Adaptive power generation optimization control system for greenhouse photovoltaic ceiling

Abstract

The invention discloses a self-adaptive power generation optimization control system for a greenhouse photovoltaic ceiling, relates to the technical field of ceiling power generation control, and is used for solving the problem that photosynthetic efficiency and yield are affected due to the fact that crops are in a state of insufficient illumination or excessive illumination in a growth stage. The method comprises the following steps: calculating illumination deviation by combining an illumination demand library and an illumination sensor; judging an illumination level according to deviation and photosynthetic response characteristics; collecting a leaf potential and a transpiration rate to calculate photosynthetic efficiency; comprehensively judging whether to adjust the angle of a photovoltaic panel, outputting an execution instruction, setting different control mechanisms, and executing a balance optimization strategy when the power generation efficiency is high. And when the power generation efficiency is low, an intelligent switching strategy is executed, the productivity is preferentially improved, the crop illumination tolerance is considered, the angle is dynamically adjusted, collaborative optimization of agriculture and energy is realized, and the adaptability and the operation efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of roof power generation control, and more specifically, to a greenhouse photovoltaic roof adaptive power generation optimization control system. Background Art

[0002] With the continuous improvement of agricultural intelligence, greenhouses, as a key component of facility agriculture, are facing challenges in energy management and environmental regulation, which have become important factors restricting the efficient growth of crops. To achieve energy self-sufficiency and reduce carbon emissions, photovoltaic modules have been gradually integrated into greenhouse roofs in recent years, enabling them to combine lighting and power generation functions. However, due to the shading characteristics of photovoltaic modules, which have a certain impact on crop growth, traditional photovoltaic greenhouses generally have the following shortcomings:

[0003] At present, most current greenhouse photovoltaic power generation systems adopt fixed angle or preset schedule adjustment mode, which fails to dynamically adjust according to the actual light demand and growth status of crops, resulting in insufficient or excessive light exposure of crops during the growth stage, affecting photosynthetic efficiency and yield, and unable to effectively balance the contradiction between "maximizing photovoltaic power generation" and "optimizing crop growth illumination". Therefore, a greenhouse photovoltaic ceiling adaptive power generation optimization control system is proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a greenhouse photovoltaic ceiling adaptive power generation optimization control system, which solves the problems raised in the above-mentioned background technology by coupling a light deviation analysis model driven by crop physiological parameters with a photovoltaic angle dynamic optimization strategy based on power generation efficiency feedback.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a greenhouse photovoltaic roof adaptive power generation optimization control system, comprising a light analysis module, an illumination assessment module, an angle optimization module, and a strategy control module. The functions of each module are as follows:

[0007] The light analysis module is used to identify the crop type and growth stage of the current crop and obtain the light intensity of the target crop. It collects the light intensity of the current crop and calculates the light deviation value of the current crop based on the light intensity of the target crop and transmits it to the illumination evaluation module;

[0008] The illumination assessment module is used to receive the illumination deviation value transmitted by the light analysis module, evaluate the illumination condition level of the current crop, detect the leaf potential change and transpiration rate of the current crop, calculate the illumination photosynthetic efficiency of the current crop, and send the illumination condition level and illumination photosynthetic efficiency to the angle optimization module;

[0009] The angle optimization module is used to comprehensively consider the lighting condition level and illumination photosynthetic efficiency input by the illumination evaluation module, determine whether to adjust the photovoltaic panel angle, set execution instructions based on the judgment results, and send the execution instructions to the strategy control module;

[0010] The strategy control module is used to receive the execution instructions passed in by the angle optimization module, and determine whether to enter the optimization mechanism based on the execution instructions. When entering the optimization mechanism, it monitors solar radiation data and photovoltaic operation data to analyze photovoltaic power generation efficiency and executes a balance optimization strategy or an intelligent switching strategy based on the photovoltaic power generation efficiency.

[0011] In a preferred embodiment, in the light analysis module, an image recognition unit is used to collect images of crops in the current greenhouse area, and the image data is processed based on a crop feature recognition model to identify the crop type and growth stage of the current crop;

[0012] Crop type indicates the specific classification information of the crops currently being planted;

[0013] Growth stage refers to the specific growth stage of a crop during its life cycle.

[0014] In a preferred embodiment, in the light analysis module, the corresponding target crop light intensity is retrieved from the light requirement database according to the identified crop type and growth stage;

[0015] The illumination sensor placed above the crop canopy collects the actual lighting conditions in real time to obtain the current illumination of the crop.

[0016] Perform the difference calculation between the target crop's light intensity and the current crop's light intensity to calculate the current crop's light deviation value;

[0017] The illumination deviation value is transmitted to the illumination evaluation module.

[0018] In a preferred embodiment, in the illumination assessment module, two critical thresholds are set by deriving the photosynthesis-illuminance response curve of the crop, namely, the lower threshold of suitable illumination and the upper threshold of suitable illumination;

[0019] When the light deviation value is less than the lower limit threshold of light suitability, the light condition level is determined to be sufficient;

[0020] When the illumination deviation value is greater than or equal to the lower threshold value of illumination suitability and less than or equal to the upper threshold value of illumination suitability, the illumination condition level is determined to be suitable;

[0021] When the illumination deviation value is greater than the illumination suitability upper limit threshold, the illumination condition level is determined to be insufficient.

[0022] In a preferred embodiment, in the illumination evaluation module, the potential values ​​of different positions on the leaf surface are obtained in real time by a leaf potential sensor configured in the greenhouse environment;

[0023] The potential value is calculated using the numerical differentiation method to obtain the instantaneous rate of change of the leaf potential. The average value of the instantaneous rate of change calculated at multiple time points is taken to obtain the leaf potential change.

[0024] The stomatal conductance sensor and ambient humidity and temperature sensor installed in the greenhouse environment collect the stomatal conductance and relative humidity of the crop leaf surface in real time. Combined with the leaf surface temperature, the transpiration rate is calculated using a standard transpiration calculation model.

[0025] After normalizing the leaf potential change and transpiration rate, a weighted average calculation is performed to obtain the current crop illumination photosynthetic efficiency;

[0026] The light condition level and illuminance photosynthetic efficiency are transmitted to the angle optimization module.

[0027] In a preferred embodiment, in the angle optimization module, the illuminance photosynthetic efficiency is fuzzified, and the fuzzy set is defined as high, medium and low. According to the set threshold range, a first-level fuzzy rule base is established;

[0028] The photosynthesis grade is derived based on the first-level fuzzy rule base, including excellent, medium and poor;

[0029] The photosynthesis level is combined with the light condition level to establish a second-layer fuzzy rule base, which is used to determine whether to adjust the photovoltaic panel angle;

[0030] The Mamdani-type reasoning method and the maximum-minimum synthesis method are used to infer the output fuzzy membership set, which is defuzzified using the centroid method to obtain the adjusted discriminant output value.

[0031] In a preferred embodiment, in the angle optimization module, if the adjustment judgment output value is less than or equal to the adjustment judgment threshold, the photovoltaic panel angle is not adjusted;

[0032] If the adjustment judgment output value is greater than the adjustment judgment threshold, the photovoltaic panel angle is adjusted;

[0033] When the judgment result is to adjust the angle of the photovoltaic panel, a lighting optimization trigger instruction is generated;

[0034] When the result of the determination is that the photovoltaic panel angle is not to be adjusted, an angle maintenance confirmation instruction is generated;

[0035] The lighting optimization trigger instruction or angle maintenance confirmation instruction is transmitted to the strategy control module.

[0036] In a preferred embodiment, in the strategy control module, a lighting optimization trigger instruction or an angle maintenance confirmation instruction is received, and when the lighting optimization trigger instruction is received, the optimization mechanism is entered;

[0037] When receiving the angle hold confirmation command, the current operating state is maintained and the system operates according to the established photovoltaic angle;

[0038] When entering the optimization mechanism, solar radiation data is collected in real time through the solar radiation sensor configured on the top of the greenhouse;

[0039] Solar radiation data refers to the total solar radiation energy received per unit area per unit time;

[0040] Photovoltaic operation data includes the output voltage and output current of the photovoltaic panel. The product of the output voltage and output current is taken as the photovoltaic output power.

[0041] In a preferred embodiment, in the strategy control module, the photovoltaic output power is divided by the product of the solar radiation data and the effective area of ​​the photovoltaic panel to obtain the photovoltaic power generation efficiency;

[0042] Count the photovoltaic power generation efficiency that can be achieved at full power output under standard irradiation conditions, and take the percentile as the power generation efficiency judgment threshold;

[0043] If the photovoltaic power generation efficiency is greater than or equal to the power generation efficiency judgment threshold, it is determined to execute the balance optimization strategy;

[0044] If the photovoltaic power generation efficiency is less than the power generation efficiency judgment threshold, it is determined to execute the intelligent switching strategy.

[0045] Technical effects and advantages of the present invention:

[0046] The present invention obtains the crop type and growth stage through image recognition technology, combines the light demand database and illumination sensor data, calculates the current crop light deviation value, determines the light condition level according to the light deviation and the crop photosynthetic response characteristics, collects leaf potential and transpiration rate through sensors, calculates the illumination photosynthetic efficiency, determines whether the photovoltaic panel angle needs to be adjusted based on the light condition level and illumination photosynthetic efficiency, outputs execution instructions, enters different control mechanisms according to the execution instructions, and executes a balance optimization strategy when the power generation efficiency is high to maintain the dual balance of crop light adaptability and power generation stability; when the power generation efficiency is low, executes an intelligent switching strategy, gives priority to improving photovoltaic production capacity, takes into account the crop's tolerance to light, dynamically adjusts the photovoltaic module angle, realizes the coordinated optimization of the dual goals of agriculture and energy, improves the adaptability, light management accuracy and overall operating efficiency of the greenhouse photovoltaic roof system, improves crop quality and yield while ensuring the optimization of the power generation system operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart for implementing the greenhouse photovoltaic ceiling adaptive power generation optimization control system of the present invention.

[0048] Figure 2 This is a module schematic diagram of the greenhouse photovoltaic ceiling adaptive power generation optimization control system of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figures 1 to 2 The greenhouse photovoltaic roof adaptive power generation optimization control system includes a light analysis module, an illumination evaluation module, an angle optimization module, and a strategy control module. The functions of each module are as follows:

[0051] The light analysis module is used to identify the crop type and growth stage of the current crop and obtain the light intensity of the target crop. It collects the light intensity of the current crop and calculates the light deviation value of the current crop based on the light intensity of the target crop and transmits it to the illumination evaluation module;

[0052] The illumination assessment module is used to receive the illumination deviation value transmitted by the light analysis module, evaluate the illumination condition level of the current crop, detect the leaf potential change and transpiration rate of the current crop, calculate the illumination photosynthetic efficiency of the current crop, and send the illumination condition level and illumination photosynthetic efficiency to the angle optimization module;

[0053] The angle optimization module is used to comprehensively consider the lighting condition level and illumination photosynthetic efficiency input by the illumination evaluation module, determine whether to adjust the photovoltaic panel angle, set an execution instruction (lighting optimization trigger instruction or angle maintenance confirmation instruction) based on the judgment result, and send the execution instruction to the strategy control module;

[0054] The strategy control module is used to receive the execution instructions passed in by the angle optimization module, and determine whether to enter the optimization mechanism based on the execution instructions. When entering the optimization mechanism, it monitors solar radiation data and photovoltaic operation data, analyzes photovoltaic power generation efficiency, and executes a balance optimization strategy or an intelligent switching strategy based on the photovoltaic power generation efficiency.

[0055] The specific implementation is as follows:

[0056] In the light analysis module, the image recognition unit collects images of the crops in the current greenhouse area, and processes the image data based on the crop feature recognition model to identify the crop type and growth stage of the current crop.

[0057] The crop type represents the specific classification information of the currently planted crops, including but not limited to tomatoes, cucumbers, lettuce, etc. In a specific implementation, the crop type is identified based on the planting type of the selected crops in the actual planting area. The crop type is output by extracting and matching multidimensional vectors of visual feature parameters such as crop leaf morphology, stem structure, color distribution, and fruit shape;

[0058] The growth stage indicates the specific growth stage of a crop in its life cycle, including but not limited to the emergence stage, growth stage, flowering stage, and fruiting stage. In specific implementation, the growth stage is determined based on the actual crop growth characteristics and stage division standards. By identifying key growth characteristics of the crop, such as plant height, number of leaves, internode length, and bud or fruit appearance status, combined with time series image analysis and stage feature threshold model, the current crop growth stage is output.

[0059] It should be noted that the image recognition unit refers to a functional component configured in the light receiving analysis module for collecting crop images and performing feature extraction and classification recognition on crop image information; the crop feature recognition model refers to a crop visual feature classification model constructed based on a deep learning algorithm and deployed in the image recognition unit, which is used to identify the type and growth stage of crop targets in the input image.

[0060] Based on the identified crop type and growth stage, the corresponding target crop light intensity is retrieved from the light demand database. The target crop light intensity is a standard value preset based on the optimal light conditions required for the growth of different crops at different periods. The setting of the target crop light intensity is determined comprehensively based on the crop type and the physiological light requirements of its growth stage. Specifically, it includes referring to the recommended light range given in agricultural planting standards, crop cultivation manuals and other literature, combining the experience of agricultural experts with actual planting conditions such as region and season, to calibrate parameters. In a machine learning model with adaptive learning function, the initial set value is dynamically corrected based on long-term monitoring data, which will not be elaborated here.

[0061] The illumination sensor placed above the crop canopy collects the actual lighting conditions in real time to obtain the current crop illumination.

[0062] After obtaining the target crop light intensity and the current crop's illumination, the two are subtracted to calculate the current crop's illumination deviation value. The illumination deviation value reflects the numerical difference between the current illumination and the target illumination. A positive value indicates insufficient illumination, while a negative value indicates excessive illumination.

[0063] It should be explained that the above-mentioned light demand database refers to a structured data set that is pre-constructed and stores the light demand parameters corresponding to various target crops at various growth stages. It is specifically organized in the form of a two-dimensional or multi-dimensional mapping table and has a structured retrieval function; the illuminance sensor refers to an optical sensing device that is configured above the crop and is used to measure the visible light intensity per unit area.

[0064] After completing the calculation of the illumination deviation value, the light analysis module transmits the illumination deviation value to the illumination evaluation module, providing basic data support for subsequent illumination adaptability evaluation and photovoltaic angle adjustment.

[0065] In the illumination assessment module, after receiving the illumination deviation value transmitted by the light analysis module, the illumination status of the current crop is quantitatively evaluated, and then the illumination condition level of the crop is determined.

[0066] By performing interval mapping operation on the illumination deviation value, the illumination condition level is divided into three levels: sufficient, suitable and insufficient;

[0067] Two critical thresholds are set by deducing the crop's photosynthetic-illuminance response curve: the lower threshold for suitable light and the upper threshold for suitable light. These thresholds are used to determine the allowable deviation range of the light deviation value. The lower threshold for suitable light corresponds to the starting point where the photosynthetic rate changes from zero to an effective positive value when the light intensity changes from low to high, indicating that the light meets the minimum requirement for basic photosynthesis to start. The upper threshold for suitable light corresponds to the upper limit point where the photosynthetic efficiency no longer increases significantly with continued light increase, indicating the physiological saturation point of the crop photosynthetic system to light.

[0068] The specific division logic of lighting condition levels is as follows:

[0069] When the light deviation value is less than the lower limit threshold of light suitability, the light condition level is determined to be sufficient;

[0070] When the illumination deviation value is greater than or equal to the lower threshold value of illumination suitability and less than or equal to the upper threshold value of illumination suitability, the illumination condition level is determined to be suitable;

[0071] When the illumination deviation value is greater than the illumination suitability upper limit threshold, the illumination condition level is determined to be insufficient.

[0072] It should be noted that the photosynthetic-illuminance response curve refers to a typical physiological response curve that reflects the relationship between the photosynthesis rate of plants and changes in light intensity. It is used to describe the photosynthetic efficiency and response ability of plants under different light conditions.

[0073] After the light condition level is determined, the leaf potential changes and transpiration rate of the current crop are detected to calculate the illumination photosynthetic efficiency of the current crop;

[0074] The leaf potential sensor configured in the greenhouse environment is used to obtain the potential values ​​at different positions on the leaf surface in real time. The instantaneous change rate of the leaf potential is calculated using the numerical differentiation method. The average of the instantaneous change rates calculated at multiple time points is taken to obtain the leaf potential change.

[0075] The stomatal conductance sensor and ambient humidity and temperature sensor are configured in the greenhouse environment to collect the stomatal conductance and relative humidity of the crop leaf surface in real time. Combined with the leaf surface temperature, the standard transpiration calculation model is used to calculate the water evaporation rate per unit area, that is, the transpiration rate. The calculation formula is as follows:

[0076] ;

[0077] in, is the transpiration rate, is the stomatal conductance, is standard atmospheric pressure, is the vapor pressure difference, is the blade surface temperature, is the relative humidity, Tetens formula is used to calculate the saturated water vapor pressure at the blade surface temperature. The formula is defined as .

[0078] It should be noted that the leaf potential sensor refers to an electrochemical sensing device used to detect changes in the cell membrane potential of crop leaves in real time. By attaching a non-polarized electrode to the surface of the crop leaves, the potential difference between the leaf tissue cells and the reference electrode is used to reflect the dynamic activity of the electron transport chain in plant photosynthesis; the numerical differentiation method refers to a mathematical calculation method that approximates the derivative value of a function at a certain point based on a limited number of sampling point data in the absence of an explicit function expression; the stomatal conductance sensor refers to a plant physiological parameter detection device used to measure the changes in water vapor flux caused by the opening and closing state of the stomata of plant leaves in real time; the ambient humidity and temperature sensor refers to a sensing device used to measure leaf temperature and relative humidity in real time; the standard transpiration calculation model refers to a mathematical model established based on the relationship between plant physiological parameters and environmental meteorological conditions, which is used to calculate the transpiration rate of plant leaves per unit area per unit time.

[0079] After normalizing the leaf potential change and transpiration rate, a weighted average calculation is performed to obtain the current crop illumination photosynthetic efficiency. The calculation formula is as follows:

[0080] ;

[0081] in, is the photosynthetic efficiency of illumination, To normalize the leaf potential change, is the normalized transpiration rate, and It is a weight parameter obtained through experimental calibration, reflecting the contribution of leaf potential changes and transpiration rate to photosynthetic efficiency. Illumination photosynthetic efficiency comprehensively reflects the effects of electrochemical activity of photosynthesis and water metabolism rate on crop photosynthetic efficiency.

[0082] The light condition level and illuminance photosynthetic efficiency are transmitted to the angle optimization module.

[0083] In the angle optimization module, the illumination condition level and illumination photosynthetic efficiency input by the illumination evaluation module are received, and the illumination photosynthetic efficiency is fuzzy processed. The fuzzy set is defined as high, medium and low, and quantified through the triangular membership function. According to the set threshold range, the first layer of fuzzy rule base is established.

[0084] In this embodiment, the threshold range is determined by the frequency distribution curve of illumination photosynthetic efficiency in historical operation data and industry limit standards, which will not be described in detail here.

[0085] The photosynthesis level is derived based on the first-level fuzzy rule base, whose fuzzy sets include excellent, medium and poor. The fuzzy rule examples are as follows:

[0086] If the illumination photosynthetic efficiency is high, the photosynthesis level is excellent;

[0087] If the illumination photosynthetic efficiency is medium, the photosynthesis level is medium;

[0088] If the illumination photosynthetic efficiency is low, the photosynthesis level is poor;

[0089] The membership degree of each fuzzy output item was obtained by calculating the membership degree and the maximum-minimum synthesis method. The fuzzy output was then defuzzified using the centroid method to obtain a photosynthesis grade score with a value interval of [0,1]. The photosynthesis grade was determined based on the photosynthesis judgment interval.

[0090] In this embodiment, the photosynthetic determination interval uses the three-part method to set the boundary value, corresponding to the fuzzy set center point, and can be adjusted based on the frequency distribution statistics of historical data during operation and the plant photosynthesis rate limit in the industry standard.

[0091] The photosynthesis level derived from the illumination photosynthetic efficiency is combined with the light condition level to establish a second-layer fuzzy rule base. The second-layer fuzzy rule base is used to determine whether to adjust the photovoltaic panel angle. The fuzzy rule examples are as follows:

[0092] If the photosynthesis level is excellent and the light condition level is high, the PV panel angle is not adjusted;

[0093] If the photosynthesis level is medium and the light condition level is medium, the PV panel angle is not adjusted;

[0094] If the photosynthesis level is poor and the light condition level is high, adjust the photovoltaic panel angle;

[0095] If the photosynthesis level is medium and the light condition level is high, adjust the photovoltaic panel angle.

[0096] In the fuzzy reasoning process, the Mamdani-type reasoning method and the maximum-minimum synthesis method are used to infer the output fuzzy membership set, which is defuzzified by the centroid method to obtain the adjusted discriminant output value with a value interval of [0,1].

[0097] The adjustment decision threshold is set. In this embodiment, the adjustment decision threshold is set to the median value of the fuzzy output, which can be dynamically set through historical operation data and optimization training.

[0098] If the adjustment judgment output value is less than or equal to the adjustment judgment threshold, the photovoltaic panel angle is not adjusted;

[0099] If the adjustment judgment output value is greater than the adjustment judgment threshold, the photovoltaic panel angle is adjusted.

[0100] It should be noted that the triangular membership function is a commonly used fuzzy membership function, which is used to represent the degree of subordination of elements in a fuzzy set to a certain semantic label; the Mamdani-type reasoning method is a widely used fuzzy rule reasoning method, the core of which is to perform fuzzy mapping and fuzzy output on the input membership based on the rule set; the maximum-minimum synthesis method is a logical operation method used in fuzzy reasoning to synthesize the relationship between multiple rule premises and conclusions. Its core idea is to use the minimum operation to represent the rule matching strength and the maximum operation to aggregate multiple rule results; the centroid method is a commonly used defuzzification method, which represents the exact value of the output variable by calculating the centroid position of the fuzzy output function graph, and is used to convert the fuzzy output set into a single precise numerical result. It will not be described in detail here.

[0101] When the judgment result is to adjust the angle of the photovoltaic panel, a lighting optimization trigger instruction is generated; when the judgment result is not to adjust the angle of the photovoltaic panel, an angle maintenance confirmation instruction is generated to maintain the current configuration, and the lighting optimization trigger instruction or the angle maintenance confirmation instruction is transmitted to the strategy control module.

[0102] In the strategy control module, the light optimization trigger instruction or the angle maintenance confirmation instruction is received. After receiving the light optimization trigger instruction, the optimization mechanism is entered; when the angle maintenance confirmation instruction is received, the current operating state is maintained and the operation is carried out according to the established photovoltaic angle.

[0103] When entering the optimization mechanism, the strategy control module collects solar radiation data in real time through the solar radiation sensor configured on the top of the greenhouse. Solar radiation data refers to the total solar radiation energy received per unit area per unit time.

[0104] It should be explained that the solar radiation sensor refers to an optical sensing device used to measure the total solar radiation energy received per unit area per unit time. In this embodiment, it is used to collect solar radiation data on the top of the greenhouse in real time.

[0105] The collection of photovoltaic operation data includes the output voltage and output current of the photovoltaic panel, and the product of the output voltage and output current is used as the photovoltaic output power;

[0106] The data is collected in real time through the built-in electrical measurement module of the photovoltaic system. The sampling frequency is set according to the system requirements, generally once per second or once per minute.

[0107] The photovoltaic power generation efficiency is calculated based on solar radiation data, the effective area of ​​the photovoltaic panel and the photovoltaic output power. The calculation formula is: ,in, is the photovoltaic output power, is the effective area of ​​the photovoltaic panel, is the solar radiation data, is the photovoltaic power generation efficiency.

[0108] It needs to be explained that the effective area of ​​a photovoltaic panel refers to the physical surface area in the photovoltaic module that actually participates in the reception of light energy and can be used for photoelectric conversion. The effective area of ​​a photovoltaic panel is a fixed value of the photovoltaic panel design specifications and can be obtained through the photovoltaic structure archive database; the photovoltaic structure archive database refers to a special data set that is pre-established and stores the structural parameters of photovoltaic modules deployed in the greenhouse photovoltaic roof.

[0109] The photovoltaic power generation efficiency that can be achieved by full power output under standard irradiation conditions is calculated, and the percentile is used as the threshold for judging the power generation efficiency. The above standard irradiation conditions are determined according to the International Electrotechnical Commission standards, including a light intensity of 1000 watts per square meter, an ambient temperature of 25 degrees Celsius, and a standard solar spectrum of AM1.5, to ensure the uniformity of the test environment and the comparability of the data. The percentile value is not unique. In this embodiment, the range is between 70% and 80%. The specific value is adjusted according to actual application requirements and data distribution characteristics.

[0110] Compare the photovoltaic power generation efficiency with the power generation efficiency judgment threshold:

[0111] If the photovoltaic power generation efficiency is greater than or equal to the power generation efficiency judgment threshold, it is determined to execute the balance optimization strategy;

[0112] If the photovoltaic power generation efficiency is less than the power generation efficiency judgment threshold, it is determined to execute the intelligent switching strategy.

[0113] The balanced optimization strategy ensures that the crop lighting needs are met while maintaining high power generation efficiency, and minimizes energy consumption and mechanical wear caused by frequent adjustments of photovoltaic panels.

[0114] During the specific implementation process, the first step is to evaluate whether the current crop lighting status is within the appropriate range. If the crop lighting conditions are in line with the set physiological light requirement range and the current photovoltaic panel angle is stable without significant fluctuations, the existing angle configuration will remain unchanged. If there is a slight deviation, such as the light is in a critical state but has not exceeded the tolerance range, a slight adjustment will be made based on the light feedback signal to optimize the crop lighting balance.

[0115] The intelligent switching strategy prioritizes improving photovoltaic power generation capacity and allows for significant reconstruction and adjustment of the photovoltaic panel angle within the range of light fluctuations that crops can withstand.

[0116] When executing the intelligent switching strategy, a comprehensive analysis is conducted on the theoretical power generation capacity that can be obtained at different photovoltaic panel angles under the current environment. Combining the prediction results with the current system operating status, the angle with the highest power generation potential is selected as the adjustment target, and actual angle switching is implemented.

[0117] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0118] Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0119] As used herein, the singular forms "a," "an," and "the" may also include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include," "comprising," "having," and the like specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Furthermore, the term "and / or" as used in this specification includes any and all combinations of the relevant listed items.

[0120] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0121] The above description of the disclosed embodiments will enable those skilled in the art to implement or use various modifications of these embodiments, and it will be apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Greenhouse photovoltaic roof adaptive power generation optimization control system, characterized by: It includes light analysis module, illumination evaluation module, angle optimization module and strategy control module. The functions of each module are as follows: The light analysis module is used to identify the crop type and growth stage of the current crop and obtain the light intensity of the target crop. It collects the light intensity of the current crop and combines it with the light intensity of the target crop to calculate the light deviation value of the current crop and transmit it to the illumination assessment module; The illumination assessment module is used to receive the illumination deviation value transmitted by the light analysis module, evaluate the illumination condition level of the current crop, detect the leaf potential change and transpiration rate of the current crop, calculate the illumination photosynthetic efficiency of the current crop, and send the illumination condition level and illumination photosynthetic efficiency to the angle optimization module; The angle optimization module is used to comprehensively consider the lighting condition level and illumination photosynthetic efficiency input by the illumination evaluation module, determine whether to adjust the photovoltaic panel angle, set execution instructions based on the judgment results, and send the execution instructions to the strategy control module; The strategy control module is used to receive the execution instructions passed in by the angle optimization module, and determine whether to enter the optimization mechanism based on the execution instructions. When entering the optimization mechanism, it monitors solar radiation data and photovoltaic operation data, analyzes photovoltaic power generation efficiency, and executes a balance optimization strategy or an intelligent switching strategy based on the photovoltaic power generation efficiency.

2. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 1 is characterized by: In the light analysis module, the image recognition unit collects images of the crops in the current greenhouse area and processes the image data based on the crop feature recognition model to identify the crop type and growth stage of the current crop; Crop type indicates the specific classification information of the crops currently being planted; Growth stage refers to the specific growth stage of a crop during its life cycle.

3. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 1 is characterized by: In the light analysis module, the corresponding target crop light intensity is retrieved from the light requirement database based on the identified crop type and growth stage; The illumination sensor placed above the crop canopy collects the actual lighting conditions in real time to obtain the current illumination of the crop. Perform a difference calculation between the target crop's light intensity and the current crop's light intensity to obtain the current crop's light deviation value; The illumination deviation value is transmitted to the illumination evaluation module.

4. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 1 is characterized by: In the illumination assessment module, two critical thresholds are set by deriving the crop's photosynthesis-illuminance response curve, namely the lower threshold for suitable illumination and the upper threshold for suitable illumination; When the light deviation value is less than the lower limit threshold of light suitability, the light condition level is determined to be sufficient; When the illumination deviation value is greater than or equal to the lower threshold value of illumination suitability and less than or equal to the upper threshold value of illumination suitability, the illumination condition level is determined to be suitable; When the illumination deviation value is greater than the illumination suitability upper limit threshold, the illumination condition level is determined to be insufficient.

5. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 1 is characterized by: In the illumination evaluation module, the potential values ​​at different positions on the leaf surface are obtained in real time through the leaf potential sensor configured in the greenhouse environment; The potential value is calculated using the numerical differentiation method to obtain the instantaneous rate of change of the leaf potential. The average value of the instantaneous rate of change calculated at multiple time points is taken to obtain the leaf potential change. The stomatal conductance sensor and ambient humidity and temperature sensor installed in the greenhouse environment collect the stomatal conductance and relative humidity of the crop leaf surface in real time. Combined with the leaf surface temperature, the transpiration rate is calculated using a standard transpiration calculation model. After normalizing the leaf potential change and transpiration rate, a weighted average calculation is performed to obtain the current crop illumination photosynthetic efficiency; The light condition level and illuminance photosynthetic efficiency are transmitted to the angle optimization module.

6. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 1, characterized in that: In the angle optimization module, the illumination photosynthetic efficiency is fuzzified, and the fuzzy set is defined as high, medium and low. According to the set threshold range, the first layer of fuzzy rule base is established; The photosynthesis grade is derived based on the first-level fuzzy rule base, including excellent, medium and poor; The photosynthesis level is combined with the light condition level to establish a second-layer fuzzy rule base, which is used to determine whether to adjust the photovoltaic panel angle; The Mamdani-type reasoning method and the maximum-minimum synthesis method are used to infer the output fuzzy membership set, which is defuzzified using the centroid method to obtain the adjusted discriminant output value.

7. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 6, characterized in that: In the angle optimization module, if the adjustment judgment output value is less than or equal to the adjustment judgment threshold, the photovoltaic panel angle is not adjusted; If the adjustment judgment output value is greater than the adjustment judgment threshold, the photovoltaic panel angle is adjusted; When the judgment result is to adjust the angle of the photovoltaic panel, a lighting optimization trigger instruction is generated; When the result of the determination is that the photovoltaic panel angle is not to be adjusted, an angle maintenance confirmation instruction is generated; The lighting optimization trigger instruction or angle maintenance confirmation instruction is transmitted to the strategy control module.

8. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 7, characterized in that: In the strategy control module, upon receiving the lighting optimization trigger instruction or the angle maintenance confirmation instruction, the optimization mechanism is entered; When receiving the angle hold confirmation command, the current operating state is maintained and the system operates according to the established photovoltaic angle; When entering the optimization mechanism, solar radiation data is collected in real time through the solar radiation sensor configured on the top of the greenhouse; Solar radiation data refers to the total solar radiation energy received per unit area per unit time; Photovoltaic operation data includes the output voltage and output current of the photovoltaic panel. The product of the output voltage and output current is taken as the photovoltaic output power.

9. The greenhouse photovoltaic roof adaptive power generation optimization control system according to claim 8, characterized in that: In the strategy control module, the photovoltaic output power is divided by the product of solar radiation data and the effective area of ​​the photovoltaic panel to obtain the photovoltaic power generation efficiency; Count the photovoltaic power generation efficiency that can be achieved at full power output under standard irradiation conditions, and take the percentile as the power generation efficiency judgment threshold; If the photovoltaic power generation efficiency is greater than or equal to the power generation efficiency judgment threshold, it is determined to execute the balance optimization strategy; If the photovoltaic power generation efficiency is less than the power generation efficiency judgment threshold, it is determined to execute the intelligent switching strategy.

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