Photovoltaic greenhouse installation decision-making method and device based on microclimate simulation

Abstract

The invention is suitable for the technical field of greenhouse construction, and provides a photovoltaic greenhouse installation decision-making method and device based on microclimate simulation, and the method comprises the steps: obtaining photovoltaic laying data and indoor and outdoor initial environment data; constructing a photovoltaic greenhouse microclimate environment model based on photovoltaic laying data and indoor and outdoor initial environment data; optimizing the photovoltaic greenhouse microclimate environment model by modifying different photovoltaic laying modes, and obtaining an optimized photovoltaic greenhouse microclimate environment model; the optimized photovoltaic greenhouse microclimate environment model is used for outputting photovoltaic greenhouse microclimate environment parameters matched with the crop planting environment, so that an installation decision scheme of the photovoltaic greenhouse is obtained. According to the method, the sunlight greenhouse with different laying type light-transmitting photovoltaic modules can be adopted, the dynamic mathematical model is adopted according to the internal and external environment data of the greenhouse, the microclimate hourly dynamic simulation model is established, and management suggestions are provided for the installation decision of the photovoltaic greenhouse.

Description

Technical Field

[0001] The present application belongs to the technical field of greenhouse construction, and in particular, relates to a photovoltaic greenhouse installation decision-making method and device based on microclimate simulation. Background Art

[0002] The microclimate of a solar greenhouse mainly includes the temperature, relative humidity, CO2 concentration, and light intensity in the greenhouse. The factors that affect these environmental factors in a solar greenhouse mainly include the growth of crops in the greenhouse, the structure of the greenhouse, soil, fertilization, irrigation, outdoor weather conditions, and equipment in the greenhouse. A solar greenhouse microclimate model can be a useful tool for greenhouse growers to predict the greenhouse environment. An accurate model can accurately evaluate and predict changes in the microclimate in the greenhouse. Many greenhouse models in the literature attempt to predict indoor air temperature and relative humidity at specific locations, and some of them have been extended to predict CO2 concentrations.

[0003] Some researchers have developed a greenhouse energy model that uses different values ​​of cover transmittance instead of a constant, taking into account diffuse, beam and ground reflected radiation. The greenhouse area studied is very small, only 15m 2 , only natural ventilation, no auxiliary heating, and no artificial lighting. The researchers assumed that the cover and air temperatures were uniform, there was no evaporation from the soil, and ventilation was ignored by assuming that the vents were closed. A 5-minute time step was used in the model, and the simulation results were consistent with the air, but the convective heat transfer paths between layers in the greenhouse energy model are an important step to ensure accurate simulation. There is also work by some related people that summarizes existing research on convective heat transfer in greenhouses, including convective heat transfer between soil and greenhouse air, between greenhouse air and glass, and between glass and outdoor air, as well as a dynamic greenhouse climate model developed for tomato greenhouses in Europe, including greenhouse air renewal rates and crop canopy resistance to water vapor transport. Auxiliary heating, lighting, and forced ventilation are not included in the model. The experimental greenhouse also has crops at different stages of development, which makes it difficult to assign some model values.

[0004] The inventors have considered that most of the research in the above materials and related technologies focuses on the cultivation of some crops and plants, which has limitations to a certain extent. Summary of the invention

[0005] The embodiments of the present application provide a photovoltaic greenhouse installation decision method and device based on microclimate simulation, which can solve the problem of limitations in greenhouse model research.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a photovoltaic greenhouse installation decision method based on microclimate simulation, comprising:

[0008] Obtain photovoltaic laying data and initial indoor and outdoor environmental data;

[0009] Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a photovoltaic greenhouse microclimate environmental model is constructed;

[0010] The photovoltaic greenhouse microclimate environmental model is optimized by modifying different photovoltaic laying methods, and an optimized photovoltaic greenhouse microclimate environmental model is obtained; the optimized photovoltaic greenhouse microclimate environmental model is used to output photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment;

[0011] Based on the microclimate environmental parameters of the photovoltaic greenhouse that match the crop planting environment, an installation decision plan for the photovoltaic greenhouse is obtained.

[0012] In one achievable manner of the first aspect, the photovoltaic greenhouse microclimate environmental model is optimized by modifying different photovoltaic laying methods, and the optimized photovoltaic greenhouse microclimate environmental model is obtained, including:

[0013] Based on modifying different photovoltaic laying methods, updated photovoltaic laying data and updated indoor and outdoor environmental data are obtained;

[0014] Based on the updated photovoltaic laying data and updated indoor and outdoor environmental data, the photovoltaic greenhouse microclimate environmental model is optimized.

[0015] In one achievable manner of the first aspect, a photovoltaic greenhouse microclimate environment model is constructed based on photovoltaic laying data and initial indoor and outdoor environmental data, including:

[0016] Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic temperature simulation is constructed;

[0017] Based on the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic simulation of humidity is constructed;

[0018] Based on the initial indoor and outdoor environmental data, a mathematical model for dynamic simulation of CO2 concentration hour by hour is constructed;

[0019] Based on the hourly dynamic simulation mathematical models of temperature, humidity and CO2 concentration, a photovoltaic greenhouse microclimate environment simulation model is established.

[0020] In one achievable manner of the first aspect, based on photovoltaic laying data and initial indoor and outdoor environmental data, a temperature hourly dynamic simulation mathematical model is constructed, including:

[0021] Based on the photovoltaic laying data, confirm the solar radiation energy reaching the greenhouse;

[0022] Based on the initial indoor and outdoor environmental data, confirm the total heat loss; the total heat loss includes the heat loss caused by natural ventilation, the heat loss caused by crop transpiration, the heat loss caused by heat transfer between the air and soil in the greenhouse, the heat loss caused by heat exchange between the walls in the greenhouse and the outside air, and the heat loss caused by energy exchange between the air inside and outside the greenhouse through the covering material;

[0023] Based on the solar radiation energy reaching the greenhouse and the total heat loss, and according to the principle of heat balance, a mathematical model for hourly dynamic temperature simulation is constructed.

[0024] In one possible implementation of the first aspect, the solar radiation energy Q reaching the greenhouse radin (t) is expressed as:

[0025]

[0026] Where t is time; f is the proportion of global radiation absorbed by the greenhouse covering structure; s is the area of ​​the photovoltaic module laying surface; n is the transmittance of the photovoltaic module; i is the coverage percentage of the photovoltaic module; Q rad (t) is the outdoor solar radiation.

[0027] In an achievable manner of the first aspect, the temperature hourly dynamic simulation mathematical model is expressed as:

[0028]

[0029] Where t is time; Pa is air density; Vg is greenhouse volume; Ca is air specific heat capacity; Q radin (t) is the solar radiation energy reaching the greenhouse; Q nv (t) is the heat loss caused by natural ventilation; Q trant (t) is the heat loss caused by crop transpiration; Q soil (t) is the heat loss caused by heat transfer between air and soil in the greenhouse; Q wall (t) is the heat loss caused by heat exchange between the greenhouse wall and the outside air; Q exch (t) is the heat loss caused by the energy exchange between the air inside and outside the greenhouse through the covering material; x1 is the control variable; T in (t) is the indoor temperature.

[0030] In an achievable manner of the first aspect, based on initial indoor and outdoor environmental data, a humidity hourly dynamic simulation mathematical model is constructed, including:

[0031] Based on the initial environmental data indoors and outdoors, the water vapor change rate is determined; the water vapor change rate includes the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation, and the water vapor change rate caused by condensation of the greenhouse cover envelope;

[0032] Based on the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation and the water vapor change rate caused by condensation of the greenhouse cover envelope, and according to the mass balance principle of water vapor in indoor air, a mathematical model for hourly dynamic simulation of humidity is constructed.

[0033] In an achievable manner of the first aspect, the hourly dynamic simulation mathematical model of humidity is expressed as:

[0034]

[0035] Where, t is time; V g is the greenhouse volume; ρ in is the indoor water vapor density; x1 is the control variable; E trant (t) The rate of change of water vapor caused by transpiration of indoor crops; E nv is the water vapor change rate caused by natural ventilation; E cond (t) is the rate of change of water vapor caused by condensation in the greenhouse cover envelope.

[0036] In an achievable manner of the first aspect, based on initial indoor and outdoor environmental data, a mathematical model for dynamic simulation of CO2 concentration hour by hour is constructed, including:

[0037] Based on the initial environmental data indoors and outdoors, determine the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area, and the soil CO2 exhalation intensity per unit greenhouse area;

[0038] Based on the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area and the soil CO2 exhalation intensity per unit greenhouse area, a mathematical model for hourly dynamic simulation of CO2 concentration is constructed.

[0039] In a second aspect, an embodiment of the present application provides a photovoltaic greenhouse installation decision device based on microclimate simulation, which executes the photovoltaic greenhouse installation decision method based on microclimate simulation as in the first aspect, including:

[0040] A data acquisition module is used to acquire photovoltaic laying data and initial indoor and outdoor environmental data;

[0041] Model building module, used to build a photovoltaic greenhouse microclimate environment model based on photovoltaic laying data and initial indoor and outdoor environmental data;

[0042] The model simulation module is used to optimize the photovoltaic greenhouse microclimate environment model by modifying different photovoltaic laying methods, and obtain the optimized photovoltaic greenhouse microclimate environment model; the optimized photovoltaic greenhouse microclimate environment model is used to output photovoltaic greenhouse microclimate environment parameters that match the crop planting environment;

[0043] The result output module is used to obtain the installation decision plan of the photovoltaic greenhouse based on the microclimate environmental parameters of the photovoltaic greenhouse that match the crop planting environment.

[0044] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0045] The photovoltaic greenhouse installation decision method and device based on microclimate simulation in the embodiment of the present application take into account that different crops, different varieties of the same crops, and different growth stages of crops have different environmental requirements. In order to ensure that the environment of crops planted in the photovoltaic greenhouse is in a suitable state throughout the life cycle, it is necessary to simulate the microclimate environment in the greenhouse under different photovoltaic laying modes. Taking the solar greenhouse as the research object, a solar greenhouse microclimate simulation model is constructed, which realizes the accurate simulation of the microclimate change of the solar greenhouse, and provides a theoretical basis for the installation decision of the solar greenhouse. In the face of different climatic conditions and crop types, this method can flexibly respond to various environmental challenges by adjusting the parameters in the photovoltaic greenhouse microclimate environment model.

[0046] The beneficial effects of the above-mentioned second aspect embodiment refer to the beneficial effects of the first aspect embodiment, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 This is an application scenario diagram of a photovoltaic greenhouse installation decision method and device based on microclimate simulation provided in an embodiment of the present application;

[0049] Figure 2 It is a flow chart of a photovoltaic greenhouse installation decision method based on microclimate simulation provided in one embodiment of the present application;

[0050] Figure 3 This is a simulation flow chart of a photovoltaic greenhouse microclimate environment model provided in an embodiment of the present application;

[0051] Figure 4 is a sensor distribution diagram of a greenhouse model provided in an embodiment of the present application;

[0052] Figure 5 This is a flowchart of a specific construction method of a photovoltaic greenhouse microclimate environment model provided in an embodiment of the present application;

[0053] Figure 6 This is a schematic diagram of a solar greenhouse temperature simulation model provided in an embodiment of the present application;

[0054] Figure 7 This is a schematic diagram of a solar greenhouse humidity simulation model provided in an embodiment of the present application;

[0055] Figure 8 This is a schematic diagram of a solar greenhouse CO2 concentration simulation model provided in an embodiment of the present application;

[0056] Fig. 9 This is a schematic diagram of a photovoltaic greenhouse microclimate environment simulation model provided in an embodiment of the present application;

[0057] Fig.10 It is a schematic diagram of a simulation process for a microclimate environment in a greenhouse under different photovoltaic laying modes provided by an embodiment of the present application;

[0058] Fig.11 It is a structural schematic diagram of a photovoltaic greenhouse installation decision-making device based on microclimate simulation provided in one embodiment of the present application. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0060] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0061] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0062] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0063] References to "an embodiment", "one embodiment" or "some embodiments" described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0064] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0065] Reference Figure 1 As shown, it is an application scenario diagram of the photovoltaic greenhouse installation decision method and device based on microclimate simulation of the present invention. The prediction method and device run in a distributed control center 101. The control center 101 is connected to each sensor 102 by wire or wireless, and each sensor 102 sends various data such as installation structure data and environmental data obtained from the photovoltaic greenhouse 103 to the control center 101. The control center 101 analyzes various data to form a dynamic simulation process of the photovoltaic greenhouse, and adjusts the simulation conditions such as the installation structure and environment of the photovoltaic greenhouse 103 according to the dynamic simulation process.

[0066] Figure 2 is a flow chart of a photovoltaic greenhouse installation decision method based on microclimate simulation provided by an embodiment of the present application, with reference to Figure 2 The photovoltaic greenhouse installation decision method based on microclimate simulation includes:

[0067] Step 201, obtaining photovoltaic installation data and initial indoor and outdoor environmental data.

[0068] Exemplarily, the initial environmental data may include external climate conditions and indoor crop growth status. External climate conditions include temperature, humidity, CO2 concentration, illumination, wind speed, wind direction, ventilation conditions and ventilation time, etc.; indoor crop growth status: physiological characteristics of target plants, photosynthesis rate, transpiration rate, respiration rate, leaf area index, etc.

[0069] Photovoltaic laying data includes indoor soil conditions, building heat absorption characteristics, etc. Indoor soil conditions include soil temperature, moisture content, etc.

[0070] Step 202: construct a photovoltaic greenhouse microclimate environment model based on photovoltaic laying data and initial indoor and outdoor environmental data.

[0071] Due to the complexity of the greenhouse system, in the process of establishing a dynamic model of greenhouse microclimate for prediction purposes, the model simulation process is as follows: Figure 3 As shown, some modeling simplification conditions need to be set:

[0072] (1) Since the temperature and humidity of the air in the greenhouse have spatial distribution characteristics, partial differential equations should be used to describe them. However, since it is impossible to deploy a large number of sensors in the greenhouse, in the process of greenhouse temperature and humidity dynamic modeling, it is assumed that the temperature and humidity of the air in the greenhouse are uniformly distributed.

[0073] (2) Instead of building a temperature dynamic model for the inner and outer surfaces of the greenhouse cover, the cover is considered as an interface for heat exchange between the air inside and outside the greenhouse.

[0074] (3) Instead of building a dynamic model of indoor soil temperature and humidity, the soil temperature and humidity are directly measured and regarded as the boundary conditions of the dynamic model of indoor air temperature and humidity.

[0075] (4) Since there is a large amount of indoor and outdoor air exchange during natural ventilation, the heat loss in the greenhouse caused by greenhouse gaps, etc. is ignored.

[0076] (5) It is assumed that relevant physical factors in the greenhouse do not change with temperature and time, such as the physical properties of the covering layer, the specific heat capacity of air, etc.

[0077] Under the above conditions, the indoor microclimate is simulated, the simulation results are compared with the actual collected data, and the errors are compared and analyzed to further verify the accuracy of the established microclimate model.

[0078] Step 203, optimizing the photovoltaic greenhouse microclimate environment model by modifying different photovoltaic laying methods, and obtaining an optimized photovoltaic greenhouse microclimate environment model.

[0079] Among them, the optimized photovoltaic greenhouse microclimate environmental model is used to output photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment.

[0080] The inventors took into account that the choice of photovoltaic laying method will affect the changes in environmental factors such as temperature, humidity, and CO2 concentration in the solar greenhouse, and the microclimate changes in the solar greenhouse will affect the growth of greenhouse crops. Therefore, it is necessary to continuously optimize the photovoltaic greenhouse microclimate environmental model by exploring and modifying different photovoltaic laying methods to obtain various parameters that match the crop planting environment.

[0081] Step 204, obtaining a photovoltaic greenhouse installation decision plan based on the photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment.

[0082] This embodiment takes into account that different crops, different varieties of the same crops, and different growth stages of crops have different environmental requirements. In order to ensure that the environment of crops planted in photovoltaic greenhouses is in a suitable state throughout their life cycle, it is necessary to simulate the microclimate environment in the greenhouse under different photovoltaic laying modes. Taking the solar greenhouse as the research object, a solar greenhouse microclimate simulation model is constructed to achieve accurate simulation of the microclimate change of the solar greenhouse, providing a theoretical basis for the installation decision of the solar greenhouse. In the face of different climatic conditions and crop types, this method can flexibly respond to various environmental challenges by adjusting the parameters in the photovoltaic greenhouse microclimate environment model.

[0083] In one embodiment, the above-mentioned various data are obtained through light sensors, temperature and humidity sensors, photosynthetic active sensors and carbon dioxide sensors, such as Figure 4 Greenhouse model shown.

[0084] The test site is located at 38°~39° and 113°~116° east longitude, belonging to the warm temperate continental monsoon climate zone, with an annual sunshine time of about 2500-2900 hours. There are 32 sensors in total, such as Figure 2 The sensor distribution positions of the four areas in the greenhouse are shown in Figure 1. Indoor temperature and humidity, light three-in-one sensor, temperature and humidity, CO2 three-in-one sensor (on a bracket 1m above the ground), ultrasonic wind speed sensor (on a bracket 1m above the ground) and photosynthetically active radiation sensor (on a bracket 1m above the ground) are used to measure the temperature and humidity, photosynthetically active radiation, CO2 concentration, indoor wind speed and other data of the four areas of the solar greenhouse every minute for 24 hours. Outdoor light shutter boxes and wind speed sensors are used to collect data on illuminance, solar radiation, temperature and humidity, and wind speed. The types and models of sensors used are shown in Table 1.

[0085] Table 1 Test Instruments

[0086]

[0087]

[0088] In this embodiment, accurate and complete data are obtained through various types of sensors to prepare for the subsequent construction of a photovoltaic greenhouse microclimate environment model.

[0089] In one embodiment, see Figure 5 , the specific construction method of the photovoltaic greenhouse microclimate environment model is described in detail, then step 202 includes:

[0090] Step 2021, based on the photovoltaic laying data and the initial indoor and outdoor environmental data, construct a mathematical model for hourly dynamic temperature simulation.

[0091] For example, the temperature in a greenhouse is not only affected by solar radiation, but also by natural ventilation, heat loss from the covering layer, heat dissipation from the wall, heat dissipation from the soil, crop transpiration loss, etc.

[0092] Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic temperature simulation is constructed, including:

[0093] Based on the photovoltaic laying data, confirm the solar radiation energy reaching the greenhouse;

[0094] Based on the initial indoor and outdoor environmental data, confirm the total heat loss; the total heat loss includes the heat loss caused by natural ventilation, the heat loss caused by crop transpiration, the heat loss caused by heat transfer between the air and soil in the greenhouse, the heat loss caused by heat exchange between the walls in the greenhouse and the outside air, and the heat loss caused by energy exchange between the air inside and outside the greenhouse through the covering material;

[0095] Based on the solar radiation energy reaching the greenhouse and the total heat loss, and according to the principle of heat balance, a mathematical model for hourly dynamic temperature simulation is constructed.

[0096] The mathematical model of temperature hourly dynamic simulation is expressed as:

[0097]

[0098] Where, t is time; P a is the air density, in g·m -3 ; V g is the greenhouse volume in m 3 ; Ca is the specific heat capacity of air, in J·(g℃)- 1 ;Q radin (t) is the solar radiation energy reaching the greenhouse, in W; Q nv (t) is the heat loss caused by natural ventilation, in W; Q trant (t) is the heat loss caused by crop transpiration, in W; Q soil (t) is the heat loss caused by heat transfer between air and soil in the greenhouse, in W; Qwall (t) is the heat loss caused by heat exchange between the greenhouse wall and the outside air, in W; Q exch (t) is the heat loss caused by the energy exchange between the air inside and outside the greenhouse through the covering material, and the unit is W; x1 is the control variable, and its value is 0 and 1, 0 means that the natural ventilation of the greenhouse is closed, and 1 means that the natural ventilation of the greenhouse is open; T in (t) is the indoor temperature.

[0099] The main reason for indoor energy changes is solar radiation. When laying photovoltaic modules on the south slope of the greenhouse, the main factors affecting indoor solar radiation include greenhouse covering structure, photovoltaic module transmittance and coverage, etc. The solar radiation energy Q reaching the greenhouse radin (t) is expressed as:

[0100]

[0101] Where t is time; f is the proportion of global radiation absorbed by the greenhouse cover structure, which is a constant; s is the area of ​​the light module laying surface, in m 2 ; n is the transmittance of the photovoltaic module; i is the coverage percentage of the photovoltaic module; Q rad (t) is the outdoor solar radiation energy, in W.

[0102] Natural ventilation in greenhouses is a common cooling method. The heat loss caused by natural ventilation is expressed as:

[0103]

[0104] in, is the natural ventilation rate, in m 3 ·s- 1 ; T out (t) is the outdoor temperature.

[0105] Taking into account wind pressure and heat pressure, the natural ventilation rate of the greenhouse The expression is:

[0106]

[0107] Among them, v out (t) is the outdoor wind speed, in m·s -1 ; A ev (t) is the effective area of ​​greenhouse ventilation opening, in m 2 ψ is the wind pressure constant, χ is the flow constant, h is the vertical height of the vent from the ground, and g is the gravitational acceleration, in m·s -2 .

[0108] When the outdoor wind speed reaches 2m·s -1When the indoor natural ventilation is above 100°, the wind pressure plays a major role, and the greenhouse natural ventilation rate is and greenhouse effective ventilation area A ev (t) The calculation expression is:

[0109]

[0110] A ev =2A vent sin(α / 2) (6)

[0111] Among them, A vent is the total area of ​​the greenhouse vents, in m 2 ; α is the angle between the vent and the ground.

[0112] For example, the greenhouse cover will cause indoor heat loss due to its physical properties. The heat loss is mainly related to the temperature difference between the inside and outside of the greenhouse, the area of ​​the cover and the heat transfer coefficient of the cover material. The heat loss Q caused by the energy exchange between the air inside and outside the greenhouse through the cover material exch (t) is expressed as:

[0113] Q exch (t) = A c ξ(T in (t)-T out (t)) (7)

[0114] Where ξ is the heat transfer coefficient of the greenhouse covering material; A c is the area of ​​the greenhouse cover, in m 2 .

[0115] For example, indoor crop transpiration can cause heat loss, and the heat loss caused by crop transpiration is Q trant (t) is expressed as:

[0116] Q trant (t) = λE trant (t) (8)

[0117] Among them, E trant (t) is the rate of change of water vapor caused by transpiration of indoor crops, in g·s -1 ; λ is the latent heat constant of evaporation of water, in J·g -1 .

[0118] For example, soil can transfer heat to the outside world, so the soil factor in the greenhouse will affect the change of indoor temperature. The heat loss Q caused by heat transfer between air and soil in the greenhouse soil (t) is expressed as:

[0119] Q soil (t)=1.86Ag (T in (t)-T soil (t)) 4 / 3 (9)

[0120] Among them, T soil (t) is the indoor soil surface temperature, in °C; A g is the indoor soil area, in m 2 .

[0121] For example, the wall has a heat storage function. The heat storage capacity of the wall is related to the material and thickness of the wall. The heat loss Q caused by heat exchange between the wall in the greenhouse and the outside air is wall (t) is expressed as:

[0122] Q wall (t) = KA s (T in (t)-T out (t)) / D (10)

[0123] Where K is the thermal conductivity of the wall material; A s is the wall area, in m 2 ; D is the wall thickness, in m.

[0124] This step takes into account that the temperature in the greenhouse is not only affected by solar radiation, but also by natural ventilation, heat loss from the cover, heat dissipation from the wall, heat dissipation from the soil, crop transpiration loss, etc., and accurately simulates the temperature changes in the microclimate of the solar greenhouse. In this way, in the face of different climatic conditions and crop types, this method can flexibly respond to various environmental challenges by adjusting the temperature parameters in the photovoltaic greenhouse microclimate environmental model.

[0125] Step 2022, based on the initial indoor and outdoor environmental data, construct a mathematical model for hourly dynamic simulation of humidity.

[0126] For example, the main reasons affecting the change of humidity in the greenhouse are the change of crop transpiration and the change of condensation of the greenhouse cover. Then, step 2022 includes:

[0127] Based on the initial indoor and outdoor environmental data, the water vapor change rate is determined; the water vapor change rate includes the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation, and the water vapor change rate caused by condensation of the greenhouse cover envelope.

[0128] Based on the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation and the water vapor change rate caused by condensation of the greenhouse cover envelope, and according to the mass balance principle of water vapor in indoor air, a mathematical model for hourly dynamic simulation of humidity is constructed.

[0129] The mathematical model of hourly dynamic simulation of humidity is expressed as:

[0130]

[0131] Among them, V g is the greenhouse volume; ρ in is the indoor water vapor density; E trant (t) The rate of change of water vapor caused by indoor crop transpiration, in g·s -1 ; E nv is the rate of change of water vapor caused by natural ventilation, in g·s -1 ; E cond (t) is the rate of change of water vapor caused by condensation of the greenhouse cover envelope, in g·s -1 .

[0132] This embodiment uses relative humidity and water vapor density conversion for calculation. Relative humidity is equal to water vapor density ρ in The saturated water vapor density at the corresponding temperature Compared to the saturated water vapor density of air The calculation formula is expressed as:

[0133]

[0134] Among them, e * is the saturated water vapor pressure of the air; M is the molar mass of water; R is the molar gas constant; T is the air temperature when calculating the saturated water vapor of the air.

[0135] The saturated water vapor pressure in the air is only related to the temperature. * The calculation formula is expressed as:

[0136] e * =e z exp(17.4T / (239+T)) (13)

[0137] Among them, e z The saturated water vapor pressure of air at 0℃; exp() is an exponential function with the natural constant e as the base.

[0138] For example, crop transpiration is an important cause of indoor humidity changes. The water vapor change rate E caused by indoor crop transpiration is trant The calculation formula of (t) is expressed as:

[0139]

[0140] Among them, R i ' n (t) is the solar radiation of the crop canopy, in W·m-2 ; Δ(t) is the slope of saturated water vapor pressure change, in Pa·℃ -1 ; γ is the humidity coefficient, unit is Pa·℃ -1 ; is the saturated water vapor pressure of air, e in (t) is the actual water vapor pressure, in Pa; r s (t) is the crop stomatal resistance, r b (t) is the aerodynamic resistance of the blade surface, in units of s·m -1 ; LAI is the crop leaf area index.

[0141] Solar radiation above the crop canopy R i ' n The calculation formula of (t) is expressed as:

[0142] R i ' n (t) = (1-exp(-β·LAI))R in (t) (15)

[0143] Where β is the extinction coefficient; R in (t) is the canopy solar radiation power, in W·m- 2 The calculation formula of Δ(t) is as follows:

[0144]

[0145] Stomatal resistance r s (t) and aerodynamic drag r b (t) are respectively expressed as:

[0146] r s (t) = 200 × (1 + exp (-0.05 (R in (t)-50))) (17)

[0147] r b (t) = 220c 0.2 / v in (t) 0.8 (18)

[0148] Where c is the characteristic length of crop leaves, in meters; v in (t) is the indoor wind speed, in s·m- 1

[0149] For example, the greenhouse ventilation windows are opened for natural ventilation. The change of natural ventilation and the difference in water vapor density between indoor and outdoor will affect the change of indoor humidity. The water vapor change rate caused by natural ventilation is expressed as:

[0150]

[0151] ρ in (t) is the indoor water vapor density; ρ out (t) is the outdoor water vapor density; is the natural ventilation rate of the greenhouse.

[0152] For example, the decrease in humidity in the greenhouse is related to the condensation of the cover layer caused by the temperature difference between indoor and outdoor, and the condensation is related to the dew point temperature. The calculation formula of the dew point temperature is expressed as:

[0153]

[0154] Then, the condensation of the greenhouse cover causes humidity loss, and the water vapor change rate caused by condensation of the greenhouse cover envelope is E cond (t) is expressed as:

[0155]

[0156] in, is the saturated water vapor density of the covering layer, in g·m- 3 ; T s (t) is the greenhouse cover temperature, in °C.

[0157] Step 2023, based on the initial indoor and outdoor environmental data, construct a mathematical model for dynamic simulation of CO2 concentration hour by hour.

[0158] Exemplarily, the main factors affecting the CO2 concentration in the greenhouse air include greenhouse ventilation rate, soil respiration, photosynthesis and respiration of crops. It can be summarized as: CO2 concentration in the greenhouse = photosynthesis absorption of CO2 + crop respiration + soil respiration + ventilation exchange.

[0159] Step 2023 includes:

[0160] Based on the initial indoor and outdoor environmental data, the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area and the soil CO2 exhalation intensity per unit greenhouse area were determined.

[0161] Based on the absorption intensity of CO2 per unit plant leaf area, the exhalation intensity of CO2 per unit plant leaf area and the exhalation intensity of CO2 per unit greenhouse area, a mathematical model for the hourly dynamic simulation of CO2 concentration is constructed. The mathematical model for the hourly dynamic simulation of CO2 concentration is expressed as:

[0162]

[0163] Among them, c i is the indoor air CO2 concentration, in g·m -3 ;co Outdoor air CO2 concentration, in g·m -3 ; p is the absorption intensity of CO2 per unit plant leaf area, in g·m -3 ;p r is the CO2 exhalation intensity per unit plant leaf area, in g·m -3 ;p s is the soil CO2 exhalation intensity per unit greenhouse area, in g·m -3 ; L is the ventilation volume, unit is m 3 ·s.

[0164] Indoor CO2 concentration and solar radiation will be the main factors controlling the photosynthetic rate, and the CO2 absorption intensity p per unit plant leaf area can be expressed as:

[0165]

[0166] Plant respiration causes changes in CO2 concentration. The expression of plant respiration is in exponential form. The CO2 exhalation intensity per unit plant leaf area is expressed as:

[0167]

[0168] Among them, t l is the leaf surface temperature, in °C; a, b are coefficients, and when the leaf surface temperature rises by 10 °C, p r When the leaf temperature rises from 15℃ to 28℃, the coefficient b is 1.35. The CO2 respiration intensity of plant leaves is usually one tenth of the photosynthetic rate, and the coefficient a can be 0.9×10 -6 .

[0169] Soil respiration causes changes in indoor CO2 concentration. The soil CO2 exhalation intensity per unit greenhouse area is related to soil composition, soil temperature, soil moisture, and soil utilization. The microbial activity in the soil increases with increasing temperature. When the temperature reaches about 27°C, it reaches the maximum value. At 40°C, the microbial activity will drop significantly. The expression is described in exponential form. The soil CO2 exhalation intensity per unit greenhouse area is expressed as:

[0170]

[0171] Among them, p s (0) is the CO2 release of soil at 0℃, in g·(m 2 s)- 1 ; t′ is soil temperature, in °C.

[0172] For example, in a strawberry greenhouse in winter, ventilation will cause water vapor to dissipate, resulting in a decrease in humidity in the greenhouse. Natural ventilation is usually expressed in terms of air changes or air volume, and the ventilation volume L can be expressed as:

[0173]

[0174] Where N is the number of greenhouse ventilation times, in times·h -1 ; V g is the greenhouse volume in m 3 .

[0175] Of course, the ventilation volume is calculated adaptively according to different experimental conditions. For the convenience of estimating the ventilation volume, the empirical formula used is:

[0176] L=EAv o (27)

[0177] Where A is the total area of ​​the air inlet, in m 2 ; E is the wind pressure ventilation effectiveness coefficient; v o is the outdoor wind speed, in m·s -1 .

[0178] For example, since the CO2 concentration collected by the sensor is inconsistent with the CO2 concentration unit in the mathematical model, the CO2 concentration conversion formula is given below:

[0179]

[0180] in, The unit is g·L -1 , The unit is mg·m -3 When the temperature is 0℃, 1ppm=1.964g·m -3 ; When the temperature is 25℃, 1ppm=1.796g·m -3 , where 1ppm = 1μmol·mol -1 =1μL·L -1 =1mg kg -1 .

[0181] According to the ideal gas state equation:

[0182] PV=nRT (29)

[0183] Where P is the pressure, in Pa; V is the gas volume, in m 3 ; T is temperature, unit is ℃; n is the amount of gas, unit is mol; R is the molar gas constant, unit is J·(mol℃) -1 .

[0184] At 0℃ and 1atm: V m =22.4L·mol -1 ; 25℃, 1atm: V m =24.5L·mol -1 ; M is the molecular weight of CO2, M = 44. m Substituting into formula (28) can be converted into mg·m -3 ; atm is the value of one standard atmospheric pressure.

[0185] Step 2024, based on the temperature hourly dynamic simulation mathematical model, the humidity hourly dynamic simulation mathematical model and the CO2 concentration hourly dynamic simulation mathematical model, a photovoltaic greenhouse microclimate environment simulation model is established.

[0186] For example, according to the mathematical model of hourly dynamic simulation of temperature, Matlab / Simulink is used to establish an hourly dynamic prediction model of temperature, which mainly includes: solar radiation heat generation module, ventilation heat dissipation module, ground heat dissipation module, wall heat loss module, and covering material heat dissipation module, such as Figure 6 shown.

[0187] According to the mathematical model of humidity hourly dynamic simulation, Matlab / Simulink is used to establish a humidity hourly dynamic prediction model. The prediction model mainly includes: crop transpiration module, ventilation module, condensation loss module, selection module, such as Figure 7 shown.

[0188] According to the mathematical model of hourly dynamic simulation of CO2 concentration, the hourly dynamic prediction model of CO2 concentration is established using Matlab / Simulink. The prediction model mainly includes: photosynthesis module, soil respiration module, ventilation module, plant respiration module, such as Figure 8 shown.

[0189] According to the hourly dynamic prediction model of daily temperature, hourly dynamic prediction model of humidity and hourly dynamic prediction model of CO2 concentration, Matlab2021b / Simulink is used to establish the photovoltaic greenhouse microclimate environment model, which includes these three parts: Fig. 9 As shown in the figure, the collected indoor and outdoor environmental data are input into the model, the indoor microclimate is simulated, and finally the simulated environmental data is output. In the temperature hourly dynamic prediction model, Q, RH, Ti and Tout represent solar radiation, relative humidity, indoor temperature and outdoor temperature respectively; in the humidity hourly dynamic prediction model, Ti, RH, r and Tout represent indoor temperature, relative humidity, blade characteristic length and outdoor temperature respectively; in the CO2 concentration hourly dynamic prediction model, Q, T, c and v represent solar radiation, temperature, carbon dioxide concentration and ventilation volume respectively. The simulation parameters are shown in Table 2.

[0190] Table 2 Simulation parameter settings

[0191]

[0192] In this embodiment, a photovoltaic greenhouse microclimate environment simulation model is established through three parts: daily temperature hourly dynamic prediction model, humidity hourly dynamic prediction model and CO2 concentration hourly dynamic prediction model. It accurately simulates the dynamic changes of temperature, humidity and CO2 concentration inside the greenhouse, which is of great significance for improving the production efficiency, resource utilization efficiency and ability to cope with extreme weather in greenhouse agriculture.

[0193] In one embodiment, step 203 includes:

[0194] Based on modifying different photovoltaic laying methods, updated photovoltaic laying data and updated indoor and outdoor environmental data are obtained.

[0195] Based on the updated photovoltaic laying data and updated indoor and outdoor environmental data, the photovoltaic greenhouse microclimate environmental model is optimized.

[0196] For example, different crops, different varieties of the same crop, and different growth stages of crops all have different environmental requirements. In order to ensure that the environment of the crops in the photovoltaic greenhouse is in a suitable state throughout their life cycle, it is necessary to simulate the microclimate environment in the greenhouse under different photovoltaic laying modes. When the microclimate in the greenhouse under a certain photovoltaic laying density and mode matches the environmental factors required by the crops, it can be considered that this photovoltaic laying mode is suitable for the production of this type of crop. Fig.10 shown.

[0197] In summary, the photovoltaic greenhouse installation decision method based on microclimate simulation provided in the embodiment of the present application takes into account that different crops, different varieties of the same crops, and different growth stages of crops have different environmental requirements. In order to ensure that the environment of crops planted in the photovoltaic greenhouse is in a proper state throughout the life cycle, it is necessary to simulate the microclimate environment in the greenhouse under different photovoltaic laying modes. Taking the solar greenhouse as the research object, a solar greenhouse microclimate simulation model is constructed, which realizes the accurate simulation of the microclimate change of the solar greenhouse, and provides a theoretical basis for the installation decision of the solar greenhouse. In the face of different climatic conditions and crop types, this method can flexibly respond to various environmental challenges by adjusting the parameters in the photovoltaic greenhouse microclimate environment model.

[0198] See also Fig.11An embodiment of the present application provides a photovoltaic greenhouse installation decision device based on microclimate simulation, which executes the photovoltaic greenhouse installation decision method based on microclimate simulation as in the above embodiment, including a data acquisition module 301, a model construction module 302, a model simulation module 303 and a result output module 304.

[0199] The data acquisition module 301 is used to acquire photovoltaic laying data and initial indoor and outdoor environmental data;

[0200] A model building module 302 is used to build a photovoltaic greenhouse microclimate environment model based on photovoltaic laying data and initial indoor and outdoor environmental data;

[0201] The model simulation module 303 is used to optimize the photovoltaic greenhouse microclimate environment model by modifying different photovoltaic laying methods, and obtain the optimized photovoltaic greenhouse microclimate environment model; the optimized photovoltaic greenhouse microclimate environment model is used to output photovoltaic greenhouse microclimate environment parameters that match the crop planting environment;

[0202] The result output module 304 is used to obtain a photovoltaic greenhouse installation decision plan based on the photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment.

[0203] Exemplarily, the model simulation module 303 is specifically used for:

[0204] Based on modifying different photovoltaic laying methods, updated photovoltaic laying data and updated indoor and outdoor environmental data are obtained;

[0205] Based on the updated photovoltaic laying data and updated indoor and outdoor environmental data, the photovoltaic greenhouse microclimate environmental model is optimized.

[0206] Exemplarily, the model building module 302 is specifically used for:

[0207] Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic temperature simulation is constructed;

[0208] Based on the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic simulation of humidity is constructed;

[0209] Based on the initial indoor and outdoor environmental data, a mathematical model for dynamic simulation of CO2 concentration hour by hour is constructed;

[0210] Based on the hourly dynamic simulation mathematical models of temperature, humidity and CO2 concentration, a photovoltaic greenhouse microclimate environment simulation model is established.

[0211] Exemplarily, in the model building module 302, based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a temperature hourly dynamic simulation mathematical model is built, including:

[0212] Based on the photovoltaic laying data, confirm the solar radiation energy reaching the greenhouse;

[0213] Based on the initial indoor and outdoor environmental data, confirm the total heat loss; the total heat loss includes the heat loss caused by natural ventilation, the heat loss caused by crop transpiration, the heat loss caused by heat transfer between the air and soil in the greenhouse, the heat loss caused by heat exchange between the walls in the greenhouse and the outside air, and the heat loss caused by energy exchange between the air inside and outside the greenhouse through the covering material;

[0214] Based on the solar radiation energy reaching the greenhouse and the total heat loss, and according to the principle of heat balance, a mathematical model for hourly dynamic temperature simulation is constructed.

[0215] For example, the solar radiation energy Q reaching the greenhouse radin (t) is expressed as:

[0216]

[0217] Where t is time; f is the proportion of global radiation absorbed by the greenhouse covering structure; s is the area of ​​the photovoltaic module laying surface; n is the transmittance of the photovoltaic module; i is the coverage percentage of the photovoltaic module; Q rad (t) is the outdoor solar radiation.

[0218] Exemplarily, the mathematical model of temperature hourly dynamic simulation is expressed as:

[0219]

[0220] Where t is time; Pa is air density; Vg is greenhouse volume; Ca is air specific heat capacity; Q radin (t) is the solar radiation energy reaching the greenhouse; Q nv (t) is the heat loss caused by natural ventilation; Q trant (t) is the heat loss caused by crop transpiration; Q soil (t) is the heat loss caused by heat transfer between air and soil in the greenhouse; Q wall (t) is the heat loss caused by heat exchange between the greenhouse wall and the outside air; Q exch (t) is the heat loss caused by the energy exchange between the air inside and outside the greenhouse through the covering material; x1 is the control variable; T in (t) is the indoor temperature.

[0221] Exemplarily, based on the initial indoor and outdoor environmental data, a humidity hourly dynamic simulation mathematical model is constructed, including:

[0222] Based on the initial environmental data indoors and outdoors, the water vapor change rate is determined; the water vapor change rate includes the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation, and the water vapor change rate caused by condensation of the greenhouse cover envelope;

[0223] Based on the water vapor change rate caused by indoor crop transpiration, the water vapor change rate caused by natural ventilation and the water vapor change rate caused by condensation of the greenhouse cover envelope, and according to the mass balance principle of water vapor in indoor air, a mathematical model for hourly dynamic simulation of humidity is constructed.

[0224] Exemplarily, the mathematical model of hourly dynamic simulation of humidity is expressed as:

[0225]

[0226] Where, t is time; V g is the greenhouse volume; ρ in is the indoor water vapor density; x1 is the control variable; E trant (t) The rate of change of water vapor caused by transpiration of indoor crops; E nv is the water vapor change rate caused by natural ventilation; E cond (t) is the rate of change of water vapor caused by condensation in the greenhouse cover envelope.

[0227] Exemplarily, based on the initial indoor and outdoor environmental data, a mathematical model for dynamic simulation of CO2 concentration hour by hour is constructed, including:

[0228] Based on the initial environmental data indoors and outdoors, determine the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area, and the soil CO2 exhalation intensity per unit greenhouse area;

[0229] Based on the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area and the soil CO2 exhalation intensity per unit greenhouse area, a mathematical model for hourly dynamic simulation of CO2 concentration is constructed.

[0230] It should be noted that, although several units / modules or sub-units / modules of the photovoltaic greenhouse installation decision-making device based on microclimate simulation are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.

[0231] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0232] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the photovoltaic greenhouse installation decision method based on microclimate simulation provided in the above-mentioned embodiment of the present application is implemented.

[0233] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0234] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A photovoltaic greenhouse installation decision method based on microclimate simulation, characterized in that: include: Obtain photovoltaic laying data and initial indoor and outdoor environmental data; Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, construct a photovoltaic greenhouse microclimate environmental model; The photovoltaic greenhouse microclimate environmental model is optimized by modifying different photovoltaic laying methods, and an optimized photovoltaic greenhouse microclimate environmental model is obtained; the optimized photovoltaic greenhouse microclimate environmental model is used to output photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment; Based on the photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment, an installation decision plan for the photovoltaic greenhouse is obtained.

2. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 1, characterized in that: The photovoltaic greenhouse microclimate environmental model is optimized by modifying different photovoltaic laying methods, and the optimized photovoltaic greenhouse microclimate environmental model is obtained, including: Based on modifying different photovoltaic laying methods, updated photovoltaic laying data and updated indoor and outdoor environmental data are obtained; Based on the updated photovoltaic installation data and the updated indoor and outdoor environmental data, the photovoltaic greenhouse microclimate environmental model is optimized.

3. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 1, characterized in that: The photovoltaic greenhouse microclimate environment model is constructed based on the photovoltaic laying data and the initial indoor and outdoor environmental data, including: Based on the photovoltaic laying data and the initial indoor and outdoor environmental data, a temperature hourly dynamic simulation mathematical model is constructed; Based on the initial indoor and outdoor environmental data, a mathematical model for hourly dynamic simulation of humidity is constructed; Based on the initial indoor and outdoor environmental data, a mathematical model for dynamic simulation of CO2 concentration is constructed; Based on the temperature hourly dynamic simulation mathematical model, the humidity hourly dynamic simulation mathematical model and the CO2 concentration hourly dynamic simulation mathematical model, the photovoltaic greenhouse microclimate environment simulation model is established.

4. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 3, characterized in that: The step of constructing a temperature hourly dynamic simulation mathematical model based on the photovoltaic laying data and the initial indoor and outdoor environmental data includes: Based on the photovoltaic laying data, confirm the solar radiation energy reaching the greenhouse; Based on the initial indoor and outdoor environmental data, the total heat loss is determined; the total heat loss includes heat loss caused by natural ventilation, heat loss caused by crop transpiration, heat loss caused by heat transfer between air and soil in the greenhouse, heat loss caused by heat exchange between the walls in the greenhouse and the outside air, and heat loss caused by energy exchange between air inside and outside the greenhouse through covering materials; Based on the solar radiation energy reaching the greenhouse and the total heat loss, and according to the heat balance principle, a mathematical model for hourly dynamic simulation of temperature is constructed.

5. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 4, characterized in that: The solar radiation energy Q reaching the greenhouse radin (t) is expressed as: Where t is time; f is the proportion of global radiation absorbed by the greenhouse covering structure; s is the area of ​​the photovoltaic module laying surface; n is the transmittance of the photovoltaic module; i is the coverage percentage of the photovoltaic module; Q rad (t) is the outdoor solar radiation.

6. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 3, characterized in that: The mathematical model of the hourly dynamic simulation of temperature is expressed as: Where t is time; Pa is air density; Vg is greenhouse volume; Ca is air specific heat capacity; Q radin (t) is the solar radiation energy reaching the greenhouse; Q nv (t) is the heat loss caused by natural ventilation; Q trant (t) is the heat loss caused by crop transpiration; Q soil (t) is the heat loss caused by heat transfer between air and soil in the greenhouse; Q wall (t) is the heat loss caused by heat exchange between the greenhouse wall and the outside air; Q exch (t) is the heat loss caused by the energy exchange between the air inside and outside the greenhouse through the covering material; x1 is the control variable; T in (t) is the indoor temperature.

7. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 3, characterized in that: The step of constructing a humidity hourly dynamic simulation mathematical model based on the initial indoor and outdoor environmental data includes: Based on the initial indoor and outdoor environmental data, determining a water vapor change rate; the water vapor change rate includes a water vapor change rate caused by indoor crop transpiration, a water vapor change rate caused by natural ventilation, and a water vapor change rate caused by condensation of a greenhouse cover envelope; Based on the water vapor change rate caused by the transpiration of indoor crops, the water vapor change rate caused by natural ventilation and the water vapor change rate caused by condensation of the greenhouse cover envelope, according to the mass balance principle of water vapor in indoor air, a mathematical model for hourly dynamic simulation of humidity is constructed.

8. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 3, characterized in that: The hourly dynamic simulation mathematical model of humidity is expressed as: Where, t is time; V g is the greenhouse volume; ρ in is the indoor water vapor density; x1 is the control variable; E trant (t) The rate of change of water vapor caused by transpiration of indoor crops; E nv is the water vapor change rate caused by natural ventilation; E cond (t) is the rate of change of water vapor caused by condensation in the greenhouse cover envelope.

9. The photovoltaic greenhouse installation decision method based on microclimate simulation according to claim 3, characterized in that: The method of constructing a mathematical model for dynamic simulation of CO2 concentration hour by hour based on the initial indoor and outdoor environmental data includes: Based on the initial indoor and outdoor environmental data, determining the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area, and the soil CO2 exhalation intensity per unit greenhouse area; Based on the CO2 absorption intensity per unit plant leaf area, the CO2 exhalation intensity per unit plant leaf area and the soil CO2 exhalation intensity per unit greenhouse area, a mathematical model for hourly dynamic simulation of CO2 concentration is constructed.

10. A photovoltaic greenhouse installation decision-making device based on microclimate simulation, characterized in that: The photovoltaic greenhouse installation decision method based on microclimate simulation as described in any one of claims 1 to 9 is applied, comprising: A data acquisition module is used to acquire photovoltaic laying data and initial indoor and outdoor environmental data; A model building module, used to build a photovoltaic greenhouse microclimate environment model based on the photovoltaic laying data and the initial indoor and outdoor environmental data; A model simulation module is used to optimize the photovoltaic greenhouse microclimate environment model by modifying different photovoltaic laying methods, and obtain an optimized photovoltaic greenhouse microclimate environment model; the optimized photovoltaic greenhouse microclimate environment model is used to output photovoltaic greenhouse microclimate environment parameters that match the crop planting environment; The result output module is used to obtain an installation decision plan for the photovoltaic greenhouse based on the photovoltaic greenhouse microclimate environmental parameters that match the crop planting environment.

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