WO2023087483A1

WO2023087483A1 - Greenhouse environment optimization design method considering crop production benefits and energy consumption reduction

Info

Publication number: WO2023087483A1
Application number: PCT/CN2021/140329
Authority: WO
Inventors: 李康吉; 米艳慧; 孟凡跃; 薛文平
Original assignee: 江苏大学
Priority date: 2021-11-18
Filing date: 2021-12-22
Publication date: 2023-05-25
Also published as: GB2621942A; CN114200986A; CN114200986B

Abstract

Disclosed is a greenhouse environment optimization design method considering crop production benefits and energy consumption reduction. The method comprises: actually determining key design parameters and a feasible range according to a greenhouse; establishing a three-dimensional model of a greenhouse environment by using a Computational Fluid Dynamics (CFD) tool; constructing a porous medium model, and simulating thermal interaction between crops and the surrounding environment; in the feasible range, performing multi-dimensional sampling and CFD simulation on the design parameters; extracting an environmental parameter steady-state response result, and reconstructing an environmental substitution model by using a singular value decomposition technique; establishing a crop growth model, and setting a crop growth benefit index and a uniformity index of environmental parameters; and using the environmental substitution model and multi-dimensional interpolation to quickly solve an environmental response, and implementing multi-objective optimization of the key design parameters. According to the present invention, related environmental changes and economic benefits caused by candidate environmental parameters are measured and calculated by introducing the crop model, so that the optimization design of the greenhouse environment is more targeted, the crop production benefits can be maximized, and energy conservation and emission reduction of facility agriculture are promoted at the same time.

Description

A Greenhouse Environment Optimization Design Method Considering Crop Production Benefit and Energy Saving

technical field

The invention relates to the field of facility agricultural production, in particular to a greenhouse environment optimization design method considering crop production benefits and energy saving.

Background technique

As a semi-closed ecosystem, the greenhouse can reduce the dependence of traditional agriculture on the external environment, but its self-regulation ability is limited, and the quality of the internal environment has a significant impact on crop growth, pest control, energy conservation and emission reduction. Reasonable layout and optimization of the greenhouse environment can effectively improve crop growth conditions, increase crop production and save energy consumption, which has important practical significance and practical application value for promoting the further development of facility agriculture.

Most of the current studies on the optimal design of agricultural greenhouses assume that the environmental parameters are uniformly distributed, and use lumped parameters to evaluate the growth environment of crops. For example, the Chinese Patent Publication No. CN113079881A discloses an optimal design method for a large-span variable-space solar greenhouse. By calculating the energy balance equation, the winter greenhouse temperature and heating load are obtained, and then the heat collection system and the air temperature field in the wall are calculated. performance parameters. It does not consider the spatial variation of the internal environmental parameters of the greenhouse and the optimal layout of the control equipment; the high-resolution reduced-order modeling method for the greenhouse environment disclosed in the document of the Chinese patent authorization announcement number CN107545100B is constructed by the intrinsic orthogonal decomposition (POD) technology The feature vector of the greenhouse environment can extract the high-resolution spatial and temporal information of the greenhouse environment, but it is not combined with the crop growth model, so the greenhouse optimization strategy derived from this patent cannot achieve the optimal crop production efficiency and energy saving effect.

Contents of the invention

The purpose of the present invention is to solve the problem that the current agricultural greenhouse environment design method relies too much on expert experience, resulting in low optimization accuracy and design results that are not optimal in economic benefits. An optimal design method for agricultural greenhouses considering crop production benefits and energy saving is proposed. The characteristics are: 1. Combining the model reduction technology based on singular value decomposition (SVD) and computational fluid dynamics (CFD) steady-state simulation, quickly obtain the high-resolution environmental response of the greenhouse crop area, and improve the spatial resolution of the optimal design 2. Considering the interaction between the greenhouse environment and crops, by establishing a crop growth model, the design of the greenhouse environment can maximize crop production benefits while promoting energy conservation and emission reduction.

The technical scheme adopted in the present invention is:

A greenhouse environment optimization design method considering crop production benefits and energy saving, comprising the following steps:

(1) According to the external weather conditions of the greenhouse and the characteristics of the enclosure structure, determine the key design parameters and feasible ranges that affect the greenhouse environment.

(2) Within the feasible range, the design parameters are multi-dimensionally sampled, and each sampling point forms a parameter vector.

(3) According to the external weather conditions and the greenhouse enclosure structure described in step (1), and the parameter vector described in step (2), use computational fluid dynamics tools to establish a three-dimensional model of the greenhouse environment.

(4) The crop area inside the greenhouse is constructed of porous media to simulate the thermal interaction between the crop and the surrounding environment.

(5) Run the CFD steady-state simulation for each parameter vector separately.

(6) Extract the steady-state response values of the environmental parameters of the crop area in each simulation result, including the temperature and humidity field, carbon dioxide distribution, wind speed and flow field, etc., to form the corresponding environmental response change space.

(7) Use the singular value decomposition technique to reduce the dimension of the above space, and reconstruct the corresponding low-dimensional subspace, which is used for the fast solution of the environmental response.

(8) Establish a crop growth model to obtain the production benefits of greenhouse crops under various environmental responses.

(9) Set the environmental uniformity index of the crop area, and combine the crop growth benefit index to establish the objective function of the optimal design.

(10) According to the objective function described in step (9), use a global optimization algorithm to perform multi-objective optimization on the design parameters described in step (1). The environmental response of the greenhouse in each iterative process is quickly solved by multidimensional interpolation from the low-dimensional subspace obtained in step (7).

In the step (1), the external weather conditions of the greenhouse include temperature and humidity, light intensity and wind speed; the design parameters affecting the growth environment of greenhouse crops include the range of shading rate of the ceiling, the position of the fan and the position of carbon dioxide injection, etc.

In the step (2), in order to ensure the accuracy of dimensionality reduction of the environmental parameter model, the parameter sampling method adopts multi-dimensional equidistant sampling, see Figure 3.

In described step (3), CFD tool adopts ANSYS software; The specific steps of greenhouse environment modeling include:

A, adopt the DesignModeler software in ANSYS to carry out geometric modeling to greenhouse structure; According to the parameter vector described in step (2), carry out parameter setting respectively to greenhouse roof sunshade curtain, negative pressure fan and carbon dioxide filling equipment;

B. Import the geometric model into the mesh software for mesh division, and import the divided mesh file into the fluent software;

C. Set the boundary conditions of the CFD model; select the momentum, turbulence and DO radiation models to simulate the heat convection and heat radiation phenomena in the greenhouse.

In the step (4), a porous medium is set to construct a greenhouse crop area, and sensible heat and latent heat models are used to simulate thermal interaction between crops and the environment. Specific steps include:

A. Due to the temperature difference between the crop canopy and the indoor air in the greenhouse, the sensible heat exchange Q _sem between the crop and the environment is determined by the following aerodynamic characteristics of the crop canopy:

where ρ is the air density in the greenhouse, c _p is the specific heat capacity of the air, LAI is the crop leaf area index, T _c is the canopy temperature, T _i is the room temperature, and r _a is the aerodynamic resistance of the crop leaves.

B. The crop converts the sensible heat generated by solar radiation into latent heat and releases it to the greenhouse. The latent heat Q _lat can be expressed by the following formula:

In the formula, λ is the transpiration flux, w _leaf , _wi is the absolute humidity of the crop canopy and air, and rs _sl is the stomatal resistance of the crop leaves.

In the step (6), the specific steps of extracting the steady-state response value of the environmental parameter of the crop area include:

A. According to the greenhouse crop growth area, select the plane of the crop canopy height, and extract the environmental response values of all grid points on the plane;

B. Extraction method: export the physical quantities of the selected area through fluent\export, and sort out the response matrix of each environmental parameter according to the exported profile file format;

In the step (7), the SVD technology is used to reduce the dimensionality of the environmental response variation space, and the specific steps include:

A. Use the environmental response matrix in step (6) to form a parameter matrix R. Among them, R is a matrix of order n×m, n is the number of grid points selected for the crop area inside the greenhouse, and m is the number of parameter vectors.

B. Solve n eigenvalues and corresponding n eigenvectors v _i by using the eigenvalue equation (R ^T R )v _i =λ _i v _i , and all the eigenvectors form an n×n matrix V. Use (RR ^T ) u _i =λ _i u _i to solve n eigenvalues and corresponding n eigenvectors u _i .

C. Use σ _i =RV _i /u _i to solve each singular value, and the corresponding SVD basis is expressed as:

D. Select an appropriate cut-off value k, so that the ratio of the system eigenvalues contained in the first k singular values to the total energy reaches more than 99%. Therefore, the environmental response variation space can be approximately expressed as k singular vectors

The linear combination of its coefficient a

Among them, P(n) represents the steady-state environmental response results on n grid points.

In the step (8), the dry matter quality in the crop growth model is used to represent the production benefit of the crop, and the daytime photosynthesis period of the crop is selected for the time period. Taking the lettuce crop model as an example, the first-order difference equation representing the quality of dry matter is as follows:

Among them, x _d is the mass of crop dry matter, C _αβ is the crop yield factor,

is the photosynthetic reaction rate, C _resp,d is the respiration rate parameter expressed by the amount of respiration dry matter; z _t is the average temperature of the crop area, obtained from the CFD environmental response; k is the time step. The photosynthetic reaction rate in the above formula

Calculated by the following formula:

Among them, C _pl,d is the crop effective canopy area parameter per unit mass (m ² kg ^-1 ), c _rad,phot is the light use efficiency parameter (kgJ ^-1 ); v _rad is the solar radiation amount (Wm ^-2 ), obtained by transforming CFD outdoor solar radiation and greenhouse shading rate;

and

Represent the influence parameters of temperature on photosynthesis; z _c (k) is the average carbon dioxide concentration in the crop area, which is obtained from the CFD environmental response; c _r is the carbon dioxide compensation point parameter (kgm ^-3 ).

In the step (9), the growth environment uniformity index Jeven of the crop area _is characterized by the variance sum of the temperature, humidity and carbon dioxide concentration values at the grid points of the crop area; the crop growth benefit index is subtracted from the economic benefits of crop production Electric energy consumption of fan and filling energy consumption of carbon dioxide. As a unified unit, income and energy consumption are converted into market prices, expressed by the indicator J _econ . Therefore, the objective function set of the optimization design is:

In the growth environment uniformity index J _even , T _i , H _i , C _i represent the temperature, humidity and CO ₂ concentration at grid points in the crop area, Z _t , Z _h , Z _c are their average values; N _p is the grid point The number of grid points; in the crop growth benefit index J _econ , Prc _xd is the crop market price, V _air,k is the air supply rate of the fan (m ³ /s), η _fan is the efficiency of the fan, and ΔP is the pressure difference between the inlet and outlet of the greenhouse (Pa ), Prc _e is the local electricity price; V _CO2 is the carbon dioxide injection rate (m ³ /s), ωρ is the mass fraction and density of CO ₂ respectively, Prc _co2 is the CO ₂ injection price, T _hout is the action time.

In the step (10), the optimization algorithm adopts a multi-objective MOEA/D optimization algorithm, and the optimization objective is to minimize the crop growth environment uniformity index J _even and maximize the crop growth benefit index J _econ . The number of algorithm populations is set to 500, and the number of iterations is set to 70. The environmental response of the greenhouse in each iterative process is obtained by quickly solving the multidimensional interpolation of the singular vector coefficients in step (7).

The beneficial effect after the present invention adopts above-mentioned technical scheme is:

1. The present invention considers crop production benefits and energy-saving greenhouse environment optimization design method by introducing crop models to measure crop dry matter quality and economic benefits caused by candidate environmental parameters, and at the same time by converting the economic cost of greenhouse operation energy consumption, making the greenhouse The optimized design of the environment is more targeted, and the optimization results are supported by economic benefit data, which can maximize the benefits of crop production and at the same time promote energy conservation and emission reduction in facility agriculture.

2. The present invention considers the influence of the spatial distribution of the greenhouse environment, and adopts the idea of model reduction to quickly extract environmental feature information, so that the greenhouse environment design method of the present invention can ensure the maximum environmental suitability of the crop area, and at the same time shorten the time for optimal design. Improve optimization efficiency.

Description of drawings

Fig. 1 is the flow chart of optimal design of greenhouse environment in consideration of crop production benefit and energy saving in the present invention;

Figure 2 is a schematic diagram of a three-dimensional multi-span greenhouse model. The green area in the figure is simulated as the crop growth area, and there are three candidate positions for carbon dioxide injection, marked as

circles

1, 2, and 3; the candidate positions of the negative pressure fans are determined by the mutual distance h;

Fig. 3 is a schematic diagram of sampling point distribution of multi-dimensional design parameters.

Detailed ways

In order to describe the present invention more specifically, the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation examples.

Figure 1 describes the optimal design process of the greenhouse environment considering crop production benefits and energy saving.

Figure 2 is a three-dimensional model of a multi-span greenhouse. The greenhouse is 40m long, 19m wide, and the roof is 5m high. There is a crop growing area with a width of 8m, a length of 20m and an area of ^160m2 inside the greenhouse.

Describe in detail the implementation steps of the inventive method below:

Step 1. Determine the key design parameters and feasible ranges that affect the greenhouse environment according to the climate conditions such as the temperature and humidity outside the greenhouse, light intensity and wind speed, and the characteristics of the enclosure structure. In this example, when the geometric model of the greenhouse is known, the design parameters that have a greater impact on the growth of greenhouse crops are selected as: roof shading rate, fan position and carbon dioxide injection position.

Step 2, within the feasible range, conduct multi-dimensional equidistant sampling of the above design parameters, specifically including: the sampling value of the shading rate is 0.4, 0.6, 0.8, and the sampling value of the fan position interval (that is, the mutual distance h between the fans in Figure 2) is 1m , 3m, 5m, the sampling values of carbon dioxide injection location interval (distance from the south wall) are 3m, 9m, 15m, and each group of sampling points forms a parameter vector to participate in CFD simulation, as shown in Figure 3.

Step 3, according to the external weather conditions and the greenhouse enclosure structure described in step 1, use computational fluid dynamics tools to establish a three-dimensional model of the greenhouse environment. Specific steps include:

Step 3.1, determine the location and size of the greenhouse, the wet curtain entrance and the crop area, and use the DesignModeler software to build the greenhouse geometric model, as shown in Figure 2; according to the parameter vector described in step 2, the greenhouse roof sunshade, negative pressure fan and carbon dioxide The filling equipment is parameterized separately;

In step 3.2, the geometric model is imported into the mesh software for mesh division, and the special boundaries (such as the wet curtain inlet, negative pressure fan and carbon dioxide filling port) are partially encrypted. In this example, a total of 182,491 grids are divided, and the grid files are imported into the fluent software;

Step 3.3, set the boundary conditions of the CFD model, including: the surrounding and roof enclosures are set as the temperature boundary (float glass material), the fan is set as the velocity inlet boundary, and the wet curtain is set as the pressure inlet boundary; the gas in the greenhouse is assumed to flow at a low speed The incompressible viscous Newtonian fluid, the turbulence model uses the standard k-ε model, the near-wall treatment uses the standard wall function, and the radiation model uses the DO radiation model; the component transport equation is used to simulate the flow of multi-component gases such as carbon dioxide, and the pressure velocity is used Coupling mode, adopt SIMPLE algorithm to solve.

Step 4: Use porous media to construct greenhouse crop areas, and use sensible heat and latent heat models to simulate the thermal interaction between crops and the environment. Specific steps include:

Step 4.1, set the porosity according to the characteristics of the crop; due to the temperature difference between the crop canopy and the indoor air in the greenhouse, the sensible heat exchange Q _sem between the crop and the environment is determined by the following aerodynamic characteristics of the crop canopy:

where ρ is the air density in the greenhouse, c _p is the specific heat capacity of the air, LAI is the crop leaf area index, T _c is the canopy temperature, T _i is the room temperature, and r _a is the aerodynamic resistance of the crop leaves;

In step 4.2, the crop converts the sensible heat generated by solar radiation into latent heat and releases it to the greenhouse. The latent heat Q _lat is expressed by the following formula:

Step 5, according to step 2, there are 3×3×3=27 candidate parameter vectors in this example; according to step 3 and step 4, perform CFD settings for each parameter vector, and complete the off-line CFD steady state simulation.

Step 6: Extract the steady-state response values of environmental parameters of the crop area in each simulation result, including temperature field, carbon dioxide distribution, wind speed flow field, etc., to form the corresponding environmental response change space. Specific steps include:

Step 6.1, according to the greenhouse crop growth area, select the height of the crop canopy plane to be 1.3 meters, and extract the environmental response values of all grid points on the plane, the number of grid points in this example is 4500;

Step 6.2, extraction method: export the physical quantities of the selected area through fluent\export, sort out the response matrix of each environmental parameter according to the exported profile file format, and form the change space.

Step 7, use singular value decomposition technology to reduce the dimension of the above space, and reconstruct the corresponding low-dimensional subspace. The specific steps include:

Step 7.1, use the environmental response variation space in step 6 to form a parameter matrix R. Among them, R is a matrix of order n×m, n is the number of grid points selected for the crop area inside the greenhouse, and m is the number of parameter vectors. In this example, n=4500, m=27;

In step 7.2, use (R ^T R)v _i =λ _i v _i to solve n eigenvalues and corresponding n eigenvectors v _i , and all eigenvectors are stretched into a matrix V of n×n. Use (RR ^T ) u _i =λ _i u _i to solve n eigenvalues and corresponding n eigenvectors u _i ;

In step 7.3, use σ _i =RV _i /u _i to solve each singular value, and the corresponding SVD basis is expressed as:

Step 7.4, choose an appropriate cut-off value k, so that the ratio I(k) of the system eigenvalues contained in the first k singular values to the total energy reaches more than 99%, k=5 in this example. Therefore, the environmental response variation space can be approximately expressed as k singular vectors

The linear combination of its coefficient a

In step 8, a crop growth model is established, and the dry matter quality in the crop growth model is used to represent the crop yield, so as to obtain the production efficiency of greenhouse crops under each environmental response. In this example, the cumulative amount of crop dry matter produced during photosynthesis within a week is used to represent crop yield. Taking the lettuce crop model as an example, the first-order difference equation for dry matter mass production is as follows:

Among them, x _d is the mass of dry matter of the crop; C _αβ is the crop yield factor, with a value of 0.544;

is the photosynthetic reaction rate; C _resp,d is the respiration rate parameter represented by the amount of respiration dry matter, and the value is 2.65×10 ^-7 s ^-1 ; z _t is the average temperature of the crop area, obtained from the CFD environmental response; k is the time step. Further, the photosynthetic reaction rate in the above formula

Calculated by the following formula:

Among them, C _pl,d is the crop effective canopy area parameter per unit mass (m ² kg ^-1 ), the value is 53; c _rad,phot is the light use efficiency parameter (kgJ ^-1 ), the value is 3.55×10 ^{- 9} ; v _rad is the amount of solar radiation (Wm ^-2 ), which is obtained from the transformation of CFD outdoor solar radiation and the shading rate of the greenhouse; the parameters of the influence of temperature on photosynthesis:

Take the value of 5.11×10 ^-6 ,

Take the value of 2.30×10 ^-4 ,

The value is 6.29×10 ^-4 ; z _c (k) is the average carbon dioxide concentration in the crop area, which is obtained from the CFD environmental response; c _r is the carbon dioxide compensation point parameter (kgm ^-3 ), and the value is 5.2×10 ^-5 .

Step 9: Set the environmental uniformity index of the crop area, and combine the crop growth benefit index to establish the objective function of the optimal design. Among them, the growth environment uniformity index J _even is represented by the variance sum of temperature, humidity and carbon dioxide concentration values at grid points in the crop area; the crop growth benefit index is the economic benefits of crop production minus the energy consumption of wind turbines and the filling energy of carbon dioxide. consumption. As a unified unit, income and energy consumption are converted into market prices, expressed by the indicator J _econ . Therefore, the objective function of the optimal design is:

In the growth environment uniformity index J _even , T _i , H _i , C _i represent the temperature, humidity and CO ₂ concentration at grid points in the crop area, and Z _t , Z _h , Z _c are their average values, which can be obtained by CFD The results are calculated; N _p is the number of grid points, which is 4600 _in this example; in the crop growth benefit index J _econ , Prc _xd is the crop market price; η _fan is the efficiency of the fan, which is set to 0.75 in this example; ΔP is the pressure difference between the inlet and outlet of the greenhouse (Pa), which is set to 562.5 in this example; Prc _e is the local electricity price; V _CO2 is the carbon dioxide injection rate (m/s) , this example is set to 0.02; ωρ is the mass fraction and density of CO ₂ respectively, and Prc _co2 is the CO ₂ filling price; this example considers the production of crop dry matter in a photosynthesis period of one week, and the action time T _hout is 7× 8 = 56 hours.

Step 10, according to the objective function described in step 9, use a global optimization algorithm to perform multi-objective optimization on the design parameters described in step 1. In order to improve the optimization speed, the environmental response of the greenhouse in each iterative process is obtained through the multidimensional interpolation of the singular vector coefficient a in step 7. The optimization algorithm can adopt the multi-objective MOEA/D optimization algorithm, and the optimization goal is to minimize the crop growth environment uniformity index J _even and maximize the crop growth benefit index J _econ . The number of algorithm populations is set to 500, and the number of iterations is set to 70.

In order to verify the effect of the present invention, taking the greenhouse environment of lettuce planting in East China as an example, three design parameters that have a greater impact on the growth of greenhouse crops are optimized, namely the range of ceiling shading rate, fan position and carbon dioxide injection position. By using this method, the optimal greenhouse environment design parameters can be obtained, so that the crop growth benefits in the crop area can be optimized and the energy-saving operation of the greenhouse can be maintained.

Claims

A greenhouse environment optimization design method considering crop production benefits and energy saving, characterized in that it comprises the following steps:

(1) According to the external weather conditions of the greenhouse and the characteristics of the enclosure structure, determine the key design parameters and feasible ranges that affect the greenhouse environment;

(2) Within the feasible range, the design parameters are multi-dimensionally sampled, and each sampling point forms a parameter vector;

(3) According to the external weather conditions and the greenhouse enclosure structure described in step (1), and the parameter vector described in step (2), use the computational fluid dynamics tool CFD to establish a three-dimensional model of the greenhouse environment;

(4) The crop area inside the greenhouse is constructed of porous media to simulate the thermal interaction between the crop and the surrounding environment;

(5) Running CFD steady-state simulation for each parameter vector;

(6) Extract the steady-state response value of the environmental parameters of the crop area in each simulation result, including the temperature and humidity field, carbon dioxide distribution, wind speed and flow field, etc., to form the corresponding environmental response change space;

(7) Use singular value decomposition technology to reduce the dimension of the above space, and reconstruct the corresponding low-dimensional subspace, which is used for the rapid solution of environmental response;

(8) Establish a crop growth model to obtain the production benefits of greenhouse crops under various environmental responses;

(9) Set the environmental uniformity index of the crop area, and establish the objective function of the optimal design in combination with the crop growth benefit index;

(10) According to the objective function described in step (9), use a global optimization algorithm to perform multi-objective optimization on the design parameters described in step (1). The environmental response of the greenhouse in each iterative process is quickly solved by multidimensional interpolation from the low-dimensional subspace obtained in step (7).
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, characterized in that, in the step (1), the external weather conditions of the greenhouse include temperature and humidity, light intensity and wind speed; The design parameters of the greenhouse crop growth environment include the range of the shading rate of the roof, the location of the fan and the location of the carbon dioxide injection.
A method for optimal design of greenhouse environment considering crop production benefits and energy saving according to claim 1, characterized in that in said step (2), in order to ensure the accuracy of dimension reduction of the environmental parameter model, the parameter sampling method adopts Multidimensional equidistant sampling.
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, it is characterized in that, in described step (3), computational fluid dynamics tool adopts ANSYS software; Steps include:

A, adopt the DesignModeler software in ANSYS to carry out geometric modeling to greenhouse structure; According to the parameter vector described in step (2), carry out parameter setting respectively to greenhouse roof sunshade curtain, negative pressure fan and carbon dioxide filling equipment;

B. Import the geometric model into the mesh software for mesh division, and import the divided mesh file into the fluent software;

C. Set the boundary conditions of the CFD model; select the momentum, turbulence and DO radiation models to simulate the heat convection and heat radiation phenomena in the greenhouse.
A method for optimal design of greenhouse environment considering crop production benefits and energy saving according to claim 1, characterized in that in said step (4), a porous medium is set to construct a greenhouse crop area, and sensible heat and latent heat models are used To simulate the thermal interaction between crops and the environment, the specific steps include:

4.1. Due to the temperature difference between the crop canopy and the indoor air in the greenhouse, the sensible heat exchange Q sem between the crop and the environment is determined by the following aerodynamic characteristics of the crop canopy:

where ρ is the air density in the greenhouse, c p is the specific heat capacity of the air, LAI is the crop leaf area index, T c is the canopy temperature, T i is the room temperature, and r a is the aerodynamic resistance of the crop leaves;

4.2. The crop converts the sensible heat generated by solar radiation into latent heat and releases it to the greenhouse. The latent heat Q lat can be expressed by the following formula:

In the formula, λ is the transpiration flux, w leaf , wi is the absolute humidity of the crop canopy and air, and rs sl is the stomatal resistance of the crop leaves.
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, characterized in that, in the step (6), the specific steps of extracting the steady-state response value of the crop area environmental parameters include:

6.1. According to the greenhouse crop growth area, select the plane of the crop canopy height, and extract the environmental response values of all grid points on the plane;

6.2. Extraction method: Export the physical quantities of the selected area through fluent\export, and sort out the response matrix of each environmental parameter according to the exported profile file format.
A greenhouse environment optimization design method considering crop production benefits and energy saving according to claim 1, characterized in that in the step (7), the singular value decomposition (SVD) technology is used to reduce the dimensionality of the environmental response variation space , the specific steps include:

7.1. Use the environmental response matrix in step (6) to form a parameter matrix R, where R is a matrix of order n×m, n is the number of grid points selected in the crop area inside the greenhouse, and m is the number of parameter vectors ;

7.2. Use the eigenvalue equation (R T R)v i =λ i v i to solve n eigenvalues and corresponding n eigenvectors v i , and all eigenvectors form an n×n matrix V; use (RR T )u i = λ i u i Solve n eigenvalues and corresponding n eigenvectors u i ;

7.3. Use σ i =RV i /u i to solve each singular value, and the corresponding SVD basis is expressed as:

7.4. Select an appropriate cut-off value k, so that the ratio of the system eigenvalues contained in the first k singular values to the total energy reaches more than 99%; thus, the environmental response variation space can be approximately expressed as k singular vectors
and the linear combination of its coefficient a;

Among them, P(n) represents the steady-state environmental response results on n grid points.
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, characterized in that, in the step (8), the dry matter quality in the crop growth model is used to represent the production benefit of the crop , the time period selects the daytime photosynthesis period of the crop, taking the lettuce crop model as an example, the first-order difference equation representing the dry matter quality is as follows:

Among them, x d is the mass of crop dry matter, C αβ is the crop yield factor,
is the photosynthetic reaction rate, C resp,d is the respiration rate parameter represented by the amount of respiration dry matter; z t is the average temperature of the crop area, obtained from the CFD environmental response; k is the time step, and the photosynthetic reaction rate in the above formula
Calculated by the following formula:

Among them, C pl,d is the crop effective canopy area parameter per unit mass (m 2 kg -1 ), c rad,phot is the light use efficiency parameter (kgJ -1 ); v rad is the solar radiation amount (Wm -2 ), obtained by transforming CFD outdoor solar radiation and greenhouse shading rate;

and
Represent the influence parameters of temperature on photosynthesis; z c (k) is the average carbon dioxide concentration in the crop area, which is obtained from the CFD environmental response; c r is the carbon dioxide compensation point parameter (kgm -3 ).
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, characterized in that, in the step (9), the growth environment uniformity index Jeven of the crop area is determined by the crop area network The variance and characterization of the temperature, humidity and carbon dioxide concentration values at the grid points; the crop growth benefit index is the economic benefit of crop production minus the energy consumption of fan power and the energy consumption of carbon dioxide filling, which is a unified unit, and both income and energy consumption are converted into The market price is represented by the indicator J econ , thus, the objective function set of the optimization design is:

In the growth environment uniformity index J even , T i , H i , C i represent the temperature, humidity and CO 2 concentration at grid points in the crop area, Z t , Z h , Z c are their average values; N p is the grid point The number of grid points; in the crop growth benefit index J econ , Prc xd is the crop market price, V air,k is the air supply rate of the fan (m 3 /s), η fan is the efficiency of the fan, and ΔP is the pressure difference between the inlet and outlet of the greenhouse (Pa ), Prc e is the local electricity price; V CO2 is the carbon dioxide injection rate (m 3 /s), ωρ is the mass fraction and density of CO 2 respectively, Prc co2 is the CO 2 injection price, and T hour is the action time.
A kind of greenhouse environment optimization design method considering crop production benefit and energy saving according to claim 1, it is characterized in that, in described step (10), optimization algorithm adopts multi-objective MOEA/D optimization algorithm, and optimization goal is The crop growth environment uniformity index Jeven is the smallest, and the crop growth benefit index Jecon is the largest; the number of algorithm populations is set to 500, and the number of iterations is set to 70. The greenhouse environment response in each iteration process passes through step (7) on the multidimensional singular vector coefficient The interpolation is quickly solved.