhouse thermal and humidity environment.

[0124] Specifically, the formula for calculating the rate of temperature rise is as follows:

[0125]

[0126] In the formula, K air T1 is the rate of air temperature rise, in °C / min; T2 is the final measured average temperature of indoor air, in °C; T1 is the initial measured average temperature of indoor air, in °C; Δt is the time difference between T1 and T2, in min; ΔT is the temperature difference between T1 and T2, in °C.

[0127] The formula for calculating the indoor temperature and humidity non-uniformity coefficient is as follows:

[0128]

[0129]

[0130] In the formula, S T S is the coefficient of temperature non-uniformity. R The coefficient of relative humidity non-uniformity. The average indoor air temperature is expressed in °C. The average relative humidity of indoor air, in %; σ T σ represents the root mean square error of temperature, in °C. R The value is the root mean square error of relative humidity, in percentages.

[0131] The formula for calculating the root mean square error is as follows:

[0132]

[0133]

[0134] In the formula, T i R represents the indoor air temperature measured in °C. i The relative humidity at the indoor air measurement point is expressed in %; n air This represents the number of indoor air temperature and humidity measurement points.

[0135] The formula for calculating average temperature and humidity is:

[0136]

[0137]

[0138] In the formula, The average indoor air temperature is expressed in °C. The indoor average relative humidity is expressed as a percentage.

[0139] Before adopting the entropy weight method, the calculation results of each index under different building parameters need to be standardized and positiveized. Considering that the temperature rise rate is an extremely large index, the temperature and relative humidity non-uniformity coefficients are extremely small indexes, and the average temperature and average relative humidity are interval-type indexes, the calculation results of the thermal and humid environment evaluation indexes are standardized.

[0140] This invention uses the entropy weight method to determine the weight coefficients of thermal and humid environment indicators, as shown in Table 2. The final formal expression of the thermal and humid environment weight indicators is as follows:

[0141] D i =0.3427x1+0.2188x2+0.4172x3+0.018x4+0.0033x5

[0142] Where x1 is the standardized sample value of temperature rise rate, x2 is the standardized sample value of temperature non-uniformity coefficient, x3 is the standardized sample value of relative humidity non-uniformity coefficient, x4 is the standardized sample value of average temperature, and x5 is the standardized sample value of average relative humidity. x1, x2, x3, x4, and x5 are obtained by numerical simulation under unsteady conditions based on a three-dimensional transient CFD model.

[0143] Table 2: Weighting coefficients for thermal and humid environment indicators

[0144]

[0145] Based on the above formal expression D of the thermal and humid environment weight index i The comprehensive score of each building parameter optimization scheme is calculated based on the index weights of the thermal and humid environment evaluation index, as shown in Table 3.

[0146] Table 3: Score Table for Comprehensive Evaluation Index of Environmental Parameters of Solar Greenhouse

[0147]

[0148] The optimal building parameter scheme is the one with the highest score, and the optimal parameter dimensions are obtained. As an example, as shown in Table 3 above, the optimal orthogonal scheme is A4B1C4, which has a ridge height of 5.72m, a rear wall height of 3.2m, and a horizontal projection of the rear roof of 2.1m.

[0149] As an example, before the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results, the method further includes:

[0150] Step A1: Obtain the measured results of the solar greenhouse;

[0151] Step A2: Based on the difference between the simulation results and the measured results, verify whether the three-dimensional transient CFD model is effective;

[0152] If so, then proceed to step A3, which involves calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results.

[0153] As an example, when using a 3D transient CFD model to optimize the building parameters of a solar greenhouse, the measured results of the solar greenhouse are compared with the simulation results to verify the effectiveness of the 3D transient CFD model. The simulation results obtained from a valid 3D transient CFD model can be used in the scoring calculation to evaluate the merits of the optimized building parameter scheme. It can be understood that the effectiveness of the 3D transient CFD model can be verified by calculating the root mean square error and mean absolute error between the simulation results and the measured results. When the error value is within the preset allowable error range, the 3D transient CFD model is considered effective.

[0154] It should be noted that the measured results of the solar greenhouse are the indoor and outdoor measured data of the solar greenhouse. They can be the first measured data of the initial area, or the data of real-time monitoring of the indoor and outdoor environment.

[0155] This invention provides a method and related equipment for optimizing the building parameters of a solar greenhouse. Compared with current methods for optimizing solar greenhouse building parameters, which fail to accurately reflect the actual conditions of the greenhouse and result in unsatisfactory optimization effects, this invention designs the building parameters of the solar greenhouse based on an orthogonal experimental method to determine at least one optimized building parameter scheme. A three-dimensional transient fluid dynamics (CFD) model corresponding to the optimized building parameter scheme is obtained. This three-dimensional transient CFD model is created based on solar greenhouse modeling data under thermal and humid conditions, including the solar greenhouse building parameters. Initialization results and boundary conditions of the thermal and humid environment are obtained. The boundary conditions and initialization results are input into the three-dimensional transient CFD model to calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each optimized building parameter scheme, and the optimal building parameter scheme with the highest comprehensive score is determined. In other words, CFD technology is used to more accurately calculate the indoor environmental distribution of the solar greenhouse, fully consider the heterogeneity of indoor temperature and humidity distribution, accurately calculate complex fluid mass and heat transfer within the greenhouse, and truly reflect the actual conditions of the greenhouse. Furthermore, by combining orthogonal experiments with numerical simulations using CFD models, the calculation efficiency of building parameters for solar greenhouses is improved. By introducing a comprehensive evaluation index of thermal and humid environment as the final optimization target, optimal building parameters can be obtained, providing a theoretical basis for the design and production guidance of solar greenhouses.

[0156] Based on the first embodiment described above, a system for optimizing the building parameters of a solar greenhouse is proposed. (Refer to...) Figure 2 The solar greenhouse building parameter optimization system includes:

[0157] The data acquisition module 10 is used to collect modeling data of the solar greenhouse, including solar greenhouse building parameters, crop row data, crop physiological data, indoor and outdoor environmental data, greenhouse physical property parameters and geographical location information, and transmit the above information to the data review module.

[0158] The data review module 20 reviews the collected data, verifies the validity of the data, and transmits the information to the numerical simulation cloud platform.

[0159] Module 30 of the orthogonal experimental design module selects the building parameter factors and levels of the solar greenhouse, chooses an appropriate orthogonal experimental table, generates an orthogonal experimental design for the building parameters of the solar greenhouse, thus determining that there is at least one optimized building parameter design, and transmits the information to the numerical simulation cloud platform.

[0160] The numerical simulation cloud platform 40 uses collected greenhouse building parameters and crop row information to build a 3D model of the solar greenhouse. Collected crop physiological data is loaded into the crop row region as source terms. Outdoor environmental data is loaded onto the model boundary and initialized using a fitting function. Greenhouse physical properties and geographical location information are loaded into the CFD model. A 3D transient CFD model of the solar greenhouse's thermal and humidity environment is established, and its effectiveness is verified by comparing it with measured temperature and humidity distributions. Based on this, according to an orthogonal experimental scheme, only the building parameters and dimensions are changed, while other simulation conditions remain unchanged. Unsteady-state numerical simulations are then performed, and the calculation results are transmitted to the comprehensive evaluation index module.

[0161] The comprehensive evaluation module 50 calculates the magnitude of various environmental parameter indices under different building parameter optimization schemes, uses the entropy weight method to calculate the weight of each index, obtains the expression of the comprehensive evaluation index of thermal and humid environment, performs a comprehensive score for each building parameter optimization scheme, and transmits the results to the parameter output module.

[0162] The parameter output module 60 sorts the parameters according to the comprehensive evaluation score, determines the optimal building parameter scheme and dimensions, and outputs the optimization results.

[0163] The following describes the solar greenhouse building parameter optimization device provided by the present invention. The solar greenhouse building parameter optimization device described below and the solar greenhouse building parameter optimization method described above can be referred to in correspondence.

[0164] The present invention also provides a device for optimizing the building parameters of a solar greenhouse, the device comprising:

[0165] The scheme determination module is used to design the building parameters of the solar greenhouse based on the orthogonal experimental method and determine at least one optimized scheme for the building parameters.

[0166] The model acquisition module is used to acquire the three-dimensional transient fluid dynamics CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters.

[0167] The condition acquisition module is used to acquire the initialization results and boundary conditions of the thermal and humid environment;

[0168] The scheme optimization module is used to input the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and determine the optimal building parameter scheme with the highest comprehensive score.

[0169] And / or, the condition acquisition module further includes:

[0170] The first acquisition submodule is used to acquire the first measured data of the initialization area, perform fitting processing on the first measured data to obtain the initial binary fitting function, wherein the initialization area includes the air area, the back wall area, and the soil area.

[0171] The first setting submodule is used to load the initialization binary fitting function into the three-dimensional transient CFD model, perform initialization settings on the initialization region, and obtain the initialization result;

[0172] The second acquisition submodule is used to acquire the second measured data of the boundary region, and to perform fitting processing on the second measured data to obtain the boundary condition binary fitting function. The boundary region includes the outer surface region of the rear wall, the south side region of the soil, and the north side region of the soil.

[0173] The second setting submodule is used to load the boundary condition binary fitting function into the three-dimensional transient CFD model, set the boundary conditions for the boundary region, and obtain the boundary conditions.

[0174] And / or, the condition acquisition module further includes:

[0175] The initialization results of the thermal and humid environment include temperature initialization results and humidity initialization results, wherein the first measured data includes the temperature and humidity data of the initialization area;

[0176] The boundary conditions of the thermal and humid environment include temperature boundary conditions and humidity boundary conditions, wherein the second measured data includes temperature and humidity data of the boundary region.

[0177] And / or, the scheme optimization module further includes:

[0178] The model calculation submodule is used to input the boundary conditions and the initialization results into the three-dimensional transient CFD model, which performs numerical simulation under unsteady conditions to obtain simulation results;

[0179] The scheme optimization submodule is used to calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results. The building parameter optimization scheme with the highest comprehensive score is taken as the optimal building parameter scheme.

[0180] And / or, the scheme optimization submodule further includes:

[0181] The numerical calculation unit is used to calculate the index value corresponding to each of the thermal and humid environment evaluation indicators of the building parameter optimization scheme based on the simulation results.

[0182] The weight calculation unit is used to calculate the weight of each of the thermal and humid environment evaluation indicators based on the indicator values ​​using the entropy weight method.

[0183] The scheme optimization unit is used to determine the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the index values ​​and index weights.

[0184] And / or, the scheme optimization module further includes:

[0185] The result acquisition submodule is used to acquire the measured results of the solar greenhouse;

[0186] The verification submodule is used to verify whether the three-dimensional transient CFD model is effective based on the difference between the simulation results and the measured results.

[0187] Select a submodule, which, if applicable, executes the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results.

[0188] The specific implementation of the solar greenhouse building parameter optimization device of the present invention is basically the same as the embodiments of the solar greenhouse building parameter optimization method described above, and will not be repeated here.

[0189] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a method for optimizing the building parameters of a solar greenhouse. This method includes: designing the building parameters of a solar greenhouse based on an orthogonal experimental method to determine at least one optimized building parameter scheme; obtaining a three-dimensional transient fluid dynamics (CFD) model corresponding to the optimized building parameter scheme, wherein the three-dimensional transient CFD model is created based on solar greenhouse modeling data under a hot and humid environment, and the solar greenhouse modeling data includes the solar greenhouse building parameters; obtaining the initialization results and boundary conditions of the hot and humid environment; inputting the boundary conditions and the initialization results into the three-dimensional transient CFD model; calculating the comprehensive score of the hot and humid environment evaluation index corresponding to each optimized building parameter scheme; and determining the optimal building parameter scheme with the highest comprehensive score.

[0190] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the solar greenhouse building parameter optimization method provided by the above methods. The method includes: designing solar greenhouse building parameters based on orthogonal experimental design to determine at least one building parameter optimization scheme; obtaining a three-dimensional transient fluid dynamics (CFD) model corresponding to the building parameter optimization scheme, wherein the three-dimensional transient CFD model is created based on solar greenhouse modeling data under a hot and humid environment, and the solar greenhouse modeling data includes the solar greenhouse building parameters; obtaining the initialization results and boundary conditions of the hot and humid environment; inputting the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculating the comprehensive score of the hot and humid environment evaluation index corresponding to each building parameter optimization scheme, and determining the optimal building parameter scheme with the highest comprehensive score.

[0192] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the method for optimizing the building parameters of a solar greenhouse provided by the above methods. The method includes: designing the building parameters of a solar greenhouse based on an orthogonal experimental method to determine at least one optimized building parameter scheme; obtaining a three-dimensional transient fluid dynamics (CFD) model corresponding to the optimized building parameter scheme, wherein the three-dimensional transient CFD model is created based on solar greenhouse modeling data under a hot and humid environment, and the solar greenhouse modeling data includes the solar greenhouse building parameters; obtaining the initialization results and boundary conditions of the hot and humid environment; inputting the boundary conditions and the initialization results into the three-dimensional transient CFD model; calculating the comprehensive score of the evaluation index of the hot and humid environment corresponding to each optimized building parameter scheme; and determining the optimal building parameter scheme with the highest comprehensive score.

[0193] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0194] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the building parameters of a solar greenhouse, characterized in that, The method includes: The building parameters of the solar greenhouse are designed based on the orthogonal experimental method, and at least one optimized building parameter scheme is determined. Obtain the three-dimensional transient CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters. Obtain the initialization results and boundary conditions of the thermal and humid environment; The boundary conditions and initialization results are input into the three-dimensional transient CFD model to calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and the optimal building parameter scheme with the highest comprehensive score is determined. The step of inputting the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the optimized building parameter schemes, and determining the optimal building parameter scheme with the highest comprehensive score includes: The boundary conditions and initialization results are input into a three-dimensional transient CFD model, which performs numerical simulations under unsteady conditions to obtain simulation results. Based on the simulation results, the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is calculated, and the building parameter optimization scheme corresponding to the highest comprehensive score is taken as the optimal building parameter scheme. The step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results includes: Based on the simulation results, calculate the index values ​​corresponding to each of the thermal and humid environment evaluation indicators in the building parameter optimization scheme; Based on the aforementioned index values, the index weight of each of the aforementioned thermal and humid environment evaluation indicators is calculated using the entropy weight method. Based on the index values ​​and index weights, a comprehensive score for the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is determined. Among them, the building parameters of the solar greenhouse include the greenhouse span, ridge height, rear roof length, rear wall height and front roof shape parameters; Select the building parameters of the solar greenhouse. Each building parameter of the solar greenhouse has multiple levels. The number of levels of the same building parameter of the solar greenhouse is within a certain reasonable range. Establish a multi-factor, multi-level orthogonal experimental scheme based on the selected building parameters of the solar greenhouse and their corresponding multiple levels. Select an appropriate orthogonal experimental table based on the selected number of factors and levels, and determine the optimization scheme of the building parameters of at least one solar greenhouse. Temperature rise rate, temperature and humidity non-uniformity coefficient, and average temperature and humidity were selected as thermal and humidity environment evaluation indicators. A combination of numerical simulation and orthogonal experiment was used to obtain the magnitude of each indicator of thermal and humidity environment under different building parameters.

2. The method for optimizing the building parameters of a solar greenhouse according to claim 1, characterized in that, The steps for obtaining the initialization results and boundary conditions of the thermal and humid environment include: First measured data of the initialization area is obtained, and the first measured data is fitted to obtain an initial binary fitting function. The initialization area includes an air area, a back wall area, and a soil area. The initialization binary fitting function is loaded into the three-dimensional transient CFD model, and the initialization region is initialized to obtain the initialization result. Second measured data of the boundary region is obtained, and the second measured data is fitted to obtain a binary fitting function for the boundary conditions. The boundary region includes the outer surface region of the rear wall, the south side region of the soil, and the north side region of the soil. The boundary condition binary fitting function is loaded into the three-dimensional transient CFD model, and boundary conditions are set for the boundary region to obtain the boundary conditions.

3. The method for optimizing the building parameters of a solar greenhouse according to claim 2, characterized in that, The initialization results of the thermal and humid environment include temperature initialization results and humidity initialization results, wherein the first measured data includes the temperature and humidity data of the initialization area; The boundary conditions of the thermal and humid environment include temperature boundary conditions and humidity boundary conditions, wherein the second measured data includes temperature and humidity data of the boundary region.

4. The method for optimizing the building parameters of a solar greenhouse according to claim 1, characterized in that, Before the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results, the method further includes: Obtain the measured results of the solar greenhouse; The effectiveness of the three-dimensional transient CFD model is verified based on the difference between the simulation results and the measured results. If so, then the step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results is performed.

5. A device for optimizing the building parameters of a solar greenhouse, characterized in that, The device includes: The scheme determination module is used to design the building parameters of the solar greenhouse based on the orthogonal experimental method and determine at least one optimized scheme for the building parameters. The model acquisition module is used to acquire the three-dimensional transient CFD model corresponding to the building parameter optimization scheme. The three-dimensional transient CFD model is created based on the solar greenhouse modeling data under hot and humid conditions. The solar greenhouse modeling data includes the solar greenhouse building parameters. The condition acquisition module is used to acquire the initialization results and boundary conditions of the thermal and humid environment; The scheme optimization module is used to input the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculate the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes, and determine the optimal building parameter scheme with the highest comprehensive score. The step of inputting the boundary conditions and the initialization results into the three-dimensional transient CFD model, calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the optimized building parameter schemes, and determining the optimal building parameter scheme with the highest comprehensive score includes: The boundary conditions and initialization results are input into a three-dimensional transient CFD model, which performs numerical simulations under unsteady conditions to obtain simulation results. Based on the simulation results, the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is calculated, and the building parameter optimization scheme corresponding to the highest comprehensive score is taken as the optimal building parameter scheme. The step of calculating the comprehensive score of the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes based on the simulation results includes: Based on the simulation results, calculate the index values ​​corresponding to each of the thermal and humid environment evaluation indicators in the building parameter optimization scheme; Based on the aforementioned index values, the index weight of each of the aforementioned thermal and humid environment evaluation indicators is calculated using the entropy weight method. Based on the index values ​​and index weights, a comprehensive score for the thermal and humid environment evaluation index corresponding to each of the building parameter optimization schemes is determined. Among them, the building parameters of the solar greenhouse include the greenhouse span, ridge height, rear roof length, rear wall height and front roof shape parameters; Select the building parameters of the solar greenhouse. Each building parameter of the solar greenhouse has multiple levels. The number of levels of the same building parameter of the solar greenhouse is within a certain reasonable range. Establish a multi-factor, multi-level orthogonal experimental scheme based on the selected building parameters of the solar greenhouse and their corresponding multiple levels. Select an appropriate orthogonal experimental table based on the selected number of factors and levels, and determine the optimization scheme of the building parameters of at least one solar greenhouse. Temperature rise rate, temperature and humidity non-uniformity coefficient, and average temperature and humidity were selected as thermal and humidity environment evaluation indicators. A combination of numerical simulation and orthogonal experiment was used to obtain the magnitude of each indicator of thermal and humidity environment under different building parameters.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for optimizing the building parameters of a solar greenhouse as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the building parameters of a solar greenhouse as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the building parameters of a solar greenhouse as described in any one of claims 1 to 4.

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