Photovoltaic greenhouse distributed environment regulation and light storage load collaborative management system and method

Abstract

The application relates to the technical field of intelligent control of smart agricultural greenhouses, and discloses a photovoltaic greenhouse distributed environment control and light storage load collaborative control system and method, the method comprising the following steps: S1, synchronously collecting and checking multi-source data of the whole system, synchronously collecting temperature, humidity, concentration, environment control equipment running state, photovoltaic power generation power, energy storage charge state and outdoor meteorological parameters at multiple fixed control points in the greenhouse, and performing data cleaning, abnormal data elimination and validity checking on the collected data; through integrated design of multi-source data fault-tolerant collection and checking, environment dynamic self-adaptive partitioning, crop transpiration load pre-judgment, digital twin real-time synchronization, multi-target mixed decision optimization and closed-loop iterative rolling control, the application effectively solves the defects of uneven environment control, control lag and poor adaptability to complex working conditions caused by the fixed area extensive control and passive threshold control of the existing photovoltaic greenhouse.

Description

Technical Field

[0001] This application relates to the technical field of intelligent regulation and control of smart agricultural greenhouses, and in particular to a distributed environmental regulation and photovoltaic storage load collaborative management and control system and method for photovoltaic greenhouses. Background Technology

[0002] With the rapid development of smart agriculture and new energy utilization technologies, photovoltaic agricultural greenhouses, as a composite facility agriculture carrier, are widely used in large-scale crop cultivation. Integrating photovoltaic power generation and facility cultivation functions, they can effectively revitalize agricultural land resources and achieve an organic combination of new energy power generation and agricultural production. During the photovoltaic greenhouse cultivation process, the stability and suitability of environmental parameters such as temperature, humidity, and carbon dioxide concentration directly determine the crop's growth status, yield, and quality. Simultaneously, the synergistic matching capability of photovoltaic power generation, energy storage, and environmental control loads, as well as the level of water and heat resource recycling, are core indicators for measuring the system's low-carbon, intelligent, and efficient operation. Currently, the industry generally improves the greenhouse planting environment and reduces production energy consumption by coordinating environmental control equipment with new energy supporting equipment. Distributed precision control and photovoltaic-storage-load synergistic management have also become the core development direction for the technological iteration of smart photovoltaic greenhouses.

[0003] Existing photovoltaic agricultural greenhouse control technologies mostly adopt a traditional centralized control architecture, relying on fixedly deployed sensors and environmental equipment. Temperature, humidity, ventilation, and carbon supplementation are controlled based on a single threshold logic. The accompanying photovoltaic energy storage systems are mostly independent grid-connected or simple charge-discharge energy storage structures. Environmental control, photovoltaic power generation, and energy storage load operation are independent of each other, lacking an integrated and coordinated management logic. Furthermore, current mainstream control schemes still have many systemic defects, hindering the improvement of intelligent and refined greenhouse operation. Existing greenhouse environmental control often adopts a crude control mode with fixed points and fixed areas, which cannot adapt to the dynamic environmental distribution differences within the greenhouse, making it difficult to achieve balanced control of the entire environment. This easily leads to problems such as localized high humidity, low temperature condensation, and disease induction. Traditional control systems mostly rely on fixed thresholds for passive response control, without considering crop dynamics. The existing photovoltaic-storage-load (PV-SGR) management model relies on predictive control of pre-existing disturbances such as evaporation load and condensation risk on the building envelope. However, this control is significantly delayed and cannot adapt to the differentiated control needs of complex operating conditions such as winter, continuous rainy seasons, and summer. Furthermore, the current PV-SGR management model only achieves simple self-consumption of PV power generation, lacking cross-time-period forward scheduling capabilities. It cannot optimize the charging and discharging sequence of energy storage by combining meteorological changes and time-of-use pricing, resulting in low local PV consumption and high system operating energy consumption and electricity costs. In addition, the existing technology struggles to balance multiple objectives such as crop growth adaptability, environmental control precision, resource recycling rate, and system operating economy. The control of multiple environmental parameters is prone to logical conflicts, and there is no efficient utilization path designed for surplus PV power. Water and heat resource recovery and utilization are insufficient, and the equipment operates with unbalanced load and serious energy waste over a long period of time. Overall, the intelligent and collaborative management performance has significant shortcomings. Summary of the Invention

[0004] To address the technical problems of low precision in environmental control, insufficient adaptability to multiple operating conditions, weak collaborative management and control capabilities of photovoltaic storage and load in existing photovoltaic greenhouses, and poor resource and energy consumption utilization, this application provides a distributed environmental control and collaborative management and control system and method for photovoltaic greenhouses.

[0005] Firstly, this application provides a method for distributed environmental control and coordinated management of photovoltaic storage and load in photovoltaic greenhouses, adopting the following technical solution: A method for distributed environmental regulation and coordinated management of photovoltaic storage and load in photovoltaic greenhouses, the method comprising: S1. Perform synchronous acquisition and verification of multi-source data across the entire system, synchronously acquiring temperature, humidity, and other data at multiple fixed control points within the greenhouse. The system collects data on concentration, environmental control equipment operating status, photovoltaic power generation, energy storage charge status, and outdoor meteorological parameters. It performs data cleaning, abnormal data removal, and validity verification on the collected data. When a key sensor fails, a spatiotemporal correlation interpolation algorithm is used to complete the data and trigger an alarm. When a communication interruption occurs, the local edge controller switches to autonomous mode. S2. Dynamic control area division based on real-time environmental data: Using clustering algorithms, the internal space of the greenhouse is automatically divided into multiple dynamic control areas with similar environmental characteristics based on real-time environmental data. The boundaries of each dynamic control area are updated in real time as the environment changes. S3. Through the state synchronization of the digital twin model and the prediction of crop transpiration, the verified data is input into the digital twin to achieve state synchronization; the crop transpiration prediction sub-model in the digital twin is run to predict the crop transpiration of each dynamic control area in the future prediction time domain based on environmental parameters, crop growth stage and historical data, and the crop transpiration is used as the prediction input of humidity load. S4. Based on model predictive control, a multi-objective optimization problem is constructed, with crop growth adaptability as the highest priority constraint. The system comprehensively considers environmental accuracy, operational economy, resource recycling, and system stability, and solves the cooperative optimal control command sequence online. A hybrid decision-making mechanism of multi-objective rolling optimization and expert rule base is adopted. Under normal operating conditions, the control command is obtained through optimization, and under extreme operating conditions, the expert rules provide fallback control. S5. Coordinated control command issuance and partitioned execution: Extract the first control step length command from the optimal control command sequence, decompose it and issue it to each execution unit, and execute it collaboratively according to the dynamic control region; S6. Feedback and model self-evolution: Collect the effect data after the control is executed and feed it back to the digital twin to complete parameter calibration; upload the running data to the cloud to realize the iterative update of the model.

[0006] By adopting the above technical solutions, this invention achieves fault-tolerant acquisition of multi-source data in greenhouses, completes dynamic zoning control based on clustering algorithms, realizes pre-load control by combining digital twins and evapotranspiration prediction, and effectively solves the problems of traditional extensive control and response lag by combining multi-objective optimization, expert support and closed-loop iterative rolling control, thereby improving the intelligence and precision of greenhouse control.

[0007] Optionally, in step S3, the digital twin integrates an environmental dynamics model, an energy system model, and an external disturbance prediction model; The state-space equation of the environmental dynamic model is: in, This is a state vector, containing the temperature, humidity, and other parameters of each dynamically controlled region. Concentration and energy storage state of charge, The control vector includes the core power of dehumidification, the opening degree of the damper, the flow distribution of the refrigeration system, and the charging and discharging power of the energy storage. This represents the crop transpiration disturbance vector. This is the external disturbance vector, which includes outdoor meteorological parameters and time-of-use electricity prices. Here is the state transition matrix. To control the input matrix, The input matrix is ​​the evaporation disturbance matrix.

[0008] By adopting the above technical solution, this invention constructs a digital twin state-space model that integrates multiple perturbation factors, accurately depicts the dynamic coupling relationship between greenhouse environment, equipment and crop transpiration, improves the model simulation and prediction accuracy, and provides reliable model support for the system's advanced and precise regional regulation.

[0009] Optionally, in step S4, the objective function of the multi-objective optimization problem is: in, To predict the length of the time domain, , , , , The dynamic weighting coefficients for each target component are dynamically adjusted by the central controller based on operating mode, seasonal period, electricity price, and crop growth stage. This is a target component for crop growth adaptability, representing the penalty value for environmental parameters deviating from the suitable range for the crop. This is a component representing the environmental precision target, indicating the deviation between environmental parameters and their set values ​​in each dynamically controlled area. The economic performance component represents the sum of electricity purchase costs and solar curtailment penalties. The negative value represents the efficiency of condensate and waste heat recovery, which is the target component for resource recycling. The target component for system stability characterizes the frequency of equipment start-up and shutdown and the degree of load balance. The constraints include the crop growth safety range, equipment operating boundaries, and system power balance constraints.

[0010] By adopting the above technical solution, this invention establishes a multi-objective optimization function that takes into account crop growth, environmental precision, operating costs, resource recovery and system stability. By dynamically weighting and adapting to different working conditions, and combining multi-dimensional constraints to solve for the optimal strategy, it overcomes the defects of traditional single regulation and takes into account both planting needs and economical and efficient operation.

[0011] Optionally, the rigid priority mechanism for environmental regulation includes: judge If the concentration does not meet the standard, then proceed with priority. Regulation: Turn on the fresh air supply when outdoor fresh air conditions are suitable. Activate when fresh air conditions are unsuitable Gas fertilizer supply; exist Once the concentration reaches the target, check if the temperature meets the target. If not, adjust the temperature accordingly. At low temperatures, the heat adsorption of the dehumidification core is used first to raise the temperature; at high temperatures, ventilation is used first to lower the temperature. After the temperature reaches the target, it is determined whether the humidity reaches the target. If it does not reach the target, the dehumidification core is activated to dehumidify according to the humidity load difference of each dynamic control zone. Finally, environmental control instructions are generated, and after being integrated and verified with energy dispatch instructions and resource recycling instructions, they are issued.

[0012] By adopting the above technical solution, the present invention establishes... Temperature and humidity are adjusted in stages with priority, and carbon replenishment mode is switched intelligently according to fresh air conditions. Temperature and humidity are adjusted differently for each zone. Multiple commands are integrated and verified before being issued to avoid conflicts in the control logic and ensure that the core growth environment of crops meets the standards first.

[0013] Optionally, for low temperature and high humidity operating conditions in winter, the following differentiated scheduling strategies shall be implemented: In terms of environmental control, the dehumidification core is activated first, utilizing its adsorption heat to achieve heating while dehumidifying. Establish an adsorption heat generation model, calculate the heat generation in real time, and dynamically adjust the ratio of dehumidification power to electric heating power to maximize the utilization of adsorption heat. For shaded areas with high humidity and low temperature, prioritize increasing the operating rate of dehumidification cores in these areas; The regenerated exhaust air is returned to the room after waste heat recovery and condensation treatment. In terms of energy dispatch, a strategy of prioritizing photovoltaic power, supplementing with energy storage, and using grid electricity as a safety net is implemented, utilizing off-peak electricity prices at night to charge energy storage. Multiple dehumidification units employ a polling start / stop method to balance the load; In terms of resource recycling, all condensate is recycled for irrigation, and all waste heat from the regenerated exhaust air is used to preheat fresh air.

[0014] By adopting the above technical solution, this invention employs a differentiated control strategy for low temperature and high humidity conditions in winter, dynamically adjusts the ratio of adsorption heat and electric heating power, balances temperature and humidity in different zones, and combines photovoltaic and energy storage coordinated scheduling with full recovery of water and heat, effectively reducing the energy consumption of greenhouse operation in winter and solving the problem of uneven environmental conditions.

[0015] Optionally, for continuous rainy, low-temperature, and high-humidity conditions, the following differentiated scheduling strategies may be implemented: In terms of environmental control, a condensation risk prediction model for building envelopes is established to predict the condensation risk level of each dynamic control area in real time. Differentiated dehumidification strategies are implemented based on risk levels; the higher the risk level, the greater the dehumidification intensity. start up Gas fertilizer supplementation is used to ensure crop growth; Reduce the speed of the fresh air handling unit to the lowest possible level to maintain a slight positive pressure indoors; All regenerated exhaust air is returned to the room. In terms of energy dispatch, priority is given to using energy storage for power supply, and a strategy of discharging energy storage during flat periods and charging it during off-peak periods is implemented. Optimize the operation sequence of the dehumidification core and adopt a staggered operation mode to reduce total energy consumption; In terms of abnormal protection, the rainproof louvers of the fresh air inlet automatically close in rainy or snowy weather, and the pipeline dehumidification procedure is performed regularly.

[0016] By adopting the above technical solutions, this invention implements differentiated dehumidification based on the risk classification of condensation for continuous rainy conditions, combined with energy storage for peak power supply and rain and snow protection mechanisms, effectively avoiding condensation problems in the shed, reducing energy consumption during severe weather, and improving the stability of the system under complex operating conditions.

[0017] Optionally, for high temperature and high humidity conditions in summer, the following differentiated scheduling strategies shall be implemented: In terms of environmental regulation, when If the concentration is insufficient, first determine the enthalpy of the outdoor air. If the outdoor enthalpy is lower than that inside the shed, increase the fresh air volume to replenish carbon and lower the temperature. If the enthalpy is higher than that inside the shed, then activate... Gas fertilizer supply; When the outdoor enthalpy is low, ventilation cooling is prioritized; when the outdoor enthalpy is high, the refrigeration system is activated to pre-cool and dehumidify the introduced fresh air. For the sunny, high-temperature areas, prioritize increasing the air supply and dehumidification capacity; In terms of energy dispatch, priority is given to using photovoltaic power to drive equipment, and excess power is used for energy storage charging; When photovoltaic power generation significantly exceeds the load and energy storage is full, a dehumidification capacity pre-reservation strategy is implemented: By increasing the regeneration temperature and airflow of the dehumidification core, excess photovoltaic power is converted into dehumidification capacity reserves, which are then released when humidity subsequently rises.

[0018] By adopting the above technical solution, this invention addresses the high temperature and humidity conditions in summer by intelligently switching control modes based on enthalpy value discrimination and innovatively adding a pre-reserve mechanism for the dehumidification capacity of surplus photovoltaic power, effectively improving the local photovoltaic consumption rate and solving the problems of high energy consumption and high curtailment rate in summer control.

[0019] Optionally, the coordinated scheduling of photovoltaic storage and load also includes cross-period pre-scheduling based on ultra-short-term forecasts: By accessing weather and electricity price forecasts for the next 24 hours, the system can predict photovoltaic output, environmental load, and electricity costs. Establish a cross-time period optimization model with the goal of minimizing the total 24-hour operating cost, and optimize the energy storage charging and discharging plan and equipment operation sequence; During periods when photovoltaic output significantly exceeds the load, priority is given to charging energy storage and pre-stocking dehumidification capacity; during periods when photovoltaic output is insufficient or electricity prices are high, priority is given to using energy storage and pre-stocked dehumidification capacity. The forecast data is updated in real time, and the scheduling plan is adjusted continuously.

[0020] By adopting the above technical solution, this invention combines ultra-short-term weather and electricity price forecasts to construct a cross-period energy optimization model, pre-plans the timing of energy storage charging and discharging and dehumidification storage, and continuously corrects the scheme in real time, thereby achieving optimal cross-period configuration of photovoltaic power and significantly improving the economic efficiency of system operation.

[0021] Optionally, the expert rule base mentioned in step S4 adopts a hierarchical classification design, which includes safety rules, crop survival rules, environmental stress rules, economic optimization rules and system maintenance rules in descending order of priority; When multiple rules are triggered simultaneously, the higher-priority rule overrides the lower-priority rule.

[0022] By adopting the above technical solution, the present invention constructs a hierarchical expert rule base, manages multiple types of operating rules according to priority, and high-priority rules cover low-priority rules, effectively avoiding control conflicts caused by multiple rules being triggered at the same time, and improving the system's fault tolerance and stability under extreme operating conditions.

[0023] Secondly, this application provides a distributed environmental control and photovoltaic-storage-load coordinated management system for photovoltaic greenhouses, adopting the following technical solution: A distributed environmental control and photovoltaic-storage-load coordinated management system for photovoltaic greenhouses, used to implement the distributed environmental control and photovoltaic-storage-load coordinated management method for photovoltaic greenhouses as described in any of the above-mentioned methods, the system comprising: A distributed sensing network is used to collect environmental parameters inside the greenhouse, equipment operating status, energy system data, and outdoor meteorological parameters. The distributed environmental control unit includes multiple independently controllable control points, each equipped with a distributed heat and humidity recovery dehumidification core. The gas fertilizer supply device, electric air valve group and local edge controller, the dehumidification core adopts a rotary dehumidification structure, and uses adsorption heat to achieve heating while dehumidifying; The centralized air handling and distribution unit includes a fresh air handling unit, an integrated dual-evaporator refrigeration system and an air supply duct network. The refrigeration system uses the same variable frequency compressor to drive two independent evaporators, forming a dual evaporation temperature zone loop. The water, heat and power co-supply and recovery unit includes a photovoltaic array, a hybrid energy storage system, an intelligent electrical cabinet, a condensate recovery system and a regenerative exhaust waste heat recovery system. The hybrid energy storage system consists of a lithium battery pack and a supercapacitor pack. The central intelligent control and decision-making core is equipped with a dynamic zoning module, a digital twin simulation module, a crop transpiration prediction module, a multi-objective optimization decision-making module, and an expert rule base module, which are used to execute the distributed environmental regulation and photovoltaic storage load collaborative management method of photovoltaic greenhouses.

[0024] By adopting the above technical solutions, this invention builds a modular collaborative hardware architecture, relying on the collaborative work of multi-source sensing, distributed control, air treatment, water, heat and electricity recovery and intelligent decision-making cores, to provide reliable and comprehensive hardware support for the implementation of the entire set of refined collaborative management and control methods.

[0025] In summary, this application includes at least one of the following beneficial technical effects: This invention effectively solves the defects of existing photovoltaic greenhouses caused by fixed-area extensive control and passive threshold control, such as uneven environmental management, control lag, and poor adaptability to complex working conditions. It adopts an integrated design that integrates multi-source data fault-tolerant acquisition and verification, dynamic environmental adaptive zoning, crop transpiration load pre-prediction, digital twin real-time synchronization, multi-objective hybrid decision optimization, and closed-loop iterative rolling control. At the same time, it improves the problems of low intelligence, lack of adaptive optimization capability, and weak linkage between photovoltaic and energy storage environmental management in traditional systems, and realizes refined, adaptive, and stable all-weather control of the greenhouse environment. Attached Figure Description

[0026] Figure 1 This is a flowchart of the steps of the distributed environmental regulation and photovoltaic-storage-load coordinated management method for photovoltaic greenhouses proposed in this invention.

[0027] Figure 2 This is a schematic diagram of the rigid priority mechanism for environmental regulation proposed in this invention.

[0028] Figure 3 This is a structural diagram of the smart agricultural greenhouse proposed in this invention.

[0029] Figure 4 This is a schematic diagram of the integrated dual-evaporator vapor compression refrigeration cycle system proposed in this invention.

[0030] Figure 5 This is a flowchart of the intelligent control system for smart agricultural greenhouses proposed in this invention.

[0031] Figure 6 This is the architecture diagram of the photovoltaic greenhouse distributed environmental regulation and photovoltaic-storage-load collaborative management system proposed in this invention. Detailed Implementation

[0032] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0033] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0034] This application discloses a method for distributed environmental control and coordinated management of photovoltaic storage and load in photovoltaic greenhouses, referring to... Figure 1 - Figure 5 The method includes: S1. Perform synchronous acquisition and verification of multi-source data across the entire system, synchronously acquiring temperature, humidity, and other data at multiple fixed control points within the greenhouse. Concentration, environmental control equipment operating status, photovoltaic power generation, energy storage charge status, and outdoor meteorological parameters are collected from multiple heterogeneous sources. The data is uniformly aligned in time sequence, cleaned, abnormal data is removed, and validity is verified. When a key sensor fails, a spatiotemporal correlation interpolation algorithm is used to accurately complete the missing data and trigger equipment maintenance alarms. When a communication interruption occurs, the local edge controller automatically switches to local autonomous operation mode to ensure uninterrupted system operation. S2. Based on real-time environmental data, the dynamic control area is divided into multiple dynamic control areas with similar environmental characteristics. Using a clustering algorithm, the internal space of the greenhouse is automatically divided into multiple dynamic control areas with similar environmental characteristics according to the differences in the real-time environmental distribution of the entire greenhouse. The boundary of each dynamic control area can be dynamically updated in real time as the environmental parameters inside the greenhouse change, adapting to the non-uniform environmental distribution characteristics inside the greenhouse. S3. Synchronization of digital twin model status and prediction of crop transpiration: The verified and accurate operating data is input into the pre-constructed digital twin in real time to realize the real-time synchronous update of the physical greenhouse and the virtual model; the crop transpiration prediction sub-model built into the digital twin is run, and the crop transpiration in each dynamic control area within the future prediction time domain is accurately predicted by combining real-time environmental parameters, characteristics of different growth stages of crops and historical growth and operation data, and the predicted transpiration is used as the pre-judgment input of the humidity load in the greenhouse; S4. Based on model predictive control, a multi-objective optimization problem is constructed, with crop growth adaptability as the highest priority rigid constraint. The system comprehensively considers the accuracy of environmental regulation, the economic efficiency of system operation, the recycling rate of water and heat resources, and the stable operation of equipment systems. The optimal control command sequence is solved online in a coordinated manner across the entire domain. A hierarchical hybrid decision-making mechanism of "multi-objective rolling optimization + expert rule base" is adopted. Under normal operating conditions, the precise control commands are obtained by model optimization. Under extremely complex operating conditions, the safety fallback control is achieved through the expert rule base. S5. Coordinated control command issuance and partitioned execution: Extract the real-time command of the first control step in the optimal control command sequence, decompose the global command into subdivided commands for each dynamic control area and each execution device, and issue them precisely. Complete the partitioned coordinated control execution according to the dynamic partitioned control logic. S6, closed-loop feedback and model self-evolution: real-time acquisition of environmental regulation effects and system operation data after the execution of control commands, and feedback back to the digital twin to complete online calibration and correction of model parameters; at the same time, all operation data is uploaded to the cloud platform to realize continuous iterative updates and performance optimization of the model; after the end of the current control cycle, based on the latest real-time operation status of the greenhouse, the above steps are iteratively executed to achieve all-weather adaptive, dynamic and precise control of the greenhouse environment and the photovoltaic storage system.

[0035] Through the above technical solution, this embodiment provides a method for distributed environmental regulation and coordinated management of photovoltaic greenhouses and solar energy storage load. The method effectively solves the defects of existing photovoltaic greenhouses, such as uneven environmental management, lagging regulation, and poor adaptability to complex working conditions, by adopting fixed-area extensive regulation and passive threshold regulation. It integrates multi-source data fault-tolerant acquisition and verification, dynamic environmental adaptive zoning, pre-judgment of crop transpiration load, real-time synchronization of digital twins, multi-objective hybrid decision optimization, and closed-loop iterative rolling regulation. At the same time, it improves the problems of low intelligence, lack of adaptive optimization capability, and weak linkage between photovoltaic energy storage and environmental management in traditional systems, and realizes refined, adaptive, and stable all-weather management of the greenhouse environment.

[0036] In one embodiment, in step S3, the digital twin integrates an environmental dynamics model, an energy system model, and an external disturbance prediction model; The state-space equation of the environmental dynamic model is: in, This is a state vector, containing the temperature, humidity, and other parameters of each dynamically controlled region. Concentration and energy storage state of charge data are collected in real time by on-site sensing devices and used to characterize the current overall environmental and energy operation status of the greenhouse. The control vector includes the core dehumidification power, damper opening, refrigeration system flow distribution, and energy storage charging and discharging power. It is a controllable execution variable output after system optimization, and its values ​​strictly correspond to the rated operating range of the equipment. This is the crop transpiration disturbance vector, calculated and output in real time by the transpiration prediction sub-model. It is used to quantify the humidity disturbance caused by crop transpiration in different zones and is the core input for changes in humidity load within the greenhouse. This is the external disturbance vector, obtained in real time by outdoor weather stations and electricity pricing platforms. It includes outdoor meteorological parameters and time-of-use electricity prices, and is used to characterize the random disturbances of the external environment on the greenhouse system. The state transition matrix, identified through the greenhouse's historical unloaded operation data system, is used to characterize the natural decay and evolution patterns of the greenhouse environment and energy storage status. The control input matrix, obtained through equipment calibration experiments, is used to characterize the weights of each equipment's control actions on the greenhouse environment. The transpiration disturbance input matrix is ​​obtained by fitting crop growth experiment data and is used to quantify the impact of crop transpiration on the humidity environment inside the greenhouse.

[0037] In step S4, the objective function of the multi-objective optimization problem is: in, To predict the time domain length, the preset number of rolling optimization steps for the system is obtained through calibration by matching the device's computing power with the control response speed. The typical value is 5 to 20 steps, with a single-step control duration of 1 to 5 minutes. This is used to limit the look-ahead time of a single optimization, balancing optimization accuracy and computational load. , , , , The dynamic weight coefficients for each target component are calculated in real time by the central controller based on a preset weight mapping model, combined with real-time operating mode, seasonal period, time-of-use electricity price, and crop growth stage. The values ​​range from [0,1], and the total weight is 1. For example, the weights are adjusted upwards during the crop seedling stage. Weighting and peak electricity consumption periods adjusted upwards Weights enable dynamic adaptation of target priorities. This is the target component for crop growth adaptability, calculated by the difference between real-time environmental parameters and the suitable ranges for each crop growth stage. It characterizes the degree of penalty for environmental parameters deviating from the optimal range for crop growth; a larger value indicates a higher risk of crop growth stress. The environmental precision target component is calculated by comparing the measured values ​​and set values ​​of environmental parameters in each dynamically controlled area. It is used to quantify the overall environmental control deviation and ensure the uniformity of the environment across zones. The economic performance target component is obtained by accumulating real-time electricity purchase costs and photovoltaic curtailment penalty values, and is used to constrain system electricity costs and photovoltaic resource waste. As the objective component for resource recycling, the negative values ​​of condensate and waste heat recovery efficiencies are used to inversely incentivize the system to maximize the recovery and utilization rate of hydrothermal resources. The target component for system stability is calculated by the number of equipment start-ups and shutdowns, power fluctuation amplitude, and load deviation of multiple equipment. It is used to suppress frequent equipment operation and balance equipment load. The optimization constraints include crop growth safety range constraints, equipment rated operating boundary constraints, system real-time power balance constraints, and zoned airflow coupling constraints. All of these are fixed threshold ranges preset based on greenhouse equipment parameters and crop planting standards, used to ensure the safety and feasibility of the optimization results.

[0038] Through the above technical solution, this embodiment provides a method for intelligent collaborative management and control of photovoltaic greenhouses based on multi-objective dynamic weight coupling and multi-disturbance fusion modeling. The method constructs a multi-objective optimization function with dynamic weights to match the operational needs of multiple scenarios, and combines a digital twin state space model that integrates multiple disturbance factors to accurately depict the dynamic operation law of the greenhouse. This method solves the defects of existing technologies such as poor adaptability of single-objective optimization, low model coupling accuracy, and inability to quantify multi-dimensional disturbances. It can accurately solve the optimal control strategy that takes into account crop growth, control accuracy, energy consumption cost, resource recovery and equipment stability, and greatly improve the accuracy of greenhouse management and control and the overall operational efficiency.

[0039] In one embodiment, the rigid priority mechanism for environmental regulation includes: Pre-marking based on crop variety and growth stage The standard suitable range thresholds for temperature and humidity are obtained through calibration of agricultural planting databases and experimental fitting, and are used as fixed preset parameters for the system, such as the vegetable growth period. The suitable range is 800–1200 ppm, the suitable temperature range is 18–28℃, and the suitable relative humidity range is 60%–85%. The system prioritizes determining the location inside the shed. If the concentration is within the preset suitable range, and the concentration is too low and does not meet the growth requirements, the carbon supplementation regulation logic will be executed first. Fresh air conditions are determined by outdoor meteorological sensors. If the temperature, humidity, and cleanliness of the fresh air are within the allowable range for crops, the fresh air conditions are considered suitable. At this time, the fresh air system is turned on to introduce outdoor air to replenish the system. ; If the outdoor temperature is high, low, or humid, making direct air intake unsuitable, then turn off the fresh air intake and start the ventilation system. The gas fertilizer supply device precisely replenishes carbon, and the gas fertilizer supply flow rate is based on... Dynamic matching of concentration differences; exist Once the concentration reaches the target, the system determines whether the real-time temperature inside the greenhouse is within the preset suitable range. If the temperature deviates from the threshold, temperature control is activated. When the greenhouse temperature is below the suitable lower limit, the heat adsorbed by the dehumidification core is reused first for passive heating, without the need for additional energy consumption; when the greenhouse temperature is above the suitable upper limit, fresh air ventilation is turned on first to achieve free ventilation and cooling. After the temperature parameters meet the standard, the system determines the humidity status inside the greenhouse. If the humidity is too high, it differentiates the start-up, shutdown and operation power of the dehumidification core in each area according to the real-time humidity load and humidity deviation of each dynamic control area, so as to achieve precise dehumidification in each area. Ultimately, environmental control commands, energy storage scheduling commands, and hydrothermal resource recovery commands are logically verified and integrated from multiple dimensions to avoid conflicts between multiple devices before being uniformly issued for execution, ensuring orderly control.

[0040] For low temperature and high humidity conditions in winter, the following differentiated scheduling strategies will be implemented: The parameters for determining winter operating conditions are an average daily outdoor temperature below 10℃ and a relative humidity inside the shed above 80%. These parameters are collected and determined in real time by outdoor weather stations and sensors inside the shed. In terms of environmental control, the system prioritizes the activation of the distributed dehumidification core, utilizing the adsorption heat generated during the rotary dehumidification process to achieve simultaneous dehumidification and passive heating. The core parameters of the built-in adsorption heat generation model are obtained from the equipment calibration test. The instantaneous heat generation can be calculated in real time based on the real-time operating power and regeneration temperature of the dehumidification core. The conventional dehumidification power range is 30% to 100% of the rated power, corresponding to the real-time dynamic change of the adsorption heat output. The system aims to achieve the required temperature in each zone of the greenhouse, dynamically adjusting the output power of the dehumidification core and the auxiliary power of electric heating to maximize the use of free adsorption heat and reduce the energy consumption of electric heating. For areas like the shaded sections of greenhouses that experience long-term low temperature and high humidity, the system adaptively increases the proportion of core dehumidification operation in these areas, typically increasing the operating time by 20% to 40% compared to sunny areas, thus balancing the temperature and humidity environment throughout the greenhouse. In winter, the active exhaust equipment is turned off to maintain a slightly positive pressure inside the shed. The regenerated exhaust air is returned to the shed after waste heat recovery and condensation treatment. In terms of energy dispatch, a strategy of "photovoltaic priority, energy storage supplement, and grid power as a backup" is adopted. Photovoltaic output is prioritized for supplying control equipment, and at night, the off-peak electricity price of the grid (the off-peak period from 22:00 to 8:00 the next day) is used to fully charge the energy storage. Multiple dehumidification cores adopt a polling start-stop mechanism, with each unit running continuously for no more than 2 hours, effectively balancing the equipment load; In terms of resource recycling, 100% of the condensate is recycled and stored in a water tank for crop irrigation, and all the waste heat from the regenerated exhaust is used for preheating the fresh air, thus achieving closed-loop utilization of resources.

[0041] For continuous rainy, low-temperature, and high-humidity conditions, the following differentiated scheduling strategies will be implemented: The continuous rainy weather condition is determined by a combination of continuous triggering by the rain sensor, outdoor humidity greater than 85%, and no solar irradiance, which is the operating condition that the system can automatically identify. In terms of environmental control, a built-in condensation risk prediction model for the building envelope is used. The model parameters are obtained by thermal test calibration of the greenhouse building envelope. The outdoor temperature and humidity, the temperature and humidity of the entire greenhouse and the thermal conductivity of the wall are used as input parameters to calculate the condensation risk value of each zone in real time. The risk is divided into three levels: low, medium and high. The risk threshold is a preset fixed value. High risk corresponds to the inner wall temperature being close to the dew point temperature and extremely easy to condense. The system matches the dehumidification intensity according to the risk level. The dehumidification core in high-risk areas operates at full load, the dehumidification core in medium-risk areas operates at half load, and the dehumidification core in low-risk areas operates intermittently, effectively avoiding condensation problems. Continuous start-up under rainy conditions The air fertilizer supply avoids the carbon deficiency problem caused by insufficient fresh air, while the fresh air unit is reduced to the minimum maintenance speed to only ensure a slight positive pressure inside the greenhouse, prevent the backflow of outdoor humid air, and all regenerated exhaust air is reused indoors. In terms of energy dispatch, priority is given to power supply through energy storage systems, and a price matching strategy of "discharging during normal periods and charging during off-peak periods" is implemented to avoid peak electricity price periods. Optimize the operating sequence of multiple dehumidifier core units and adopt a cross-regional staggered operation mode to avoid the high-power operation period of the equipment and reduce the overall peak energy consumption. In terms of abnormal protection, the fresh air rainproof louvers will automatically close in rainy or snowy weather, leaving only the minimum ventilation opening. The system will automatically perform a duct dehumidification procedure every 1 to 3 hours to prevent condensation and mold growth on the inner wall of the air supply duct.

[0042] For high temperature and high humidity conditions in summer, the following differentiated scheduling strategies will be implemented: The conditions for determining summer operating conditions are that the average daily outdoor temperature is above 30℃ and the solar irradiance is consistently high, which is identified in real time by meteorological parameters. In terms of environmental control, the outdoor air enthalpy value is the core judgment parameter. The enthalpy value is obtained by real-time calculation of outdoor temperature and humidity and is used to accurately determine the cold, hot and dry properties of fresh air. When the outdoor enthalpy is lower than the indoor enthalpy, the fresh air is considered of high quality, and the fresh air volume is increased to simultaneously achieve carbon replenishment and ventilation cooling; when the outdoor enthalpy is higher than the indoor enthalpy, the fresh air is considered of poor quality due to humidity and heat, and the fresh air system is shut off and activated. Gas fertilizer supply; When the outdoor temperature and humidity are high, the integrated dual evaporator refrigeration system is activated to pre-cool and dehumidify the fresh air before sending it into the greenhouse. For the sunny and high-temperature zones of the greenhouse, the air supply volume and dehumidification volume are adaptively increased, and the air volume can be increased by 30% to 50%, eliminating local high temperature and high humidity dead zones. In terms of energy dispatch, photovoltaic power is given priority to drive all control equipment, and surplus power is given priority to charge energy storage. When the photovoltaic output exceeds the limit and the energy storage charge status reaches the upper limit threshold of 95%, the dehumidification capacity pre-reserve strategy is activated. By increasing the core regeneration temperature of dehumidification (usually by 5-10℃) and the regeneration air volume, the desiccant adsorption and moisture storage capacity is enhanced, and the excess photovoltaic power is converted into the dehumidification reserve performance of the equipment. When the high temperature and high humidity load surges in the future, the reserve capacity is released to reduce the mains power consumption.

[0043] Photovoltaic-storage-load coordinated scheduling also includes cross-period pre-scheduling based on ultra-short-term forecasts: The system accesses ultra-short-term forecast data for the next 24 hours. Meteorological forecast data and time-of-use electricity price data are obtained in real time by the cloud platform, while photovoltaic output and environmental load are calculated by the built-in forecast model. The forecast update cycle is once every 15 minutes, realizing rolling updates. A cross-time period optimization model is constructed with the goal of minimizing the total operating cost throughout 24 hours. The optimization variables include the charging and discharging power of energy storage at each time moment, the start-up and shutdown sequence of the equipment, and the reserve of dehumidification capacity. The constraints include the maximum / minimum charge threshold of energy storage, the maximum operating power of the equipment, and the daily charging and discharging frequency limit. During periods of high photovoltaic power generation surplus, priority should be given to completing the pre-reservation of energy storage charging and dehumidification capacity; During periods of insufficient photovoltaic output and peak electricity prices, energy storage charging is stopped, and priority is given to releasing stored electrical energy and reserve dehumidification capacity to smooth out peaks and fill valleys and reduce operating costs. The system updates forecast data in real time and adjusts the scheduling plan on a rolling basis for each time period to ensure that long-term scheduling is adapted to real-time operating conditions.

[0044] The expert rule base mentioned in step S4 adopts a hierarchical classification design, which includes safety rules, crop survival rules, environmental stress rules, economic optimization rules, and system maintenance rules in descending order of priority. When multiple rules are triggered simultaneously, the higher-priority rule overrides the lower-priority rule; The rule base hierarchy and priority are pre-defined and fixed logic of the system. The trigger thresholds for various rules are all obtained from industry planting standards, equipment safety manuals, and a large amount of historical operating data. Among them, safety rules have the highest priority, covering emergency protection logic such as equipment overload, overheating, leakage, and communication failure; crop survival rules are next, including life-saving control logic such as extreme high and low temperatures and high humidity stress; environmental stress rules are used to correct local environmental exceedance problems; economic optimization rules are used for energy consumption reduction and cost optimization; and system maintenance rules are used for balanced equipment maintenance. When multiple rules are triggered in parallel, high-priority rules are executed first, and low-priority instructions are blocked to avoid conflicts in the control logic and ensure the safety of system operation and the survival of crops under extreme conditions.

[0045] Through the above technical solution, this embodiment provides a precise control method for photovoltaic greenhouses with multi-condition adaptive adaptation and forward-looking scheduling of photovoltaic storage load. The method solves the defects of existing greenhouses, such as chaotic control logic, weak adaptability to complex conditions, low photovoltaic absorption rate, lack of forward-looking scheduling capability, and easy conflict of multi-rule control, by setting hierarchical progressive environmental control priorities, winter / rainy / summer multi-condition differentiated control logic, photovoltaic dehumidification capacity reserve, 24-hour cross-time period energy pre-scheduling, and hierarchical rule conflict avoidance mechanism. It realizes precise control of greenhouse environment and efficient synergy of photovoltaic and energy storage under different seasons and weather scenarios, and comprehensively improves the system's adaptability to operating conditions, operational stability and economic energy saving.

[0046] This application also discloses a distributed environmental control and photovoltaic-storage-load collaborative management system for photovoltaic greenhouses, referring to... Figure 6 The system is used to implement the distributed environmental regulation and photovoltaic-storage-load coordinated management method for photovoltaic greenhouses as described in any of the above-mentioned methods, the system comprising: A distributed sensing network is used to collect greenhouse internal environmental parameters, equipment operating status, energy system data, and outdoor meteorological parameters around the clock; the distributed sensing network consists of temperature and humidity sensors deployed at multiple points. It consists of sensors, equipment status acquisition modules, photovoltaic power sensors, energy storage monitoring units and outdoor weather stations. The sensor deployment density is determined based on the greenhouse planting area. Typically, one monitoring point is set up every 80 to 120 square meters. It can collect multi-source operating data in the greenhouse in an all-round and comprehensive manner, providing raw data support for subsequent dynamic zoning, load forecasting and optimized control, and ensuring the comprehensiveness and accuracy of data collection. The distributed environmental control unit includes multiple independently controllable control points. The number and spacing of each control point are set according to the length and width of the greenhouse and the requirements for environmental uniformity, with a typical spacing of 5-8 meters. Each control point is independently equipped with a distributed heat and moisture recovery dehumidification core. The system includes a gas-fertilizer supply device, an electric air valve assembly, and a local edge controller. The distributed heat and humidity recovery dehumidification core employs a rotary dehumidification structure. Parameters such as rotary speed and regeneration temperature are calibrated based on the humidity load range within the greenhouse. A typical rotary speed ranges from 8 to 15 r / h, allowing for simultaneous heat generation during dehumidification, enabling simultaneous dehumidification and heating, and adapting to low-temperature, high-humidity conditions. The local edge controller is responsible for data preprocessing, local autonomous control, and command execution at its designated location, ensuring independent and controllable single-point control. The centralized air handling and distribution unit includes a fresh air handling unit, an integrated dual-evaporator refrigeration system, and an air supply duct network. The integrated dual-evaporator refrigeration system uses a single variable frequency compressor to drive two independent evaporators. The compressor operates at a frequency of 30–100 Hz, dynamically adjusted according to load demand. The two evaporators respectively handle fresh air pre-cooling and precise cooling of the shed, forming a closed-loop refrigeration circuit with dual evaporator temperature zones. The temperature difference between the two zones can be stably controlled within 3–8°C, and the cooling capacity can be allocated as needed to meet the graded cooling and dehumidification requirements of different operating conditions. The air supply duct network adopts a zoned air supply layout, corresponding to dynamically adjustable areas, to ensure uniform air supply. The water, heat, and power co-supply and recovery unit includes a photovoltaic array, a hybrid energy storage system, an intelligent electrical cabinet, a condensate recovery system, and a regenerative exhaust waste heat recovery system. The hybrid energy storage system consists of a lithium battery pack and a supercapacitor pack coupled together. The rated capacity of the lithium battery pack is matched to the average daily energy consumption of the greenhouse, typically ranging from 10 to 50 kWh, used to smooth the daytime base load and achieve energy storage across time periods. The rated power of the supercapacitor pack is typically range from 2 to 10 kW, used to buffer the instantaneous power surge generated by equipment start-up and shutdown, and to stabilize and protect the equipment. The intelligent electrical cabinet is used to realize intelligent switching and power distribution between photovoltaic, energy storage, mains power, and load, ensuring the safety of system power consumption. The central intelligent control and decision-making core is connected to the distributed sensing network, the distributed environmental control unit, the centralized air treatment and distribution unit, and the water, heat and electricity co-supply and recycling unit. The communication transmission interval is preset to 100-500ms to ensure real-time command interaction. Internally, it integrates preset dynamic zoning modules, digital twin simulation modules, crop transpiration prediction modules, multi-objective optimization decision-making modules, and expert rule base modules. The parameters and algorithm logic of each module are obtained through previous experimental calibration and training with historical data. It can fully execute the above-mentioned distributed environmental control and photovoltaic storage load co-management methods for photovoltaic greenhouses to achieve integrated intelligent management and control of the entire area.

[0047] Through the above technical solution, this embodiment provides a distributed environmental control and photovoltaic-storage-load collaborative management system for photovoltaic greenhouses. The system builds a dedicated hardware architecture that integrates multi-source distributed sensing, independent zone control, centralized air treatment, water, heat and electricity resource recovery, and intelligent decision-making core. It is matched with a hardware configuration of dual evaporator zoned cooling and hybrid energy storage in stages. This system solves the defects of existing greenhouse control hardware such as low integration, lack of zoned control capability, inability to recover cold and heat resources, and poor adaptability of photovoltaic and energy storage equipment. It can provide a stable and reliable hardware carrier for dynamic and precise environmental control of greenhouses, differentiated adaptation to multiple operating conditions, and collaborative scheduling of photovoltaic, energy storage and load, and greatly improve the implementation stability and operational adaptability of the entire management system.

[0048] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A photovoltaic greenhouse distributed environment regulation and light load coordination and management method, characterized in that, The method includes: S1. Perform synchronous acquisition and verification of multi-source data across the entire system, synchronously acquiring temperature, humidity, and other data at multiple fixed control points within the greenhouse. The system collects data on concentration, environmental control equipment operating status, photovoltaic power generation, energy storage charge status, and outdoor meteorological parameters. It performs data cleaning, abnormal data removal, and validity verification on the collected data. When a key sensor fails, a spatiotemporal correlation interpolation algorithm is used to complete the data and trigger an alarm. When a communication interruption occurs, the local edge controller switches to autonomous mode. S2. Dynamic control area division based on real-time environmental data: Using clustering algorithms, the internal space of the greenhouse is automatically divided into multiple dynamic control areas with similar environmental characteristics based on real-time environmental data. The boundaries of each dynamic control area are updated in real time as the environment changes. S3. Through the state synchronization of the digital twin model and the prediction of crop transpiration, the verified data is input into the digital twin to achieve state synchronization; the crop transpiration prediction sub-model in the digital twin is run to predict the crop transpiration of each dynamic control area in the future prediction time domain based on environmental parameters, crop growth stage and historical data, and the crop transpiration is used as the prediction input of humidity load. S4. Based on model predictive control, a multi-objective optimization problem is constructed, with crop growth adaptability as the highest priority constraint. The system comprehensively considers environmental accuracy, operational economy, resource recycling, and system stability, and solves the cooperative optimal control command sequence online. A hybrid decision-making mechanism of multi-objective rolling optimization and expert rule base is adopted. Under normal operating conditions, the control command is obtained through optimization, and under extreme operating conditions, the expert rules provide fallback control. S5. Coordinated control command issuance and partitioned execution: Extract the first control step length command from the optimal control command sequence, decompose it and issue it to each execution unit, and execute it collaboratively according to the dynamic control region; S6. Feedback and model self-evolution: Collect the effect data after the control is executed and feed it back to the digital twin to complete parameter calibration; upload the running data to the cloud to realize the iterative update of the model.

2. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 1, characterized in that, In step S3, the digital twin integrates an environmental dynamics model, an energy system model, and an external disturbance prediction model; The state-space equation of the environmental dynamic model is: in, This is a state vector, containing the temperature, humidity, and other parameters of each dynamically controlled region. Concentration and energy storage state of charge, The control vector includes the core power of dehumidification, the opening degree of the damper, the flow distribution of the refrigeration system, and the charging and discharging power of the energy storage. This represents the crop transpiration disturbance vector. This is the external disturbance vector, which includes outdoor meteorological parameters and time-of-use electricity prices. Here is the state transition matrix. To control the input matrix, The input matrix is ​​the evaporation disturbance matrix.

3. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 2, characterized in that, In step S4, the objective function of the multi-objective optimization problem is: in, To predict the length of the time domain, , , , , The dynamic weighting coefficients for each target component are dynamically adjusted by the central controller based on operating mode, seasonal period, electricity price, and crop growth stage. This is a target component for crop growth adaptability, representing the penalty value for environmental parameters deviating from the suitable range for the crop. This is a component representing the environmental precision target, indicating the deviation between environmental parameters and their set values ​​in each dynamically controlled area. The economic performance component represents the sum of electricity purchase costs and solar curtailment penalties. The negative value represents the efficiency of condensate and waste heat recovery, which is the target component for resource recycling. The target component for system stability characterizes the frequency of equipment start-up and shutdown and the degree of load balance. The constraints include the crop growth safety range, equipment operating boundaries, and system power balance constraints.

4. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 3, characterized in that, The rigid priority mechanism for environmental regulation includes: judge If the concentration does not meet the standard, then proceed with priority. Regulation: Turn on the fresh air supply when outdoor fresh air conditions are suitable. Activate when fresh air conditions are unsuitable Gas fertilizer supply; exist Once the concentration reaches the target, check if the temperature meets the target. If not, adjust the temperature accordingly. At low temperatures, the heat adsorption of the dehumidification core is used first to raise the temperature; at high temperatures, ventilation is used first to lower the temperature. After the temperature reaches the target, it is determined whether the humidity reaches the target. If it does not reach the target, the dehumidification core is activated to dehumidify according to the humidity load difference of each dynamic control zone. Finally, environmental control instructions are generated, and after being integrated and verified with energy dispatch instructions and resource recycling instructions, they are issued.

5. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 4, characterized in that, For low temperature and high humidity conditions in winter, the following differentiated scheduling strategies will be implemented: In terms of environmental control, the dehumidification core is activated first, utilizing its adsorption heat to achieve heating while dehumidifying. Establish an adsorption heat generation model, calculate the heat generation in real time, and dynamically adjust the ratio of dehumidification power to electric heating power to maximize the utilization of adsorption heat. For shaded areas with high humidity and low temperature, prioritize increasing the operating rate of dehumidification cores in these areas; The regenerated exhaust air is returned to the room after waste heat recovery and condensation treatment. In terms of energy dispatch, a strategy of prioritizing photovoltaic power, supplementing with energy storage, and using grid electricity as a safety net is implemented, utilizing off-peak electricity prices at night to charge energy storage. Multiple dehumidification units employ a polling start / stop method to balance the load; In terms of resource recycling, all condensate is recycled for irrigation, and all waste heat from the regenerated exhaust air is used to preheat fresh air.

6. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 5, characterized in that, For continuous rainy, low-temperature, and high-humidity conditions, the following differentiated scheduling strategies will be implemented: In terms of environmental control, a condensation risk prediction model for building envelopes is established to predict the condensation risk level of each dynamic control area in real time. Differentiated dehumidification strategies are implemented based on risk levels; the higher the risk level, the greater the dehumidification intensity. start up Gas fertilizer supplementation is used to ensure crop growth; Reduce the speed of the fresh air handling unit to the lowest possible level to maintain a slight positive pressure indoors; All regenerated exhaust air is returned to the room. In terms of energy dispatch, priority is given to using energy storage for power supply, and a strategy of discharging energy storage during flat periods and charging it during off-peak periods is implemented. Optimize the operation sequence of the dehumidification core and adopt a staggered operation mode to reduce total energy consumption; In terms of abnormal protection, the rainproof louvers of the fresh air inlet automatically close in rainy or snowy weather, and the pipeline dehumidification procedure is performed regularly.

7. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 6, characterized in that, For high temperature and high humidity conditions in summer, the following differentiated scheduling strategies will be implemented: In terms of environmental regulation, when If the concentration is insufficient, first determine the enthalpy of the outdoor air. If the outdoor enthalpy is lower than that inside the shed, increase the fresh air volume to replenish carbon and lower the temperature. If the enthalpy is higher than that inside the shed, then activate... Gas fertilizer supply; When the outdoor enthalpy is low, ventilation cooling is prioritized; when the outdoor enthalpy is high, the refrigeration system is activated to pre-cool and dehumidify the introduced fresh air. For the sunny, high-temperature areas, prioritize increasing the air supply and dehumidification capacity; In terms of energy dispatch, priority is given to using photovoltaic power to drive equipment, and excess power is used for energy storage charging; When photovoltaic power generation significantly exceeds the load and energy storage is full, a dehumidification capacity pre-reservation strategy is implemented: By increasing the regeneration temperature and airflow of the dehumidification core, excess photovoltaic power is converted into dehumidification capacity reserves, which are then released when humidity subsequently rises.

8. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 7, characterized in that, Photovoltaic-storage-load coordinated scheduling also includes cross-period pre-scheduling based on ultra-short-term forecasts: By accessing weather and electricity price forecasts for the next 24 hours, the system can predict photovoltaic output, environmental load, and electricity costs. Establish a cross-time period optimization model with the goal of minimizing the total 24-hour operating cost, and optimize the energy storage charging and discharging plan and equipment operation sequence; During periods when photovoltaic output significantly exceeds the load, priority should be given to charging energy storage and pre-stocking dehumidification capacity; During periods of insufficient photovoltaic output or peak electricity prices, priority should be given to using energy storage and pre-stored dehumidification capacity; The forecast data is updated in real time, and the scheduling plan is adjusted continuously.

9. The method for distributed environmental regulation and coordinated management of photovoltaic storage and load in a photovoltaic greenhouse according to claim 8, characterized in that, The expert rule base mentioned in step S4 adopts a hierarchical classification design, which includes safety rules, crop survival rules, environmental stress rules, economic optimization rules, and system maintenance rules in descending order of priority. When multiple rules are triggered simultaneously, the higher-priority rule overrides the lower-priority rule.

10. A distributed environmental control and photovoltaic-storage-load coordinated management system for photovoltaic greenhouses, characterized in that, The system is used to implement the distributed environmental regulation and photovoltaic-storage-load coordinated management method for photovoltaic greenhouses as described in any one of claims 1-9, the system comprising: A distributed sensing network is used to collect environmental parameters inside the greenhouse, equipment operating status, energy system data, and outdoor meteorological parameters. The distributed environmental control unit includes multiple independently controllable control points, each equipped with a distributed heat and humidity recovery dehumidification core. The gas fertilizer supply device, electric air valve group and local edge controller, the dehumidification core adopts a rotary dehumidification structure, and uses adsorption heat to achieve heating while dehumidifying; The centralized air handling and distribution unit includes a fresh air handling unit, an integrated dual-evaporator refrigeration system and an air supply duct network. The refrigeration system uses the same variable frequency compressor to drive two independent evaporators, forming a dual evaporation temperature zone loop. The water, heat and power co-supply and recovery unit includes a photovoltaic array, a hybrid energy storage system, an intelligent electrical cabinet, a condensate recovery system and a regenerative exhaust waste heat recovery system. The hybrid energy storage system consists of a lithium battery pack and a supercapacitor pack. The central intelligent control and decision-making core is equipped with a dynamic zoning module, a digital twin simulation module, a crop transpiration prediction module, a multi-objective optimization decision-making module, and an expert rule base module, which are used to execute the distributed environmental regulation and photovoltaic storage load collaborative management method of photovoltaic greenhouses.

Images