A multi-compartment environment regulation system, container and environment parameter evaluation method

Abstract

The application discloses a multi-cabin environment regulation system, a container and an environment parameter evaluation method, a first cabin root system monitoring part is used for monitoring root system parameters of vegetables, and a first cabin environment monitoring part is used for monitoring environment parameters in the first cabin; a first execution part is arranged in the first cabin; a second cabin root system monitoring part is used for monitoring respiration states of mushrooms, and a second cabin environment monitoring part is used for monitoring environment parameters in the second cabin; a second execution part is arranged in the second cabin; a control part comprises a first cabin control assembly, a second cabin control assembly and a main controller; wherein, the first cabin and the second cabin are configured to be in gas communication; and the main controller is configured to control gas on-off between the first cabin and the second cabin. Multi-cabin linkage can be formed, root systems are monitored, gas circulation between the multi-cabin rooms is ensured, and thus precise regulation and control can be performed.

Description

Technical Field

[0001] This invention belongs to the field of plant factory technology, specifically, it relates to a multi-compartment environmental control system, a container, and a method for evaluating environmental parameters. Background Technology

[0002] Traditional compartments are single-compartment structures with low gas utilization rates. For example, the oxygen produced by photosynthesis in traditional vegetable compartments is directly released, and additional carbon dioxide needs to be added to the vegetable compartments. Similarly, the respiration process in mushroom compartments requires additional carbon dioxide. Separating vegetable and mushroom compartments results in multiple resource wastes and requires additional equipment, increasing energy consumption.

[0003] Common systems focus on leaf parameters (such as leaf surface temperature and PPFD) while ignoring the root system (the core organ for crop water and fertilizer absorption), resulting in inaccurate water and fertilizer regulation and affecting crop growth efficiency. At the same time, data transmission is difficult to flexibly cover multiple compartments.

[0004] Therefore, developing a multi-compartment environmental control system, a container and environmental parameter assessment method that can form multi-compartment linkage, monitor the root system, ensure gas circulation between multiple compartments, and thus carry out precise control is an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-compartment collaborative environmental control system and a method for evaluating environmental parameters of containers and compartment plants, so as to solve the problems in the prior art, such as individual circulation control of compartments, inability to coordinate control of multiple compartments, resulting in waste of equipment resources and lack of monitoring of core organs such as root systems.

[0006] To achieve the above-mentioned objectives, the present invention employs the following technical solution:

[0007] In one aspect, the present invention proposes a multi-compartment collaborative environmental control system, comprising:

[0008] The first compartment is used for growing vegetables. The first compartment is equipped with a first compartment root system monitoring unit and a first compartment environmental monitoring unit. The first compartment root system monitoring unit is used to monitor the root system parameters of the vegetables, and the first compartment environmental monitoring unit is used to monitor the environmental parameters inside the first compartment. The first compartment is equipped with a first execution unit.

[0009] The second compartment is used for cultivating mushrooms. The second compartment is equipped with a second compartment respiration monitoring unit and a second compartment environmental monitoring unit. The second compartment root system monitoring unit is used to monitor the respiration status of the mushrooms, and the second compartment environmental monitoring unit is used to monitor the environmental parameters in the second compartment. The second compartment is also equipped with a second execution unit.

[0010] The control unit includes a first compartment control component, a second compartment control component, and a main controller. The first compartment control component is electrically connected to the main controller, and the second compartment control component is also electrically connected to the main controller. The first compartment root system monitoring unit and the first compartment environment monitoring unit feed back monitoring information to the first compartment control component. The second compartment respiration monitoring unit and the second compartment environment monitoring unit are connected to the second compartment control component. The main controller controls the first actuator based on the information fed back by the first compartment control component, and the main controller controls the second actuator based on the information fed back by the second compartment control component.

[0011] The first compartment and the second compartment are configured to be in gas communication; the main controller is configured to control the gas communication between the first compartment and the second compartment.

[0012] In some embodiments of this application, the first execution unit includes a water and fertilizer execution device, which is configured to adjust the supply of water and fertilizer according to the root parameters;

[0013] And / or, the first actuator includes a grow lamp configured to adjust the illumination time, and the first chamber root system monitoring unit controls the grow lamp to adjust the illumination time based on the monitored root system parameters;

[0014] And / or, the first actuator includes a first temperature regulating device, and the main controller controls the start or stop of the first temperature regulating device through the first compartment environment monitoring unit according to the environmental parameters;

[0015] And / or, the first actuator includes a first humidity regulating device, and the main controller controls the start or stop of the first humidity regulating device through the first cabin environment monitoring unit according to the environmental parameters;

[0016] And / or, the first actuator includes a first ventilation device configured to regulate gas exchange between the first compartment and the outside; the main controller controls the start or stop of the first ventilation device through the first compartment environment monitoring unit according to the environmental parameters;

[0017] And / or, the first compartment and the second compartment are connected via a gas exchange valve, and the main controller controls the opening and closing of the gas exchange valve.

[0018] In some embodiments of this application, the second actuator includes a second temperature regulating device, and the main controller regulates the start-up or stop of the second temperature regulating device through the second compartment environment monitoring unit according to the environmental parameters;

[0019] And / or, the second actuator includes a second humidity control device, and the main controller controls the start or stop of the second humidity control device through the second cabin environment monitoring unit according to the environmental parameters;

[0020] And / or, the second actuator includes a second ventilation device configured to regulate gas exchange between the second compartment and the outside; the main controller controls the start or stop of the second ventilation device through the second compartment environment monitoring unit according to the environmental parameters.

[0021] In another aspect, the present invention also proposes a multi-compartment mixed-planting container, comprising:

[0022] As described above, a multi-compartment collaborative environmental control system;

[0023] The container body, with the first compartment and the second compartment located inside the container body.

[0024] In another aspect, the present invention also proposes a method for evaluating environmental parameters of cabin plants, comprising:

[0025] S1: Based on LS-SVM, establish a crop growth status prediction model, considering root temperature, root humidity, root EC value, and multi-compartment data. Concentration difference and multi-compartment Concentration difference can be used to predict crop growth status.

[0026] S11: The input parameters of the crop growth status prediction model include environmental parameters and root parameters;

[0027] The output parameters of the crop growth status prediction model include the crop growth status index; the crop growth status index includes root absorption efficiency (…). ) and plant height growth rate ( );

[0028] S12: Use a Gaussian kernel function to verify the environmental parameters and the root system parameters.

[0029] Input / output vector definition:

[0030]

[0031] Model mapping relationship:

[0032]

[0033] in:

[0034] Input vector for the prediction model; Root temperature, unit: °C; EC value of the root system, unit: mS / cm; Root humidity, unit: % For multi-compartment Concentration difference, unit: μmol·mol⁻¹; For multi-compartment Concentration difference, unit: % The output vector of the prediction model (growth state index); Root absorption efficiency, unit: % Y represents the plant height growth rate, in cm / d; Y^ is the predicted value output by the prediction model. For the weight vector, For bias terms; Here is the Gaussian kernel mapping function, and the corresponding kernel function is: ; , For the sample input vector; σ is the 2-norm; σ is the Gaussian kernel parameter of LS-SVM.

[0035] S13: Model optimization objective: Use 5-fold cross-validation;

[0036] With the goal of minimizing the mean square error (MSE), the kernel parameter σ and the regularization parameter C are optimized. σ is used to determine the granularity of detection, and C determines the fault tolerance rate.

[0037]

[0038] Constrained by the primal optimization problem of LS-SVM:

[0039]

[0040] Where: N is the sample size, X i ξi represents slack variables (allowing a small amount of prediction bias); C is the LS-SVM regularization parameter; ξi is the LS-SVM slack variable.

[0041] S14: Accuracy Constraints

[0042]

[0043] in, As the coefficient of determination, This represents the relative prediction error of root-related growth status indices.

[0044] In some embodiments of this application, S2: Establishing a multi-warehouse environment optimization control model, which includes:

[0045] S21: Determine the objective function:

[0046] While ensuring the health of vegetable roots and the normal growth of mushrooms, minimize the energy consumption of gas equipment and water and fertilizer.

[0047] Primary objectives: Vegetable root absorption efficiency ≥90% (based on predictive model output); mushroom mycelial viability ≥85%;

[0048] Secondary objectives: Energy consumption for gas exchange (gas exchange valve regulation, fan operation) ≤ 30% of the original system (independent gas supply to a single compartment), and energy consumption for water and fertilizer ≤ 20% of the original system.

[0049] In some embodiments of this application, S2: establishing a multi-warehouse environment optimization control model further includes:

[0050] S22: Constraints:

[0051] Vegetable Warehouse Concentration: 400-1400 μmol·mol -1 (Adapted to the photosynthetic needs of vegetables);

[0052] Mushroom Warehouse Concentration: 18%-22%, to suit the respiratory needs of mushrooms.

[0053] Vegetable root temperature: 18-27℃ (avoid low temperature inhibiting absorption and high temperature causing root rot).

[0054] In some embodiments of this application, S2: establishing a multi-warehouse environment optimization control model further includes:

[0055] S23: Multi-algorithm collaborative adjustment:

[0056] Definition of decision variables:

[0057] ;

[0058] U: Vector of decision variables for the optimization control model;

[0059] U1: Root temperature regulation amount ((X1=X10+U1, X10 is the reference temperature), unit: ℃;

[0060] U2: Root EC value regulation amount (X2=X20+U2, X20 is the baseline EC value), unit: mS / cm;

[0061] U3: Root humidity control amount (X3 = X30 + U3, where X30 is the baseline humidity), unit: %

[0062] U4: From mushroom warehouse to vegetable warehouse Gas exchange valve opening, unit: %

[0063] U5: From vegetable warehouse to mushroom warehouse Gas exchange valve opening, unit: %

[0064] .

[0065] In some embodiments of this application, S2: establishing a multi-warehouse environment optimization control model further includes:

[0066] S24: Multi-objective optimization objective function:

[0067] With "satisfying the primary objective and minimizing the secondary objective" as the core, the NSGA-II optimization objective set is constructed as follows:

[0068] Primary objective (constraint objective): To ensure the basic needs for biological growth;

[0069]

[0070] Y1 is predicted by the LS-SVM model, and Z represents the mycelial viability of mushrooms. Strong correlation;

[0071] Secondary objective (minimum objective): Reduce system energy consumption;

[0072]

[0073] Egas: Energy consumption for gas exchange (including energy consumption for gas exchange valve regulation and energy consumption for fan operation), unit: kWh;

[0074] Egas,0: Energy consumption for gas exchange in the original system (independent gas supply to a single compartment), unit: kWh;

[0075] Ewf: Water and fertilizer energy consumption, unit: kWh;

[0076] Ewf,0: Original system water and fertilizer energy consumption, unit: kWh;

[0077] Environmental constraints:

[0078]

[0079] in, It is directly related to the opening degrees of the gas exchange valves U4 and U5;

[0080] Initial population production constraints:

[0081]

[0082]

[0083] Circular revision logic:

[0084] Internal circulation (gas exchange rate revision):

[0085] ;

[0086] ;

[0087] ;

[0088] External circulation (deviation compatibility judgment):

[0089] If the following deviation conditions are met, then U is a feasible solution; otherwise, return to the inner loop for adjustment:

[0090]

[0091] These are reference values ​​for root system parameters;

[0092] This is a reference value for gas concentration;

[0093] This represents the allowable deviation for root EC / humidity.

[0094] In some embodiments of this application, it also includes:

[0095] S3: Core Logic Closed-Loop Formula:

[0096] ;

[0097] ;

[0098] in,

[0099] U* represents the final optimized control plan to be implemented;

[0100] To find the minimum value among all feasible solutions;

[0101] The electricity cost for the gas equipment required to implement Plan U;

[0102] The water, fertilizer, and electricity costs required to implement Plan U;

[0103] , According to the LS-SVM prediction model, after implementing scheme U, the root absorption efficiency of vegetables must be ≥90%.

[0104] After implementing scheme U, the mycelial viability of mushrooms must be ≥85%.

[0105] Compared with the prior art, the advantages and positive effects of the present invention are:

[0106] By setting up a root system monitoring unit and an environmental monitoring unit in the first compartment, the root system parameters and environmental parameters of the vegetables in the first compartment can be easily detected. By setting up a respiration monitoring unit and an environmental monitoring unit in the second compartment, the respiration status and environmental parameters of the mushrooms in the second compartment can be easily detected. The first compartment control component summarizes the information in the first compartment, the second compartment control component summarizes the information in the second compartment, and the main controller summarizes the information from the first compartment control component and the second compartment control component. The main controller controls the start and stop of the first and second actuators to regulate the parameters in the compartments.

[0107] By connecting the first and second compartments, the main controller controls the gas flow between them, thereby reducing the cost of regulating the gas composition in the two compartments through additional equipment.

[0108] By sequentially employing LS-SVM to establish a crop growth status prediction model, and NSGA-II to establish a multi-compartment environmental optimization control model, the final optimized control scheme to be executed is obtained by using the core logic closed-loop formula. This enables multi-compartment linkage, root system monitoring, and gas circulation between multiple compartments, thereby achieving precise control.

[0109] Other features and advantages of the present invention will become clearer after reading the detailed embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0110] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0111] Figure 1 A flowchart illustrating the temperature regulation process between compartments;

[0112] Figure 2 A flowchart illustrating the humidity control process between compartments;

[0113] Figure 3 A flowchart illustrating the oxygen and carbon dioxide regulation process between compartments; Detailed Implementation

[0114] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0115] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0116] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0117] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections, direct connections, or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0118] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0119] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0120] In some embodiments of this application, a multi-compartment collaborative environmental control system is disclosed, comprising a first compartment, a second compartment, and a control unit. The first compartment is used for growing vegetables. The second compartment is used for growing mushrooms. The first and second compartments are interconnected, allowing for smooth gas exchange between them. The first compartment is the vegetable compartment, and the second compartment is the mushroom compartment.

[0121] Since the first compartment 100 is used for growing vegetables, the vegetables will absorb nutrients from the first compartment 100. Vegetables will release In the first compartment 100; if the first compartment 100 is closed, the contents of the first compartment 100 The concentration will continue to rise, within 100 meters of the first compartment. The concentration will continue to decrease, in order to ensure the first compartment within 100 Concentration and To maintain the concentration within the agreed range, additional equipment is required to regulate the concentration within the first chamber 100. Concentration and Concentration, thus resulting in an increase in overall power consumption.

[0122] Since the second compartment 200 is used for growing mushrooms, the mushrooms will absorb nutrients from the environment inside the second compartment 200. Mushrooms release In the second compartment 200; if the second compartment 200 is closed, the contents of the second compartment 200 The concentration will continue to rise, within 100 meters of the first compartment. The concentration will continue to decrease, in order to ensure the safety of the second compartment within 200... Concentration and To maintain the concentration within the agreed range, additional equipment is required to regulate the concentration within the second chamber (200 cubic meters). Concentration and Concentration, thus resulting in an increase in overall power consumption.

[0123] In some embodiments of this application, the first compartment 100 and the second compartment 200 are connected. Since the gas within the first compartment 100 and the second compartment 200 can circulate, the gas within the first compartment 100 and the second compartment 200 can be exchanged. , The flow.

[0124] Specifically, a gas exchange valve is installed at the connection between the first compartment 100 and the second compartment 200. When the gas exchange valve is open, gas can flow between the first compartment 100 and the second compartment 200. When the gas exchange valve is closed, the first compartment 100 and the second compartment 200 are sealed and no gas flows.

[0125] The first compartment 100 is equipped with a root system monitoring unit and an environmental monitoring unit. The root system monitoring unit is used to monitor the root parameters of the vegetables. The environmental monitoring unit is used to monitor the environmental parameters within the first compartment 100.

[0126] The second compartment 200 is equipped with a second compartment respiration monitoring unit and a second compartment environmental monitoring unit. The second compartment respiration monitoring unit is used to monitor the respiration status of the mushrooms. The second compartment environmental monitoring unit is used to monitor the environmental parameters within the second compartment 200.

[0127] The control unit includes a first compartment control component, a second compartment control component, and a main controller. The first compartment control component is electrically connected to the first compartment root system monitoring unit and the first compartment environmental monitoring unit. The second compartment control component is electrically connected to the second compartment respiratory monitoring unit and the second compartment environmental monitoring unit.

[0128] The first compartment control component collects root system parameters and environmental parameters for the first compartment.

[0129] The second compartment control component collects data on the breathing status and environmental parameters of the second compartment.

[0130] The first and second compartment control components use ZigBee wireless communication, which is suitable for short-range wireless communication and features low power consumption and low cost; it is responsible for the stable autonomous networking of a large number of low-power devices.

[0131] The main controller is electrically connected to the first compartment control component and the second compartment control component. The root system parameters and environmental parameters collected and summarized by the first compartment control component, and the respiratory status and environmental parameters collected and summarized by the second compartment control component are all fed back to the main controller for analysis and processing.

[0132] The root system parameters in the first chamber include root temperature, root EC value, and root humidity. The environmental parameters in the first chamber include... Concentration and concentration.

[0133] The respiratory status of the second compartment is indirectly reflected by sensors that detect the activity of mushroom mycelium. The generation rate. Environmental parameters of the second compartment include... concentration, Concentration, ambient temperature, and ambient humidity.

[0134] The first compartment control unit receives gas exchange commands from the main controller.

[0135] The second compartment control unit receives gas exchange commands from the main controller.

[0136] The main controller receives feedback from the first compartment control component. concentration, Concentration, and feedback from the second compartment control components. concentration, The concentration is analyzed to determine whether to open the gas exchange valve in order to regulate the gas exchange between the first and second compartments.

[0137] A first execution unit is provided in the first compartment. The first execution unit includes a water and fertilizer execution device, which is configured to adjust the supply of water and fertilizer according to root parameters.

[0138] The first actuator includes a grow lamp. The grow lamp is configured to adjust the illumination time. The first compartment root monitoring unit controls the grow lamp to adjust the illumination time based on the monitored root parameters.

[0139] A second execution unit is located in the second compartment.

[0140] The first execution unit includes a first temperature regulation device, and the main controller controls the start or stop of the first temperature regulation device through the first compartment environment monitoring unit according to environmental parameters.

[0141] The first execution unit includes a first humidity control device, and the main controller controls the start or stop of the first temperature control device through the first compartment environment monitoring unit according to environmental parameters.

[0142] The first actuator includes a first ventilation system configured to regulate gas exchange between the first compartment and the outside. The main controller controls the activation or deactivation of the first ventilation system via the first compartment environmental monitoring unit based on environmental parameters.

[0143] The first ventilation system is also configured for gas exchange between the first compartment and the outside.

[0144] The second ventilation system is also configured for gas exchange between the second compartment and the outside.

[0145] Gas exchange between the first and second compartments is achieved via a gas exchange valve, but this is insufficient to meet the gas exchange requirements between the two compartments. concentration, When the required concentration is reached, the main controller activates or deactivates the first ventilation system, enabling gas exchange between the first compartment and the outside.

[0146] In some embodiments of this application, the second execution unit further includes a second temperature regulation device, and the main controller adjusts the start-up or stop of the second temperature regulation device through the second compartment environment monitoring unit according to environmental parameters.

[0147] The second execution unit also includes a second humidity control device. The main controller adjusts the second compartment environmental monitoring unit to control the start or stop of the second humidity control device according to environmental parameters.

[0148] The second execution unit also includes a second ventilation system configured to regulate gas exchange between the second compartment and the outside. The main controller controls the activation or deactivation of the second ventilation system via the second compartment environmental monitoring unit based on environmental parameters.

[0149] Gas exchange between the first and second compartments is achieved via a gas exchange valve, but this is insufficient to meet the gas exchange requirements between the two compartments. concentration, When the required concentration is reached, the main controller activates or deactivates the second ventilation system, enabling gas exchange between the second compartment and the outside.

[0150] The main controller uses LoRa communication technology to address the IoT requirements of "ultra-long coverage + ultra-long battery life". It enables communication transmission between the main controller and the first compartment control components in the first compartment, and between the main controller and the second compartment control components in the second compartment.

[0151] Figure 1 The temperature control methods for the first and second compartments are as follows:

[0152] Step 1: Enter the desired temperature;

[0153] Step 2: Determine the difference between the internal temperature and the required temperature;

[0154] If the temperature inside the warehouse is higher than the required temperature, compare the inside temperature with the outside temperature. If the inside temperature is higher than the outside temperature, activate the first and second ventilation systems to use fresh air for cooling, achieving energy saving and consumption reduction. If the inside temperature is lower than the indoor temperature, activate the first and second temperature control systems for cooling, operating at 50% load for the first 10 minutes. After 10 minutes, dynamically adjust according to the incremental operation formula, calculating every 60 seconds to achieve low-load operation and energy saving.

[0155] △U(K)=Kp*(E(t)-E(t-1))+Ki*E(t) + Kd*(E(t)-2E(t-1)+E(t-2));

[0156] Wherein, △U(K): load change value;

[0157] Kp: Adjustment coefficient;

[0158] Ki: Adjustment coefficient;

[0159] Kd: Adjustment coefficient;

[0160] E(t): The difference between the actual temperature and the set temperature in the current cycle;

[0161] E(t-1): The difference between the actual temperature and the set temperature in the previous cycle;

[0162] E(t-2): The difference between the actual temperature and the set temperature in the previous two cycles;

[0163] If the temperature inside the warehouse is lower than the required temperature, the difference between the temperature inside the warehouse and the outdoor temperature is determined. If the temperature inside the warehouse is lower than the outdoor temperature, the first and second ventilation devices are activated to use fresh air for cooling, thereby achieving energy saving and consumption reduction. If the temperature inside the warehouse is higher than the outdoor temperature, the first and second temperature control devices are activated. For the first 10 minutes, the devices operate at 50% load. After 10 minutes, the operation is dynamically adjusted according to the incremental operation formula, and the calculation is performed every 60 seconds to achieve low-load operation and energy saving and consumption reduction.

[0164] △U(K)=Kp*(E(t)-E(t-1))+Ki*E(t) + Kd*(E(t)-2E(t-1)+E(t-2));

[0165] Wherein, △U(K): load change value;

[0166] Kp: Adjustment coefficient;

[0167] Ki: Adjustment coefficient;

[0168] Kd: Adjustment coefficient;

[0169] E(t): The difference between the actual temperature and the set temperature in the current cycle;

[0170] E(t-1): The difference between the actual temperature and the set temperature in the previous cycle;

[0171] E(t-2): The difference between the actual temperature and the set temperature in the previous two cycles;

[0172] Figure 2 The method for adjusting the humidity in the first and second compartments follows the steps described above. Figure 1 The temperature adjustment methods shown are similar:

[0173] Step 1: Enter the desired humidity;

[0174] Step 2: Determine the difference between the humidity inside the warehouse and the required humidity;

[0175] If the humidity inside the warehouse is higher than the required humidity, compare the indoor humidity with the outdoor humidity. If the indoor humidity is higher than the outdoor humidity, activate the first and second ventilation systems to use fresh air for cooling, achieving energy saving and consumption reduction. If the indoor humidity is lower than the indoor humidity, activate the first and second humidity control systems for cooling, operating at 50% load for the first 10 minutes. After 10 minutes, dynamically adjust according to the incremental operation formula, calculating every 60 seconds to achieve low-load operation and energy saving.

[0176] △U(φ)=Kpφ*(E(φ)-E(φ-1))+Kiφ*E(φ) + Kdφ*(E(φ)-2E(φ-1)+E(φ-2));

[0177] Wherein, △U(φ): load change value;

[0178] Kpφ: Adjustment coefficient;

[0179] Kiφ: Adjustment coefficient;

[0180] Kdφ: Adjustment coefficient;

[0181] E(φ): The difference between the actual humidity and the set humidity in the current cycle;

[0182] E(φ-1): The difference between the actual humidity and the set humidity in the previous cycle;

[0183] E(φ-2): The difference between the actual humidity and the set humidity in the previous two cycles;

[0184] If the humidity inside the warehouse is lower than the required humidity, the difference between the humidity inside the warehouse and the outdoor humidity is determined. If the humidity inside the warehouse is lower than the outdoor humidity, the first and second ventilation devices are activated to use fresh air for cooling, thereby achieving energy saving and consumption reduction. If the humidity inside the warehouse is higher than the outdoor humidity, the first and second humidity control devices are activated. For the first 10 minutes, the devices operate at 50% load. After 10 minutes, the operation is dynamically adjusted according to the incremental operation formula, and the calculation is performed every 60 seconds to achieve low-load operation and energy saving and consumption reduction.

[0185] △U(φ)=Kpφ*(E(φ)-E(φ-1))+Kiφ*E(φ) + Kdφ*(E(φ)-2E(φ-1)+E(φ-2));

[0186] △U(φ): Load change value;

[0187] Kpφ: Adjustment coefficient;

[0188] Kiφ: Adjustment coefficient;

[0189] Kdφ: Adjustment coefficient;

[0190] E(φ): The difference between the actual humidity and the set humidity in the current cycle;

[0191] E(φ-1): The difference between the actual humidity and the set humidity in the previous cycle;

[0192] E(φ-2): The difference between the actual humidity and the set humidity in the previous two cycles;

[0193] Figure 3 For the first and second compartments concentration, The concentration adjustment method and specific steps are as follows:

[0194] Step 1: Input Requirements concentration, concentration;

[0195] Step Two: Determine the contents of the warehouse The difference between the concentration and the set value;

[0196] If the vegetables are in the warehouse If the concentration exceeds the set value, activate the first and second ventilation systems. After 10 minutes, dynamically adjust according to the incremental operation formula, calculating every 60 seconds to achieve low-load operation and energy saving.

[0197] △U( =Kp *(E( )-E( -1))+Ki *E(O2)+KdO2*(E( )-2E ( -1)+E( -2));

[0198] △U( ): Load change value;

[0199] Kp Adjustment coefficient;

[0200] Ki Adjustment coefficient;

[0201] Kd Adjustment coefficient;

[0202] E(O2): Actual value in the current period The difference between the concentration and the set value;

[0203] E(O2-1): Actual value of the previous period The difference between the concentration and the set value;

[0204] E(O2-2): Actual value of the previous two cycles The difference between the concentration and the set value;

[0205] If the vegetable warehouse If the concentration is lower than the set value, the status quo will remain unchanged;

[0206] Step 3: Determine the contents of the warehouse The difference between the concentration and the set value;

[0207] If the mushroom storage If the concentration exceeds the set value, activate the first and second ventilation systems. After 10 minutes, dynamically adjust according to the incremental operation formula, calculating every 60 seconds to achieve low-load operation and energy saving.

[0208] △U( =Kp *(E( )-E( -1))+KiO2*E( )+KdO2*(E( )-2E ( -1)+E( -2));

[0209] Among them, △U ( ): Load change value;

[0210] Kp Adjustment coefficient;

[0211] Ki Adjustment coefficient;

[0212] Kd Adjustment coefficient;

[0213] E( ): The difference between the actual O2 concentration and the set value in the current cycle;

[0214] E ( -1): The difference between the actual O2 concentration and the set value in the previous cycle;

[0215] E ( -2): The difference between the actual O2 concentration and the set value in the previous two cycles;

[0216] If the mushroom storage If the concentration is lower than the set value, the status quo will remain unchanged.

[0217] In some embodiments of this application, the application relates to a multi-compartment mixed-use planting container, which includes the aforementioned multi-compartment collaborative environmental control system and a container body. The first compartment and the second compartment are disposed within the container body.

[0218] In some embodiments of this application, this application relates to a method for evaluating environmental parameters of cabin plants, which includes:

[0219] S1: Based on LS-SVM, establish a crop growth status prediction model, considering root temperature, root humidity, root EC value, and multi-compartment data. Concentration difference and multi-compartment Concentration difference can be used to predict crop growth status.

[0220] S11: The input parameters of the crop growth status prediction model include environmental parameters and root parameters. It uses known data to predict unknown results and predicts the growth status of vegetables and mushrooms based on root data and ambient air data.

[0221] The output parameters of the crop growth status prediction model include the crop growth status index; the crop growth status index includes root absorption efficiency (…). ) and plant height growth rate ( );

[0222] S12: Use a Gaussian kernel function to verify the environmental parameters and the root system parameters.

[0223] Input / output vector definition:

[0224]

[0225] Model mapping relationship:

[0226]

[0227] in:

[0228] Input vector for the prediction model; Root temperature, unit: °C; EC value of the root system, unit: mS / cm; Root humidity, unit: % For multi-compartment Concentration difference, unit: μmol·mol⁻¹; For multi-compartment Concentration difference, unit: % The output vector of the prediction model (growth state index); Root absorption efficiency, unit: % Y represents the plant height growth rate, in cm / d; Y^ is the predicted value output by the prediction model. For the weight vector, For bias terms; Here is the Gaussian kernel mapping function, and the corresponding kernel function is: ; , For the sample input vector; σ is the 2-norm; σ is the Gaussian kernel parameter of LS-SVM.

[0229] S13: Model optimization objective: Use 5-fold cross-validation;

[0230] With the goal of minimizing the mean square error (MSE), the kernel parameter σ and the regularization parameter C are optimized. σ is used to determine the granularity of detection, and C determines the fault tolerance rate.

[0231]

[0232] Constrained by the primal optimization problem of LS-SVM:

[0233]

[0234] Where: N is the number of samples, Xi is the slack variable (allowing a small amount of prediction bias); C is the LS-SVM regularization parameter; ξi is the LS-SVM slack variable;

[0235] Five-fold cross-validation involves dividing the data for vegetables or mushrooms into five parts and conducting five tests. In each test, four parts are used as training data, and the fifth part is used as test data. The five tests are not repeated. The average of the five tests is then used to determine the true performance of the model. The optimal values ​​for the parameters σ and C are then determined to ensure the most ideal data is obtained from the five tests. σ (kernel parameter): model judgment; C (regularization parameter): model's tolerance for errors.

[0236] S14: Accuracy Constraints

[0237]

[0238] in, As the coefficient of determination, The relative prediction error of the root-related growth status index; the prediction accuracy is ≥99.2%, and the error cannot exceed 5%, which is equivalent to predicting 100g of vegetables with an error of no more than 5g.

[0239] The LS-SVM model simulates empirical formulas based on multiple growth parameters of vegetables or mushrooms and their growth status. These formulas relate each influencing factor, its weight, and the basic growth amount to the growth status of the vegetables or mushrooms.

[0240] The Gaussian kernel mapping function is a tool for this intermediate selection rule, which can be used to analyze and obtain the corresponding suitable growth state index from the growth state data of multiple vegetables or fungi.

[0241] S2: Establish a multi-warehouse environment optimization control model, which includes:

[0242] S21: Determine the objective function:

[0243] While ensuring the health of vegetable roots and the normal growth of mushrooms, minimize the energy consumption of gas equipment and water and fertilizer.

[0244] Primary objectives: Vegetable root absorption efficiency ≥90% (based on predictive model output); mushroom mycelial viability ≥85%;

[0245] Secondary objectives: Energy consumption for gas exchange (gas exchange valve regulation, fan operation) ≤ 30% of the original system (independent gas supply to a single compartment), and energy consumption for water and fertilizer ≤ 20% of the original system.

[0246] S22: Constraints:

[0247] Vegetable Warehouse Concentration: 400-1400 μmol·mol -1 (Adapted to the photosynthetic needs of vegetables);

[0248] Mushroom Warehouse Concentration: 18%-22%, to suit the respiratory needs of mushrooms.

[0249] Vegetable root temperature: 18-27℃ (avoid low temperature inhibiting absorption and high temperature causing root rot).

[0250] S23: Multi-algorithm collaborative adjustment:

[0251] Definition of decision variables:

[0252] ;

[0253] U: Vector of decision variables for the optimization control model;

[0254] U1: Root temperature regulation amount ((X1=X10+U1, X10 is the reference temperature), unit: ℃;

[0255] U2: Root EC value regulation amount (X2=X20+U2, X20 is the baseline EC value), unit: mS / cm;

[0256] U3: Root humidity control amount (X3 = X30 + U3, where X30 is the baseline humidity), unit: %

[0257] U4: From mushroom warehouse to vegetable warehouse Gas exchange valve opening, unit: %

[0258] U5: From vegetable warehouse to mushroom warehouse Gas exchange valve opening, unit: %

[0259]

[0260] S24: Multi-objective optimization objective function:

[0261] With "satisfying the primary objective and minimizing the secondary objective" as the core, the NSGA-II optimization objective set is constructed as follows:

[0262] Primary objective (constraint objective): To ensure the basic needs for biological growth;

[0263]

[0264] Y1 is predicted by the LS-SVM model, and Z represents the mycelial viability of mushrooms. Strong correlation;

[0265] Secondary objective (minimum objective): Reduce system energy consumption;

[0266]

[0267] Egas: Energy consumption for gas exchange (including energy consumption for gas exchange valve regulation and energy consumption for fan operation), unit: kWh;

[0268] Egas,0: Energy consumption for gas exchange in the original system (independent gas supply to a single compartment), unit: kWh;

[0269] Ewf: Water and fertilizer energy consumption, unit: kWh;

[0270] Ewf,0: Original system water and fertilizer energy consumption, unit: kWh;

[0271] Environmental constraints:

[0272]

[0273] in, It is directly related to the opening degrees of the gas exchange valves U4 and U5;

[0274] Initial population production constraints:

[0275]

[0276]

[0277] Circular revision logic:

[0278] Internal circulation (gas exchange rate revision):

[0279] ;

[0280] ;

[0281] ;

[0282] If in the first cabin The concentration difference is greater than that of the second chamber. When the concentration difference is more than 200 ppm, the gas exchange valve is opened proportionally to increase the gas supply to the first chamber in the second chamber; the greater the difference, the larger the valve is opened, up to a maximum of 100%.

[0283] External circulation (deviation compatibility judgment):

[0284] If the following deviation conditions are met, then U is a feasible solution; otherwise, return to the inner loop for adjustment:

[0285]

[0286] These are reference values ​​for root system parameters;

[0287] This is a reference value for gas concentration;

[0288] This represents the allowable deviation for root EC / humidity.

[0289] S3: Core Logic Closed-Loop Formula:

[0290] ;

[0291] ;

[0292] in,

[0293] U* represents the final optimized control plan to be implemented;

[0294] To find the minimum value among all feasible solutions;

[0295] The electricity cost for the gas equipment required to implement scheme U; including the opening or closing of the gas exchange valve, the opening or closing of the first ventilation equipment, and the opening or closing of the second ventilation equipment;

[0296] The water, fertilizer, and electricity costs required to implement Plan U include the electricity costs for operating water pumps and fertilizer applicators;

[0297] , According to the LS-SVM prediction model, after implementing scheme U, the root absorption efficiency of vegetables must be ≥90%.

[0298] After implementing scheme U, the mycelial viability of mushrooms must be ≥85%.

[0299] Among all the control measures that allow vegetables and mushrooms to grow normally, choose the one that saves the most electricity and water as the final implementation plan.

[0300] The complete process of automatic adjustment of the core logic closed-loop formula system is as follows:

[0301] The first compartment contains the root system monitoring section, the first compartment contains the environmental monitoring section, and the second compartment contains the respiration monitoring section. These two sections monitor root temperature, root salinity, root humidity, and other parameters in both compartments. , Concentration and mushroom vitality are both transmitted to the main controller;

[0302] Using the LS-SVM model, calculate the root absorption for choosing option A and option B.

[0303] All programs with "root absorption <90%" or "mushroom vitality <85%" were eliminated.

[0304] Eliminate all impossible solutions such as "adjusting the root temperature to 30℃" and "opening the gas exchange valve to 110%";

[0305] Of all the remaining options that "can make crops grow well", choose the one with the lowest combined electricity cost for gas and electricity cost for water and fertilizer.

[0306] The optimal solution is fed back to the first compartment, the second compartment, and the control unit, and then repeated after 5 minutes (the time value can be set), forming a closed loop.

[0307] The key to the above process is that the core of the formula is to first ensure the plant's growth, and then consider energy conservation; the order cannot be changed.

[0308] The incorrect logic is to first find the most energy-efficient solution and then check if the crop can survive. The correct logic is to first identify all solutions that will ensure the crop's survival, and then select the most energy-efficient one. This minimizes energy consumption without sacrificing yield and quality. The preceding LS-SVM prediction, NSGA-II optimization, multi-compartment gas circulation, and root monitoring all serve to provide data and computational support for this formula.

[0309] The LS-SVM model is used to predict the current and future growth status of plants. The parameter range scheme for optimal plant growth obtained by NSGA-II is finally determined based on the core logic closed-loop formula to obtain the specific parameter adjustment scheme for energy consumption under the premise of ensuring the absorption efficiency of vegetable roots and the vitality index of mushroom mycelium.

[0310] The optimal solution was found using a multi-warehouse environment optimization control model, and the details are as follows:

[0311] Multiple candidate solutions were shortlisted, and the computer randomly generated various combinations of switches. For example, the root temperature was set to 22℃, and the mushroom compartment was switched to the vegetable compartment. The gas exchange valve opening is 30%; or, the root temperature is 23℃, and the gas is transferred from the mushroom storage compartment to the vegetable storage compartment. The gas exchange valve opening is 35%.

[0312] First round of elimination: Use the LS-SVM prediction model to calculate the "root absorption efficiency" and "mycelial vigor" for each scheme, and remove all schemes with efficiency below 90% and 85% respectively.

[0313] The second round of elimination: remove all schemes that do not meet the physical red lines, such as those with a root temperature of 30℃ and a gas exchange valve opening of 110%.

[0314] Let the remaining excellent solutions "learn from each other", for example, combine the root temperature of different solutions with the opening parameters of the gas exchange valve to generate better new solutions;

[0315] Repeat the above steps multiple times. Among the several solutions obtained, select the one with the lowest combined electricity and water costs, which will be the final implementation plan.

[0316] The above model calculations include acceleration mechanisms, such as the inner and outer loops.

[0317] During the inner loop process, when detected If the concentration exceeds 200 ppm, the gas exchange valve can be opened proportionally without waiting for a complete optimization calculation;

[0318] During the external circulation process, a complete optimization calculation is performed every 5 minutes to check the overall deviation of root temperature, water and fertilizer, and gas concentration, and to fine-tune all parameters. This ensures the reaction speed without wasting electricity due to frequent equipment adjustments.

[0319] After adjustment using the above model:

[0320] Vegetable Warehouse Stable at 1200±50ppm (completely supplied by mushrooms, no need to turn on the engine) generator);

[0321] Mushroom Warehouse The concentration remained stable at 20±1% (completely supplied by vegetables, without the need for oxygenation machines).

[0322] The average weight of lettuce has increased from 138.7g / head to 150-160g / head;

[0323] The growth cycle of mushrooms is shortened by 5-7 days, and the yield is increased by 15%.

[0324] The overall system energy consumption is reduced by more than 35% compared to the original.

[0325] The multi-compartment environmental optimization control model is a computational model that constantly calculates "how to make crops grow best while saving the most money." It takes into account the needs of vegetables and mushrooms, equipment capacity, water costs, and electricity costs, and finally outputs a perfect control plan that allows the two compartments to cooperate with each other.

[0326] Whenever possible, the various aspects and features described and shown in the specification can be applied individually, and these individual aspects can serve as the subject of a divisional application.

[0327] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions claimed by the present invention.

Claims

1. A multi-compartment collaborative environmental control system, characterized in that, include: The first compartment is used for growing vegetables. The first compartment is equipped with a first compartment root system monitoring unit and a first compartment environmental monitoring unit. The first compartment root system monitoring unit is used to monitor the root system parameters of the vegetables, and the first compartment environmental monitoring unit is used to monitor the environmental parameters inside the first compartment. The first compartment is equipped with a first execution unit. The second compartment is used for cultivating mushrooms. The second compartment is equipped with a second compartment respiration monitoring unit and a second compartment environmental monitoring unit. The second compartment root system monitoring unit is used to monitor the respiration status of the mushrooms, and the second compartment environmental monitoring unit is used to monitor the environmental parameters in the second compartment. The second compartment is also equipped with a second execution unit. The control unit includes a first compartment control component, a second compartment control component, and a main controller. The first compartment control component is electrically connected to the main controller, and the second compartment control component is also electrically connected to the main controller. The first compartment root system monitoring unit and the first compartment environment monitoring unit feed back monitoring information to the first compartment control component. The second compartment respiration monitoring unit and the second compartment environment monitoring unit are connected to the second compartment control component. The main controller controls the first actuator based on the information fed back by the first compartment control component, and the main controller controls the second actuator based on the information fed back by the second compartment control component. The first compartment and the second compartment are configured to be in gas communication; the main controller is configured to control the gas communication between the first compartment and the second compartment.

2. The multi-compartment collaborative environmental control system according to claim 1, characterized in that, The first execution unit includes a water and fertilizer execution device, which is configured to adjust the supply of water and fertilizer according to the root parameters; And / or, the first actuator includes a grow lamp configured to adjust the illumination time, and the first chamber root system monitoring unit controls the grow lamp to adjust the illumination time based on the monitored root system parameters; And / or, the first actuator includes a first temperature regulating device, and the main controller controls the start or stop of the first temperature regulating device through the first compartment environment monitoring unit according to the environmental parameters; And / or, the first actuator includes a first humidity regulating device, and the main controller controls the start or stop of the first humidity regulating device through the first cabin environment monitoring unit according to the environmental parameters; And / or, the first actuator includes a first ventilation device configured to regulate gas exchange between the first compartment and the outside; the main controller controls the start or stop of the first ventilation device through the first compartment environment monitoring unit according to the environmental parameters; And / or, the first compartment and the second compartment are connected via a gas exchange valve, and the main controller controls the opening and closing of the gas exchange valve.

3. The multi-compartment collaborative environmental control system according to claim 1, characterized in that, The second actuator includes a second temperature regulating device, and the main controller adjusts the start or stop of the second temperature regulating device through the second compartment environment monitoring unit according to the environmental parameters; And / or, the second actuator includes a second humidity control device, and the main controller controls the start or stop of the second humidity control device through the second cabin environment monitoring unit according to the environmental parameters; And / or, the second actuator includes a second ventilation device configured to regulate gas exchange between the second compartment and the outside; the main controller controls the start or stop of the second ventilation device through the second compartment environment monitoring unit according to the environmental parameters.

4. A multi-compartment mixed planting container, characterized in that, include: The multi-compartment collaborative environmental control system as described in any one of claims 1 to 3; The container body, with the first compartment and the second compartment located inside the container body.

5. A method for evaluating environmental parameters of cabin plants, characterized in that, include: S1: Based on LS-SVM, establish a crop growth status prediction model, considering root temperature, root humidity, root EC value, and multi-compartment data. Concentration difference and multi-compartment Concentration difference can be used to predict crop growth status. S11: The input parameters of the crop growth status prediction model include environmental parameters and root parameters; The output parameters of the crop growth status prediction model include the crop growth status index; the crop growth status index includes root absorption efficiency (…). ) and plant height growth rate ( ); S12: Use a Gaussian kernel function to verify the environmental parameters and the root system parameters. Input / output vector definition: ； Model mapping relationship: ； in: Input vector for the prediction model; Root temperature, unit: °C; EC value of the root system, unit: mS / cm; Root humidity, unit: % For multi-compartment Concentration difference, unit: μmol·mol⁻¹; For multi-compartment Concentration difference, unit: % The output vector of the prediction model (growth state index); Root absorption efficiency, unit: % Y represents the plant height growth rate, in cm / d; Y^ is the predicted value output by the prediction model. For the weight vector, For bias terms; Here is the Gaussian kernel mapping function, and the corresponding kernel function is: ; , For the sample input vector; σ is the 2-norm; σ is the Gaussian kernel parameter of LS-SVM. S13: Model optimization objective: Use 5-fold cross-validation; With the goal of minimizing the mean square error (MSE), the kernel parameter σ and the regularization parameter C are optimized. σ is used to determine the granularity of detection, and C determines the fault tolerance rate. ； Constrained by the primal optimization problem of LS-SVM: ； Where: N is the sample size, X i ξi represents slack variables (allowing a small amount of prediction bias); C is the LS-SVM regularization parameter; ξi is the LS-SVM slack variable. S14: Accuracy Constraints ； in, As the coefficient of determination, This represents the relative prediction error of root-related growth status indices.

6. The method for evaluating cabin plant environmental parameters according to claim 5, characterized in that, S2: Establish a multi-warehouse environment optimization control model, which includes: S21: Determine the objective function: While ensuring the health of vegetable roots and the normal growth of mushrooms, minimize the energy consumption of gas equipment and water and fertilizer. Primary objectives: Vegetable root absorption efficiency ≥90% (based on predictive model output); mushroom mycelial viability ≥85%; Secondary objectives: Energy consumption for gas exchange (gas exchange valve regulation, fan operation) ≤ 30% of the original system (independent gas supply to a single compartment), and energy consumption for water and fertilizer ≤ 20% of the original system.

7. The method for evaluating cabin plant environmental parameters according to claim 5, characterized in that, S2: Establish a multi-warehouse environment optimization control model, which also includes: S22: Constraints: Vegetable Warehouse Concentration: 400-1400 μmol·mol -1 (Adapted to the photosynthetic needs of vegetables); Mushroom Warehouse Concentration: 18%-22%, to suit the respiratory needs of mushrooms. Vegetable root temperature: 18-27℃ (avoid low temperature inhibiting absorption and high temperature causing root rot).

8. The method for evaluating cabin plant environmental parameters according to claim 5, characterized in that, S2: Establish a multi-warehouse environment optimization control model, which also includes: S23: Multi-algorithm collaborative adjustment: Definition of decision variables: ； U: Vector of decision variables for the optimization control model; U1: Root temperature regulation amount ((X1=X10+U1, X10 is the reference temperature), unit: ℃; U2: Root EC value regulation amount (X2=X20+U2, X20 is the baseline EC value), unit: mS / cm; U3: Root humidity control amount (X3 = X30 + U3, where X30 is the baseline humidity), unit: % U4: From mushroom warehouse to vegetable warehouse Gas exchange valve opening, unit: % U5: From vegetable warehouse to mushroom warehouse Gas exchange valve opening, unit: % 。 9. The method for evaluating cabin plant environmental parameters according to claim 5, characterized in that, S2: Establish a multi-warehouse environment optimization control model, which also includes: S24: Multi-objective optimization objective function: With "satisfying the primary objective and minimizing the secondary objective" as the core, the NSGA-II optimization objective set is constructed as follows: Primary objective (constraint objective): To ensure the basic needs for biological growth; ； Y1 is predicted by the LS-SVM model, and Z represents the mycelial viability of mushrooms. Strong correlation; Secondary objective (minimum objective): Reduce system energy consumption; ； Egas: Energy consumption for gas exchange (including energy consumption for gas exchange valve regulation and energy consumption for fan operation), unit: kWh; Egas,0: Energy consumption for gas exchange in the original system (independent gas supply to a single compartment), unit: kWh; Ewf: Water and fertilizer energy consumption, unit: kWh; Ewf,0: Original system water and fertilizer energy consumption, unit: kWh; Environmental constraints: ； in, It is directly related to the opening degrees of the gas exchange valves U4 and U5; Initial population production constraints: ； ； Circular revision logic: Internal circulation (gas exchange rate revision): ； ； ； External circulation (deviation compatibility judgment): If the following deviation conditions are met, then U is a feasible solution; otherwise, return to the inner loop for adjustment: ； These are reference values ​​for root system parameters; This is a reference value for gas concentration; This represents the allowable deviation for root EC / humidity.

10. The method for evaluating cabin plant environmental parameters according to claim 5, characterized in that, Also includes: S3: Core Logic Closed-Loop Formula: ； ； in, U* represents the final optimized control plan to be implemented; To find the minimum value among all feasible solutions; The electricity cost for the gas equipment required to implement Plan U; The water, fertilizer, and electricity costs required to implement Plan U; , According to the LS-SVM prediction model, after implementing scheme U, the root absorption efficiency of vegetables must be ≥90%. After implementing scheme U, the mycelial viability of mushrooms must be ≥85%.

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