A photovoltaic support control method, network control unit and system

Abstract

The application discloses a photovoltaic support control method, a network control unit and a system, relates to the technical field of photovoltaic power generation, and comprises the following steps: calculating the photosynthetically active radiation reduction amount and the power generation power change amount of each candidate tracking angle relative to the light angle; acquiring the crop light marginal effect coefficient under each candidate tracking angle, determining the crop light marginal effect value according to the crop light marginal effect coefficient and the photosynthetically active radiation reduction amount, and simultaneously determining the power generation marginal effect value according to the power generation power change amount and the real-time electricity price; determining the comprehensive marginal effect value according to the crop light marginal effect value and the power generation marginal effect value, taking the comprehensive marginal effect value as a decision direction signal to determine the target tracking angle and to issue the target tracking angle for execution. The application can quantitatively determine the marginal effect of shading on power generation and crops in real time, automatically selects the tracking angle with the optimal comprehensive marginal effect through dynamic balancing, and realizes the dynamic optimal balance of power generation income and crop yield.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation technology, and in particular to a photovoltaic support control method, network control unit and system. Background Technology

[0002] The agricultural-solar hybrid system deploys photovoltaic modules above farmland to achieve three-dimensional and composite land use. The tracking angle of the photovoltaic tracking bracket determines both the photovoltaic power generation and the effective photosynthetic radiation received by the crops below the panel.

[0003] Existing tracking and control schemes mainly include astronomical tracking, fixed yield priority, and fixed parameter weighting. The astronomical tracking method takes maximizing power generation as its sole objective, ignoring crop light requirements and easily leading to crop yield reduction. The fixed yield priority method places crop light requirements in the highest priority, making it unable to respond to electricity price signals and resulting in lost power generation revenue. The fixed parameter weighting method uses a fixed power generation-agriculture weighting coefficient throughout the year, which cannot adapt to electricity price fluctuations and the differences in shading sensitivity of crops at different growth stages. Summary of the Invention

[0004] In view of the above problems, this application provides a photovoltaic support control method, a network control unit, and a system to achieve a dynamic optimal balance between power generation revenue and crop yield. The specific solution is as follows: The first aspect of this application provides a photovoltaic support control method, including: Multiple candidate tracking angles for the photovoltaic bracket are obtained, and the reduction in photosynthetically active radiation and the change in power generation of each candidate tracking angle relative to the yield angle are determined, wherein the yield angle is the tracking angle that minimizes shading of the ground under the current sun position. Obtain the crop illumination marginal effect coefficient corresponding to each of the candidate tracking angles. The crop illumination marginal effect coefficient represents the marginal effect of a unit change in photosynthetically active radiation on the crop at the corresponding candidate tracking angle. Based on the reduction in photosynthetically active radiation corresponding to the candidate tracking angle and the crop light marginal effect coefficient, determine the crop light marginal effect value of the candidate tracking angle relative to the yielding angle; Based on the change in power generation corresponding to the candidate tracking angle and the real-time electricity price, determine the marginal effect value of power generation of the candidate tracking angle relative to the light-giving angle; Based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle is determined, and the target tracking angle is determined according to the comprehensive marginal effect value. Control the photovoltaic bracket to track the target tracking angle.

[0005] In one possible implementation, obtaining the crop illumination marginal effect coefficients corresponding to each of the candidate tracking angles includes: Obtain physiological characteristic parameters of the crop at its current growth stage. These physiological characteristic parameters include light saturation point, shading sensitivity period penalty coefficient, upper limit of optimal temperature, and maximum growth temperature. The current growth stage of the crop is determined based on the cumulative effective accumulated temperature. Based on the physiological characteristic parameters, the preset crop light marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle, the crop light marginal effect coefficient corresponding to each candidate tracking angle is determined.

[0006] In one possible implementation, determining the crop illumination marginal effect coefficient corresponding to each candidate tracking angle based on the physiological characteristic parameters, a preset crop illumination marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle includes: Determine the relative level of photosynthetically active radiation corresponding to each of the candidate tracking angles, wherein the relative level of photosynthetically active radiation is the ratio of the photosynthetically active radiation corresponding to the candidate tracking angle to the light saturation point; Based on the piecewise function of crop light marginal value and the relative level of photosynthetically active radiation corresponding to each candidate tracking angle, the first crop light marginal effect coefficient corresponding to each candidate tracking angle is determined. in: When the relative level of photosynthetically active radiation is less than 1, the piecewise function is determined to be in the light-deficient region, the marginal effect coefficient of light for the first crop is positive, and it increases as photosynthetically active radiation decreases; When the relative level of photosynthetically active radiation is greater than or equal to 1 and less than a preset light suppression threshold, the piecewise function is determined to be in the light saturation zone, and the marginal effect coefficient of light for the first crop is 0. When the relative level of photosynthetically active radiation is greater than or equal to a preset light suppression threshold, the piecewise function is determined to be in the light over-intensity zone, the marginal effect coefficient of light on the first crop is negative, and decreases as photosynthetically active radiation increases.

[0007] In one possible implementation, determining the crop illumination marginal effect coefficient corresponding to each of the candidate tracking angles based on the physiological characteristic parameters, a preset crop illumination marginal effect model, and the photosynthetically active radiation corresponding to each of the candidate tracking angles further includes: The correction ratio for the shading sensitive period is determined based on the penalty coefficient for the shading sensitive period. The correction ratio for the shading sensitive period is 1 in non-shading sensitive periods and greater than 1 in shading sensitive periods. The marginal effect coefficient of light for the second crop is determined based on the marginal effect coefficient of light for the first crop and the correction factor for the shading sensitivity period.

[0008] In one possible implementation, determining the crop illumination marginal effect coefficient corresponding to each of the candidate tracking angles based on the physiological characteristic parameters, a preset crop illumination marginal effect model, and the photosynthetically active radiation corresponding to each of the candidate tracking angles further includes: When the ambient temperature is greater than the upper limit of the optimal temperature and the marginal effect coefficient of light for the second crop is less than 0, a temperature stress factor is determined based on the degree to which the ambient temperature exceeds the upper limit of the optimal temperature, wherein the ambient temperature is positively correlated with the temperature stress factor and the temperature stress factor is greater than 1. Based on the second crop light marginal effect coefficient and the temperature stress factor, the crop light marginal effect coefficient corresponding to the candidate tracking angle is determined.

[0009] In one possible implementation, determining the reduction in photosynthetically active radiation and the change in power generation of each of the candidate tracking angles relative to the light angle includes: For each candidate tracking angle, combined with meteorological irradiance data and photovoltaic system parameters, the shading analysis function is called to simulate and obtain the average photosynthetically active radiation at the candidate tracking angle, and the photovoltaic power generation function is called to simulate and obtain the AC power generation corresponding to the candidate tracking angle. Obtain the reference photosynthetically active radiation and reference power generation corresponding to the aforementioned light angle; The reduction in photosynthetically active radiation is obtained by subtracting the ground-average photosynthetically active radiation corresponding to the candidate tracking angle from the reference photosynthetically active radiation. The change in power generation is obtained by subtracting the reference power generation from the AC power generation corresponding to the candidate tracking angle.

[0010] In one possible implementation, determining the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, and determining the target tracking angle based on the comprehensive marginal effect value, includes: The sum of the crop illumination marginal effect value and the power generation marginal effect value corresponding to each candidate tracking angle is determined as the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle. The candidate tracking angle corresponding to the maximum comprehensive marginal effect value is determined as the target tracking angle.

[0011] One possible implementation also includes: When the crop is in its growing season and the total horizontal irradiance is greater than the weak light irradiance threshold, the step of obtaining multiple candidate tracking angles of the photovoltaic support is performed. In the absence of effective solar irradiance at night, the target tracking angle is determined to be 0, and the current control cycle is terminated. If the crop is not in its growing season or the total horizontal irradiance is less than or equal to the weak light irradiance threshold, and the real-time electricity price is greater than or equal to 0, then the target tracking angle is determined to be the astronomical sun-tracking angle; if the real-time electricity price is less than 0, then the target tracking angle is determined to be the light-yielding angle.

[0012] A second aspect of this application provides a network control unit, including at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the network control unit can implement the photovoltaic bracket control method of the first aspect or any implementation thereof.

[0013] A third aspect of this application provides a photovoltaic tracking control system, comprising: a network control unit and a tracking control unit; The network control unit and the tracking control unit are communicatively connected; The tracking control unit has a built-in tilt sensor and is parallel to the photovoltaic module; The tracking control unit is used to drive the photovoltaic module to rotate to the target tracking angle in response to the target tracking angle sent by the network control unit; The network control unit is used to execute the photovoltaic support control method of the first aspect or any implementation thereof. Using the above technical solution, the photovoltaic support control method provided in this application obtains multiple candidate tracking angles of the photovoltaic support and determines the reduction in photosynthetically active radiation and the change in power generation relative to the yielding angle for each candidate tracking angle, thereby obtaining the crop illumination marginal effect coefficient for each candidate tracking angle. Based on this, the crop illumination marginal effect value of the candidate tracking angle relative to the yielding angle is determined according to the reduction in photosynthetically active radiation and the crop illumination marginal effect coefficient, thereby quantifying the illumination marginal effect of rotating the support in the sun-tracking direction relative to the yielding angle on the crop. Furthermore, the power generation marginal effect value of the candidate tracking angle relative to the yielding angle is determined according to the change in power generation and the real-time electricity price, thereby quantifying the power generation marginal effect of rotating the support in the sun-tracking direction relative to the yielding angle. Based on this, the comprehensive marginal effect value is used as a decision-making direction signal. The comprehensive marginal effect value of each candidate tracking angle relative to the yield angle is determined based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle. This comprehensive marginal effect value characterizes the net trade-off between the change in power generation revenue and the change in crop illumination revenue after increasing shading relative to the yield angle, and the target tracking angle is determined accordingly. This process enables the system to quantify in real time the marginal effects of blocking a single beam of sunlight on power generation and crops, and to automatically select the tracking angle with the optimal comprehensive marginal effect through a unified technical framework for trade-off comparison. This effectively solves the technical problem that existing tracking control strategies are unable to cope with complex and ever-changing real-time environmental demands, leading to difficulties in achieving a dynamic optimal balance between power generation revenue and crop yield. Therefore, it avoids unreasonable resource allocation caused by fixed weights or a single target orientation, improving the overall operating efficiency and dual economic and ecological benefits of the agricultural-solar complementary system under different electricity price fluctuations and crop growth stages. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0015] Figure 1 A schematic flowchart illustrating a photovoltaic support control method provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a method for obtaining the marginal effect coefficient of crop illumination corresponding to candidate tracking angles, provided in an embodiment of this application. Figure 3 A schematic diagram of a network control unit provided in an embodiment of this application; Figure 4This is a schematic diagram of a photovoltaic tracking control system provided in an embodiment of this application. Detailed Implementation

[0016] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0017] As will be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0018] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0019] This application provides a photovoltaic (PV) bracket control method applied to a network control unit (NCU). The photovoltaic bracket control method of this application embodiment will be described in detail below with reference to the accompanying drawings.

[0020] Reference Figure 1 , Figure 1 This is a flowchart illustrating a photovoltaic support control method provided in an embodiment of this application, as shown below. Figure 1 As shown in the embodiment of this application, a photovoltaic support control method may include steps 101 to 106, which are described in detail below.

[0021] Step 101: Obtain multiple candidate tracking angles for the photovoltaic bracket and determine the reduction in photosynthetically active radiation and the change in power generation relative to the light-emitting angle for each candidate tracking angle.

[0022] When the total horizontal irradiance (GHI) (W / m²) is greater than the low light irradiance threshold (e.g., 100 W / m²), the area is considered to have sufficient sunlight. When crops are in their growing season and the total horizontal irradiance exceeds the low light irradiance threshold, the tracking angle of the photovoltaic support will not only affect crop growth but also the photovoltaic power generation.

[0023] Candidate tracking angles refer to the set of tilt angles that the photovoltaic support may take at the current moment. They can be generated by uniformly and discretely sampling within the mechanically permissible angle range of the photovoltaic support (such as -90° to 90°) according to a preset step size, or by finely sampling with variable step size based on the neighborhood of the historical best angle.

[0024] The light angle is set to the tracking angle that minimizes ground shading under the current sun position. Using the light angle as the core reference, this corresponds to the state where the long side of the photovoltaic module is parallel to the direction of the sun's projection on the ground. In this state, the overlap of shadows between adjacent photovoltaic support rows is minimized, maximizing the average effective photosynthetic radiation received per unit area of ​​the ground.

[0025] For each candidate tracking angle, simulation calculations are performed in conjunction with meteorological environment and the physical characteristics of photovoltaic system to determine the reduction in photosynthetically active radiation and the change in power generation.

[0026] For example, the system acquires current meteorological irradiance data (including total horizontal irradiance, direct irradiance, diffuse irradiance, and solar position information such as zenith angle and azimuth angle) and photovoltaic system parameters (such as support axis azimuth angle, row spacing, and module installation height). It then calls a shadow analysis function to calculate the shadow distribution formed by the photovoltaic modules on the ground at a specific candidate tracking angle, and integrates this to obtain the ground-level average photosynthetically active radiation (PAR). Simultaneously, it calls the photovoltaic power generation functions with built-in inclined plane irradiance models, module temperature models, and DC power models to simulate the AC power generation at that candidate tracking angle. After obtaining the baseline PAR and baseline power generation corresponding to the tracking angle, it subtracts the ground-level average PAR corresponding to the candidate tracking angle from the baseline PAR to obtain the reduction in PAR representing the additional shading caused by the angle adjustment. Finally, it subtracts the baseline power generation from the AC power generation corresponding to the candidate tracking angle to obtain the change in power generation representing the increase or decrease in power generation revenue resulting from the angle adjustment.

[0027] Step 102: Obtain the crop illumination marginal effect coefficient corresponding to each candidate tracking angle. The crop illumination marginal effect coefficient represents the marginal effect of a unit change in photosynthetically active radiation on the crop at the corresponding candidate tracking angle.

[0028] The crop light marginal effect coefficient quantifies the sensitivity of crops to changes in light intensity, that is, the specific impact of each increase or decrease in photosynthetically active radiation (PAER) on crop growth under a specific light environment and crop growth state. This coefficient is dynamically determined based on crop physiological characteristics. The determination process includes, for example,: determining the light saturation point corresponding to the current growth stage of the crop; calculating the ratio of the actual PAER received by the ground at the candidate tracking angle to the light saturation point, i.e., the relative level of PAER; and mapping based on a piecewise function of crop light marginal value. This piecewise function divides light conditions into insufficient light, light saturation, and excessive light zones according to the relative level of PAER: in the insufficient light zone, the coefficient is positive and increases as the light level decreases, indicating that light has a significant promoting effect on crops; in the light saturation zone, the coefficient approaches 0, indicating that increased light no longer brings additional growth benefits; in the excessive light zone, the coefficient is negative, indicating that strong light will cause photoinhibition damage to crops.

[0029] To more accurately reflect the actual physiological state of crops, the determination of the crop light marginal effect coefficient can be modified by incorporating environmental factors. For example, when crops are in shading-sensitive periods (such as flowering and grain-filling stages), a shading-sensitive period penalty coefficient is introduced to weight and amplify the initially calculated coefficient, making the system more sensitive to shading behavior during these sensitive periods and prioritizing light protection. Alternatively, when the ambient temperature exceeds the upper limit of the crop's optimum temperature, a temperature stress factor is used to negatively correct the coefficient, giving higher positive value to shading protection behavior under high temperature and strong light combinations. Through the comprehensive evaluation of these multi-dimensional factors, the obtained crop light marginal effect coefficient can reflect the real marginal contribution of a unit change in light intensity to the crop in real time and accurately.

[0030] Step 103: Determine the crop illumination marginal effect value of the candidate tracking angle relative to the yield angle based on the reduction of photosynthetically active radiation corresponding to the candidate tracking angle and the crop illumination marginal effect coefficient.

[0031] This step transforms the impact of light on crops into quantifiable value indicators. The specific calculation logic is: Crop Light Marginal Effect Value = Reduction in Photosynthetically Active Radiation × Crop Light Marginal Effect Coefficient. This product operation has a clear physical meaning: when the reduction in photosynthetically active radiation is positive (shading occurs) and the crop light marginal effect coefficient is positive (crops lack light, light is scarce), the calculation result is positive, meaning that shading behavior leads to the loss of valuable resources; if the crop is in a photoinhibited state, the marginal effect coefficient is negative, and the calculation result from shading is negative, meaning that shading behavior produces a positive protective benefit by avoiding harmful light. Through this calculation, the impact of shading behavior on crops under different light conditions is uniformly mapped to the same value dimension.

[0032] Step 104: Determine the marginal effect value of power generation of the candidate tracking angle relative to the yield angle based on the change in power generation corresponding to the candidate tracking angle and the real-time electricity price.

[0033] This step converts the increase in physical power generated by photovoltaic (PV) into marginal economic benefits. Real-time electricity prices, as a market signal, directly reflect the current economic value of a unit of electricity. The formula for calculating the marginal effect value of power generation is: Marginal effect value of power generation = Change in power generation × Real-time electricity price. Here, the change in power generation can be positive (increasing power generation relative to adjusting the tracking angle) or negative (reducing power generation), and the real-time electricity price can be positive (profit from electricity sales) or negative (power generation during periods of negative electricity prices incurs fees or penalties). The product of these two values ​​accurately assesses the economic consequences of adjusting the tracking angle: for example, during peak electricity price periods, even sacrificing a small amount of crop sunlight, if the resulting change in power generation is significant, it may yield a large positive marginal effect value, indicating that the angle has a significant economic advantage; while during periods of negative electricity prices, the marginal effect value may be negative, indicating that increasing power generation will lead to economic losses, and the system tends to reduce power generation.

[0034] Step 105: Based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, determine the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle, and determine the target tracking angle according to the comprehensive marginal effect value.

[0035] After obtaining the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, the directional signals output by the two are fused in the same decision space to select the target tracking angle with the optimal comprehensive trade-off. Specifically, the crop illumination marginal effect value and power generation marginal effect value are used as a unified reference point. They respectively represent the crop-side marginal impact and power generation-side marginal impact of the candidate tracking angle relative to this reference point due to increased shading. The two do not need to be dimensionally consistent; they only participate in the calculation as decision direction signals, and are summed to obtain the comprehensive marginal effect value. This comprehensive marginal effect value is not the actual net economic benefit, but rather a decision signal indicating the merits of the photovoltaic support rotation direction: a comprehensive marginal effect value greater than 0 indicates that adjusting the support towards the sun-tracking direction relative to the illumination angle is beneficial; a comprehensive marginal effect value less than 0 indicates that adjusting the support towards the sun-tracking direction relative to the illumination angle is harmful. All candidate tracking angles are traversed, and the angle with the largest comprehensive marginal effect value is selected as the target tracking angle. This embodiment abandons the direct comparison of the two physical absolute values ​​of power generation and illumination, and makes decisions only based on the marginal increment of the light angle relative to the benchmark for each candidate tracking angle. The system can automatically respond to fluctuations in electricity prices and changes in crop physiological state at any time, and maximize the overall benefits of the agricultural-solar complementary system under dynamic trade-offs.

[0036] Step 106: Control the photovoltaic support to track the target tracking angle.

[0037] The target tracking angle is transmitted to the tracking control unit via a network or other communication link. The tracking control unit, acting as the actuator of the photovoltaic support structure, typically incorporates a tilt sensor, motor driver, and control logic. It is configured to receive angle commands from the network control unit and parse them to control the motor rotation.

[0038] During execution, the tracking control unit reads the actual tilt angle of the photovoltaic support and compares it with the target tracking angle, calculates the difference and rotation direction, and then outputs a corresponding pulse signal or drive current to the motor to rotate the photovoltaic support until the actual tilt angle matches the target tracking angle or is within the allowable error range. Through this closed-loop control process, the photovoltaic support adjusts to the optimal posture in real time, maximizing the use of solar resources for power generation while ensuring crop growth, thus achieving the coordinated and optimized operation of the agricultural-solar complementary system.

[0039] The photovoltaic support control method provided in this embodiment uses the light-gathering angle as a physical benchmark to calculate in real time the precise impact of light shading on crop growth and the economic gains and losses caused by changes in power generation under different tracking angles. It dynamically weighs the relationship between these two factors through marginal analysis. This decision-making mechanism based on marginal effects enables the photovoltaic support to automatically adjust to the angle that maximizes overall net benefit based on the crop's current light saturation state, shading sensitivity, and real-time electricity market price signals. This effectively solves the problem that traditional fixed-weight methods cannot adapt to dynamic environmental changes, achieving refined and adaptive control of the agricultural-photovoltaic complementary system under different growth stages and market conditions.

[0040] In one optional embodiment, step 102 in the above embodiments includes the following steps A1-A2: Step A1: Obtain the physiological characteristic parameters of the crop at the current growth stage. The physiological characteristic parameters include light saturation point, shading sensitivity period penalty coefficient, upper limit of optimal temperature, and maximum growth temperature.

[0041] The current growth stage of the crop is determined based on the cumulative effective accumulated temperature.

[0042] The current growth stage represents a discrete state of crop growth and development. This stage is not determined by a simple calendar date, but by a biological process driven by the total effective heat experienced by the crop itself. By calculating the daily effective accumulated temperature and accumulating it from the sowing date, a timescale reflecting the actual physiological age of the crop is formed. The calculation process for the cumulative effective accumulated temperature includes: collecting the daily maximum and minimum temperature data, combining them with the baseline temperature corresponding to the crop variety, and calculating the daily effective accumulated temperature (GDD) using the following formula. The daily GDDs from the sowing date are then accumulated to obtain a dynamically updated cumulative effective accumulated temperature sequence: ; ; In the formula, The highest temperature of the day, in °C; The lowest temperature of the day, in °C; This is the crop base temperature, expressed in °C. The formula filters out low-temperature periods that are ineffective for crop growth by accumulating effective heat above the base temperature. Based on this accumulated effective temperature, it queries a preset crop database for the accumulated temperature threshold range corresponding to each growth stage, matching the current accumulated temperature value to the specific growth stage (e.g., seedling stage, flowering stage, fruiting stage, etc.).

[0043] After determining the current growth stage, the light saturation point, shading sensitivity penalty coefficient, upper limit of optimum temperature, and maximum growth temperature are extracted from the parameter set corresponding to that stage. The light saturation point (LSP) refers to the critical value of light intensity at which the crop's photosynthetic rate no longer increases with increasing light intensity under given environmental conditions; it is usually measured as the photosynthetic photon flux density per unit area per unit time (e.g., μmol / m² / s). The shading sensitivity penalty coefficient is a dimensionless parameter typically ranging from 0 to 1, used to quantify the crop's sensitivity to light deficiency or shading during specific physiological periods (e.g., flowering and pollination period, tuber enlargement period); this coefficient is 0 during non-sensitive periods. The upper limit of optimum temperature and maximum growth temperature define the temperature boundary between the suitable growth zone and the heat stress zone for the crop; their units are typically °C. Obtaining these parameters lays the physiological foundation for subsequent precise quantification of the marginal impact of light on crops.

[0044] Step A2: Based on physiological characteristic parameters, a preset crop light marginal effect model, and photosynthetically active radiation corresponding to each candidate tracking angle, determine the crop light marginal effect coefficient corresponding to each candidate tracking angle.

[0045] The preset crop light marginal effect model can be a computational model. This model includes at least a piecewise function for crop light marginal value, which is used to calculate the crop light marginal effect coefficient corresponding to the candidate tracking angle. The preset crop light marginal effect model may also include a correction function based on the shading sensitivity period penalty coefficient and a correction function based on the temperature stress factor.

[0046] Please see Figure 2 One implementation of step 102 in the above embodiments includes the following steps 1021-1024: Step 1021: Obtain the light saturation point, shading sensitivity period penalty coefficient, optimal temperature upper limit, and maximum growth temperature corresponding to the crop at the current growth stage.

[0047] Step 1022: Determine the first crop light marginal effect coefficient corresponding to the candidate tracking angle based on the piecewise function of crop light marginal value and the photosynthetically active radiation corresponding to the candidate tracking angle.

[0048] The piecewise function of crop light marginal value is a mapping model that transforms the physical intensity of light into a marginal value measure in an economic sense. Based on the light response curve of crop photosynthesis, this function uses a mathematical piecewise approach to describe the differentiated contribution of increasing light intensity by one unit to crop growth at different light levels. Photosynthetically active radiation (PAR) refers to a specific band of solar radiation (typically 400 nm to 700 nm) that can be used for photosynthesis by green plants; its value is calculated from a determined candidate tracking angle and meteorological data.

[0049] In one possible implementation, the process of determining the marginal effect coefficient of light for the first crop includes: firstly, calculating the relative level of photosynthetically active radiation (PADR) with respect to the light saturation point; secondly, calculating the marginal effect coefficient using piecewise logic based on the interval in which this relative level falls. Specifically, if PADR is less than the light saturation point, it indicates insufficient light, and increasing light has a positive promoting effect; in this case, the marginal effect coefficient of light for the first crop is positive, and its value is related to the distance of light from the saturation point. If PADR is in the saturation zone (i.e., greater than the light saturation point but not reaching the inhibition threshold), it indicates that the light has met the crop's maximum photosynthetic requirements, and increasing light no longer produces additional benefits; in this case, the marginal effect coefficient of light for the first crop is 0. If PADR exceeds the light inhibition threshold, it indicates that excessive light leads to light inhibition, and increasing light will instead damage the crop; in this case, the marginal effect coefficient of light for the first crop is negative. This calculation process is formalized as the following decision logic: when hour: ; when hour: ; when hour: ; ; In the formula, This represents the marginal effect coefficient of light on the first crop. This represents the photosynthetically active radiation corresponding to the candidate tracking angle. This represents the light saturation point. Through the piecewise function described above, the nonlinear biological photosynthetic response process is transformed into a quantifiable and comparable numerical index, thus achieving the integration of biological characteristics with engineering control logic.

[0050] Step 1023: Correct the marginal effect coefficient of light for the first crop using the shading sensitivity period penalty coefficient to obtain the marginal effect coefficient of light for the second crop.

[0051] The application of the shading sensitivity period penalty coefficient is a targeted amplification mechanism for the marginal effect coefficient of light on the first crop, aiming to reflect the differentiated tolerance of crops to light changes at different growth stages. The correction process includes: detecting whether the current growth stage belongs to the preset shading sensitivity period list; if so, retrieving the corresponding shading sensitivity period penalty coefficient. According to the penalty coefficient of the shading sensitive period Determine the correction ratio for the shading sensitive period Therefore, based on the first crop light effect coefficient Correction ratio for the shading sensitive period Determine the marginal effect coefficient of light for the second crop. For example, the marginal effect coefficient of light for the first crop can be compared with... Multiplying these yields the marginal effect coefficient of light on the second crop. This corrective logic is mathematically expressed as: ; In the formula, This represents the marginal effect coefficient of light on the second crop. This represents the marginal effect coefficient of light on the first crop. This represents the penalty coefficient during the shading sensitivity period. When... When the value is 0 (non-sensitive period), the correction ratio for the shading sensitive period is... When the value is 1, the marginal effect coefficient of light for the second crop remains unchanged; when When the value is greater than 0 (sensitive period), the correction factor for the shading sensitive period is... If the value is greater than 1, the absolute value of the marginal effect coefficient of the second crop's light is amplified. This amplification mechanism changes the weight distribution in subsequent decisions: in the stage when crops are extremely sensitive to lack of light, even if higher electricity prices drive shading for power generation, the crop loss caused by shading has a greater weight in the overall evaluation function due to the significant increase in the positive marginal effect coefficient, thereby inhibiting the system from making decisions on excessive shading.

[0052] Step 1024: When the ambient temperature is greater than the upper limit of the optimal temperature and the marginal effect coefficient of the second crop light is less than 0, the marginal effect coefficient of the second crop light is corrected by using the ambient temperature, the upper limit of the optimal temperature and the highest growth temperature to obtain the marginal effect coefficient of the crop light corresponding to the candidate tracking angle.

[0053] Temperature stress correction is a dynamic compensation process for changes in crop photosynthetic characteristics under high-temperature stress. When the ambient temperature exceeds the upper limit of the optimum temperature, the crop's photosynthetic apparatus is damaged, and the photoinhibition effect caused by strong light intensifies. At this time, the positive protective effect of shading (i.e., reducing photosynthetically active radiation) is significantly enhanced. The correction process includes: real-time collection of ambient temperature data and comparison with the upper limit of the optimum temperature corresponding to the current growth stage; if the ambient temperature is greater than the upper limit of the optimum temperature and the marginal effect coefficient of the second crop light is negative (indicating that the current light is already in the inhibition zone, and shading is theoretically beneficial to the crop), then a temperature stress factor is constructed based on the ambient temperature, the upper limit of the optimum temperature, and the highest growth temperature. The calculation logic of this temperature stress factor can be: ; ; In the formula, Indicates ambient temperature. This indicates the upper limit of the optimal temperature. This represents the maximum growth temperature. Subsequently, based on the second crop light marginal effect coefficient and the temperature stress factor, the final crop light marginal effect coefficient is determined. For example, the temperature stress factor is multiplied by the second crop light marginal effect coefficient to obtain the final crop light marginal effect coefficient. ; This correction formula uses linear interpolation to proportionally increase the correction factor when the temperature is between the upper limit of the optimum and the maximum growth temperature, up to a maximum of 2.0 times. Through this dynamic correction, when encountering high-temperature heat waves, it can automatically identify the dual damage of strong sunlight to crops (photoinhibition combined with heat stress), thus significantly increasing the marginal value of shading and guiding the photovoltaic support system to actively increase the shading intensity to protect the crops. This process not only demonstrates the synergistic effect of light and temperature factors on crop growth but also ensures the robustness of the control strategy under extreme weather conditions.

[0054] In another optional embodiment, a specific implementation of step 101 in the above embodiments includes the following steps 1011-1014: Step 1011: For each candidate tracking angle, combine meteorological irradiance data and photovoltaic system parameters, call the shadow analysis function to simulate and obtain the average photosynthetically active radiation at the candidate tracking angle, and call the photovoltaic power generation function to simulate and obtain the AC power generation corresponding to the candidate tracking angle.

[0055] Meteorological irradiance data, serving as the fundamental input for solar radiation environment calculations, originates from meteorological station monitoring equipment or satellite remote sensing inversion data. It includes parameters such as solar zenith angle, solar azimuth angle, total horizontal irradiance, direct irradiance, and diffuse irradiance. Photovoltaic system parameters define the physical architecture and equipment characteristics of the photovoltaic power station, including support axis azimuth angle, maximum tracking angle, row spacing, lowest point height of the modules above the ground, physical dimensions of the modules in the row spacing direction, peak power of the modules, power temperature coefficient, and system efficiency. Based on the above input data, physical simulations are performed for each candidate tracking angle generated at the current moment using shading analysis functions and photovoltaic power generation functions. The shading analysis function simulates the shading of the photovoltaic modules on the ground at different tilt angles and azimuths. By calculating the relative relationship between sunlight and module geometry, it accurately quantifies the received irradiance of the ground area and converts the irradiance value into Photosynthetically Active Radiation (PAR), thus obtaining the average PAR of the ground at each candidate tracking angle. The photovoltaic power generation function is based on the physical model of the photovoltaic module. It combines the current irradiance conditions and temperature parameters to calculate the DC power output of the module at the corresponding angle. It further considers system losses such as inverter efficiency and finally calculates the AC power generation corresponding to each candidate tracking angle.

[0056] Step 1012: Obtain the reference photosynthetically active radiation and reference power generation corresponding to the light angle.

[0057] The yield angle refers to the angle of the photovoltaic (PV) support that minimizes ground shading under the current solar position. Using the yield angle as a key reference benchmark, the same shading analysis function and PV power generation function are used to calculate the illumination and power generation state at that specific angle. Specifically, using current meteorological irradiance data, the ground illumination distribution at the yield angle is simulated using the shading analysis function to calculate the average ground irradiance at that angle, and this is converted into a baseline photosynthetically active radiation (PAR). Simultaneously, the module operating state at this angle is simulated using the PV power generation function to obtain the corresponding baseline power generation. These two baseline values ​​represent the ground illumination level and power generation level that the PV system can maintain while maximizing crop sunlight protection, providing a comparative benchmark for subsequently evaluating the changes brought about by other candidate angles.

[0058] Step 1013: Subtract the ground-average photosynthetically active radiation corresponding to the candidate tracking angle from the baseline photosynthetically active radiation to obtain the reduction in photosynthetically active radiation.

[0059] The reduction in photosynthetically active radiation (RADR) characterizes the amount of light resources lost by crops at a given candidate tracking angle relative to the yield angle. This is quantified by subtracting the average RADR at the ground level at the candidate angle from the baseline RADR. A positive result indicates that the candidate angle reduces the RADR reaching the ground compared to the yield angle, thus producing shading. A zero result indicates that the lighting effect is the same as the yield angle. A negative result indicates that the angle provides better ground lighting conditions than the yield angle. This value accurately reflects the direct impact of different tracking angles on the crop growth environment.

[0060] Step 1014: Subtract the reference power generation from the AC power generation corresponding to the candidate tracking angle to obtain the change in power generation.

[0061] The change in power generation represents the increase or decrease in the photovoltaic system's power generation capacity when using a candidate tracking angle relative to the yield angle. This is quantified by performing a subtraction operation: subtracting the baseline power generation from the AC power generation corresponding to the candidate angle. A positive result indicates that the candidate angle increases power generation compared to the yield angle, resulting in greater power generation benefits. A negative result indicates that the angle sacrifices some power generation for other benefits, such as reduced crop shading or compliance with inverse power limits. The calculation results of the reduction in photosynthetically active radiation and the change in power generation will serve as key input data for subsequent marginal effect analysis and trade-off decisions.

[0062] In one optional implementation, a specific implementation of step 105 in the above embodiments includes the following steps 1051-1052: Step 1051: The sum of the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle is determined as the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle.

[0063] Based on the marginal effect values ​​of crop illumination and power generation corresponding to the candidate tracking angles, their sum is calculated and determined as the comprehensive marginal effect value for each candidate tracking angle. This comprehensive marginal effect value is not a quantitative indicator evaluating the absolute overall benefit of the candidate tracking angle, but rather a decision direction indication signal relative to the benchmark yield angle. It characterizes the net trade-off between the marginal impact on power generation and the marginal impact on crops after the candidate angle increases shading compared to the yield angle. The marginal effect values ​​of crop illumination and power generation are output as independent marginal direction signals and superimposed in the same decision space. Thus, each candidate tracking angle obtains a value characterizing its comprehensive superiority or inferiority relative to the benchmark yield angle. The magnitude of this value directly reflects the superiority of the candidate tracking angle relative to the benchmark yield angle, while taking into account both crop growth and power generation benefits.

[0064] Step 1052: Determine the candidate tracking angle corresponding to the maximum comprehensive marginal effect value as the target tracking angle.

[0065] The system iterates through and compares the comprehensive marginal effect values ​​of all candidate tracking angles, selects the maximum value, and determines the candidate tracking angle corresponding to this maximum value as the target tracking angle. This maximum value represents the optimal posture that maximizes both crop light efficiency and power generation efficiency under current environmental conditions, crop growth status, and electricity price levels. After determining the target tracking angle, the system outputs it as a control command to guide the drive actuator to adjust the photovoltaic module's posture. This optimization mechanism based on normalized weighted sums enables the technical solution to automatically and objectively select the optimal solution that balances the dual needs of agriculture and electricity in complex and ever-changing agro-photovoltaic complementary scenarios.

[0066] In one possible implementation, this application embodiment also provides complete control logic coverage of the photovoltaic support control method. The method further includes: when the crop is in its growing season and the total horizontal irradiance is greater than the weak light irradiance threshold, performing the step of acquiring multiple candidate tracking angles of the photovoltaic support; when there is no effective solar irradiance at night, determining the target tracking angle as 0 and ending the current control cycle; when the crop is not in its growing season or the total horizontal irradiance is less than or equal to the weak light irradiance threshold, if the real-time electricity price is greater than or equal to 0, determining the target tracking angle as the astronomical solar tracking angle, and if the real-time electricity price is less than 0, determining the target tracking angle as the light-yielding angle.

[0067] When the total horizontal irradiance (GHI) (W / m²) is greater than the low light irradiance threshold (e.g., 100 W / m²), the area is considered to be in a sufficient light-saturated stage. When crops are in their growing season and the total horizontal irradiance exceeds the low light irradiance threshold, the tracking angle of the photovoltaic (PV) support will affect not only crop growth but also PV power generation. Therefore, the target tracking angle with the largest overall marginal effect can be determined by executing the PV support control method corresponding to steps 101-106 of the above embodiments.

[0068] For operating conditions with no effective solar irradiance at night, the first step is to determine whether it is currently nighttime. This is determined by monitoring the solar zenith angle or total horizontal irradiance. If the solar zenith angle is greater than 90 degrees or the total horizontal irradiance is less than or equal to 0, it is confirmed to be nighttime with no effective solar irradiance. Under these conditions, the photovoltaic modules cannot perform effective photoelectric conversion, and crops do not require photosynthesis at night. Based on this, the target tracking angle is set to 0 degrees, i.e., the photovoltaic support is controlled to return to a horizontal position. This posture not only helps reduce dust accumulation on the module surface but also significantly reduces the wind pressure on the support under wind loads, thereby improving the safety and stability of the system. After determining the target tracking angle, the subsequent calculation process of the current control cycle is directly terminated, and complex marginal effect analysis is no longer performed, thus effectively reducing the computational load on the controller.

[0069] For scenarios where crops are not in their growing season or where total horizontal irradiance is less than or equal to the low light irradiance threshold, the control strategy focuses on power generation optimization or risk mitigation. Crops not in their growing season refers to the period before sowing or after harvest, when there are no crops in the field or crops have ceased growing, and the ground light demand is zero. Total horizontal irradiance less than or equal to the low light irradiance threshold (e.g., less than 100 W / m²) indicates extremely low ambient light intensity, and the shading of photovoltaic modules no longer has a substantial negative impact on crop growth. In either of these situations, the control objective shifts from agricultural-solar balance to a single power generation optimization mode oriented towards economic efficiency. Real-time electricity prices are further introduced as a decision-making basis; when the real-time electricity price is greater than or equal to 0, it indicates that power generation has positive economic value. In this case, the target tracking angle is determined as the astronomical solar tracking angle, which is the optimal incident angle calculated based on the current sun position so that the surface of the photovoltaic module is perpendicular to the incident light. This control method maximizes solar irradiance capture, ensuring economic benefits while avoiding unnecessary angle search calculations when there is no need for agricultural protection.

[0070] When crops are not in their growing season or under low light conditions, and the real-time electricity price is less than 0, the system enters a negative electricity price avoidance mode. Negative electricity prices typically occur when there is an oversupply of electricity, leading to negative market prices. Continuing to generate electricity in this situation would result in economic losses. Since crop light protection is not currently a concern, the system prioritizes mitigating economic risks and determines the target tracking angle as the yield angle. The yield angle refers to the tracking angle that minimizes ground shading under the current sun position, typically manifested as the photovoltaic modules being parallel to the sunlight. This posture minimizes the irradiance received by the inclined surfaces of the modules, thereby proactively reducing power generation to avoid losses from negative electricity prices, while simultaneously minimizing ground shading and maintaining a good ground lighting environment. This logic collectively constitutes a rapid response mechanism for special operating conditions such as non-growing seasons, low light, and nighttime, ensuring the economic efficiency and adaptability of the photovoltaic support control system under different external environments.

[0071] Please see Figure 3 This application also provides a network control unit, including: At least one processor 301 and a memory 302 connected to the processor 301, wherein: the memory 302 is used to store a computer program; and the processor 301 is used to execute the computer program so that the network control unit can implement any of the photovoltaic bracket control methods provided in the embodiments of this application.

[0072] Each module in the aforementioned network control unit can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of the computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0073] Please see Figure 4 This application also provides a photovoltaic tracking control system, which includes a network control unit and a tracking control unit; The network control unit and the tracking control unit are connected in communication. The tracking control unit has a built-in tilt sensor and is parallel to the photovoltaic module; The tracking control unit is used to drive the photovoltaic module to rotate to the target tracking angle in response to the target tracking angle sent by the network control unit; The network control unit is used to execute any of the photovoltaic bracket control methods provided in the embodiments of this application.

[0074] This application also provides a computer program product, including computer-readable instructions, which, when executed on a network control unit, cause the network control unit to implement any of the photovoltaic bracket control methods provided in this application.

[0075] This application also provides a computer-readable storage medium carrying one or more computer programs. When the one or more computer programs are executed by a network control unit, the network control unit can implement any of the photovoltaic bracket control methods provided in this application.

[0076] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0078] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0079] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A photovoltaic support control method, characterized in that, include: Multiple candidate tracking angles for the photovoltaic bracket are obtained, and the reduction in photosynthetically active radiation and the change in power generation of each candidate tracking angle relative to the yield angle are determined, wherein the yield angle is the tracking angle that minimizes shading of the ground under the current sun position. Obtain the crop illumination marginal effect coefficient corresponding to each of the candidate tracking angles. The crop illumination marginal effect coefficient represents the marginal effect of a unit change in photosynthetically active radiation on the crop at the corresponding candidate tracking angle. Based on the reduction in photosynthetically active radiation corresponding to the candidate tracking angle and the crop light marginal effect coefficient, determine the crop light marginal effect value of the candidate tracking angle relative to the yielding angle; Based on the change in power generation corresponding to the candidate tracking angle and the real-time electricity price, determine the marginal effect value of power generation of the candidate tracking angle relative to the light-giving angle; Based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle is determined, and the target tracking angle is determined according to the comprehensive marginal effect value. Control the photovoltaic bracket to track the target tracking angle.

2. The photovoltaic support control method according to claim 1, characterized in that, The step of obtaining the crop illumination marginal effect coefficient corresponding to each of the candidate tracking angles includes: Obtain physiological characteristic parameters of the crop at its current growth stage. These physiological characteristic parameters include light saturation point, shading sensitivity period penalty coefficient, upper limit of optimal temperature, and maximum growth temperature. The current growth stage of the crop is determined based on the cumulative effective accumulated temperature. Based on the physiological characteristic parameters, the preset crop light marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle, the crop light marginal effect coefficient corresponding to each candidate tracking angle is determined.

3. The photovoltaic support control method according to claim 2, characterized in that, The step of determining the crop light marginal effect coefficient corresponding to each candidate tracking angle based on the physiological characteristic parameters, the preset crop light marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle includes: Determine the relative level of photosynthetically active radiation corresponding to each of the candidate tracking angles, wherein the relative level of photosynthetically active radiation is the ratio of the photosynthetically active radiation corresponding to the candidate tracking angle to the light saturation point; Based on the piecewise function of crop light marginal value and the relative level of photosynthetically active radiation corresponding to each candidate tracking angle, the first crop light marginal effect coefficient corresponding to each candidate tracking angle is determined. in: When the relative level of photosynthetically active radiation is less than 1, the piecewise function is determined to be in the light-deficient region, the marginal effect coefficient of light for the first crop is positive, and it increases as photosynthetically active radiation decreases; When the relative level of photosynthetically active radiation is greater than or equal to 1 and less than a preset light suppression threshold, the piecewise function is determined to be in the light saturation zone, and the marginal effect coefficient of light for the first crop is 0. When the relative level of photosynthetically active radiation is greater than or equal to a preset light suppression threshold, the piecewise function is determined to be in the light over-intensity zone, the marginal effect coefficient of light on the first crop is negative, and decreases as photosynthetically active radiation increases.

4. The photovoltaic support control method according to claim 3, characterized in that, The step of determining the crop illumination marginal effect coefficient corresponding to each candidate tracking angle based on the physiological characteristic parameters, a preset crop illumination marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle further includes: The correction ratio for the shading sensitive period is determined based on the penalty coefficient for the shading sensitive period. The correction ratio for the shading sensitive period is 1 in non-shading sensitive periods and greater than 1 in shading sensitive periods. The marginal effect coefficient of light for the second crop is determined based on the marginal effect coefficient of light for the first crop and the correction ratio for the shading sensitive period.

5. The photovoltaic support control method according to claim 4, characterized in that, The step of determining the crop illumination marginal effect coefficient corresponding to each candidate tracking angle based on the physiological characteristic parameters, a preset crop illumination marginal effect model, and the photosynthetically active radiation corresponding to each candidate tracking angle further includes: When the ambient temperature is greater than the upper limit of the optimal temperature and the marginal effect coefficient of light for the second crop is less than 0, a temperature stress factor is determined based on the degree to which the ambient temperature exceeds the upper limit of the optimal temperature, wherein the ambient temperature is positively correlated with the temperature stress factor and the temperature stress factor is greater than 1. Based on the second crop light marginal effect coefficient and the temperature stress factor, the crop light marginal effect coefficient corresponding to the candidate tracking angle is determined.

6. The photovoltaic support control method according to claim 1, characterized in that, Determining the reduction in photosynthetically active radiation and the change in power generation relative to the light-gathering angle for each of the candidate tracking angles includes: For each candidate tracking angle, combined with meteorological irradiance data and photovoltaic system parameters, the shading analysis function is called to simulate and obtain the average photosynthetically active radiation at the candidate tracking angle, and the photovoltaic power generation function is called to simulate and obtain the AC power generation corresponding to the candidate tracking angle. Obtain the reference photosynthetically active radiation and reference power generation corresponding to the aforementioned light angle; The reduction in photosynthetically active radiation is obtained by subtracting the ground-average photosynthetically active radiation corresponding to the candidate tracking angle from the reference photosynthetically active radiation. The change in power generation is obtained by subtracting the reference power generation from the AC power generation corresponding to the candidate tracking angle.

7. The photovoltaic support control method according to claim 1, characterized in that, The step of determining the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle based on the crop illumination marginal effect value and power generation marginal effect value corresponding to each candidate tracking angle, and determining the target tracking angle based on the comprehensive marginal effect value, includes: The sum of the crop illumination marginal effect value and the power generation marginal effect value corresponding to each candidate tracking angle is determined as the comprehensive marginal effect value of each candidate tracking angle relative to the light-giving angle. The candidate tracking angle corresponding to the maximum comprehensive marginal effect value is determined as the target tracking angle.

8. The photovoltaic support control method according to claim 1, characterized in that, Also includes: When the crop is in its growing season and the total horizontal irradiance is greater than the weak light irradiance threshold, the step of obtaining multiple candidate tracking angles of the photovoltaic support is performed. In the absence of effective solar irradiance at night, the target tracking angle is determined to be 0, and the current control cycle is terminated. If the crop is not in its growing season or the total horizontal irradiance is less than or equal to the weak light irradiance threshold, and the real-time electricity price is greater than or equal to 0, then the target tracking angle is determined to be the astronomical sun-tracking angle; if the real-time electricity price is less than 0, then the target tracking angle is determined to be the light-yielding angle.

9. A network control unit, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the network control unit can implement the photovoltaic bracket control method as described in any one of claims 1 to 8.

10. A photovoltaic tracking control system, characterized in that, include: Network control unit and tracking control unit; The network control unit and the tracking control unit are communicatively connected; The tracking control unit has a built-in tilt sensor and is parallel to the photovoltaic module; The tracking control unit is used to drive the photovoltaic module to rotate to the target tracking angle in response to the target tracking angle sent by the network control unit; The network control unit is used to execute the photovoltaic bracket control method as described in any one of claims 1 to 8.

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