Greenhouse crop growth simulation method and device

Abstract

The present invention provides a greenhouse crop growth simulation method and device. By setting up a virtual carbon pool and dynamically monitoring its reserves, a photosynthesis inhibition function affected by air temperature is constructed to more accurately determine the photosynthesis rate and net carbohydrate production of greenhouse crops. The cumulative weight of the greenhouse crop's structural dry matter within a target time period is then determined based on the carbohydrate-to-structural dry matter conversion factor. This facilitates determining the updated structural dry matter weight of the greenhouse crop, simulating greenhouse crop growth and accurately accounting for the adverse effects of extreme air temperatures, particularly extreme low temperatures, on crop growth. The greenhouse crop growth simulation method of the present invention also significantly improves the greenhouse crop growth model's compatibility with large-scale air temperature inputs and its control-oriented applicability.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to a greenhouse crop growth simulation method and device. Background Art

[0002] Greenhouses provide a controlled environment for crop growth. Most greenhouses feature controllable structural components or environmental conditioning devices, such as ventilation windows and heating systems. In practice, greenhouse environmental control is primarily based on the experience of the grower or equipment developer, as well as general knowledge of protected horticulture. Heuristic control is implemented using simple on-off controllers or PI-series controllers. To achieve the desired indoor environment, growers adjust the controller's setpoint values. These settings, which define the greenhouse's environmental trajectory or constrain equipment operation, directly impact greenhouse energy and resource consumption, as well as crop growth and development.

[0003] However, these settings cannot accurately explain future dynamics, that is, growers cannot know the specific impact of these settings and cannot accurately simulate the growth status of crops, which leads to inefficient greenhouse environmental control. Optimal control of the greenhouse environment can provide higher control efficiency by balancing the economic benefits of crop production with the operating costs of environmental control equipment during the crop growth period. Unlike traditional heuristic greenhouse environmental control methods, optimal control requires quantitative analysis of theoretical knowledge about greenhouses, environmental control equipment and crops, and requires prediction of future dynamics of the greenhouse environment and crops as well as weather changes, all of which are reflected by numerical dynamic models. The grower's goal is generally to maximize net profit, which is also described in detail by a mathematical cost function.

[0004] System models are the foundation for constructing optimal control models. For greenhouse environmental control, these primarily include greenhouse environmental models and crop growth models. In terms of simulating crop potential growth, crop growth models are mathematical algorithms that quantitatively and dynamically describe the crop growth, development, and yield formation processes in response to the environment, genetic characteristics, and production management. The performance of greenhouse optimal control systems depends largely on the accuracy and applicability of crop growth models.

[0005] However, existing greenhouse crop models fail to fully account for the adverse effects of extreme temperatures on crop growth and therefore cannot fully meet the application requirements of optimal control. For one thing, existing greenhouse crop models are primarily designed for greenhouses with robust environmental control capabilities. They are not applicable to lower-end greenhouses, including low-end multi-span greenhouses, solar greenhouses, and plastic greenhouses, where crop growth conditions are poor and extreme temperatures are frequent. For example, some greenhouse crop models only allow for an input temperature range of 5–40°C, but in lower-end greenhouses, indoor temperatures can easily drop below 5°C or exceed 40°C. Another example is that in some greenhouse crop models, extreme nighttime low temperatures are described as beneficial for dry matter accumulation due to reduced respiratory expenditure, but in reality, they inhibit crop growth. On the other hand, optimal control allows for a certain range of greenhouse air temperature fluctuations, seeking to trade off minor yield or quality losses for greater net benefits. For example, during extremely low temperatures, a trade-off favors lower greenhouse air temperatures for optimal crop growth. Therefore, for control purposes, greenhouse crop growth models need to accurately describe the impact of extreme high or low temperatures on crop production processes and accommodate a wider range of air temperature inputs.

[0006] Conventional greenhouse crop models cannot fully account for the adverse effects of greenhouse environmental parameters on crop growth, particularly those associated with extreme temperatures, and therefore cannot fully meet the application requirements for optimal control. Therefore, a greenhouse crop growth simulation method is urgently needed to more accurately simulate crop growth under greenhouse environmental parameters. Summary of the Invention

[0007] The present invention provides a greenhouse crop growth simulation method and device to solve the defect in the prior art that it is difficult to accurately simulate crop growth under extreme temperatures, and to achieve the effect of accurately simulating crop growth under various greenhouse environments.

[0008] The present invention provides a greenhouse crop growth simulation method, comprising: determining environmental data in a greenhouse within a target period starting from a target time, the structural dry matter weight of the greenhouse crops at the target time, and the storage volume of a virtual carbon pool corresponding to the greenhouse crops at the target time, wherein the environmental data includes at least air temperature, light radiation, and carbon dioxide concentration;

[0009] determining a photosynthesis inhibition function and a photosynthesis rate of the greenhouse crops during the target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the storage of the virtual carbon pool at the target time, and the maximum storage of the virtual carbon pool at the target time;

[0010] Determining the cumulative weight of structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and a conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis losses;

[0011] The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. When the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of the greenhouse crops during the target period is inhibited.

[0012] The maximum reserves of the virtual carbon pool at the target moment are positively correlated with the structural dry matter weight of the greenhouse crops at the target moment; the change in reserves of the virtual carbon pool during the target period is associated with the structural dry matter weight of the greenhouse crops at the target moment, the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and the potential relative growth rate of the greenhouse crops during the target period.

[0013] According to a greenhouse crop growth simulation method provided by the present invention, after determining the cumulative weight of structural dry matter of the greenhouse crops within the target period, the method further comprises:

[0014] Based on the cumulative weight of the structural dry matter of the greenhouse crops during the target period, the weight of the structural dry matter of the greenhouse crops at the target moment, and the loss weight of the crops during the target time period, the updated structural dry matter weight of the greenhouse crops is determined, and based on the change in the reserves of the virtual carbon pool during the target period and the reserves of the virtual carbon pool at the target moment, the updated reserves of the virtual carbon pool are determined; the loss weight includes at least the weight of the structural dry matter of the greenhouse crops removed by agricultural operations.

[0015] According to a greenhouse crop growth simulation method provided by the present invention, the cumulative weight of the structural dry matter of the greenhouse crops in the target period is determined by the following formula:

[0016]

[0017] Among them, dX d is the cumulative weight of the structural dry matter of greenhouse crops during the target period; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; cβ is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c α The conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period.

[0018] According to a greenhouse crop growth simulation method provided by the present invention, the photosynthesis inhibition function of the greenhouse crop in the target period is determined by the following formula:

[0019]

[0020] Among them, X d is the structural dry matter weight of greenhouse crops at the target moment; h buf is the photosynthesis inhibition function of greenhouse crops during the target period; RGR max is the potential relative growth rate of greenhouse crops under the environmental data; C buf is the reserve of the virtual carbon pool at the target time; C buf,max is the maximum reserve of the virtual carbon pool at the target time.

[0021] According to a greenhouse crop growth simulation method provided by the present invention, the change in the carbohydrate reserves in the virtual carbon pool is determined by the following formula:

[0022]

[0023] Among them, X d is the structural dry matter weight of the greenhouse crops at the target time; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; c β is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c α The conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period, RGR max is the potential relative growth rate of greenhouse crops under the environmental data; dC buf is the reserve change of the virtual carbon pool during the target period; (0≤C buf ≤C buf,max ) represents a state constraint on the target time and the updated reserves of the virtual carbon pool.

[0024] According to a greenhouse crop growth simulation method provided by the present invention, the photosynthesis rate of the greenhouse crop in the target period is expressed as the product A of the photosynthesis rate and the photosynthesis inhibition function. C ·h buf ,in:

[0025]

[0026] Among them, X d is the structural dry matter weight of greenhouse crops at the target time, k PAR is the extinction coefficient of crop canopy to photosynthetically active radiation; SLA is specific leaf area; σ r is the ratio of root dry matter weight to plant dry matter weight; X c is the carbon dioxide concentration in the greenhouse; X t is the air temperature of the greenhouse, Γ T20 Q is the carbon dioxide compensation point at 20℃; 10,Γ is Q for the carbon dioxide compensation point 10 Factor; r b is the boundary layer resistance; r s is the stomatal resistance; r c is the carboxylation resistance; ε0 is the light energy utilization efficiency in the absence of photorespiration under high carbon dioxide concentration conditions; σ PAR is the ratio of photosynthetically active radiation to shortwave radiation; I is the amount of shortwave radiation above the crop canopy.

[0027] According to a greenhouse crop growth simulation method provided by the present invention, the carboxylation resistance is determined by the following formula:

[0028] r c =c rc,1 ·X t 2 +c rc,2 ·X t +c rc,3 ;

[0029] Among them, c rc,1 、c rc,2 and c rc,3 is the coefficient of the second-order polynomial fitting function of carboxylation resistance, c rc,1 =0.28, c rc,2 =26.04, c rc,3 =784.46.

[0030] The present invention also provides a greenhouse crop growth simulation device, comprising:

[0031] a first processing module, configured to determine environmental data within the greenhouse within a target period starting from a target time, the structural dry matter weight of greenhouse crops at the target time, and the storage volume of a virtual carbon pool corresponding to the greenhouse crops at the target time, wherein the environmental data includes at least air temperature, light radiation, and carbon dioxide concentration;

[0032] a second processing module, configured to determine a photosynthesis inhibition function and a photosynthesis rate of the greenhouse crops during the target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the storage of the virtual carbon pool at the target time, and the maximum storage of the virtual carbon pool at the target time;

[0033] a third processing module, configured to determine the cumulative weight of the structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and a conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis loss;

[0034] The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. When the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of the greenhouse crops during the target period is inhibited.

[0035] The maximum reserves of the virtual carbon pool at the target moment are positively correlated with the structural dry matter weight of the greenhouse crops at the target moment; the change in reserves of the virtual carbon pool during the target period is associated with the structural dry matter weight of the greenhouse crops at the target moment, the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and the potential relative growth rate of the greenhouse crops during the target period.

[0036] According to a greenhouse crop growth simulation device provided by the present invention, the greenhouse crop growth simulation device also includes a fourth processing module, which is used to determine the updated structural dry matter weight of the greenhouse crops based on the cumulative weight of the structural dry matter of the greenhouse crops in the target period, the structural dry matter weight of the greenhouse crops at the target moment, and the loss weight of the crops in the target time period, and determine the updated reserve of the virtual carbon pool based on the reserve change of the virtual carbon pool in the target period and the reserve of the virtual carbon pool at the target moment; the loss weight at least includes the weight of the structural dry matter of the greenhouse crops removed by agricultural operations.

[0037] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the greenhouse crop growth simulation methods described above is implemented.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the greenhouse crop growth simulation methods described above.

[0039] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the greenhouse crop growth simulation methods described above.

[0040] The greenhouse crop growth simulation method and device provided by the present invention set up a virtual carbon pool and dynamically monitor the reserves of the virtual carbon pool, construct a photosynthesis inhibition function affected by air temperature, so as to determine a more accurate photosynthesis rate and net carbohydrate production of greenhouse crops. Then, based on the conversion factor of carbohydrates into structural dry matter, the cumulative weight of the structural dry matter of the greenhouse crops in the target period is obtained, thereby facilitating the determination of the updated structural dry matter weight of the greenhouse crops, realizing the simulation of greenhouse crop growth, and accurately explaining the adverse effects of extreme air temperatures, especially extreme low temperatures, on crop growth.

[0041] Furthermore, the greenhouse crop growth simulation method and device provided by the present invention optimize the carboxylation resistance expression, so that the greenhouse crop growth simulation can allow a larger air temperature input range, greatly improving the compatibility of the greenhouse crop growth model with large-scale air temperature input and its control-oriented applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is one of the flow charts of the greenhouse crop growth simulation method provided by the present invention;

[0044] Figure 2 This is the second flow chart of the greenhouse crop growth simulation method provided by the present invention;

[0045] Figure 3 This is a comparison chart of the simulation effects of the greenhouse crop growth simulation method provided by the present invention;

[0046] Figure 4 This is a schematic diagram of the storage changes of the virtual carbon pool in the greenhouse crop growth simulation method provided by the present invention;

[0047] Figure 5 This is a schematic structural diagram of the greenhouse crop growth simulation device provided by the present invention;

[0048] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] The following combination Figures 1-6 The greenhouse crop growth simulation method and device of the present invention are described.

[0051] The greenhouse crop growth simulation method according to the present invention can be executed by a processor. Of course, in other embodiments, the execution entity can also be a server. The execution entity type is not limited herein. The greenhouse crop growth simulation method according to the present invention will be described below using a processor as an example.

[0052] like Figure 1 As shown, the greenhouse crop growth simulation method according to the embodiment of the present invention mainly includes step 110, step 120 and step 130.

[0053] Step 110 , determining the environmental data in the greenhouse within the target period starting from the target time, the structural dry matter weight of the greenhouse crops at the target time, and the storage capacity of the virtual carbon pool corresponding to the greenhouse crops at the target time.

[0054] It should be noted that the target time period is a simulated cycle of crop growth, the target time period can be set according to actual needs, and the target time is the starting time of the target time period.

[0055] For example, the target time period may be 5 minutes or 10 minutes long, and there is no limitation on the target time period here.

[0056] Environmental data at least include air temperature, light radiation and carbon dioxide concentration.

[0057] It is understandable that air temperature affects the temperature of the crop plants, and thus affects the various metabolic activities, growth, and development of the crop plants. In some embodiments, the air temperature can be directly used as the temperature of the crop plants to analyze the metabolic activities of the crop plants.

[0058] In this embodiment, a temperature sensor may be provided in the greenhouse to obtain the air temperature, thereby enabling real-time monitoring of the greenhouse air temperature.

[0059] As an important environmental factor, light radiation has a wide range of regulatory effects on plant growth and development. The light-induced and regulated development of plants is called photomorphogenesis, and shortwave light, in particular, has a significant impact on plant growth and development.

[0060] In this embodiment, a radiation sensor may be used to monitor light radiation and obtain shortwave radiation or photosynthetically active radiation above the crop canopy.

[0061] As an important environmental factor, carbon dioxide concentration also has a wide range of impacts on plant growth and development, especially affecting the efficiency of plant photosynthesis, and thus affecting the reaction of plant organic matter synthesis.

[0062] In this embodiment, a carbon dioxide sensor may be installed in the greenhouse to monitor the carbon dioxide concentration in the greenhouse.

[0063] In this embodiment, the environmental data can be determined by real-time monitoring of the environment in the greenhouse, thereby obtaining accurate crop growth simulation results in the greenhouse environment.

[0064] Of course, in other embodiments, the environmental data can also be customized. By customizing the environmental data, more environmental types can be covered, thereby obtaining richer simulation results. There is no restriction on the method of determining the environmental data here.

[0065] The structural dry matter weight of the greenhouse crops at the target time is used to represent the structural dry matter weight of the greenhouse crops at the starting time of the target time period.

[0066] The structural dry matter weight of greenhouse crops at the target moment can be obtained by estimation. For example, plants with a growth state similar to that of the plants to be simulated can be processed and weighed to obtain a reference structural dry matter weight of the greenhouse crops to be simulated, and then the structural dry matter weight of the crops in the greenhouse can be estimated based on the reference structural dry matter weight.

[0067] It should be noted that the virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops.

[0068] The storage capacity of the virtual carbon pool corresponding to greenhouse crops at the target time can be obtained by updating the storage capacity of the virtual carbon pool in the previous period. During the entire simulation process, at the beginning of the simulation, the initial capacity of the virtual carbon pool can be zero, and the storage capacity is continuously and dynamically updated during the simulation.

[0069] In this embodiment, the virtual carbon pool is used to regulate the rate of photosynthesis. When the reserves of the virtual carbon pool at the target time are greater than or equal to the maximum reserves of the virtual carbon pool at the target time, the photosynthesis of greenhouse crops in the target period is inhibited.

[0070] like Figure 2 As shown, carbohydrates in the virtual carbon pool simultaneously flow to sustain respiration, growth conversion, and dry matter distribution. Under extreme temperature conditions, greenhouse crops and their organs experience reduced potential growth rates, reduced growth potential, and suppressed dry matter distribution in both the aboveground and root parts of the crop. This inhibits crop dry matter distribution, correspondingly reducing the flow of carbohydrates from the virtual carbon pool to the growth conversion process, leading to carbohydrate accumulation in the virtual carbon pool. When the carbohydrate reserves in the virtual carbon pool exceed the maximum reserves, photosynthesis is inhibited, affecting structural dry matter accumulation and crop growth.

[0071] The maximum storage of the virtual carbon pool at the target time is positively correlated with the structural dry matter weight of greenhouse crops at the target time, that is, the maximum storage of the virtual carbon pool of greenhouse crops with different structural dry matter weights is also different.

[0072] The storage change of the virtual carbon pool during the target period is related to the structural dry matter weight of greenhouse crops at the target moment, the photosynthesis rate of greenhouse crops during the target period, the maintenance respiration rate of greenhouse crops during the target period, and the potential relative growth rate of greenhouse crops during the target period.

[0073] In some embodiments, the change in the carbohydrate reserve in the virtual carbon pool is determined by the following formula:

[0074]

[0075] Among them, X d is the structural dry matter weight of greenhouse crops at the target time; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; c β is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c αThe conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period, RGR max is the potential relative growth rate of greenhouse crops under environmental data; dC buf is the change in the reserves of the virtual carbon pool during the target period; (0≤C buf ≤C buf,max ) represents the state constraints on the target time and the updated virtual carbon pool reserves.

[0076] Step 120, based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the reserves of the virtual carbon pool at the target time, and the maximum reserves of the virtual carbon pool at the target time, determine the photosynthesis inhibition function and photosynthesis rate of the greenhouse crops in the target period.

[0077] It should be noted that the photosynthesis rate is a way of expressing the strength of photosynthesis, also known as "photosynthetic intensity". The photosynthesis rate can be expressed by the weight of carbon dioxide absorbed or carbohydrates produced by greenhouse crops per unit time, per unit leaf area, or per unit cultivated area. When different parameters are used for characterization, the meaning of the photosynthesis rate is also different. For example, in this embodiment, the photosynthesis rate of the crop A C It indicates the amount of carbon dioxide assimilated and absorbed by all greenhouse crops through photosynthesis per unit time and per unit cultivated area.

[0078] In this embodiment, the virtual carbon pool is used to regulate the rate of photosynthesis. Under extreme temperature conditions, the potential growth rate of greenhouse crops and their organs decreases, inhibiting dry matter distribution. This in turn reduces the flow of carbohydrates from the virtual carbon pool to the growth and transformation stages, leading to carbohydrate accumulation in the virtual carbon pool. When the carbohydrate reserves in the virtual carbon pool reach the maximum virtual carbon pool capacity, photosynthesis is inhibited. The degree of photosynthesis inhibition can be represented by a photosynthesis inhibition function.

[0079] The photosynthesis inhibition function of greenhouse crops during the target period is determined by the following formula:

[0080]

[0081] Among them, X d is the structural dry matter weight of greenhouse crops at the target time; h buf is the photosynthesis inhibition function of greenhouse crops during the target period; RGR max is the potential relative growth rate of greenhouse crops under environmental data; C buf is the storage of the virtual carbon pool at the target time; Cbuf,max is the maximum storage of the virtual carbon pool at the target time.

[0082] It should be noted that the photosynthesis rate obtained according to the calculation formula of photosynthesis rate in the prior art does not take into account the impact of the conversion of carbohydrates inside crops on the photosynthesis rate under extreme temperatures.

[0083] In this embodiment, the photosynthesis rate of greenhouse crops in the target period is expressed as the product of the photosynthesis rate and the photosynthesis inhibition function: A C ·h buf ,in:

[0084]

[0085] Among them, X d is the structural dry matter weight of greenhouse crops at the target time, k PAR is the extinction coefficient of crop canopy to photosynthetically active radiation; SLA is specific leaf area; σ r is the ratio of root dry matter weight to plant dry matter weight; X c is the carbon dioxide concentration in the greenhouse; X t is the air temperature of the greenhouse, Γ T20 Q is the carbon dioxide compensation point at 20℃; 10,Γ is Q for the carbon dioxide compensation point 10 Factor; r b is the boundary layer resistance; r s is the stomatal resistance; r c is the carboxylation resistance; ε0 is the light energy utilization efficiency in the absence of photorespiration under high carbon dioxide concentration conditions; σ PAR is the ratio of photosynthetically active radiation to shortwave radiation; I is the amount of shortwave radiation above the crop canopy.

[0086] It should be noted that A C is a calculation formula for the photosynthesis rate. However, in the embodiment of the present invention, taking into account the situation where photosynthesis is inhibited, a photosynthesis inhibition function is introduced to calculate the photosynthesis rate of greenhouse crops in the target period, thereby obtaining a more accurate photosynthesis rate.

[0087] In the above calculation company, the carboxylation resistance is determined by the following formula:

[0088] r c =c rc,1 ·X t 2 +c rc,2 ·X t +c rc,3 ;

[0089] Among them, crc,1 、c rc,2 and c rc,3 is the coefficient of the second-order polynomial fitting function of carboxylation resistance, c rc,1 =0.28, c rc,2 =26.04, c rc,3 =784.46.

[0090] In this embodiment, the parameters of the carboxylation resistance expression are optimized, thereby enabling the calculation model to allow a larger temperature input range, thereby simulating crop growth under extreme temperature conditions more accurately.

[0091] Step 130 , determining the cumulative weight of the structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and the conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis loss.

[0092] It should be noted that greenhouse crops produce carbohydrates through photosynthesis, and greenhouse crops consume carbohydrates during respiration to maintain normal vital signs. In addition, greenhouse crops also need to consume carbohydrates during growth, respiration, and the synthesis and transformation of crop structural dry matter.

[0093] The maintenance respiration rate indicates the rate of maintenance respiration required for greenhouse crops to maintain normal vital signs. The carbohydrate conversion factor (CF) determined by growth respiration and synthetic losses indicates the conversion factor required to convert carbohydrates remaining after maintenance respiration into structural dry matter during greenhouse crop growth.

[0094] The cumulative weight of structural dry matter of greenhouse crops during the target period is determined by the following formula:

[0095]

[0096] Among them, dX d is the cumulative weight of structural dry matter of greenhouse crops during the target period; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; c β is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c α The conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period.

[0097] In this embodiment, the amount of carbohydrates produced by photosynthesis of greenhouse crops can be determined based on the photosynthesis rate of greenhouse crops during the target period under extreme temperature conditions, and the carbohydrates consumed by greenhouse crops for maintenance respiration during the target period can be determined based on the maintenance respiration rate of greenhouse crops during the target period. Furthermore, based on the conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis loss, the cumulative weight of structural dry matter of greenhouse crops during the target period converted from the remaining carbohydrates can be determined.

[0098] According to an embodiment of the present invention, a greenhouse crop growth simulation method is provided. By setting a virtual carbon pool and dynamically monitoring the reserves of the virtual carbon pool, a photosynthesis inhibition function affected by air temperature is constructed to determine a more accurate photosynthesis rate and net carbohydrate production of greenhouse crops. Then, based on the conversion factor of carbohydrates into structural dry matter, the cumulative weight of the structural dry matter of the greenhouse crops in the target period is obtained, and then the structural dry matter weight of the greenhouse crops is determined to be updated, thereby simulating the growth of greenhouse crops and accurately explaining the adverse effects of extreme air temperatures, especially extreme low temperatures, on crop growth. In some embodiments, after determining the cumulative weight of the structural dry matter of the greenhouse crops in the target period, the greenhouse crop growth simulation method of the embodiment of the present invention further includes: determining the structural dry matter weight of the greenhouse crops updated based on the cumulative weight of the structural dry matter of the greenhouse crops in the target period, the structural dry matter weight of the greenhouse crops at the target time, and the weight loss of the crops in the target time period, and determining the updated reserves of the virtual carbon pool based on the reserve change of the virtual carbon pool in the target period and the reserve of the virtual carbon pool at the target time.

[0099] It should be noted that the weight loss includes at least the weight of structural dry matter of greenhouse crops removed by agricultural operations, such as defoliation and pruning.

[0100] In the process of simulating crop growth, a first-order nonlinear ordinary differential equation containing two inputs, time and crop growth status, can be constructed. Preferably, a fourth-order to fifth-order Runge-Kutta algorithm is used for numerical solution, and the crop growth status output including the structural dry matter weight of greenhouse crops is output.

[0101] On this basis, monitoring simulation is carried out according to multiple target time periods. After the simulation of the current target time period is completed, the cumulative weight of the structural dry matter of the greenhouse crops in the target time period and the structural dry matter weight of the greenhouse crops at the target moment are added together, and the loss weight of the crops in the target time period is subtracted to determine the updated structural dry matter weight of the greenhouse crops after a monitoring simulation cycle. At the same time, based on the change in the reserves of the virtual carbon pool in the target time period and the reserves of the virtual carbon pool at the target moment, the updated reserves of the virtual carbon pool are determined to provide initial input for the monitoring simulation of the next time period.

[0102] Under the greenhouse crop growth model architecture constructed by the greenhouse crop growth simulation method of an embodiment of the present invention, the structural dry matter accumulation of crops and the changes in carbohydrate storage in the virtual carbon pool use the same material flow path, but have two different and parallel material flow logics. More specifically, there is no restriction on the material flow of dry matter accumulation after photosynthesis, and only the photosynthesis rate may be inhibited. In the logic of photosynthetic inhibition, the material flow from the virtual carbon pool to the growth conversion and distribution link is limited by the potential growth rate of the crop. At the same time, this material flow does not represent the actual growth rate of the crop, but only affects the storage status of the virtual carbon pool.

[0103] In this embodiment, the crop growth state is described by a single state variable, the model description method is concise, the amount of calculation can be reduced, and the simulator runs efficiently.

[0104] like Figure 3 As shown, Figure 3 The figure is a schematic diagram showing a comparison between a simulated value of crop dry matter weight obtained by simulating the growth of a greenhouse crop using the greenhouse crop growth simulation method according to an embodiment of the present invention and an actual measured value.

[0105] The simulation effects of the present invention are demonstrated below using a typical solar greenhouse in northern China for winter lettuce cultivation as an example. In this example, a typical solar greenhouse lacks environmental control equipment and an automated control system. It possesses only two essential controllable structural components: top and side windows and a thermal blanket. Environmental control is entirely manual and primarily based on the grower's experience, resulting in limited controllability.

[0106] In this case, the greenhouse environment inputs for the greenhouse crop growth simulation program include air temperature, CO2 concentration, and shortwave radiation, which last for 45 days. Extreme low temperatures occur occasionally during this period, with the lowest air temperature reaching 3.9°C. The crop structural dry matter mass at the target time is 0.0020 kg m -2 The initial value of carbohydrate storage in the virtual carbon pool is set to 0 kg m -2 .

[0107] Figure 3The output of the greenhouse crop growth simulation program, namely, the structural dry matter weight of greenhouse crops, is shown. For most of the time, the simulated values for the structural dry matter weight of greenhouse crops fall within the 95% confidence interval of the measured values, with a relative root mean square error (RRMSE) of 11.97%, indicating good model performance.

[0108] Figure 4 The graph shows the changes in carbohydrate storage in the virtual carbon pool, an intermediate variable, during greenhouse crop growth simulation. It shows that during the early and middle stages of crop growth, carbohydrate accumulation in the carbon pool is frequently limited by its maximum capacity. This indicates that photosynthesis is inhibited by factors such as low temperatures. This demonstrates that the greenhouse crop growth simulation method of the present invention is functioning as intended, enabling the model to accurately account for the adverse effects of extreme air temperatures, particularly extreme low temperatures, on crop growth.

[0109] When the target time period is set to a relatively short duration, the greenhouse crop growth simulation method of the present invention can simulate instantaneous changes in crop growth, closely resembling actual crop growth conditions. This method shares the same timescale as greenhouse environmental changes and greenhouse environmental models, allowing for seamless integration and facilitating the development of greenhouse environment optimal control algorithms. Therefore, the present invention improves the applicability of greenhouse crop growth models for control purposes.

[0110] The greenhouse crop growth simulation device provided by the present invention is described below. The greenhouse crop growth simulation device described below and the greenhouse crop growth simulation method described above can be referenced to each other.

[0111] like Figure 5 As shown, the greenhouse crop growth simulation device according to the embodiment of the present invention mainly includes a first processing module 510 , a second processing module 520 and a third processing module 530 .

[0112] The first processing module 510 is used to determine environmental data in the greenhouse within a target period starting from a target time, the structural dry matter weight of the greenhouse crops at the target time, and the storage volume of the virtual carbon pool corresponding to the greenhouse crops at the target time, wherein the environmental data includes at least air temperature, light radiation, and carbon dioxide concentration;

[0113] The second processing module 520 is configured to determine a photosynthesis inhibition function and a photosynthesis rate of the greenhouse crops within a target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the storage of the virtual carbon pool at the target time, and the maximum storage of the virtual carbon pool at the target time;

[0114] The third processing module 530 is configured to determine the cumulative weight of the structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and the conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis losses.

[0115] The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. If the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of greenhouse crops during the target period is inhibited.

[0116] The maximum storage of the virtual carbon pool at the target time is positively correlated with the structural dry matter weight of greenhouse crops at the target time; the storage change of the virtual carbon pool during the target period is associated with the structural dry matter weight of greenhouse crops at the target time, the photosynthesis rate of greenhouse crops during the target period, the maintenance respiration rate of greenhouse crops during the target period, and the potential relative growth rate of greenhouse crops during the target period.

[0117] According to the greenhouse crop growth simulation device provided by an embodiment of the present invention, a virtual carbon pool is set up and the reserves of the virtual carbon pool are dynamically monitored, thereby constructing a photosynthesis inhibition function affected by air temperature to determine a more accurate photosynthesis rate and net carbohydrate production of greenhouse crops. Then, based on the conversion factor of carbohydrates into structural dry matter, the cumulative weight of the structural dry matter of the greenhouse crops in the target period is obtained, thereby facilitating the determination of the updated structural dry matter weight of the greenhouse crops, realizing the simulation of greenhouse crop growth, and accurately explaining the adverse effects of extreme air temperatures, especially extreme low temperatures, on crop growth.

[0118] In some embodiments, the greenhouse crop growth simulation device of an embodiment of the present invention also includes a fourth processing module, which is used to determine the updated structural dry matter weight of the greenhouse crops based on the cumulative weight of the structural dry matter of the greenhouse crops in the target period, the structural dry matter weight of the greenhouse crops at the target moment, and the loss weight of the crops in the target time period, and determine the reserves of the updated virtual carbon pool based on the change in the reserves of the virtual carbon pool in the target period and the reserves of the virtual carbon pool at the target moment; the loss weight includes at least the weight of the structural dry matter of the greenhouse crops removed by agricultural operations.

[0119] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6As shown, the electronic device may include: a processor (processor) 610, a communication interface (Communications Interface) 620, a memory (memory) 630 and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call the logic instructions in the memory 630 to execute the greenhouse crop growth simulation method, which includes: determining the environmental data in the greenhouse within the target period from the target moment, the structural dry matter weight of the greenhouse crops at the target moment, and the reserves of the virtual carbon pool corresponding to the greenhouse crops at the target moment, the environmental data at least including air temperature, light radiation and carbon dioxide concentration; based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target moment, the reserves of the virtual carbon pool at the target moment and the maximum reserves of the virtual carbon pool at the target moment, determining the photosynthesis inhibition function and the photosynthesis rate of the greenhouse crops in the target period; based on the photosynthesis rate of the greenhouse crops in the target period, the maintenance respiration rate of the greenhouse crops in the target period and the conversion of carbohydrates into structural dry matter determined by growth respiration and material synthesis loss The conversion factor determines the cumulative weight of the structural dry matter of greenhouse crops during the target period; the virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops; the virtual carbon pool is used to regulate the rate of photosynthesis; when the reserves of the virtual carbon pool at the target time are greater than or equal to the maximum reserves of the virtual carbon pool at the target time, the photosynthesis of greenhouse crops during the target period is inhibited; the maximum reserves of the virtual carbon pool at the target time are positively correlated with the weight of the structural dry matter of greenhouse crops at the target time; the change in the reserves of the virtual carbon pool during the target period is associated with the weight of the structural dry matter of greenhouse crops at the target time, the photosynthesis rate of greenhouse crops during the target period, the maintenance respiration rate of greenhouse crops during the target period, and the potential relative growth rate of greenhouse crops during the target period.

[0120] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0121] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the greenhouse crop growth simulation method provided by the above methods, which includes: determining the environmental data in the greenhouse within the target period from the target moment, the structural dry matter weight of the greenhouse crops at the target moment, and the reserves of the virtual carbon pool corresponding to the greenhouse crops at the target moment, the environmental data at least including air temperature, light radiation and carbon dioxide concentration; based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target moment, the reserves of the virtual carbon pool at the target moment and the maximum reserves of the virtual carbon pool at the target moment, determining the photosynthesis inhibition function and photosynthesis rate of the greenhouse crops within the target period; based on the photosynthesis rate of the greenhouse crops within the target period, the maintenance respiration rate of the greenhouse crops within the target period The cumulative weight of the structural dry matter of greenhouse crops during the target period is determined by the conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis losses. The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. When the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of greenhouse crops during the target period is inhibited. The maximum storage of the virtual carbon pool at the target time is positively correlated with the weight of the structural dry matter of greenhouse crops at the target time. The change in the storage of the virtual carbon pool during the target period is associated with the weight of the structural dry matter of greenhouse crops at the target time, the photosynthesis rate of greenhouse crops during the target period, the maintenance respiration rate of greenhouse crops during the target period, and the potential relative growth rate of greenhouse crops during the target period.

[0122] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to execute the greenhouse crop growth simulation method provided by the above-mentioned methods, the method comprising: determining the environmental data in the greenhouse within a target period from a target moment, the structural dry matter weight of the greenhouse crops at the target moment, and the reserves of the virtual carbon pool corresponding to the greenhouse crops at the target moment, the environmental data comprising at least air temperature, light radiation, and carbon dioxide concentration; determining the photosynthesis inhibition function and photosynthesis rate of the greenhouse crops within the target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target moment, the reserves of the virtual carbon pool at the target moment, and the maximum reserves of the virtual carbon pool at the target moment; determining the photosynthesis inhibition function and photosynthesis rate of the greenhouse crops within the target period based on the photosynthesis rate of the greenhouse crops within the target period, the maintenance respiration rate of the greenhouse crops within the target period, and the loss caused by growth respiration and material synthesis. The conversion factor of carbohydrates determined by the virtual carbon pool into structural dry matter is used to determine the cumulative weight of the structural dry matter of greenhouse crops during the target period; the virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops; the virtual carbon pool is used to regulate the rate of photosynthesis; when the reserves of the virtual carbon pool at the target time are greater than or equal to the maximum reserves of the virtual carbon pool at the target time, the photosynthesis of greenhouse crops during the target period is inhibited; the maximum reserves of the virtual carbon pool at the target time are positively correlated with the weight of the structural dry matter of greenhouse crops at the target time; the change in the reserves of the virtual carbon pool during the target period is associated with the weight of the structural dry matter of greenhouse crops at the target time, the photosynthesis rate of greenhouse crops during the target period, the maintenance respiration rate of greenhouse crops during the target period, and the potential relative growth rate of greenhouse crops during the target period.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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. However, 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 various embodiments of the present invention.

Claims

1. A greenhouse crop growth simulation method, characterized in that: include: Determining environmental data within the greenhouse within a target period starting from a target time, the structural dry matter weight of greenhouse crops at the target time, and the storage capacity of a virtual carbon pool corresponding to the greenhouse crops at the target time, wherein the environmental data includes at least air temperature, light radiation, and carbon dioxide concentration; determining a photosynthesis inhibition function and a photosynthesis rate of the greenhouse crops during the target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the storage of the virtual carbon pool at the target time, and the maximum storage of the virtual carbon pool at the target time; Determining the cumulative weight of structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and a conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis losses; The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. When the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of the greenhouse crops during the target period is inhibited. The maximum storage of the virtual carbon pool at the target time is positively correlated with the structural dry matter weight of the greenhouse crop at the target time; the storage change of the virtual carbon pool during the target period is associated with the structural dry matter weight of the greenhouse crop at the target time, the photosynthesis rate of the greenhouse crop during the target period, the maintenance respiration rate of the greenhouse crop during the target period, and the potential relative growth rate of the greenhouse crop during the target period; The photosynthesis inhibition function of greenhouse crops during the target period is determined by the following formula: Among them, X d is the structural dry matter weight of greenhouse crops at the target moment; h buf is the photosynthesis inhibition function of greenhouse crops during the target period; RGR max is the potential relative growth rate of greenhouse crops under the environmental data; C buf is the reserve of the virtual carbon pool at the target time; C buf,max is the maximum reserve of the virtual carbon pool at the target time.

2. The greenhouse crop growth simulation method according to claim 1, characterized in that: After determining the cumulative weight of the structural dry matter of the greenhouse crops within the target period, the method further includes: Based on the cumulative weight of the structural dry matter of the greenhouse crops during the target period, the weight of the structural dry matter of the greenhouse crops at the target moment, and the loss weight of the crops during the target time period, the updated structural dry matter weight of the greenhouse crops is determined, and based on the change in the reserves of the virtual carbon pool during the target period and the reserves of the virtual carbon pool at the target moment, the updated reserves of the virtual carbon pool are determined; the loss weight includes at least the weight of the structural dry matter of the greenhouse crops removed by agricultural operations.

3. The greenhouse crop growth simulation method according to claim 1, characterized in that: The cumulative weight of structural dry matter of greenhouse crops during the target period is determined by the following formula: Among them, dX d is the cumulative weight of the structural dry matter of greenhouse crops during the target period; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; c β is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c α The conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period.

4. The greenhouse crop growth simulation method according to claim 1, characterized in that: The change in the carbohydrate reserves in the virtual carbon pool is determined by the following formula: Among them, X d is the structural dry matter weight of the greenhouse crops at the target time; dt represents the duration of the target period; A C is the photosynthesis rate of crops; R d is the maintenance respiration rate of greenhouse crops during the target period; c β is the conversion factor of carbohydrates into structural dry matter determined by growth, respiration and material synthesis losses; c α The conversion factor for the conversion of carbon dioxide assimilated by photosynthesis into carbohydrate equivalents; h buf is the photosynthesis inhibition function of greenhouse crops during the target period, RGR max is the potential relative growth rate of greenhouse crops under the environmental data; dC buf is the reserve change of the virtual carbon pool during the target period; (0≤C buf ≤C buf,max ) represents a state constraint on the target time and the updated reserves of the virtual carbon pool.

5. The greenhouse crop growth simulation method according to any one of claim 1, characterized in that: The photosynthesis rate of greenhouse crops during the target period is expressed as the product of the photosynthesis rate and the photosynthesis inhibition function: A C ·h buf ,in: Among them, X d is the structural dry matter weight of greenhouse crops at the target time, k PAR is the extinction coefficient of crop canopy to photosynthetically active radiation; SLA is specific leaf area; σ r is the ratio of root dry matter weight to plant dry matter weight; X c is the carbon dioxide concentration in the greenhouse; X t is the air temperature of the greenhouse, Γ T20 Q is the carbon dioxide compensation point at 20℃; 10,Γ is Q for the carbon dioxide compensation point 10 Factor; r b is the boundary layer resistance; r s is the stomatal resistance; r c is the carboxylation resistance; ε0 is the light energy utilization efficiency in the absence of photorespiration under high carbon dioxide concentration conditions; σ PAR is the ratio of photosynthetically active radiation to shortwave radiation; I is the amount of shortwave radiation above the crop canopy.

6. The greenhouse crop growth simulation method according to any one of claim 5, characterized in that: The carboxylation resistance is determined by the following formula: r c =c rc,1 ·X t 2 +c rc,2 ·X t +c rc,3 ; Among them, c rc,1 、c rc,2 and c rc,3 is the coefficient of the second-order polynomial fitting function of carboxylation resistance, c rc,1 =0.28, c rc,2 =26.04, c rc,3 =784.

46.

7. A greenhouse crop growth simulation device, characterized in that: include: a first processing module, configured to determine environmental data within the greenhouse within a target period starting from a target time, the structural dry matter weight of greenhouse crops at the target time, and the storage volume of a virtual carbon pool corresponding to the greenhouse crops at the target time, wherein the environmental data includes at least air temperature, light radiation, and carbon dioxide concentration; a second processing module, configured to determine a photosynthesis inhibition function and a photosynthesis rate of the greenhouse crops during the target period based on the environmental data, the potential relative growth rate of the greenhouse crops under the environmental data, the structural dry matter weight of the greenhouse crops at the target time, the storage of the virtual carbon pool at the target time, and the maximum storage of the virtual carbon pool at the target time; a third processing module, configured to determine the cumulative weight of the structural dry matter of the greenhouse crops during the target period based on the photosynthesis rate of the greenhouse crops during the target period, the maintenance respiration rate of the greenhouse crops during the target period, and a conversion factor of carbohydrates into structural dry matter determined by growth respiration and material synthesis loss; The virtual carbon pool is used to temporarily store carbohydrates produced by photosynthesis of greenhouse crops. The virtual carbon pool does not constitute a component of the structural dry matter of greenhouse crops and is not included in the weight of the structural dry matter of greenhouse crops. The virtual carbon pool is used to regulate the rate of photosynthesis. When the storage of the virtual carbon pool at the target time is greater than or equal to the maximum storage of the virtual carbon pool at the target time, the photosynthesis of the greenhouse crops during the target period is inhibited. The maximum storage of the virtual carbon pool at the target time is positively correlated with the structural dry matter weight of the greenhouse crop at the target time; the storage change of the virtual carbon pool during the target period is associated with the structural dry matter weight of the greenhouse crop at the target time, the photosynthesis rate of the greenhouse crop during the target period, the maintenance respiration rate of the greenhouse crop during the target period, and the potential relative growth rate of the greenhouse crop during the target period; The photosynthesis inhibition function of greenhouse crops during the target period is determined by the following formula: Among them, X d is the structural dry matter weight of greenhouse crops at the target moment; h buf is the photosynthesis inhibition function of greenhouse crops during the target period; RGR max is the potential relative growth rate of greenhouse crops under the environmental data; C buf is the reserve of the virtual carbon pool at the target time; C buf,max is the maximum reserve of the virtual carbon pool at the target time.

8. The greenhouse crop growth simulation device according to claim 7, characterized in that: It also includes a fourth processing module, which is used to determine the updated structural dry matter weight of the greenhouse crops based on the cumulative weight of the structural dry matter of the greenhouse crops during the target period, the structural dry matter weight of the greenhouse crops at the target moment, and the loss weight of the crops during the target time period, and determine the updated reserves of the virtual carbon pool based on the reserve change of the virtual carbon pool during the target period and the reserves of the virtual carbon pool at the target moment; the loss weight at least includes the weight of the structural dry matter of the greenhouse crops removed by agricultural operations.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the greenhouse crop growth simulation method according to any one of claims 1 to 6 is implemented.

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