Energy control method for agricultural planting greenhouse system

Abstract

The present invention provides an energy management method for an agricultural planting greenhouse system, including: constructing a growth model for each stage of the growth cycle of target plants according to the growth change rules of each stage of the growth cycle of the target plants; constructing a load transferable equipment model and a load reducible equipment model of the greenhouse system according to the electricity consumption characteristics of the load equipment in the greenhouse system; constructing a thermodynamics model of the greenhouse system according to the dynamic relationship of energy interaction between the air in the greenhouse system and the surrounding environment; constructing a light intensity model of the greenhouse system according to the historical light data and historical meteorological data of the greenhouse system; constructing an energy coupling matrix of the greenhouse system based on the load reducible equipment model, the light intensity model and the thermodynamics model; constructing an energy consumption model for the growth of the target plants according to the growth model and the energy coupling matrix to conduct energy management on the greenhouse system. The present invention reduces the energy consumption cost of the greenhouse system while ensuring the growth rate and quality of the target plants.

Description

Technical Field

[0001] The present invention relates to the field of agricultural energy management, and particularly to an energy control method for an agricultural planting greenhouse system. Background Art

[0002] Currently, the research on the energy management system of greenhouse systems at home and abroad mainly focuses on the precise control of the greenhouse environment. For example, methods such as the adaptive fuzzy PID algorithm and the RBF neural network model are used. These systems aim to achieve high-robust control of the plant growth environment, but they ignore the differences in energy consumption requirements at different growth stages of plants and the energy consumption costs at different times, as well as the impact of different energy inputs on plant growth. As a result, the greenhouse blindly consumes a large amount of energy consumption costs in pursuit of a slight increase in the plant growth rate, greatly reducing the economy of the greenhouse and making the greenhouse energy consumption cost relatively high. Summary of the Invention

[0003] An embodiment of the present invention provides an energy control method for an agricultural planting greenhouse system, which can reduce the energy consumption cost of the greenhouse system.

[0004] In a first aspect, an embodiment of the present invention provides an energy control method for an agricultural planting greenhouse system, and the method includes the following steps:

[0005] According to the growth change rules of each stage of the growth cycle of the target type of plants, construct the growth models of each stage of the growth cycle of the target type of plants; and according to the electricity consumption characteristics of the load devices in the greenhouse system, construct the load transferable device model and the load reducible device model of the greenhouse system; and according to the dynamic relationship of the energy interaction between the air in the greenhouse system and the surrounding environment, construct the thermodynamic model of the greenhouse system; and according to the historical light data and historical meteorological data of the greenhouse system, construct the light intensity model of the greenhouse system;

[0006] Based on the load reducible device model, the light intensity model, and the thermodynamic model, construct the energy coupling matrix of the greenhouse system;

[0007] According to the growth model and the energy coupling matrix, construct the growth energy consumption model of the target type of plants;

[0008] Based on the growth energy consumption model, perform energy control on the greenhouse system.

[0009] The present invention constructs a load transfer device model and a load reduction device model for a greenhouse system, a light intensity model for the greenhouse system, and a thermal power model for the greenhouse system. An energy coupling matrix among electric power, heat, and light loads is constructed through the load reduction device model, the light intensity model, and the thermal power model of the greenhouse system. At the same time, a growth model for each stage of the growth cycle of target plants is also constructed. By analyzing the variation relationships among heat load, light load, temperature, light intensity, and the dry matter accumulation rate of plants, an energy consumption model for target plants' growth, which describes the relationship between the plant growth process and the energy consumption of the greenhouse, is established. Through the energy consumption model for target plants' growth, the relationship between the newly increased profit of plant growth and the energy consumption cost of the greenhouse can be obtained. Furthermore, the energy management and control of the greenhouse system can be carried out based on the relationship between the newly increased profit of plant growth and the energy consumption cost of the greenhouse, while ensuring the growth rate and quality of the target plants and reducing the energy consumption cost of the greenhouse system. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 is a structural diagram of an intelligent greenhouse system provided by an embodiment of the present invention;

[0012] Figure 2 is a schematic diagram of a temperature-plant growth relationship curve provided by an embodiment of the present invention;

[0013] Figure 3 is a schematic diagram of a light intensity-plant growth relationship curve provided by an embodiment of the present invention;

[0014] Figure 4 is a schematic diagram of a plant growth-thermal load curve provided by an embodiment of the present invention;

[0015] Figure 5 is a schematic diagram of a plant growth-light load curve provided by an embodiment of the present invention;

[0016] Figure 6 is a signaling diagram of an intelligent greenhouse energy management and control system provided by an embodiment of the present invention. Detailed Embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] The steps of the energy control method for the agricultural planting greenhouse system include the following steps:

[0019] 101. According to the growth change rules of each stage of the growth cycle of the target type of plant, construct the growth models of each stage of the growth cycle of the target type of plant.

[0020] In the embodiments of the present invention, the above-mentioned target type of plant may be a leafy vegetable plant, and the above-mentioned growth cycle may include three stages. Specifically, the above-mentioned growth cycle may include three stages: the germination stage, the seedling stage, and the vigorous growth stage.

[0021] Optionally, according to the historical experimental data and relevant materials of the target type of plant in the germination stage, extract the growth curve of the target type of plant in the germination stage, and fit the growth curve to obtain the growth rate model of the target type of plant in the germination stage; according to the dry matter change rules under the photosynthesis and respiration of the target type of plant in the seedling stage and the vigorous growth stage, construct the dynamic dry matter accumulation model of the target type of plant; based on the growth rate model in the germination stage and the dynamic dry matter accumulation model, construct the growth models of each stage of the growth cycle of the target type of plant.

[0022] The above-mentioned historical experimental data may be the historical experimental data of the target type of plant in planting experiments in each agricultural base, and the above-mentioned relevant materials may be materials such as papers and magazines recording the research on the growth rules of the target type of plant in the germination stage.

[0023] Taking the target type of plant as a leafy vegetable plant for illustration, during the process of a leafy vegetable plant developing from a seed to the best harvest state, its growth cycle can be divided into three stages: the germination stage, the seedling stage, and the vigorous growth stage. The germination stage refers to the period from the germination of the plant seeds to the initial appearance of true leaves, the seedling stage refers to the period when the first true leaf of the plant is fully flattened, and the vigorous growth stage refers to the period when the plant leaves expand rapidly.

[0024] Select the growth model indicators at different stages, perform curve fitting according to the historical experimental data and relevant materials to obtain the growth rate model of the leafy vegetable plant in the germination stage, and establish the dynamic dry matter accumulation model of the leafy vegetable plant according to the dry matter change rules under the photosynthesis and respiration in the seedling stage and the vigorous growth stage.

[0025] Specifically, in the embodiments of the present invention, the germination rate is used as the growth model index of leafy vegetable plants during the germination period. According to historical experimental data and relevant information, curve fitting is performed to obtain the germination period growth rate models of the following formulas (1) to (3):

[0026] D t = e d TE p IT q (1)

[0027]

[0028] In formulas (1) to (3), D t represents the seed germination rate at time period t (unit: d -1 ); TE is the temperature effect factor; IT represents the photoperiod effect factor; p represents the temperature sensitivity coefficient during the germination period obtained by fitting; q represents the light sensitivity coefficient during the germination period obtained by fitting; T t represents the greenhouse air temperature at time period t (unit: °C); T gm represents the upper temperature limit for seed germination (unit: °C), T gb represents the lower temperature limit for seed germination (unit: °C); T go represents the optimum temperature for seed germination (unit: °C); t I represents the daily light duration of the seed soil (unit: h); t Ib represents the daily critical light duration of the seed soil (unit: h); t Io represents the daily optimum light duration of the seed soil (unit: h).

[0029] In this stage, the product of the growth rate and the time period for each time period is the germination progress of that time period. Then, the sum of the progress of all time periods in this stage should be 100%, that is, the constraint condition of the plant during the germination period is the following formula (4):

[0030]

[0031] Furthermore, in the embodiments of the present invention, the dry matter accumulation rate can be used as the growth rate index of the target plants in the seedling stage and the vigorous growth stage. According to the dry matter change law of the target plants under photosynthesis and respiration in the seedling stage, a dry matter dynamic accumulation model of the target plants in the seedling stage is constructed, and according to the dry matter change law of the target plants under photosynthesis and respiration in the vigorous growth stage, a dry matter dynamic accumulation model of the target plants in the vigorous growth stage is constructed; according to the dry matter dynamic accumulation model of the target plants in the seedling stage and the dry matter dynamic accumulation model of the target plants in the vigorous growth stage, a dry matter dynamic accumulation model of the target plants is constructed.

[0032] It should be noted that photosynthesis and respiration are the most important life processes in plants. During the seedling stage and the vigorous growth stage, the plant can accumulate dry matter through photosynthesis and consume part of the dry matter through respiration to promote the growth of mesophyll cells and accelerate the expansion of leaf area. Since there is a definite linear relationship between the dry matter accumulation of the plant and its actual fresh weight, the embodiments of the present invention can use the dry matter accumulation rate as an index to evaluate the growth rate of the plant in the two stages of the seedling stage and the vigorous growth stage, and establish a dynamic dry matter accumulation model applicable to the seedling stage and the vigorous growth stage, where the model parameters in different growth stages are different, as shown in the following formula (5):

[0033]

[0034] In formula (5), M d,t represents the dry matter accumulation per unit area of land at time t (unit: kg·m -2 ); φ phot represents the photosynthetic rate per unit area of land (unit: kg·m -2 ·s -1 ); φ resp represents the respiration rate per unit area of land (unit: kg·m -2 ·s -1 ); c α represents the photosynthesis conversion rate; c β represents the growth factor of the plant in different growth stages.

[0035] Among them, the photosynthesis of the plant is closely related to factors such as its own dry matter accumulation, temperature, light, and photosynthetic characteristics in different stages. φ phot can be described by the following formulas (6) to (13):

[0036] φ phot =φ photmax Z M (6)

[0037]

[0038] Z I =εc par I t (9)

[0039]

[0040] σ c =c c1 T t 2 +c c2 T t +c c3 (13)

[0041] In formulas (6) to (13): φ photmax represents the total CO2 photosynthetic absorption rate of plants per unit land area (unit: kg·m -2 ·s -1 ); I t represents the light intensity per unit area of land within period t (unit: W / m 2 ); c k represents the extinction coefficient of the plant canopy; c d represents the leaf area conversion ratio per unit dry weight (unit: m 2 kg -1 ); c τ represents the proportion of root dry weight at different growth stages; Z M , Z I , Z T , Z bs represent functions related to dry matter, light, temperature, and other constants of photosynthesis; c par represents the effective rate of intercepted photosynthetically active radiation by the plant canopy; C CO2 represents the CO2 concentration in the greenhouse (unit: kg / m 3 ); ε represents the photosynthesis conversion efficiency; Γ represents the CO2 compensation point (unit: kg / m 3 ); T c1 represents the reference temperature for plant photosynthesis; c Γ represents the CO2 compensation point at the reference T c1 temperature (unit: kg / m 3 ); c t_Γ represents the temperature influence factor of the CO2 compensation point Γ; σ b represents the boundary layer conductance of the plant leaf (unit: m / s); σ s represents the stomatal conductance of the plant leaf (unit: m / s); σ c represents the CO2 hydroxylation conductance inside the plant leaf (unit: m / s); c c1 , c c2 , c c3 represent polynomial-related parameters of the hydroxylation conductance.

[0042] Furthermore, the respiration of plants is closely related to factors such as its own dry matter accumulation, temperature, and respiratory characteristics at different stages. φ resp can be described by the following formula (14):

[0043]

[0044] In formula (14): T c2 represents the reference temperature for plant respiration (unit: °C); c s represents at the reference Tc2 At a temperature, the respiration rate of the parts other than the plant roots (unit: s -1 ); c resp represents the respiration maintenance factor at different growth stages.

[0045] Furthermore, the constraint conditions of the plant in the seedling stage are as shown in the following formulas (15) to (17):

[0046]

[0047] In formulas (15) to (17): represents the upper limit of the total dry matter accumulation in the seedling stage (unit: kg·m -2 ), represents the lower limit of the total dry matter accumulation in the seedling stage (unit: kg·m -2 ); represents the upper limit of the survival temperature of the plant in the seedling stage (unit: °C), represents the lower limit of the survival temperature of the plant in the seedling stage (unit: °C); represents the upper limit of the acceptable light intensity of the plant in the seedling stage (unit: W / m 2 ), represents the lower limit of the acceptable light intensity of the plant in the seedling stage (unit: W / m 2 ).

[0048] Furthermore, the constraint conditions of the plant in the vigorous growth stage are as shown in the following formulas (18) to (20):

[0049]

[0050] In formulas (18) to (20): represents the upper limit of the total dry matter accumulation in the vigorous growth stage (unit: kg·m -2 ), represents the lower limit of the total dry matter accumulation in the vigorous growth stage (unit: kg·m -2 ); represents the upper limit of the survival temperature of the plant in the vigorous growth stage (unit: °C), represents the lower limit of the survival temperature of the plant in the vigorous growth stage (unit: °C); the upper limit of the acceptable light intensity of the plant in the vigorous growth stage (unit: W / m 2 ), represents the lower limit of the acceptable light intensity of the plant in the vigorous growth stage (unit: W / m 2 ).

[0051] Furthermore, the plant output profit RES can be deduced from the plant dry matter weight, as shown in the following formula (21):

[0052] RES = λv c f A s M d,t (1 - c τ ) (21)

[0053] In formula (21): λ v represents the plant price (unit: ￥ / kg); c f represents the fresh weight conversion coefficient of the plant; A s represents the total planting area of the greenhouse (unit: m 2 ).

[0054] 102. According to the electricity consumption characteristics of the load equipment in the greenhouse system, a load transferable equipment model and a load reducible equipment model of the greenhouse system are constructed.

[0055] In the embodiment of the present invention, the above greenhouse system may be an intelligent greenhouse system. As Figure 1 described above, the above intelligent greenhouse system includes: LED supplementary lights, wet curtains, negative pressure fans, solar collectors, circulating water pumps, hot water storage tanks, light-shielding curtains, water supply and drainage equipment, fog and disease prevention equipment, greenhouse heating pipes, etc. Among them, the above wet curtain and negative pressure fan constitute a wet curtain - fan cooling system, which can be called a cooling equipment. The LED supplementary lights are supplementary lighting equipment. The solar collector, circulating water pump, and hot water storage tank are heating equipment. The light-shielding curtain is a light-shielding equipment.

[0056] Furthermore, the main electrical equipment in the above intelligent greenhouse system includes: The supplementary lighting equipment (LED supplementary lights) adjusts the brightness of the LED light source to assist sunlight in enhancing the indoor lighting intensity and extending the lighting time; the light-shielding equipment (light-shielding curtain) changes the light-shielding degree by adjusting the opening and closing angle of the curtain blades to control the solar radiation intensity entering the room; the heating equipment (solar collector, circulating water pump, hot water storage tank) converts the collected solar energy into heat energy and stores it in the hot water storage tank, and heats the greenhouse through the heating pipes; the cooling equipment (wet curtain - fan cooling system) converts the sensible heat of the extracted external air into latent heat, conveys cold air for greenhouse cooling, and at the same time plays a ventilation role; the water supply and drainage equipment supplies agricultural production water to the greenhouse regularly and quantitatively every day through a water pump; the fog and disease prevention equipment generates a space electric field through a DC high-voltage power supply for the disease prevention of greenhouse plants. Among them, the light-shielding curtain has a short working time and a small power, and its influence on the overall power can be ignored.

[0057] It should be noted that the above-mentioned electrical equipment can also be called load equipment. Since the types and load characteristics of the above-mentioned electrical equipment are different, in order to make the designed intelligent greenhouse system practical, it is necessary to determine the usage requirements of different electrical equipment according to factors such as the electrical characteristics of these equipment and different growth stages of plants, and classify and summarize the electrical equipment. According to the flexibility of load regulation, the loads of the intelligent greenhouse system in the embodiments of the present invention are divided into two categories: uncontrollable loads and controllable loads. The water supply and drainage equipment in the embodiments of the present invention must carry out irrigation work on time, so the load of the water supply and drainage equipment belongs to the uncontrollable load. Further, the controllable load is divided into transferable load and reducible load according to the regulation characteristics. The transferable load refers to a load that is not limited to a specific time and has little impact on normal agricultural production, such as the load of the fog removal and disease prevention equipment. The reducible load refers to a load whose electrical load is reduced to a certain extent, but the achieved effect is still within an acceptable range. The loads of such equipment mainly include the loads of LED supplementary lights, wet curtain - fan cooling systems, heating equipment, etc. The load equipment of the above-mentioned intelligent greenhouse system includes uncontrollable load equipment and controllable load equipment. The uncontrollable load equipment includes water supply and drainage equipment, and the controllable load equipment includes fog removal and disease prevention equipment, LED supplementary lights, wet curtain - fan cooling systems, heating equipment, etc. Further, the controllable load equipment includes load transferable equipment and load reducible equipment. Among them, the controllable load equipment includes fog removal and disease prevention equipment, and the load reducible equipment includes supplementary lighting equipment, cooling equipment, and heating equipment.

[0058] Optionally, a load transferable equipment model of the greenhouse system can be constructed according to the operation period, operation duration, and operation power of the above-mentioned load transferable equipment; a load reducible equipment model of the greenhouse system can be constructed according to the germination rate of the target type of plants in the germination period, the dry matter accumulation increment of the target type of plants in the seedling period, and the dry matter accumulation increment limit of the target type of plants in the vigorous growth period; a load transferable equipment model and a load reducible equipment model of the greenhouse system can be constructed according to the above-mentioned load transferable equipment model and the above-mentioned load reducible equipment model.

[0059] Further, in the intelligent greenhouse system, the fog removal and disease prevention equipment has a certain degree of electrical flexibility, and its load can be transferred from the peak electricity consumption or peak electricity price period to other low - price idle periods. At the same time, to ensure the operation effect, the fog removal and disease prevention equipment is only allowed to be reliably scheduled within a preset effective time period and must complete the operation task before the cut - off time. Let the operation duration of the load transferable equipment be l SF (unit: h), and the allowable operation time range is TR SF =[t SF,s , t SF,e , and the actual operation power at time t is P SF,t (unit: kW), and the minimum operation power is (unit: kW), the maximum operating power is (unit: kW), then the load transfer equipment of the greenhouse system needs to satisfy the following formulas (22) to (24):

[0060] t SF,e -t SF,s ≥l SF (22)

[0061]

[0062] Suppose the power transferred by the defogging and disease prevention equipment from the current time period t to the subsequent time period t' is P SF,t→t '(unit: kW), and the maximum transfer duration is t tmax (unit: h), then the sum of the transferred powers must be less than the maximum value that the user can transfer in the current time period (unit: kW), that is, it satisfies the constraints of the following formulas (25) and (26):

[0063]

[0064] t + t tmax ≤t SF,e (26)

[0065] Finally, since the amount of electricity required for the load transfer equipment to complete the task is certain, the power after transfer satisfies the constraint of the following formula (27):

[0066]

[0067] In formula (27): Δt SF,t represents the operating time of the defogging and disease prevention equipment in each transfer time period (unit: h); E SF represents the total daily energy demand of the defogging and disease prevention equipment (unit: kW·h).

[0068] Furthermore, from the growth models of each stage of the growth cycle of the target plants, it can be seen that the effects of temperature and light on the plant growth rate are as Figure 2 and Figure 3 shown. Near the maximum point of the plant growth rate, appropriate changes in environmental factors such as temperature and light intensity basically do not affect the growth rate, but will have a greater impact on the energy consumption changes of LED supplementary lights, wet curtain - fan cooling systems, and heating equipment. Therefore, in the embodiments of the present invention, a load reduction equipment model corresponding to the load reduction equipment is established. While satisfying the set plant growth constraints, the operating state of such equipment is changed to reduce power consumption.

[0069] For the plants in the germination stage, the germination rate constraint of the plant seeds in the time period t is shown in the following formula (28):

[0070]

[0071] In formula (28): represents the lower limit of the set seed germination rate within the time period t (unit: d -1 ), represents the upper limit of the set seed germination rate within the time period t (unit: d -1 ).

[0072] Similarly, for plants in the seedling stage and the vigorous growth stage, the increment of dry matter accumulation of the plants within the time period t should satisfy the growth constraint as shown in formula (29) below:

[0073]

[0074] In formula (29): represents the lower limit of the set dry matter accumulation amount within the time period t (unit: kg·m -2 ), represents the upper limit of the set dry matter accumulation amount within the time period t (unit: kg·m -2 ).

[0075] Furthermore, from the above formulas (28) and (29), the reducible load ΔP of the greenhouse system in the embodiments of the present invention can be obtained t The model is as shown in formula (30) below:

[0076] ΔP C,t =ΔP LED,t +ΔP PT,t +ΔP PF,t (30)

[0077] In the formula: ΔP C,t represents the total system load reduction amount within the time period t (unit: kW); ΔP LED,t represents the load reduction amount of the LED supplementary light within the time period t (unit: kW); ΔP PT,t represents the load reduction amount of the heating equipment within the time period t (unit: kW); ΔP PF,t represents the load reduction amount of the wet curtain - fan cooling system within the time period t (unit: kW).

[0078] Among them, the linear relationship between the unit operating power of the LED supplementary light and the light intensity is as shown in formula (31) below:

[0079] P LED,t =I LED,t ·L LED (31)

[0080] In formula (31): P LED,tRepresents the operating power of the LED supplementary light during period t (unit: kW); I LED,t Represents the actual value of the illumination intensity of the LED supplementary light during period t (unit: W / m 2 ); L LED Represents the linearized power-illumination intensity factor of the LED supplementary light.

[0081] Furthermore, the load that can be reduced, ΔP, of the LED supplementary light LED,t The model is shown in the following equations (32) and (33):

[0082]

[0083] In equations (32) and (33): Represents the expected value of the illumination intensity of the LED supplementary light during period t (unit: W / m 2 ); Represents the maximum load reduction of the LED supplementary light during period t (unit: kW).

[0084] The temperature increase device consists of a heat storage unit and a heating unit. The heat changes of each unit are controlled by the circulating pumps on both sides. Assuming that the enthalpy drop of the carrier per unit flow during the heat storage process of the temperature increase device is a fixed value, the linearized relationship between the operating power and heat of each unit of the temperature increase device is shown in the following equations (34) to (36):

[0085] Q PTc,t = h c,t η PTc P PTc,t (34)

[0086]

[0087] Q PTd,t = h d η PTd P PTd,t (36)

[0088] In equations (34) to (36): Q PTc,t Represents the stored heat of the heat storage unit of the temperature increase device during period t (unit: J); h c,t Represents the enthalpy increase of the carrier per unit flow in the solar collector during period t (unit: J / kg); η PTc Represents the unit power flow of the heat storage unit of the temperature increase device (unit: kg / kW); P PTc,t Represents the operating power of the heat storage unit of the temperature increase device during period t (unit: kW); A PT Represents the total area of the solar collector in the temperature increase device (unit: m 2 ); β PTIndicates the effective absorption and storage rate of solar energy collectors for solar radiation (unit: J / (m 2 ·℃)); I G,t Indicates the solar radiation intensity within the time period t (unit: W / m 2 ); Indicates the maximum flow rate of the flow carrier in the solar energy collector (unit: kg); Q PTd,t Indicates the heat released by the heating unit of the temperature increase device within the time period t (unit: J); h d Indicates the enthalpy drop per unit flow carrier in the heat release stage of the heating unit of the temperature increase device (unit: J / kg); P PTd,t Indicates the operating power of the heating unit of the temperature increase device within the time period t (unit: kW); η PTd Indicates the unit power flow rate of the heating unit of the temperature increase device (unit: kg / kW).

[0089] According to the linear relationship between the operating power and heat of each unit of the above temperature increase device, the load that can be reduced, ΔP, of the temperature increase device PT,t The model is shown in the following formulas (37) to (41):

[0090] ΔP PT,t = ΔP PTc,t + ΔP PTd,t (37)

[0091]

[0092] In formulas (37) to (41): L PTd Indicates the linearized power-temperature factor of the heating unit of the temperature increase device; Indicates the expected greenhouse temperature within the time period t (unit: °C); T t Indicates the actual greenhouse temperature within the time period t (unit: °C); ρ air Indicates the air density (unit: kg / m 3 ); C air Indicates the specific heat capacity at constant pressure of air (unit: J / (kg·°C)); V G Indicates the greenhouse volume (unit: m 3 ); η r Indicates the absorption rate of greenhouse air for the released thermal energy; Indicates the maximum load reduction of the heating unit of the temperature increase device within the time period t (unit: kW).

[0093] The cooling capacity of the wet curtain - fan cooling system is closely related to external environmental factors, and its operating power P PF and the heat Q PF absorbed from the greenhouse are related as shown in the following formulas (42) to (44):

[0094] QPF,t = η PF P PF,t ρ air C air (T pad,t - 15)(42)

[0095] T pad,t = T o,t - η pad (T o,t - T wb,t )(43)

[0096]

[0097] In equations (42) to (44): Q PF,t represents the heat absorbed by the wet curtain - fan cooling system during time period t (unit: J); P PF,t represents the operating power of the wet curtain - fan cooling system during time period t (unit: kW); η PF represents the operating efficiency of the wet curtain - fan cooling system; T pad,t represents the temperature of the cold air passing through the wet curtain in the wet curtain - fan cooling system during time period t (unit: °C); T o,t represents the outdoor air temperature during time period t (unit: °C); η pad represents the wet curtain refrigeration efficiency; T wb,t represents the wet - bulb temperature of the outdoor air during time period t (unit: °C); R ho represents the outdoor relative humidity.

[0098] According to the relationship between the operating power P PF of the above - mentioned wet curtain - fan cooling system and the heat Q PF absorbed from the greenhouse, the load reduction ΔP LED,t model of the wet curtain - fan cooling system is shown in the following equations (45) to (47):

[0099] ΔP PF,t = (T t - T t * )·L PF (45)

[0100]

[0101] In equations (45) to (47): L PF represents the linearized power - temperature factor of the wet curtain - fan cooling system; represents the maximum load reduction of the wet curtain - fan cooling system during time period t (unit: kW).

[0102] 203. Construct a thermodynamic model of the greenhouse system based on the dynamic relationship of energy interaction between the air inside the greenhouse system and the surrounding environment.

[0103] In the embodiment of the present invention, the thermodynamic model of the greenhouse system can be constructed according to the thermodynamic effect and the amount of change in the heat of the greenhouse air, the heat exchange amount between the greenhouse air and the greenhouse soil, the heat exchange amount between the greenhouse air and the greenhouse wall, the heat exchange amount between the greenhouse air and the outside air, the heat absorbed by plant transpiration, the surface soil temperature, and the wall temperature in each time period.

[0104] Furthermore, it can be assumed that the indoor air does not absorb solar radiation and the radiation heat transfer process is not considered. According to the thermodynamic effect, a thermodynamic model of the intelligent greenhouse system is established to describe the dynamic relationship of heat interaction between the air inside the system and the surrounding environment, and to predict the change in the indoor air temperature. The thermodynamic model of the intelligent greenhouse system can be shown as the following formulas (48) to (52):

[0105] Q t =η r ·Q PTd,t +Q s,t +Q w,t +Q o,t -Q p,t -Q PF,t (48)

[0106]

[0107] Q s,t =α s A s (T s,t -T t ) (50)

[0108] Q w,t =α w A w (T w,t -T t ) (51)

[0109] Q o,t =(α g A g +α o A v )(T o,t -T t ) (52)

[0110] In formulas (48) to (52): Q t represents the amount of change in the heat of the greenhouse air in time period t (unit: J); Q s,tRepresents the heat exchange amount between the greenhouse air and the greenhouse soil within the time period t (unit: J); Q w,t Represents the heat exchange amount between the greenhouse air and the greenhouse wall within the time period t (unit: J); Q o,t Represents the heat exchange amount between the greenhouse air and the outside air within the time period t (unit: J); Q p,t Represents the heat absorbed by plant transpiration within the time period t (unit: J); T s,t Represents the surface soil temperature within the time period t (unit: °C); T w,t Represents the wall surface temperature within the time period t (unit: °C); α s Represents the direct convective heat transfer coefficient between the greenhouse air and the soil, α w Represents the direct convective heat transfer coefficient between the greenhouse air and the wall, α o Represents the direct convective heat transfer coefficient between the greenhouse air and the outside air; α g Represents the indirect convective heat transfer coefficient between the greenhouse air and the outside air through the glass (unit: J / (m 2 ·°C)); A s Represents the soil area (unit: m 2 ); A w Represents the greenhouse area (unit: m 2 ); A g Represents the glass area (unit: m 2 ); A v Represents the ventilation opening area (unit: m 2 ).

[0111] Among them, the internal heat change of the heating equipment satisfies the following relationships of formulas (53) to (56):

[0112] E PT,t = η PTe E PT,t-1 + Q PTc,t - Q PTd,t (53)

[0113]

[0114] In formulas (53) to (56): E PT,t Represents the total heat stored in the heat storage unit of the heating equipment within the time period t (unit: J); η PTe Represents the storage efficiency of the heat storage unit; P PTc,t Represents the operating power of the heat storage unit of the heating equipment within the time period t (unit: kW); Represents the upper limit of the total heat of the heat storage unit within the time period t (unit: J), Represents the lower limit of the total heat of the heat storage unit within the time period t (unit: J); Represents the upper limit of the stored thermal energy of the heat storage unit of the heating equipment within the time period t (unit: J); Represents the upper limit of the thermal energy released by the heating unit of the warming device within the time period t (unit: J).

[0115] The greenhouse wall can directly absorb the external solar radiation and also exchange heat with the air inside and outside the greenhouse. Therefore, the thermal dynamic model of the greenhouse wall can be as described in Equation (57) below:

[0116] A w I G,t β w = A w [α w (T w,t - T t ) + α' w (T w,t - T o,t )] (57)

[0117] In Equation (57): β w Represents the effective absorptivity of the wall surface to solar radiation; α' w Represents the direct convective heat transfer coefficient between the wall and the external air (unit: J / (m 2 ·°C)).

[0118] The surface soil can absorb the solar radiation passing through the greenhouse glass and the shading curtain, and can also exchange heat with the air inside the greenhouse and the deep soil. Assuming that the deep soil is always in a constant temperature state during the heat exchange process, that is, the temperature is basically unchanged, the thermal dynamic model of the greenhouse surface soil is as shown in Equation (58) below:

[0119]

[0120] In Equation (58): τ represents the light transmittance of the greenhouse glass; ω t Represents the shading degree of the shading curtain within the time period t; β s Represents the effective absorptivity of the soil to solar radiation; α cs Represents the convective heat transfer coefficient between the indoor surface soil and the deep constant temperature soil (unit: J / (m2·°C)); δ cs Represents the depth of the deep constant temperature soil (m); T cs Represents the temperature of the deep constant temperature soil (unit: °C).

[0121] Transpiration refers to the process by which water is lost from the surface of plant leaves in the form of water vapor to the atmosphere. It can reduce the temperature of the leaf surface and is also the main driving force for plants to absorb and transport water, which helps the transport of mineral elements and organic substances synthesized in the roots. This process is relatively complex and is not only affected by external environmental conditions but also controlled by the plants themselves. During the germination period, the plants do not have transpiration, that is, the heat absorbed by transpiration in this stage is 0, as shown in Equation (59) above:

[0122] Q p,t =0 (59)

[0123] In the embodiment of the present invention, the heat absorbed by the transpiration of leafy plants in the seedling stage and the vigorous growth stage can be expressed by the following equations (60) to (63):

[0124]

[0125] δ t =a1T t 2 +a2T t +a3 (61)

[0126]

[0127] L=c d (1-c τ )M d,t (63)

[0128] In equations (60) to (63), ΔH represents the potential heat energy of water vapor (in J / kg); δ t Represents the slope of the water vapor saturation curve; C' air Indicates the specific heat capacity of air at constant pressure (unit is J / (kg·K)); VPD t represents the air vapor pressure difference (in kPa); γ represents the measurement constant (in kPa / K); R hg Represents the relative humidity in the greenhouse; a1, a2, a3 represent the slope coefficients of the water vapor saturation curve.

[0129] 204. Based on the historical lighting data and historical meteorological data of the greenhouse system, a light intensity model of the greenhouse system is constructed.

[0130] In the embodiment of the present invention, the following all-day solar radiation intensity prediction model can be fitted based on historical lighting data and meteorological data to predict the solar radiation intensity at any time period in the future.

[0131] Furthermore, a solar radiation intensity prediction model for the greenhouse system can be constructed based on the historical lighting data and historical meteorological data of the greenhouse system; a lighting load model for the lighting load equipment can be constructed based on the changing relationship between the operating power and light intensity of the lighting load equipment; and a lighting intensity model for the greenhouse system can be constructed based on the solar radiation intensity prediction model and the lighting load model.

[0132] Specifically, the solar radiation intensity prediction model can be expressed as follows:

[0133]

[0134] In Equation (64): v s represents the seasonal influence factor of solar radiation; τ w represents the weather influence factor of solar radiation; τ p1 and τ p2 represent the predicted correction coefficients of solar radiation; b1, b2, b3, b4, b5, and b6 represent the fitting coefficients of solar radiation.

[0135] Combining the above solar radiation intensity prediction model (Equation (64)) with the relationship between the operating power of the lighting load device and the change in light intensity (Equation (31)), the light intensity model of the greenhouse system can be obtained as shown in the following Equations (65) to (67):

[0136]

[0137] ω min ≤ω t ≤ω max (67)

[0138] In Equations (65) to (67): I lim represents the lower limit of light intensity (W / m 2 ); ω max represents the upper limit of the light shading rate of the light shading curtain, and ω min represents the lower limit of the light shading rate of the light shading curtain.

[0139] 205. Based on the load curtailment device model, the light intensity model, and the thermodynamics model, an energy coupling matrix of the greenhouse system is constructed.

[0140] In the embodiment of the present invention, the above load curtailment device model, the above light intensity model, and the above thermodynamics model can be equivalently modeled through the predicted multi-energy flow energy coupling matrix to obtain the energy coupling matrix of the above greenhouse system.

[0141] Specifically, the above load curtailment device model, the above light intensity model, and the above thermodynamics model can be combined, and equivalent modeling can be performed by describing the multi-energy flow relationships of various loads and devices through the multi-energy flow energy coupling matrix to characterize the comprehensive external characteristics of the intelligent greenhouse system after participating in the integrated energy demand response. The energy coupling matrix represents the coupling relationship between the states of each device in the multi-energy flow system and the electricity, heat, and light loads. Specifically, the above energy coupling matrix is shown in the following Equations (68) and (69):

[0142]

[0143] Q c,t =Q s,t +Q w,t +Qo,t (69)

[0144] In Formulas (68) and (69): L represents the energy demand matrix, representing the change in energy demand in the system; E represents the energy input matrix, representing various energies generated in the system and the energy input from the outside; C represents the energy coupling matrix, indicating the relationship between the energy input matrix and the energy demand matrix; represents the total electrical energy demand of the system within time period t; represents the total heat energy demand of the system within time period t; represents the total light energy demand of the system within time period t; Q c,t represents the total heat exchange amount (J) between the greenhouse air and other objects within time period t; P w represents the operating power (kW) of the water supply and drainage equipment in the greenhouse system.

[0145] 206. According to the growth model and the energy coupling matrix, construct an energy consumption model for the growth of the target type of plants.

[0146] In the embodiments of the present invention, the energy consumption model of the above-mentioned target type of plants in the seedling stage and the energy consumption model of the above-mentioned target type of plants in the vigorous growth stage can be constructed by combining the above-mentioned growth model and the above-mentioned energy coupling matrix; according to the energy consumption model in the above-mentioned seedling stage and the energy consumption model in the above-mentioned vigorous growth stage, construct the energy consumption model for the growth of the above-mentioned target type of plants.

[0147] Specifically, by combining the energy coupling matrix of the greenhouse system established above with the growth model of the plants, an energy consumption model of the plants can be established to express the relationship between the change in the plant growth rate and the energy consumption of the greenhouse system, and further explore the relationship between the newly added profit of plant growth and the energy consumption cost of the greenhouse, so as to realize the energy consumption analysis of the plant growth process. Since plants cannot create profits during the germination period, the embodiments of the invention mainly explore the energy consumption models of plant growth in the seedling stage and the vigorous growth stage. More specifically, the relationship between the change in the plant growth rate and the change in the greenhouse heat load is shown in the following Formulas (70) to (74):

[0148]

[0149] In Formulas (70) to (74): Δφ phot,T represents the change in the photosynthetic rate when the temperature changes (the unit is kg·m -2 ·s -1 ); Δφ resp,T represents the change in the respiration rate when the temperature changes (the unit is kg·m -2 ·s -1 ).

[0150] Furthermore, the relationship between the change in plant growth and the change in greenhouse light load is shown in the following formulas (75) to (77):

[0151]

[0152]

[0153] In formulas (75) to (77): Δφ phot,I represents the change in photosynthetic rate when the light intensity changes (unit: kg·m -2 ·s -1 ).

[0154] From the above change rate of plant growth rate, it can be obtained that the plant growth rate should satisfy the constraint of the following formula (78):

[0155]

[0156] Thus, it can be obtained that under a certain condition, after changing the heat load and light load of the greenhouse by using greenhouse electrical equipment respectively, the change situation of the plant growth rate is as Figure 4 、 Figure 5 shown.

[0157] 207. Perform energy control on the greenhouse system based on the growth energy consumption model.

[0158] In the embodiment of the present invention, it can be assumed that the humidity and CO2 concentration in the greenhouse always remain unchanged. After establishing the energy consumption model of the greenhouse system under the growth requirements of plants, a scheduling plan is formulated with the goal of maximizing the total income of the greenhouse. Since meteorological changes will cause deviations in the prediction and control of temperature and light, it is also necessary to correct the model parameters according to the real-time updated environmental information. According to the dynamic characteristic differences of different types of loads, this patent will adopt multi-time scale rolling optimization to control the greenhouse environment: first, the temperature of the system is rolled and optimized every 30 minutes, and the scheduling plan for 24 hours after each rolling optimization; then, within these 30 minutes, the light intensity of the system is rolled and optimized every 5 minutes, and the scheduling plan for 1 hour after each rolling optimization.

[0159] Optionally, it is possible to calculate with the goal of maximizing the difference between the profit brought by the increase in dry matter of the above target plant and the energy consumption cost, and combine the above load transfer equipment model to optimize the above growth energy consumption model to obtain the energy consumption optimization model of the above greenhouse system; perform energy control on the above greenhouse system based on the above energy consumption optimization model.

[0160] Specifically, when the plant is in the germination period, the germination process cannot bring direct profit. Therefore, in this stage, the goal should be to ensure that the plant germination rate is within the set range and the overall electricity cost is the least. The objective function of its energy consumption optimization model is:[[]]

[0161]

[0162] In the formula: t0 represents the starting period of optimization; t end represents the ending period of optimization; P grid,t represents the power obtained from the power grid side within period t (unit: kW); λ grid,t represents the electricity price within period t (unit: ¥ / kWh).

[0163] As can be seen from formula (21), when the plant is in the seedling stage and the vigorous growth stage, the increase in the dry weight of the leaves within each period can bring direct profit. Therefore, in the embodiment of the present invention, with the maximum pure profit, that is, the difference between the profit brought by the increase in the dry weight of the plant and the electricity cost being the largest as the goal, the objective function of the energy optimization model is shown in the following formula (80):

[0164]

[0165] Furthermore, in addition to the above formulas (1)-(78), the constraint conditions of the energy optimization model should also include constraint conditions such as the power balance of the greenhouse system and the plant growth rate, as specifically shown in the following formulas (81) to (88):

[0166] P grid,t = P LED,t + P PTd,t + P PTc,t + P PF,t + P SF,t + P W (81)

[0167] D t min ≤ D t ≤ D t max (82)

[0168]

[0169] x E = {1,0} E∈{LED,PTd,PTc,PF} (88)

[0170] In formulas (81) to (88): P w represents the operating power of the water supply and drainage equipment in the greenhouse system (unit: kW); represents the upper limit of the set plant germination rate (unit: d -1 ), represents the lower limit of the set plant germination rate (unit: d -1 ); represents the upper limit of the set dry matter accumulation of the plant within period t (unit: kg·m-2 ) represents the lower limit of the dry matter accumulation of the plant within the set time period t (unit: kg·m -2 ) represents the upper limit of the power of the LED supplementary light (unit: kW), represents the lower limit of the power of the LED supplementary light (unit: kW); represents the upper limit of the power of the heating unit of the temperature increase equipment (unit: kW), represents the lower limit of the power of the heating unit of the temperature increase equipment (unit: kW); represents the upper limit of the power of the heat storage unit of the temperature increase equipment (unit: kW), represents the lower limit of the power of the heat storage unit of the temperature increase equipment (unit: kW); represents the upper limit of the power of the wet curtain - fan cooling system (unit: kW), represents the lower limit of the power of the wet curtain - fan cooling system (unit: kW); x E represents the operation status indication variable, 1 represents that the E - type equipment is in the operation state, and 0 represents that the E - type equipment is in the stop state.

[0171] Further, as Figure 6 shown, the execution process of the energy management and control system for intelligent greenhouses can be as follows: First, according to the current actual solar radiation intensity information, correct the parameters of the solar radiation intensity prediction model, and make a prediction of the solar radiation I within the optimization period and input it into the energy optimization model of the greenhouse system; input the outdoor air temperature data T G,t , the real - time electricity price λ o,t , information, and the relevant control information of the controllable load equipment (such as the working time range [t grid,t of the load - transferable equipment, the operation power limit range [P SF,s , t SF,e of various electrical equipment, etc.); then, according to the current growth status and dry matter accumulation M min of the plant, set the range of the plant growth rate at each time period through the plant growth model max (or d0 ); after that, based on the plant growth rate range, formulate the optimal energy - using plan according to the energy optimization model of the greenhouse system; finally, the intelligent controller executes the scheduling plan for the controllable equipment until entering the next optimization period. (or )

[0172] In the embodiment of the present invention, a load transfer device model and a load reduction device model of the greenhouse system, a light intensity model of the greenhouse system, and a thermodynamic model of the greenhouse system are constructed. An energy coupling matrix between electric power, heat, and light loads is constructed through the load reduction device model of the greenhouse system, the light intensity model of the greenhouse system, and the thermodynamic model of the greenhouse system. At the same time, a growth model for each stage of the growth cycle of the target type of plants is constructed. By analyzing the change relationships among the heat load, the light load, the temperature, the light intensity, and the dry matter accumulation rate of the plants, an energy consumption model for the growth of the target type of plants describing the relationship between the plant growth process and the energy consumption of the greenhouse is established. Through the energy consumption model for the growth of the target type of plants, the relationship between the newly added profit of plant growth and the energy consumption cost of the greenhouse can be obtained. Furthermore, the energy management and control of the greenhouse system can be carried out based on the relationship between the newly added profit of plant growth and the energy consumption cost of the greenhouse, while ensuring the growth rate and quality of the target plants and reducing the energy consumption cost of the greenhouse system.

[0173] At the same time, the embodiment of the present invention establishes an energy consumption optimization model for the greenhouse system. Based on the differences in the dynamic characteristics of plant growth in different stages and different types of loads, the real-time data such as the external solar radiation and temperature are continuously updated to correct the model parameters. Combining with the time-of-use electricity price information, a multi-time scale rolling optimization method is used with the maximum total income of the greenhouse in different stages as the goal to solve the optimal control variables, so as to achieve the maximum production benefit of the greenhouse.

[0174] The above-disclosed are only the preferred embodiments of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. An energy control method for an agricultural planting greenhouse system, characterized in that, The method includes the following steps: Construct growth models for each stage of the growth cycle of the target plant class according to the growth change laws of each stage of the growth cycle of the target plant class; and construct a load transferable equipment model and a load reducible equipment model for the greenhouse system according to the electricity consumption characteristics of the load equipment in the greenhouse system; and construct a thermo-dynamic model for the greenhouse system according to the dynamic relationship of energy interaction between the air in the greenhouse system and the surrounding environment; and construct a light intensity model for the greenhouse system according to the historical light data and historical meteorological data of the greenhouse system; the growth cycle of the target plant class includes three stages: germination stage, seedling stage, and vigorous growth stage; the load equipment in the greenhouse system includes load transferable equipment and load reducible equipment; Construct an energy coupling matrix for the greenhouse system based on the load reducible equipment model, the light intensity model, and the thermo-dynamic model; Construct an energy consumption model for the growth of the target plant class according to the growth model and the energy coupling matrix; Perform energy management and control on the greenhouse system based on the energy consumption model for growth; The step of constructing growth models for each stage of the growth cycle of the target plant class according to the growth change laws of each stage of the growth cycle of the target plant class specifically includes: extracting the growth curve of the target plant class in the germination stage according to the historical experimental data and relevant materials of the target plant class in the germination stage, and fitting the growth curve to obtain the germination rate model of the target plant class in the germination stage; and constructing a dynamic dry matter accumulation model of the target plant class according to the dry matter change laws under photosynthesis and respiration of the target plant class in the seedling stage and the vigorous growth stage; constructing growth models for each stage of the growth cycle of the target plant class based on the germination rate model in the germination stage and the dynamic dry matter accumulation model; The step of constructing a load transferable equipment model and a load reducible equipment model for the greenhouse system according to the electricity consumption characteristics of the load equipment in the greenhouse system specifically includes: constructing a load transferable equipment model for the greenhouse system according to the operation period, operation duration, and operation power of the load transferable equipment; and constructing a load reducible equipment model for the greenhouse system according to the germination rate of the target plant class in the germination stage, the dry matter accumulation increment of the target plant class in the seedling stage, and the dry matter accumulation increment of the target plant class in the vigorous growth stage; constructing a load transferable equipment model and a load reducible equipment model for the greenhouse system according to the load transferable equipment model and the load reducible equipment model; The steps of constructing the load reduction equipment model of the greenhouse system according to the germination rate of the target plant type in the germination stage, the dry matter accumulation increment of the target plant type in the seedling stage, and the dry matter accumulation increment of the target plant type in the vigorous growth stage include: taking the germination rate of the target plant type in the germination stage, the dry matter accumulation increment of the target plant type in the seedling stage, and the dry matter accumulation increment of the target plant type in the vigorous growth stage as constraint conditions, and taking the total load reduction amount of the light load equipment and the temperature load equipment as the objective function to construct the load reduction equipment model of the greenhouse system; Among them, the growth rate model in the germination stage is shown in formulas (1) to (3): D t = e d TE p IT q (1) In formulas (1) to (3), D t represents the seed germination rate at time period t; TE is the temperature effect factor; IT represents the photoperiod effect factor; p represents the temperature sensitivity coefficient of the germination period obtained by fitting; q represents the light sensitivity coefficient of the germination period obtained by fitting; T t represents the greenhouse air temperature at time period t; T gm represents the upper temperature limit for seed germination, T gb represents the lower temperature limit for seed germination; T go represents the optimum temperature for seed germination; t I represents the daily light duration of the seed soil; t Ib represents the daily critical light duration of the seed soil; t Io represents the daily optimum light duration of the seed soil; e is the natural constant; d is the proportionality coefficient obtained by fitting experimental data, used to adjust the overall output level of the seed germination rate model; The dry matter dynamic accumulation model is specifically shown in formula (5) below: In formula (5), M d,t represents the dry matter accumulation of land per unit area at time period t; φ phot represents the photosynthetic rate of land per unit area; φ resp represents the respiration rate of land per unit area; c α represents the photosynthesis conversion rate; c β represents the growth factor of plants at different growth stages; The load transfer equipment of the greenhouse system satisfies the following formulas (22) to (24): t SF,e -t SF,s ≥l SF (22) Among them, the operation duration of the load transfer device is l SF , and the allowable operation time range is TR SF = [t SF,s , t SF,e . The actual operation power at time t is P SF,t , the minimum operation power is The maximum operation power is The load reduction equipment model of the greenhouse system is as shown in formula (30) below: ΔP C,t = ΔP LED,t + ΔP PT,t + ΔP PF,t (30) Where: ΔP C,t represents the total system load reduction during period t; ΔP LED,t represents the load reduction of LED supplementary lights during period t; ΔP PT,t represents the load reduction of the temperature increase equipment during period t; ΔP PF,t represents the load reduction of the wet curtain - fan cooling system during period t; The load reduction amount ΔP of the LED supplementary light during the time period t LED,t As shown in the following formulas (32) and (33): In equations (32) and (33): represents the expected value of the illumination intensity of the LED supplementary light during time period t; represents the maximum load reduction of the LED supplementary light during time period t; I LED,t represents the actual value of the illumination intensity of the LED supplementary light during time period t; L LED represents the linearized power-illumination intensity factor of the LED supplementary light; The load reduction amount ΔP of the heating equipment during the time period t PT,t As shown in the following formulas (37) to (41): ΔP PT,t = ΔP PTc,t + ΔP PTd,t (37) In formulas (37) to (41): L PTd represents the linearized power-temperature factor of the heating unit of the temperature increase device; represents the expected value of the greenhouse temperature during period t; ρ air represents the air density; C air represents the specific heat capacity of air at constant pressure; V G represents the greenhouse volume; η r represents the absorption rate of the greenhouse air to the released heat energy; represents the maximum load reduction of the heating unit of the temperature increase device during period t; h d represents the enthalpy drop of the unit flow carrier in the heat release stage of the heating unit of the temperature increase device; h c,t represents the enthalpy increase of the unit flow carrier in the solar collector during period t; η PTd represents the unit power flow of the heating unit of the temperature increase device; ΔP PTc,t represents the change in the operating power of the heat storage unit of the temperature increase device during period t; ΔP PTd,t represents the change in the operating power of the heating unit of the temperature increase device during period t; The load reduction ΔP of the wet curtain - fan cooling system during period t PF,t As shown in the following formulas (45) to (47): In formulas (45) to (47): η PF represents the operating efficiency of the wet curtain - fan cooling system; L PF represents the linearized power - temperature factor of the wet curtain - fan cooling system; represents the maximum load reduction of the wet curtain - fan cooling system within time period t; T pad represents the temperature of the cold air passing through the wet curtain in the wet curtain - fan cooling system.

2. The energy control method of the agricultural planting greenhouse system according to claim 1, characterized in that The steps of constructing the dry matter dynamic accumulation model of the target plant type according to the dry matter change rules under photosynthesis and respiration of the target plant type in the seedling stage and the vigorous growth stage specifically include: Taking the dry matter accumulation rate as the growth rate index of the target plant type in the two stages of the seedling stage and the vigorous growth stage, constructing the dry matter dynamic accumulation model of the target plant type in the seedling stage according to the dry matter change rules under photosynthesis and respiration of the target plant type in the seedling stage; and constructing the dry matter dynamic accumulation model of the target plant type in the vigorous growth stage according to the dry matter change rules under photosynthesis and respiration of the target plant type in the vigorous growth stage; Constructing the dry matter dynamic accumulation model of the target plant type according to the dry matter dynamic accumulation model of the target plant type in the seedling stage and the dry matter dynamic accumulation model of the target plant type in the vigorous growth stage.

3. The energy control method of the agricultural planting greenhouse system according to claim 1, characterized in that, The steps of constructing the light intensity model of the greenhouse system according to the historical light data and historical meteorological data of the greenhouse system include: Constructing the solar radiation intensity prediction model of the greenhouse system according to the historical light data and historical meteorological data of the greenhouse system; Constructing the light load model of the light load equipment according to the change relationship between the operating power of the light load equipment and the light intensity; Constructing the light intensity model of the greenhouse system according to the solar radiation intensity prediction model and the light load model; Among them, the solar radiation intensity prediction model is shown in formula (64) below: In Equation (64): τ s represents the seasonal influence factor of solar radiation; τ w represents the weather influence factor of solar radiation; τ p1 and τ p2 represent the prediction correction coefficients of solar radiation; b1, b2, b3, b5, and b6 represent the fitting coefficients of solar radiation; The light intensity model of the greenhouse system is shown in formulas (65) to (67) below: ω min ≤ ω t ≤ ω max (67) In formulas (65) to (67): I t represents the light intensity of the land per unit area within the time period t; τ represents the light transmittance of the greenhouse glass; ω t represents the shading rate of the shading curtain within the time period t; P G,t represents the solar radiation intensity within the time period t; P LED,t represents the operating power of the LED supplementary light within the time period t; I lim represents the lower limit of the light intensity; ω max represents the upper limit of the shading rate of the shading curtain, ω min represents the lower limit of the shading rate of the shading curtain.

4. The energy control method of the agricultural planting greenhouse system according to claim 1, characterized in that, The steps of constructing the thermal power model of the greenhouse system according to the dynamic relationship of energy interaction between the air in the greenhouse system and the surrounding environment include: Constructing the thermal power model of the greenhouse system according to the thermodynamic effect and the greenhouse air heat change amount, the heat exchange amount between the greenhouse air and the greenhouse soil, the heat exchange amount between the greenhouse air and the greenhouse wall, the heat exchange amount between the greenhouse air and the outside air, the heat absorbed by plant transpiration, the surface soil temperature, and the wall temperature in each time period of the greenhouse system; The thermal power model of the greenhouse system is shown in the following equations (48) to (52): Q t = η r ·Q PTd,t + Q s,t + Q w,t + Q o,t - Q p,t - Q PF,t (48) Q s,t = α s A s (T s,t - T t ) (50) Q w,t = α w A w (T w,t - T t ) (51) Q o,t = (α g A g + α o A v )(T o,t - T t )(52) In equations (48) to (52): Q t represents the change in the heat of the greenhouse air during period t; Q PTd,t represents the heat released by the heating unit of the temperature increase device during period t; Q s,t represents the heat exchange amount between the greenhouse air and the greenhouse soil during period t; Q w,t represents the heat exchange amount between the greenhouse air and the greenhouse wall during period t; Q o,t represents the heat exchange amount between the greenhouse air and the outside air during period t; Q p,t represents the heat absorbed by plant transpiration during period t; Q PF,t represents the heat absorbed by the wet curtain - fan cooling system during period t; ρ air represents the air density; V G represents the greenhouse volume; C air represents the specific heat capacity of air at constant pressure; T t represents the actual value of the greenhouse temperature during period t; T s,t represents the surface soil temperature during period t; T w,t represents the wall surface temperature during period t; α s represents the direct convective heat transfer coefficient between the greenhouse air and the soil, α w represents the direct convective heat transfer coefficient between the greenhouse air and the wall, α o represents the direct convective heat transfer coefficient between the greenhouse air and the outside air; α g represents the indirect convective heat transfer coefficient between the greenhouse air and the outside air through the glass; A s represents the soil area, A w represents the greenhouse area, A g represents the glass area, A v represents the ventilation opening area; T o,t represents the outdoor air temperature during period t.

5. The energy control method of the agricultural planting greenhouse system according to claim 4, characterized in that The steps of constructing the energy coupling matrix of the greenhouse system based on the load curtailment equipment model, the light intensity model, and the thermal power model specifically include: Through the predicted multi-energy flow energy coupling matrix, equivalent modeling is performed on the load transferable equipment model, the load curtailment equipment model, the light intensity model, and the thermal power model to obtain the energy coupling matrix of the greenhouse system; The energy coupling matrix is shown in the following equations (68) and (69): Q c,t = Q s,t + Q w,t + Q o,t (69) In equations (68) and (69): L represents the energy demand matrix, which represents the variation of energy demand in the system; E represents the energy input matrix, which represents various energies generated in the system and the energies input from the outside; C represents the energy coupling matrix, which represents the relationship between the energy input matrix and the energy demand matrix. represents the total electrical energy demand of the system during period t; represents the total heat energy demand of the system during period t; represents the total light energy demand of the system during period t; represents the maximum flow rate of the flow carrier in the solar collector; A PT represents the total area of the solar collectors in the temperature increase device; β PT represents the effective absorption and storage rate of the solar collectors for solar radiation; η PTc represents the unit power flow rate of the heat storage unit of the temperature increase device; Q c,t represents the total heat exchange amount between the greenhouse air and other objects during period t; P w represents the operating power of the water supply and drainage equipment in the greenhouse system; T pad,t represents the temperature of the cold air passing through the wet curtain in the wet curtain - fan cooling system during period t; Q PTc,t represents the stored heat of the heat storage unit of the temperature increase device during period t; Q PTd,t represents the released heat of the heating unit of the temperature increase device during period t; τ represents the light transmittance of the greenhouse glass; I G,t represents the solar radiation intensity during period t.

6. The energy control method of the agricultural planting greenhouse system according to claim 5, characterized in that, The steps of constructing the energy consumption model for the growth of the target type of plants based on the growth model and the energy coupling matrix specifically include: Combining the growth model and the energy coupling matrix, constructing the energy consumption model of the target type of plants in the seedling stage and constructing the energy consumption model of the target type of plants in the vigorous growth stage; Based on the energy consumption model in the seedling stage and the energy consumption model in the vigorous growth stage, constructing the energy consumption model for the growth of the target type of plants; In the energy consumption model for growth, the relationship between the change in the plant growth rate and the change in the greenhouse heat load is shown in the following equations (70) to (74): In equations (70) to (74): c α represents the photosynthesis conversion rate; c β represents the growth factor of the plant at different growth stages; Δφ phot,T represents the change in photosynthesis rate when the temperature changes; Δφ resp,T represents the change in respiration rate when the temperature changes; Z M 、Z I 、Z T 、Z bs represent the functions related to dry matter, light, temperature, and other constants of photosynthesis; c c2 、c c3 represent the polynomial-related parameters of hydroxylation conductance; represents the CO2 concentration in the greenhouse; Γ represents the CO2 compensation point; c τ represents the proportion of root dry weight at different growth stages; T c2 represents the reference temperature of plant respiration; c s represents the respiration rate of the part of the plant other than the roots at the reference T c2 temperature; c r is the respiration rate of the roots of leafy vegetables at the reference temperature T c2 ; c resp represents the respiration maintenance factor at different growth stages; ΔZ T represents the change in the temperature-related function; ΔT t represents the change in the actual value of the greenhouse temperature within the time period t; represents the change in the total thermal energy demand of the system within the time period t.

7. The energy control method of the agricultural planting greenhouse system according to claim 6, characterized in that, The steps of performing energy management and control on the greenhouse system based on the energy consumption model for growth specifically include: Calculating with the maximum difference between the profit brought by the increase in the dry matter of the target plant and the energy consumption cost as the goal, and combining the load transferable equipment model, optimizing the energy consumption model for growth to obtain the energy consumption optimization model of the greenhouse system; Performing energy management and control on the greenhouse system based on the energy consumption optimization model; Among them, the objective function of the energy consumption optimization model is: Where: t0 represents the starting period of optimization; t end represents the ending period of optimization; P grid,t represents the power obtained from the grid side during period t; λ grid,t represents the electricity price during period t; Δt represents the change amount of period t, and f G represents the energy consumption optimization target.

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