Intelligent agricultural shelter digital twin collaborative management method and system

Abstract

The present application relates to the field of intelligent agricultural shelter control technology, in particular to an intelligent agricultural shelter digital twin collaborative management and control method and system; the method comprises the following steps: dividing the multi-layer three-dimensional planting area in the shelter into crown layer digital twin units, obtaining size parameters, surface layer environment parameters and inner layer environment parameters; calculating crown layer closure and forward transition amount, and calculating surface layer boundary layer index and inner layer wet stagnation index according to the difference between surface layer and inner layer wind speed and water vapor content; further calculating crown layer misplacement intensity, and distributing crown flow guide execution amount and wet exhaust execution amount according to crown layer misplacement intensity and inner layer wet stagnation index; after executing control, re-collecting parameters to enter the next control cycle. The present application can identify the misplacement of surface layer boundary layer and inner layer wet stagnation caused by crown layer closure transition, and improve the accuracy of shelter digital twin collaborative management and control.

Description

Technical Field

[0001] This invention relates to the field of smart agricultural modular control technology, specifically to a digital twin collaborative management and control method and system for smart agricultural modular control. Background Technology

[0002] A smart agriculture modular unit is an agricultural production unit that integrates multi-layered vertical planting racks, supplemental lighting equipment, circulating fans, air diversion devices, return air dehumidification devices, temperature and humidity sensors, wind speed sensors, image acquisition devices, and a digital twin platform into a closed or semi-closed box. It can digitally monitor and automatically control the crop growth environment within a limited space. As crops in the modular unit enter the rapid growth stage from the seedling stage, the leaf projection area, canopy height, and the degree of leaf overlap between adjacent plants continuously increase, and the multi-layered canopy gradually changes from a sparse ventilation state to a closed wind-blocking state. During this process, the circulating airflow in the modular unit tends to flow along the gaps above the canopy, in the rack channels, or below the light panels, forming surface skimming, while the wind speed inside the canopy and in the leaf overlap area decreases, making it difficult for the water vapor generated by crop transpiration to be discharged in time, forming inner layer stagnation. As a result, a misalignment can easily occur between the measurable environment on the surface of the canopy and the actual inner environment that the crop experiences, making it difficult for the digital twin model to accurately reflect the true controlled state inside the multi-layered canopy.

[0003] Existing smart agriculture modular control methods typically rely on overall temperature and humidity within the modular control area, sensor data from the passageway, environmental data above the canopy, or actuator feedback as the primary control basis, generating control commands for ventilation, airflow diversion, and dehumidification. While these methods can meet basic control needs when the canopy is sparse or the environment is relatively uniform, in scenarios with multi-layered, vertical planting and a continuously closed canopy, the wind speed, temperature, and humidity detected from the passageway or above the canopy cannot fully represent the true state inside the canopy. Especially when the surface environment appears normal while the inner layer exhibits low wind speed and high moisture retention, the digital twin model can easily mistake the measurable surface environment for the actual controlled environment of the crop. This leads to inaccurate allocation of airflow diversion and dehumidification commands, making it difficult to promptly coordinate adjustments to the inner humid areas, thereby affecting the uniformity of crop growth within the modular control area, the control of localized disease risks, and the accuracy of digital twin collaborative control. Summary of the Invention

[0004] The purpose of this invention is to address the problem in the background technology that, as the canopy closure of crops in multi-layered three-dimensional planting cabins continues to increase, a misalignment occurs between the measurable environment on the surface and the actual controlled environment inside the canopy. This leads to the difficulty for digital twin models to accurately identify the surface grazing and the inner stagnant state, resulting in inaccurate allocation of flow guidance and dehumidification coordinated control commands. The invention proposes a digital twin coordinated control method and system for smart agricultural cabins.

[0005] The technical solution of this invention: A digital twin collaborative control method for smart agricultural modular shelters, comprising: S1. Divide the multi-layer three-dimensional planting area in the smart agriculture modular unit into canopy digital twin units, and obtain the size parameters, surface environment parameters and inner environment parameters of the canopy digital twin units. S2. Calculate the canopy closure degree of the current control cycle based on the size parameters, and determine the positive transition amount by combining the canopy closure degree of the previous control cycle. S3. Calculate the surface grazing index and the inner layer wet stagnation index based on the surface environmental parameters and the inner layer environmental parameters; S4. Based on the canopy closure degree, the positive transition amount, the surface grazing index, and the inner layer hysteresis index, the canopy dislocation intensity is calculated. S5. Based on the canopy misalignment intensity and the inner layer moisture retention index, allocate and calculate the canopy diversion and dehumidification execution amounts; S6. Send instructions to the corresponding actuators according to the inflow and dehumidification execution amounts, and after execution, reacquire the size parameters, surface environment parameters, and inner environment parameters to enter the next control cycle.

[0006] Preferably, the dimensional parameters include the leaf projection area, the effective area of ​​the planting tray, the current height of the canopy, and the net height that can grow in the current layer; the surface environmental parameters include surface wind speed, surface temperature, and surface relative humidity; and the inner layer environmental parameters include inner layer wind speed, inner layer temperature, and inner layer relative humidity.

[0007] Preferably, calculating the canopy closure degree for the current control cycle based on the dimensional parameters includes: The ratio of the projected area of ​​the leaf to the effective area of ​​the planting tray is used as the degree of canopy coverage in the horizontal direction. The ratio of the current height of the canopy to the net height that can grow in the layer is taken as the degree of occupancy of the canopy in the vertical direction; The canopy closure degree is obtained by multiplying the coverage degree and the occupancy degree.

[0008] Preferably, the positive transition amount is determined by combining the canopy closure degree of the previous control cycle, including: The difference between the canopy closure degree in the current control cycle and the canopy closure degree in the previous control cycle is used as the canopy closure degree transition value. If the canopy closure transition is greater than zero, then the canopy closure transition is extracted as a positive transition. If the canopy closure transition is not greater than zero, then the positive transition is set to zero.

[0009] Preferably, the calculation of the surface grazing index and the inner layer wet hysteresis index based on the surface environmental parameters and the inner layer environmental parameters includes: Calculate the ratio of the difference between the surface wind speed and the inner wind speed to the sum of the surface wind speed and the inner wind speed. If the ratio is greater than zero, the ratio is used as the surface glide index. If the ratio is not greater than zero, the surface glide index is set to zero. Calculate the surface water vapor content based on the surface temperature and the surface relative humidity; Calculate the water vapor content of the inner layer based on the inner layer temperature and the inner layer relative humidity; Calculate the ratio of the difference between the inner layer water vapor content and the surface layer water vapor content to the sum of the inner layer water vapor content and the surface layer water vapor content. If the ratio is greater than zero, the ratio is used as the inner layer moisture retention index. If the ratio is not greater than zero, the inner layer moisture retention index is set to zero.

[0010] Preferably, the canopy dislocation intensity is calculated based on the canopy closure, the positive transition amount, the surface grazing index, and the inner layer hysteresis index, including: The canopy closure degree is added to the positive transition amount to obtain the comprehensive value of morphological change; The canopy dislocation intensity is obtained by multiplying the comprehensive value of the morphological change, the surface grazing index, and the inner layer hysteresis index. The canopy misalignment intensity is written into the canopy digital twin unit.

[0011] Preferably, the calculation of the canopy diversion and dehumidification execution amounts is based on the canopy misalignment intensity and the inner layer moisture retention index, including: Obtain the total canopy misalignment intensity of all canopy digital twin units within the shelter; Based on the proportion of the current canopy misalignment intensity of the digital twin unit in the total canopy misalignment intensity, the total flow execution margin that can be used to enhance the ingress flow is allocated to obtain the ingress flow execution amount; Calculate the sum of the products of the canopy misalignment intensity and the inner layer hysteresis index for each canopy digital twin unit; Based on the proportion of the product of the current canopy digital twin unit corresponding to the canopy misalignment intensity and the inner layer moisture retention index in the sum of the products, the total moisture removal execution margin that can be used to enhance moisture removal is allocated to obtain the moisture removal execution amount; The total flow guiding execution margin is determined by the current adjustable range of the flow guiding actuator, and the total dehumidification execution margin is determined by the current adjustable range of the dehumidification actuator.

[0012] Preferably, issuing instructions to the corresponding actuators based on the inflow diversion amount and the dehumidification amount includes: The ingress flow control quantity is sent to the stratified circulating fan or flow control actuator to adjust the ingress flow direction or ingress flow intensity of the corresponding canopy digital twin unit; The dehumidification control is sent to the return air vent, dehumidification valve, or dehumidification actuator to adjust the return air dehumidification intensity of the corresponding canopy digital twin unit.

[0013] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: This invention divides the multi-layered, three-dimensional planting area within a smart agriculture modular unit into canopy digital twin units, acquiring dimensional parameters, surface environmental parameters, and inner environmental parameters for each unit. This allows the digital twin model to synchronize the physical modular unit's state from two dimensions: canopy morphology and the internal and external environments of the canopy. Canopy closure is calculated using leaf projection area, effective planting tray area, current canopy height, and the net scalable height of the current layer. Combined with the previous control cycle, the positive transition amount is determined, enabling the identification of the crop's transition from a sparsely ventilated state to a closed, wind-resistant state. The surface grazing index is calculated based on the difference between surface and inner wind speeds, and the inner moisture retention index is calculated based on the difference between surface and inner moisture content. This allows for the determination of whether airflow only flows along the top of the canopy or through the canopy channels, and whether moisture retention has occurred within the canopy. This avoids the digital twin model relying solely on measurable surface conditions to judge the modular unit's environmental state, resulting in a more accurate representation of the crop's truly controlled environment. This invention further calculates the canopy misalignment intensity by combining canopy closure degree, positive transition amount, surface grazing index, and inner layer stagnation index, and writes the canopy misalignment intensity into the canopy digital twin unit, enabling the digital twin platform to directly characterize the degree of misalignment between surface grazing and inner layer stagnation. Based on this, the canopy guiding execution amount is allocated according to the canopy misalignment intensity, and the dehumidification execution amount is allocated according to the canopy misalignment intensity and inner layer stagnation index. This ensures that the guiding and dehumidification capabilities are preferentially applied to the canopy area with higher misalignment and more obvious stagnation, rather than being uniformly controlled according to the overall average environment of the container or a fixed execution amount. By re-acquiring the size parameters, surface environment parameters, and inner layer environment parameters after execution and entering the next control cycle, this invention forms a process of canopy state identification, misalignment intensity calculation, collaborative control allocation, and feedback update, thereby improving the accuracy of digital twin collaborative management of multi-layer three-dimensional planting container and reducing the risk of local stagnation and inconsistent growth within the canopy. Attached Figure Description

[0014] Figure 1 This is a flowchart of a digital twin collaborative control method for smart agricultural modular shelters proposed in this invention; Figure 2 This is a block diagram of a smart agricultural modular digital twin collaborative control system proposed in this invention; Figure 3This is a schematic diagram of the division of the canopy digital twin unit and the sampling positions of the surface and inner layers proposed in this invention. Detailed Implementation

[0015] Example 1, as Figure 1 As shown, the present invention proposes a digital twin collaborative management and control method for smart agricultural modular shelters, comprising: In this embodiment, the smart agriculture modular unit refers to an agricultural production unit with a closed or semi-closed box structure, and equipped with multi-layer planting racks, environmental sensors, crop status acquisition devices, and environmental control actuators inside the box. The smart agriculture modular unit can collect, model, and control the crop canopy status, environmental status, and actuator status within the modular unit through a controller and a digital twin platform. The smart agriculture modular unit is equipped with multi-layer planting racks, planting trays, supplemental lighting, tiered circulating fans, airflow actuators, return air vents, dehumidification valves, dehumidification actuators, image acquisition devices, depth acquisition devices, wind speed sensors, and temperature sensors. The system includes a temperature sensor, a relative humidity sensor, a controller, and a digital twin platform. Multi-layer planting racks support leafy vegetables, seedlings, medicinal plants, or other crops suitable for container cultivation. Layered circulating fans and flow-guiding actuators change the direction and intensity of airflow entering the canopy from different planting layers. Return air vents, dehumidification valves, and dehumidification actuators change the return air dehumidification intensity in different canopy areas. The digital twin platform establishes a mapping relationship between the physical container, multi-layer planting racks, canopy digital twin units, sensor data, and actuator states, and updates the state of the corresponding canopy digital twin unit based on the controller's calculation results.

[0016] In this embodiment, a canopy digital twin unit refers to a digital object in the digital twin platform corresponding to a planting tray, a partial area of ​​a planting tray, or a continuous planting area within the physical container. The boundaries of the canopy digital twin unit are jointly determined by the planting tray boundary, the planting layer boundary, and the effective range of the flow guide actuator and the dehumidification actuator. When a planting tray corresponds to a set of flow guide actuators and dehumidification actuators, the planting tray is considered as a canopy digital twin unit. When a planting tray is acted upon by multiple sets of flow guide actuators and dehumidification actuators, the planting tray is further divided into multiple canopy digital twin units according to the range of the actuators. When multiple continuous planting trays are acted upon by the same set of flow guide actuators and dehumidification actuators, the continuous planting tray area is considered as a canopy digital twin unit. Each canopy digital twin unit establishes a control association with at least one actuator capable of changing its canopy flow guide state or dehumidification state. A canopy digital twin unit includes at least the spatial location of the area, the layer information, the effective area of ​​the planting tray, the net height that can grow in the layer, canopy morphology data, surface environment data, inner layer environment data, canopy closure degree, positive transition amount, surface runoff index, inner layer moisture retention index, canopy misalignment intensity, and the control correlation of the corresponding actuators. Through the canopy digital twin unit, the digital twin platform can no longer rely solely on the average environmental state of the entire container as the basis for control, but can instead control the crop canopy state of different layers and different areas. In this embodiment, the surface layer refers to the location adjacent to the upper boundary of the canopy and the area where the shelf channel, the gap below the light panel, or the main flow area of ​​the air supply airflow passes. The surface layer is used to characterize the environmental state when the air supply airflow or circulating airflow sweeps across the upper canopy, the shelf channel, or the gap below the light panel. The surface layer sampling position is set in the airflow area between the upper boundary of the canopy and the upper light panel, the bottom plate of the shelf, or the air supply channel, ensuring that the sensor probe is not covered by the leaves. The inner layer refers to the leaf overlap area between the top surface of the canopy and the cultivation surface, especially near the location where crop transpiration water vapor is generated and retained. The inner layer is used to characterize the actual internal environmental state of the canopy that the crop experiences. The inner layer sampling position is set between the upper boundary of the canopy and the cultivation surface, ensuring that the sensor probe is located in the leaf overlap area. When the canopy height changes with crop growth, the surface layer sampling position is updated to the airflow area above the canopy along with the upper boundary of the canopy, and the inner layer sampling position is updated to the leaf overlap area inside the canopy along with the canopy height. As the leaf projection area, canopy height, and leaf overlap increase continuously during the rapid growth period of crops, the surface environment and the inner environment may be significantly different. Therefore, in this embodiment, surface environmental parameters and inner environmental parameters are collected separately to identify the surface grazing and inner hygroscopic misalignment caused by the transition of multi-layer canopy closure.

[0017] The method in this embodiment includes the following steps; S1. Divide the multi-layer three-dimensional planting area in the smart agriculture cabin into canopy digital twin units, and obtain size parameters, surface environment parameters and inner environment parameters for the canopy digital twin units. Specifically, in this embodiment, the controller reads the multi-layer planting rack layout information of the smart agriculture cabin and divides the multi-layer three-dimensional planting area in the cabin into multiple canopy digital twin units according to the planting tray, the partial area of ​​the planting tray, or the continuous planting area; assuming there are m canopy digital twin units in the cabin, the i-th canopy digital twin unit is denoted as unit i, where i is the number of the canopy digital twin unit, and the value of i ranges from 1 to m; In this embodiment, the control cycle refers to the time period corresponding to the controller completing one parameter acquisition, index calculation, execution quantity allocation, instruction issuance, and execution feedback. After one control cycle ends, the size parameters, surface environment parameters, and inner environment parameters are reacquired, and the next control cycle begins. The current control cycle and the previous control cycle are used to distinguish the changes in the canopy state during two adjacent closed-loop control processes. For unit i, obtain its dimensional parameters; the dimensional parameters include the blade projected area. Effective area of ​​planting tray Current height of the canopy and the net height that can grow in the layer The size parameters referred to in this embodiment are geometric parameters used to characterize the degree to which the canopy occupies planting space in the horizontal and vertical directions. These size parameters are not used to characterize crop type or nutritional status, but rather to calculate canopy closure to determine whether the crop canopy has changed from a sparse to a closed state. The leaf projection area refers to the combined projection area of ​​all crop leaves in the canopy digital twin unit on the planting plate plane. The overlapping area of ​​overlapping leaves is not counted repeatedly. The controller performs crop region segmentation on the canopy image to obtain the leaf projection mask. The number of pixels belonging to the leaf region in the leaf projection mask is taken as the number of pixels in the leaf region. The leaf projection area is calculated by combining the camera calibration scale. In one embodiment, the blade projected area Calculate as follows: ; in, Represents the projected area of ​​the blade in unit i; The number of pixels in the canopy image of unit i that are identified as the leaf projection union region; This represents the actual area corresponding to a single pixel in the canopy image of unit i; in this way, the image recognition result can be converted into an area parameter that can participate in the calculation of canopy closure. This represents the effective area of ​​the planting tray in unit i, i.e., the planar area within the canopy digital twin unit that can be used for crop growth. This parameter can be written into the digital twin platform according to the geometric dimensions of the planting tray during the container modeling process, or it can be read from the digital twin platform by the controller. If unit i corresponds to a complete planting tray, then... The effective area of ​​the planting tray after deducting the border, supports, and non-plantable areas; if unit i corresponds to a continuous planting area, then This represents the effective area of ​​crops actually supported within the continuous planting area; if unit i corresponds to a local area of ​​a planting tray, then... This refers to the planar area within this local region that can be used for crop growth; The current height of the canopy in unit i represents the height of the crop from the cultivation surface to the upper boundary of the canopy. In this embodiment, the current height of the canopy refers to the difference between the height of the upper boundary of the canopy and the height of the cultivation surface. The height of the cultivation surface is determined by the installation height of the planting tray in the digital twin model, and the height of the upper boundary of the canopy is determined by the height data of the leaf area in the depth image. To avoid the influence of isolated leaf tips or noise points on the height calculation, the height of the upper boundary of the canopy is determined by the stable upper boundary height formed by continuous leaf points. The continuous leaf points refer to height points that are spatially continuous with adjacent leaf points in the depth image and belong to the leaf projection mask range. The current height of the canopy obtained in this way can reflect the height of the main body of the canopy, rather than the height of isolated noise. This indicates the allowable net height of the layer where unit i is located, which is the net height of space allowed for crop growth from the cultivation surface to the upper light panel, shelf base plate, pipeline or other restrictive structures within the planting layer. This parameter is usually a structural attribute of the planting layer and can be written when the container digital twin model is established, or it can be read from the digital twin platform during the control cycle.

[0018] For unit i, its surface environmental parameters are also obtained; the surface environmental parameters include surface wind speed. Surface temperature and surface relative humidity The surface environment parameters in this embodiment refer to the parameters used to characterize the environmental state of the airflow area above the canopy; the surface environment parameters are used to determine whether the measurable environment above the canopy collected by the digital twin model may obscure the real environment inside the canopy. Surface wind speed Wind speed data was collected by a wind speed sensor located near the upper boundary of the canopy; surface temperature... and surface relative humidity Temperature and humidity data are collected by a temperature and humidity sensor located near the upper boundary of the canopy; the surface sampling location is located adjacent to the gap between the upper boundary of the canopy and the shelf channel or the lower part of the light panel, which is used to reflect the environmental conditions when the airflow passes over the canopy or the shelf channel; For unit i, its inner environmental parameters are also obtained; the inner environmental parameters include the inner wind speed. Inner layer temperature and inner relative humidity The inner environmental parameters in this embodiment refer to the parameters used to characterize the environmental state of the leaf overlap area inside the canopy. The inner environmental parameters are used to reflect the actual controlled environment of the crop and to determine whether there is low wind speed and water vapor retention inside the canopy. Inner wind speed Data is collected by miniature wind speed sensors located inside or in the middle of the canopy; inner layer temperature. and inner relative humidity Temperature and humidity data are collected by temperature and humidity sensors located inside or in the middle of the canopy; the inner layer sampling location is located in the middle between the top surface of the canopy and the cultivation surface, close to the overlapping area of ​​leaves and the crop transpiration location, to reflect the actual environmental conditions inside the canopy that the crop is subjected to. In this step, , , , , , , , , and The canopy digital twin cell corresponding to cell i is written as input data for subsequent calculations.

[0019] S2. Calculate the canopy closure degree of the current control cycle based on the size parameters, and determine the positive transition amount by combining the canopy closure degree of the previous control cycle. Specifically, in this embodiment, the controller calculates the canopy closure degree of the current control cycle based on the size parameters of unit i. The canopy closure degree of this embodiment refers to the overall degree of occupation of the planting space by the crop canopy within the canopy digital twin unit. The canopy closure degree reflects both the shading of leaves in the horizontal direction and the occupation of the canopy in the vertical direction, and is used to determine whether the crop canopy tends to obstruct airflow into the canopy interior. Since the overlapping of crop leaves in the multi-layer vertical planting container will affect both planar shading and ventilation space in the vertical direction, it is difficult to accurately characterize the change of the canopy from a sparse state to a closed state by using only the leaf projection area or only the canopy height. Therefore, this embodiment combines the calculation of the coverage in the horizontal direction and the occupancy in the vertical direction. Specifically, the projected area of ​​the blade Effective area of ​​planting tray The ratio of the canopy height to the horizontal canopy height is used as the degree of canopy coverage. Net height of growth in the same layer The ratio of the coverage degree to the occupancy degree is used as the degree of canopy occupancy in the vertical direction; the canopy closure degree is obtained by multiplying the coverage degree by the occupancy degree. ; Canopy closure The calculation formula is as follows: ; in, This indicates the canopy closure degree of unit i in the current control cycle; Represents the projected area of ​​the blade in unit i; This represents the effective area of ​​the planting tray in unit i; This indicates the current height of the canopy in cell i; This represents the net height that can grow in the layer containing unit i; using the above formula, when the projected area of ​​crop leaves increases and the canopy height approaches the net height that can grow in the layer, the canopy closure degree... An increase in size indicates that the canopy is more likely to block airflow from entering the inner layer.

[0020] After obtaining the canopy closure degree for the current control cycle, the controller reads the canopy closure degree of the corresponding unit i from the digital twin platform for the previous control cycle and determines the canopy closure degree transition amount. The canopy closure transition is used to characterize the change in canopy closure degree in the current control cycle relative to the previous control cycle. Canopy closure transition amount The calculation formula is as follows: ; in, This represents the canopy closure transition of unit i; This indicates the canopy closure degree of unit i in the current control cycle; This indicates the canopy closure degree of unit i in the previous control cycle; This represents the actual time interval between two adjacent control cycles; when the canopy closure transition... A value greater than zero indicates an increase in canopy closure; when the canopy closure transition value... When the value is not greater than zero, it indicates that the canopy closure has not been enhanced or has been reduced, and the current change will not be used as a factor for closure enhancement in subsequent dislocation strength calculations. When the system is in the first control cycle and there is no canopy closure degree of the previous control cycle, the canopy closure degree of the current control cycle is used as the canopy closure degree of the previous control cycle, or the positive transition value is set to zero, so that the calculation of the first control cycle is not interrupted due to the lack of historical data. In this embodiment, the positive transition amount refers to the increase in canopy closure degree in the current control cycle relative to the previous control cycle. The positive transition amount only represents the process of canopy closure degree enhancement, not the process of canopy closure degree reduction. By setting the positive transition amount, the subsequent canopy misalignment intensity calculation can focus on the impact of the canopy changing from a sparse ventilation state to a closed wind-blocking state. positive transition quantity The calculation formula is as follows: ; in, This represents the positive transition amount of unit i; This represents the canopy closure transition of unit i; when When greater than zero, equal ;when When not greater than zero, Zeroing the value; this process allows subsequent calculations to focus on the effects of canopy closure enhancement on surface grazing and inner layer hygroscopicity, avoiding the mistaken use of the canopy as the cause of misalignment enhancement when the canopy is not enhanced. After this step is completed, the controller will and Write the canopy digital twin unit corresponding to unit i as the input for subsequent canopy misalignment intensity calculation.

[0021] S3. Calculate the surface grazing index and the inner layer wet stagnation index based on the surface environmental parameters and the inner layer environmental parameters; Specifically, the controller calculates the surface grazing index based on the surface and inner environmental parameters of unit i. and inner layer moisture retention index The surface glide index is used to characterize whether airflow mainly flows along the upper surface of the canopy or the tiered channels, without fully penetrating into the interior of the canopy. In this embodiment, the surface glide index refers to the degree of positive dominance of surface wind speed relative to inner wind speed. The larger the surface glide index, the more the airflow tends to flow along the upper surface of the canopy or the tiered channels, rather than penetrating into the interior of the canopy. This index is used to identify the phenomenon of airflow bypassing the interior of the canopy after the canopy is closed. The inner layer moisture retention index is used to characterize whether there is water vapor retention inside the canopy relative to the surface layer. In this embodiment, the inner layer moisture retention index refers to the degree to which the water vapor content in the inner layer is positively higher than that in the surface layer. The larger the inner layer moisture retention index, the more difficult it is for the water vapor generated by crop transpiration to be discharged from the inside of the canopy. This index is used to identify low wind speed, high water vapor load and moisture retention state inside the canopy. The controller is based on the surface temperature and surface relative humidity Calculate surface water vapor content According to the inner layer temperature and inner relative humidity Calculate the water vapor content in the inner layer Since relative humidity is greatly affected by temperature, the actual water vapor load corresponding to the same relative humidity at different temperatures is not the same. Therefore, in this embodiment, temperature and relative humidity are converted into water vapor content before being used for inner layer moisture stagnation judgment. For any sampling location x, the water vapor content The calculation formula is as follows: ; in, Choose s or n; s represents the surface sampling position; n represents the inner sampling position; This indicates the water vapor content of unit i at sampling position x; This represents the relative humidity of unit i at sampling position x; The temperature of unit i at sampling position x is represented by e; e represents the natural constant. In the calculation of water vapor content, The unit is Celsius. The unit is percentage. The unit is grams per cubic meter; the constant in the formula is used to convert the actual water vapor content in the air based on temperature and relative humidity, so that the water vapor state of the surface and inner layers under different temperature conditions can be compared; when x takes the value of s, the surface water vapor content is obtained. When x takes the value of n, the inner layer water vapor content is obtained. .

[0022] The controller is based on the surface wind speed. and inner wind speed Determine wind speed difference data and calculate surface grazing index. If the surface wind speed is significantly greater than the inner wind speed, it indicates that the airflow mainly flows along the surface of the canopy or the tiered channels, and does not fully penetrate the interior of the canopy, indicating a high degree of surface grazing. If the surface wind speed is not greater than the inner wind speed, it indicates that there is no positive grazing advantage of the surface relative to the inner layer, and the surface grazing index is set to zero. Surface grazing index The calculation formula is as follows: ; in, The surface grazing index represents unit i; Represents the surface wind speed of unit i; This represents the inner wind speed of unit i; This represents a computationally stable quantity to prevent the denominator from being zero. In this embodiment, The meaning is the same in all normalized percentage calculations; they are only used to ensure that the denominator is not zero. This can normalize wind speed differences to a proportion relative to the overall current airflow level, avoiding the incomparability of indicators under different wind speed levels; The controller is based on the surface water vapor content and inner layer water vapor content Determine the water vapor difference data and calculate the inner layer moisture retention index. If the water vapor content in the inner layer is greater than that in the surface layer, it indicates that the water vapor produced by crop transpiration has not been discharged from the canopy in time, and there is moisture retention inside the canopy. If the water vapor content in the inner layer is not greater than that in the surface layer, it indicates that there is no positive moisture retention in the inner layer relative to the surface layer, and the moisture retention index in the inner layer is set to zero. Inner layer moisture retention index The calculation formula is as follows: ; in, The inner layer hysteresis index of unit i; This indicates the water vapor content in the inner layer of unit i; This represents the surface water vapor content of unit i; This represents a stable quantity used to prevent the denominator from being zero. Through this formula, the inner layer moisture retention index can reflect the degree to which the water vapor content in the inner layer is positively higher than that in the surface layer, thereby identifying the state in which moisture inside the canopy is difficult to expel. After this step is completed, the controller will and Write the corresponding canopy digital twin unit to unit i as input for subsequent canopy misalignment intensity calculation and moisture discharge execution allocation.

[0023] S4. Based on the canopy closure degree, the positive transition amount, the surface grazing index, and the inner layer hysteresis index, the canopy dislocation intensity is calculated. Specifically, the controller is based on the canopy closure of unit i. Positive transition quantity Surface glide index and inner layer moisture retention index Calculate the canopy dislocation strength In this embodiment, the canopy misalignment intensity refers to the comprehensive deviation between the surface grazing state and the inner wet stagnant state caused by the canopy closure enhancement in the canopy digital twin unit; the canopy misalignment intensity is used to characterize the degree of representative mismatch that may exist between the state obtained by the digital twin model based on the measurable surface environment and the actual controlled state inside the crop canopy. The controller will control the canopy closure. With positive transition quantity The values ​​are added together to obtain a comprehensive morphological change value; this comprehensive morphological change value reflects both the current degree of canopy closure and the degree of closure enhancement relative to the previous control cycle; the comprehensive morphological change value and the surface grazing index are then combined. and inner layer moisture retention index Perform a series of multiplications to obtain the canopy dislocation intensity. ; Canopy dislocation strength The calculation formula is as follows: ; in, Indicates the canopy dislocation intensity of unit i; This represents the canopy closure degree of unit i; This represents the positive transition amount of unit i; The surface grazing index represents unit i; The inner layer hysteresis index represents unit i. The reason for using multiplication is that this embodiment aims to identify not simply high humidity or wind speed differences, but rather the simultaneous occurrence of surface grazing and inner layer hysteresis misalignment against a background of enhanced canopy closure. When canopy closure is low or no closure enhancement has occurred, even if there are local wind speed fluctuations, they are not directly considered as misalignment caused by a canopy closure transition. When the surface wind speed is not higher than the inner layer wind speed, it indicates that there is no significant surface grazing. Taking zero or a lower value will not amplify the canopy dislocation intensity; when the inner layer water vapor content is not higher than the surface layer water vapor content, it indicates that there is no significant inner layer hygroscopicity. Taking zero or a low value will not amplify the canopy dislocation intensity; only when the canopy morphology tends to be closed, airflow is more biased towards the surface, and water vapor is retained in the inner layer, will the canopy dislocation intensity increase. Only then will it increase significantly; Obtain the canopy dislocation strength Then, the controller will Write the canopy digital twin unit corresponding to unit i, enabling the digital twin platform to... This characterizes the degree of misalignment between the surface grazing and the inner wet stagnant layer of the canopy digital twin unit. Through this write operation, the digital twin model no longer judges whether the container environment is normal based solely on the channel side or the canopy surface environment, but can identify the deviation between the actual canopy internal environment and the measurable surface environment that the crop is subjected to. After this step is completed This serves as the direct input for the subsequent allocation of inflow and outflow volumes.

[0024] S5. Based on the canopy misalignment intensity and the inner layer moisture retention index, allocate and calculate the canopy diversion and dehumidification execution amounts; Specifically, the controller determines the canopy misalignment intensity based on the canopy digital twin unit of each canopy layer. and inner layer moisture retention index Allocate and calculate the amount of traffic diverted to the crown. and dehumidification execution volume The ingress flow control amount is used to adjust the direction or intensity of the airflow entering the canopy interior of the corresponding canopy digital twin unit. In this embodiment, the ingress flow control amount refers to the control increment allocated to the canopy digital twin unit to enhance the airflow entering the canopy interior. The ingress flow control amount can correspond to the air volume increment or operating ratio increment of the stratified circulating fan, or it can correspond to the angle increment or opening increment of the flow control actuator toward the canopy interior. The dehumidification execution amount is used to adjust the return air dehumidification intensity of the corresponding canopy digital twin unit; the dehumidification execution amount refers to the control increment allocated to the canopy digital twin unit to enhance the ability of the canopy to expel humid air; the dehumidification execution amount can correspond to the return air vent opening increment, the dehumidification valve opening increment, the dehumidification actuator operating power increment, or the operating percentage increment; The controller obtains the total flow execution margin currently available for enhancing ingress flow. The total flow diversion execution margin refers to the total remaining execution capacity of the container that can be used to enhance the flow diversion into the canopy at the current moment; the total flow diversion execution margin is obtained by summing the differences between the maximum allowable flow diversion execution state of the stratified circulating fan or flow diversion actuator corresponding to each canopy digital twin unit and the current flow diversion execution state; Total traffic diversion execution margin The calculation formula is as follows: ; in, Indicates the total flow routing execution margin; This indicates the maximum permissible flow control state of the flow control actuator or stratified circulating fan corresponding to the kth canopy digital twin unit; This indicates the current flow guidance execution state of the flow guide actuator or stratified circulating fan corresponding to the kth canopy digital twin unit; m represents the total number of canopy digital twin units; the flow guidance execution state is used to uniformly characterize the air volume, operating ratio, and air supply intensity of the stratified circulating fan, or the opening, angle, and swing position of the flow guide actuator, etc., which can enhance the flow guidance into the canopy. The controller acquires the total canopy misalignment intensity of all canopy digital twin units within the shelter. The calculation formula is as follows: ; in, represents the total canopy misalignment intensity of all canopy digital twin units within the shelter; m represents the total number of canopy digital twin units within the shelter. This represents the canopy dislocation intensity of the k-th canopy digital twin unit; k represents the number of the canopy digital twin unit; when A value greater than zero indicates that at least one canopy digital twin unit within the shelter exhibits surface grazing and inner hysteresis misalignment; the controller is based on the canopy misalignment intensity of the current unit i. In the total intensity of canopy dislocation The proportion allocated to the total flow execution margin currently available for enhancing inbound flow routing. The ingress flow execution volume of unit i is obtained. ; Execution volume of traffic diversion The calculation formula is as follows: ; in, This represents the amount of ingress flow executed by unit i; Indicates the canopy dislocation intensity of unit i; This represents the sum of canopy misalignment intensities of all canopy digital twin units within the makeshift hospital; This indicates the total flow execution margin currently available for enhancing inbound flow; when When the value is zero, it indicates that none of the canopy digital twin units have formed a misalignment state that requires enhanced coronary flow guidance. In this case, the coronary flow guidance execution amount of unit i will be adjusted. Set to zero; The controller obtains the total dehumidification execution margin currently available for enhanced dehumidification. In this embodiment, the total dehumidification execution margin refers to the total remaining execution capacity of the container that can be used to enhance dehumidification at the current moment. The total dehumidification execution margin is obtained by summing the differences between the maximum allowable dehumidification execution state of the return air vent, dehumidification valve or dehumidification actuator corresponding to each canopy digital twin unit and the current dehumidification execution state. Total dehumidification margin The calculation formula is as follows: ; in, Indicates the total dehumidification margin; This indicates the maximum permissible dehumidification execution state of the dehumidification actuator corresponding to the kth canopy digital twin unit; This indicates the current dehumidification execution status of the dehumidification actuator corresponding to the kth canopy digital twin unit; m represents the total number of canopy digital twin units; the dehumidification execution status is used to uniformly characterize the return air vent opening, dehumidification valve opening, dehumidification actuator power, dehumidification actuator operating ratio, and other execution states that can enhance dehumidification. The controller calculates the sum of the products of the canopy misalignment intensity and the inner layer hysteresis index for each canopy digital twin unit. The calculation formula is as follows: ; in, This represents the sum of the products of the canopy misalignment intensity and the inner layer hysteresis index for each canopy digital twin unit; This represents the canopy misalignment intensity of the k-th canopy digital twin unit; This represents the inner layer moisture hysteresis index of the k-th canopy digital twin unit; m represents the total number of canopy digital twin units within the shelter; when When the value is greater than zero, it indicates that at least one canopy digital twin unit within the shelter simultaneously exhibits canopy dislocation strength and inner layer hysteresis behavior; the controller is based on the canopy dislocation strength corresponding to the current unit i. With inner layer moisture retention index The product of the products in the sum of the products The proportion allocated to the total dehumidification execution margin currently available for enhancing dehumidification. The dehumidification execution amount of unit i is obtained. ; Dehumidification execution volume The calculation formula is as follows: ; in, This indicates the amount of moisture removal performed by unit i; Indicates the canopy dislocation intensity of unit i; The inner layer hysteresis index of unit i; This represents the sum of the products of the canopy misalignment intensity and the inner layer hysteresis index for each canopy digital twin unit; This indicates the total dehumidification execution margin currently available for enhancing dehumidification; when When the value is zero, it indicates that none of the canopy digital twin units have formed an inner layer moisture stagnation misalignment state requiring enhanced dehumidification. In this case, the dehumidification execution amount of unit i will be adjusted. Set to zero; Through the above methods, the execution volume of traffic redirection is increased. Mainly based on canopy dislocation strength The proportion is allocated so that the guiding capacity is preferentially applied to units with a high degree of misalignment between the surface flow and the inner moisture retention; the dehumidification execution volume Then, further combining the inner layer moisture retention index The system allocates resources to prioritize dehumidification capacity to units where moisture retention is more pronounced within the canopy. This avoids the problem of controlling only based on fixed airflow, fixed dehumidification capacity, or average humidity within the container, allowing limited execution capacity to be allocated around the actual controlled state within the canopy. After this step is completed, the controller will and As the control output of unit i, it is sent to the next step for the issuance of actuator instructions.

[0025] S6. Send instructions to the corresponding actuators according to the inflow and dehumidification execution amounts, and re-acquire the size parameters, surface environment parameters, and inner environment parameters after execution to enter the next control cycle; In this embodiment, the controller performs the ingress flow based on the input flow of unit i. and dehumidification execution volume Send control commands to the corresponding actuator; Regarding the execution volume of the inbound traffic diversion The controller sends the data to the stratified circulating fan or flow guide actuator corresponding to unit i; when the flow guide execution amount acts on the stratified circulating fan or flow guide actuator, the controller determines the target flow guide execution state based on the current flow guide execution state and the flow guide execution amount: ; in, This indicates the target flow guiding execution state of the stratified circulating fan or flow guiding actuator corresponding to unit i; This indicates the current flow control status of the stratified circulating fan or flow control actuator corresponding to unit i; This represents the amount of ingress flow executed by unit i; This indicates the maximum allowable flow control state for the stratified circulating fan or flow control actuator corresponding to unit i; If the target of the execution is a tiered circulating fan, the controller will determine the appropriate action based on the specified parameters. Adjust the airflow, operating ratio, or air supply intensity of the stratified circulating fan to enhance the airflow and enable it to enter the canopy interior of the corresponding canopy digital twin unit; if the target is a flow guide actuator, the controller will adjust accordingly. Adjusting the flow angle, opening, or swing position of the flow guide actuator redirects the airflow that originally swept along the upper surface of the canopy or the shelf passages to the interior of the canopy. In this way, the flow guide control does not aim to simply increase the total air volume, but rather to weaken the surface flow and enhance the airflow's ability to enter the interior of the canopy. For dehumidification execution volume The controller sends the data to the return air vent, dehumidification valve, or dehumidification actuator corresponding to unit i. When the dehumidification execution quantity is applied to the return air vent, dehumidification valve, or dehumidification actuator, the controller determines the target dehumidification execution state based on the current dehumidification execution state and the dehumidification execution quantity. ; in, This indicates the target dehumidification execution state of the dehumidification actuator corresponding to unit i; This indicates the current dehumidification execution state of the dehumidification actuator corresponding to unit i; This indicates the amount of moisture removal performed by unit i; This indicates the maximum allowable dehumidification execution state of the dehumidification actuator corresponding to unit i; If the target of the execution is a return air vent, the controller will determine the appropriate action based on the specified conditions. Adjust the return air vent opening or return air ratio to enhance the return air capacity of the corresponding canopy area; if the target is a dehumidification valve, the controller will adjust accordingly. Adjust the opening of the dehumidification valve to enhance the humid air removal capacity of the corresponding area; if the target is a dehumidification actuator, the controller will adjust accordingly. Adjusting the power or operating percentage of the dehumidifier can enhance the dehumidification capacity of the corresponding area. In this way, the dehumidification control can target the inner canopy area with more significant humidity retention, rather than simply dehumidifying based on the average relative humidity of the entire cabin.

[0026] After executing one control cycle, the controller reacquires the size parameters, surface environment parameters, and inner environment parameters of unit i; specifically, it reacquires the blade projected area. Effective area of ​​planting tray Current height of the canopy Net height that can grow in the layer Surface wind speed Surface temperature Surface relative humidity Inner wind speed Inner layer temperature and inner relative humidity Among them, the effective area of ​​the planting tray and the net height that can grow in the layer Structural attributes that can be used as digital twin units of the canopy can be reread from the digital twin platform, including the projected area of ​​the blades. and current height of the canopy The surface environmental parameters and inner environmental parameters can be reacquired by the corresponding sensors; After the newly acquired data enters the next control cycle, the controller executes steps S2 to S6 again to recalculate the canopy closure. Positive transition quantity Surface glide index Inner layer moisture retention index Canopy dislocation strength , Execution volume of traffic diversion and dehumidification execution volume By repeatedly executing continuous control cycles, the digital twin platform can dynamically update itself in accordance with the continuous growth of the crop canopy and changes in the container environment, thereby forming collaborative management and control.

[0027] Through the above implementation methods, this embodiment can identify the environmental representativeness mismatch problem that occurs when the canopy changes from a sparse state to a closed state within a multi-layered three-dimensional planting container. Specifically, when the crop is in the seedling stage or the canopy is relatively sparse, the leaf projection area is small, the canopy height is low, and the canopy closure is... The level is low, the difference between surface and inner airflow is small, and the canopy dislocation intensity is low. It will not increase significantly; at this time, the controller will not over-allocate the limited flow and dehumidification capacity to this unit. When crops enter their rapid growth phase, the leaf projection area increases, the canopy height rises, the overlap of leaves between adjacent plants intensifies, and the canopy closure increases. Increase; if during this process, the surface wind speed Significantly greater than the inner wind speed The surface grazing index An increase indicates that airflow mainly flows along the surface of the canopy or through stratigraphic channels; if the water vapor content in the inner layer... Greater than surface water vapor content The inner layer moisture retention index An increase indicates that water vapor inside the canopy has not been expelled in time; at this point, the canopy dislocation strength... As the size increases, the digital twin platform can identify the misalignment between surface grazing and inner stagnant water in the canopy digital twin unit, and will not misjudge the actual controlled environment of the crop as normal simply because the surface environmental parameters are normal. During the control execution phase, the controller will allocate the total execution margin. The total dehumidification margin is allocated to the digital twin units of the canopy with significant misalignment according to the proportion of canopy misalignment intensity. The data is allocated to the digital twin unit of the canopy where the inner layer moisture retention is more obvious, based on the product ratio of the canopy misalignment intensity and the inner layer moisture retention index; both the diversion control and the dehumidification control are based on the actual controlled state inside the canopy, rather than on the average environmental state of the container or the measurable environment on the surface of the canopy. Therefore, this embodiment can solve the problem that digital twin models in multi-layer three-dimensional planting cabins tend to equate the measurable surface environment with the real environment inside the canopy, improve the accuracy of digital twin collaborative management and control in smart agriculture cabins, help reduce local moisture retention, reduce disease risk, improve crop growth uniformity, and enhance the allocation efficiency of diversion and dehumidification execution capabilities.

[0028] Example 2, as Figure 2 As shown, the present invention proposes a smart agriculture modular facility digital twin collaborative control system, used to execute the smart agriculture modular facility digital twin collaborative control method described in Embodiment 1, comprising: The data acquisition module is used to divide the multi-layer three-dimensional planting area in the smart agriculture cabin into canopy digital twin units, and to acquire the size parameters, surface environment parameters and inner environment parameters of the canopy digital twin units. The closed transition module is used to calculate the canopy closure degree of the current control cycle based on the size parameters, and to determine the positive transition amount by combining the canopy closure degree of the previous control cycle. The misalignment identification module is used to calculate the surface grazing index and the inner layer wet stagnation index based on the surface environmental parameters and the inner layer environmental parameters. The intensity calculation module is used to calculate the canopy dislocation intensity based on the canopy closure degree, the positive transition amount, the surface grazing index, and the inner layer hysteresis index. The execution allocation module is used to allocate the calculated canopy diversion execution amount and moisture drainage execution amount according to the canopy misalignment intensity and the inner layer moisture retention index; The feedback control module is used to issue instructions to the corresponding actuators according to the inflow and dehumidification execution amounts, and after execution, reacquire the size parameters, surface environment parameters, and inner environment parameters to enter the next control cycle.

[0029] like Figure 3 As shown, a planting tray 2 is installed inside the planting rack 1. The area where the planting tray 2 and the corresponding crop canopy 4 are located is divided into canopy digital twin units 3. The canopy digital twin unit 3 is used for a calculable and controllable canopy area in the corresponding digital twin platform. The coverage area of ​​the crop canopy 4 on the plane of the planting tray 2 forms the projected leaf area 5. The area in the planting tray 2 that can be used for crop growth forms the effective planting tray area 6. The distance between the crop canopy 4 from the cultivation surface 10 to the upper boundary 9 of the canopy is the current canopy height 7. The distance from the cultivation surface 10 to the light panel or the bottom of the shelf is... The allowable clearance distance for crop growth between the boards 15 is the usable growth clearance height 8. The surface sampling position 11 is set in the airflow area between the upper boundary 9 of the canopy and the light board or shelf base plate 15, and the surface wind speed, surface temperature and surface relative humidity are collected by the surface environment sensor 13. The inner sampling position 12 is set in the leaf overlapping area inside the crop canopy 4, and the inner wind speed, inner temperature and inner relative humidity are collected by the inner environment sensor 14, providing a data basis for calculating the canopy closure degree, surface grazing index, inner moisture retention index and canopy misalignment intensity.

[0030] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for collaborative management and control of a smart agricultural modular facility using digital twins, characterized in that, Includes the following steps: S1. Divide the multi-layer three-dimensional planting area in the smart agriculture modular unit into canopy digital twin units, and obtain the size parameters, surface environment parameters and inner environment parameters of the canopy digital twin units. S2. Calculate the canopy closure degree of the current control cycle based on the size parameters, and determine the positive transition amount by combining the canopy closure degree of the previous control cycle. S3. Calculate the surface grazing index and the inner layer wet stagnation index based on the surface environmental parameters and the inner layer environmental parameters; S4. Based on the canopy closure degree, the positive transition amount, the surface grazing index, and the inner layer hysteresis index, the canopy dislocation intensity is calculated. S5. Based on the canopy misalignment intensity and the inner layer moisture retention index, allocate and calculate the canopy diversion and dehumidification execution amounts; S6. Send instructions to the corresponding actuators according to the inflow and dehumidification execution amounts, and after execution, reacquire the size parameters, surface environment parameters, and inner environment parameters to enter the next control cycle.

2. The method for collaborative control of a smart agricultural modular facility using digital twins according to claim 1, characterized in that, The dimensional parameters include the leaf projection area, the effective area of ​​the planting tray, the current height of the canopy, and the net height that can grow in the current layer; the surface environmental parameters include the surface wind speed, the surface temperature, and the surface relative humidity; the inner layer environmental parameters include the inner layer wind speed, the inner layer temperature, and the inner layer relative humidity.

3. The method for collaborative management and control of a smart agricultural modular facility using digital twins according to claim 2, characterized in that, Calculating the canopy closure degree for the current control cycle based on the stated size parameters includes: The ratio of the projected area of ​​the leaf to the effective area of ​​the planting tray is used as the degree of canopy coverage in the horizontal direction. The ratio of the current height of the canopy to the net height that can grow in the layer is taken as the degree of occupancy of the canopy in the vertical direction; The canopy closure degree is obtained by multiplying the coverage degree and the occupancy degree.

4. The method for collaborative control of a smart agricultural modular facility using digital twins according to claim 3, characterized in that, The positive transition amount is determined by combining the canopy closure degree of the previous control cycle, including: The difference between the canopy closure degree in the current control cycle and the canopy closure degree in the previous control cycle is used as the canopy closure degree transition value. If the canopy closure transition is greater than zero, then the canopy closure transition is extracted as a positive transition. If the canopy closure transition is not greater than zero, then the positive transition is set to zero.

5. The method for collaborative management and control of a smart agricultural modular facility using digital twins according to claim 2, characterized in that, Based on the surface environmental parameters and the inner environmental parameters, the surface grazing index and the inner wet hysteresis index are calculated, including: Calculate the ratio of the difference between the surface wind speed and the inner wind speed to the sum of the surface wind speed and the inner wind speed. If the ratio is greater than zero, the ratio is used as the surface glide index. If the ratio is not greater than zero, the surface glide index is set to zero. Calculate the surface water vapor content based on the surface temperature and the surface relative humidity; Calculate the water vapor content of the inner layer based on the inner layer temperature and the inner layer relative humidity; Calculate the ratio of the difference between the inner layer water vapor content and the surface layer water vapor content to the sum of the inner layer water vapor content and the surface layer water vapor content. If the ratio is greater than zero, the ratio is used as the inner layer moisture retention index. If the ratio is not greater than zero, the inner layer moisture retention index is set to zero.

6. The method for collaborative management and control of a smart agricultural modular facility using digital twins according to claim 5, characterized in that, Based on the canopy closure, the positive transition amount, the surface grazing index, and the inner layer hysteresis index, the canopy dislocation intensity is calculated, including: The canopy closure degree is added to the positive transition amount to obtain the comprehensive value of morphological change; The canopy dislocation intensity is obtained by multiplying the comprehensive value of the morphological change, the surface grazing index, and the inner layer hysteresis index. The canopy misalignment intensity is written into the canopy digital twin unit.

7. The method for collaborative management and control of a smart agricultural modular facility using digital twins according to claim 6, characterized in that, Based on the canopy misalignment intensity and the inner layer moisture retention index, the calculated canopy drainage and moisture removal volumes are allocated, including: Obtain the total canopy misalignment intensity of all canopy digital twin units within the shelter; Based on the proportion of the current canopy misalignment intensity of the digital twin unit in the total canopy misalignment intensity, the total flow execution margin that can be used to enhance the ingress flow is allocated to obtain the ingress flow execution amount; Calculate the sum of the products of the canopy misalignment intensity and the inner layer hysteresis index for each canopy digital twin unit; Based on the proportion of the product of the current canopy digital twin unit corresponding to the canopy misalignment intensity and the inner layer moisture retention index in the sum of the products, the total moisture removal execution margin that can be used to enhance moisture removal is allocated to obtain the moisture removal execution amount; The total flow guiding execution margin is determined by the current adjustable range of the flow guiding actuator, and the total dehumidification execution margin is determined by the current adjustable range of the dehumidification actuator.

8. The method for collaborative management and control of a smart agricultural modular facility using digital twins according to claim 7, characterized in that, Instructions are issued to the corresponding actuators based on the inflow and dehumidification execution amounts, including: The ingress flow control quantity is sent to the stratified circulating fan or flow control actuator to adjust the ingress flow direction or ingress flow intensity of the corresponding canopy digital twin unit; The dehumidification control is sent to the return air vent, dehumidification valve, or dehumidification actuator to adjust the return air dehumidification intensity of the corresponding canopy digital twin unit.

9. A smart agricultural modular facility digital twin collaborative control system, used to execute the smart agricultural modular facility digital twin collaborative control method according to any one of claims 1-8, characterized in that, Specifically, it includes: The data acquisition module is used to divide the multi-layer three-dimensional planting area in the smart agriculture cabin into canopy digital twin units, and to acquire the size parameters, surface environment parameters and inner environment parameters of the canopy digital twin units. The closed transition module is used to calculate the canopy closure degree of the current control cycle based on the size parameters, and to determine the positive transition amount by combining the canopy closure degree of the previous control cycle. The misalignment identification module is used to calculate the surface grazing index and the inner layer wet stagnation index based on the surface environmental parameters and the inner layer environmental parameters. The intensity calculation module is used to calculate the canopy dislocation intensity based on the canopy closure degree, the positive transition amount, the surface grazing index, and the inner layer hysteresis index. The execution allocation module is used to allocate the calculated canopy diversion execution amount and moisture drainage execution amount according to the canopy misalignment intensity and the inner layer moisture retention index; The feedback control module is used to issue instructions to the corresponding actuators according to the inflow and dehumidification execution amounts, and after execution, reacquire the size parameters, surface environment parameters, and inner environment parameters to enter the next control cycle.

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