Crop growth environment intelligent control system based on photovoltaic support

Abstract

The invention relates to the technical field of photovoltaic agriculture and intelligent control, in particular to a crop growth environment intelligent control system based on a photovoltaic support. The system comprises a multi-dimensional data acquisition module, a light and shadow geometric calculation module, a wind load risk assessment module and a self-adaptive cooperative control module. The system constructs a light and shadow coupling model by acquiring crop parameters, cable tension and illumination data; the core of the method is that a grading height adjusting instruction is generated according to a calculated non-shielding critical height and a wind load risk grade: when the structure is safe, the grading height is accurately adjusted to the critical height, and when the structure is in a risk interval, the grading height is forcibly reduced to a risk avoiding height; precise matching of biological requirements and mechanical adjustment is achieved, and the safety of the support structure is guaranteed while photosynthesis of crops is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic agriculture and intelligent control technology, in particular to a crop growth environment intelligent control system based on a photovoltaic support. BACKGROUND

[0002] With the rapid popularization of photovoltaic agricultural technology, the integration of photovoltaic facilities and agricultural planting is increasingly deepening, and the coupling relationship between the photovoltaic support system and the crop growth environment becomes significantly complex; this complexity poses a severe challenge to the fine management and control of facilities, especially in balancing light acquisition and structural safety; Currently, fixed gear adjustment or simple control strategies based on environmental wind speed are generally used; however, traditional adjustment modes often fail to accurately match the dynamic biological needs of crops for light at different growth stages, resulting in limited photosynthetic efficiency of crops; at the same time, relying solely on external wind speed monitoring cannot truly reflect the stress vibration state of the support structure itself, and the structural wind load safety is easily overlooked when pursuing light efficiency; this current situation of mechanical adjustment and biological demand disconnection, and lack of structural body perception ability, not only affects crop yield, but also easily causes structural instability and even collapse accidents; Therefore, how to realize the accurate matching of photovoltaic support adjustment and biological needs of crops, and maximize the light efficiency under the premise of ensuring the structural wind load safety, has become a problem to be solved in the field. SUMMARY

[0003] To solve the above technical problems, the present application provides a crop growth environment intelligent control system based on a photovoltaic support, specifically, the technical solution of the present application comprises: A multi-dimensional data acquisition module for acquiring biological growth parameters of target crops, real-time tension data of load cables of a photovoltaic support, and light geometric parameters of an external environment; A light shadow geometric solving module for constructing a light shadow coupling model based on the biological growth parameters and the light geometric parameters, and solving a no-shading critical height that meets the photosynthesis needs of target crops at the current time; A wind load risk assessment module for extracting high-frequency vibration components in the real-time tension data and comparing them with a preset structural stability threshold to generate a structural wind load risk level; An adaptive cooperative control module for generating a height adjustment instruction for the photovoltaic support according to the structural wind load risk level and the no-shading critical height; Wherein, the height adjustment instruction is configured to adjust the photovoltaic support to the no-shading critical height when the structural wind load risk level is in a safe interval, and to forcibly adjust the photovoltaic support to a preset risk-avoiding minimum height when the structural wind load risk level is in a dangerous interval.

[0004] Preferably, the multi-dimensional data acquisition module acquires biological growth parameters of the target crop, including: identifying current gear information of a physical coding disc arranged on a photovoltaic support adjusting mechanism; mapping corresponding biological growth parameters from a preset biological database based on the current gear information; wherein the biological growth parameters at least include a minimum light requirement threshold, a crop canopy growth curve, and a maximum allowable wind speed threshold.

[0005] Preferably, the light-shade geometry solving module solves an unobstructed critical height satisfying photosynthesis requirements of the target crop, including: calling current latitude and longitude data and time data, calculating a solar elevation angle and a solar azimuth angle; combining the crop canopy height in the biological growth parameters, calculating a geometric boundary of a shadow projected by the photovoltaic panel on the ground; taking the geometric boundary not covering the crop canopy and the photovoltaic panel height being within a preset mechanical stroke range as constraint conditions, solving a minimum height value satisfying the constraint conditions, and defining the minimum height value as the unobstructed critical height.

[0006] Preferably, the wind load risk assessment module generates a structure wind load risk level, which is configured to perform the following steps: performing frequency spectrum analysis on the real-time tension data, separating a low-frequency component representing cable slackness and a high-frequency component representing wind-induced vibration; calculating a root mean square value of the amplitude of the high-frequency component as a current wind load intensity index; acquiring the maximum allowable wind speed threshold in the biological growth parameters, and converting the maximum allowable wind speed threshold into a corresponding tension amplitude limit as a structure stability threshold by using a preset wind speed-tension conversion model; comparing the wind load intensity index with the structure stability threshold: if the wind load intensity index is greater than the structure stability threshold, determining that the structure wind load risk level is in a dangerous interval; if the wind load intensity index is less than or equal to the structure stability threshold, determining that the structure wind load risk level is in a safe interval.

[0007] Preferably, the adaptive cooperative control module further includes a logic locking unit, which is configured to: in response to the structure wind load risk level being in the dangerous interval, activating a logic lock state; In the logic closed state, all external artificial adjustment signals except the height-lowering risk-avoiding instruction are shielded, and the photovoltaic support is locked at the risk-avoiding lowest height until the wind load intensity index corresponding to the real-time tension data restores to the safety interval and lasts for a preset observation period.

[0008] Preferably, the system further comprises an execution feedback module, configured to: monitor a height position feedback signal of the photovoltaic support after the execution of the height adjustment instruction; calculate a deviation value between the height position feedback signal and the unobstructed critical height; if the deviation value is out of a preset control dead zone range and the current structural wind load risk level is in the safety interval, generate a fine tuning compensation instruction to correct the height of the photovoltaic support.

[0009] Preferably, the system further comprises a tension compensation unit, configured to: judge the physical slack state of the cable based on the low-frequency component; when the low-frequency component shows that the tension is lower than a preset minimum tension threshold, generate a tension tightening instruction to drive the reel mechanism to act until the real-time tension data restores to a preset reference tension range.

[0010] Preferably, the adaptive cooperative control module further performs the following constraint logic when generating the height adjustment instruction: obtain a mechanical stroke limit parameter of the current photovoltaic support; compare the unobstructed critical height with the mechanical stroke limit parameter; if the unobstructed critical height is out of the upper limit of the mechanical stroke limit parameter, correct the target value of the height adjustment instruction to the upper limit value of the mechanical stroke limit parameter.

[0011] Compared with the prior art, the present application has the following beneficial effects: 1. The system of the present application can obtain crop biological growth parameters through a multi-dimensional data acquisition module, and construct a light-shadow coupling model by using a light-shadow geometry solving module, so as to reversely deduce an unobstructed critical height that satisfies the condition that the crops are not obstructed at the current time; this mechanism breaks the rigid mode of traditional photovoltaic support fixed gear adjustment, and can dynamically adjust according to the actual height and light demand of crops at different growth stages, so as to ensure that the crop canopy obtains sufficient light while avoiding the extra energy consumption and wear caused by the blind rise of the mechanical mechanism to the highest point, and realizes the precise matching of biological demand and mechanical adjustment. 2. This system uses a wind load risk assessment module to perform spectral analysis on the real-time tension data of load-bearing cables and extracts high-frequency components that characterize wind-induced vibration as wind load intensity indicators. Compared with the traditional indirect monitoring method that relies solely on external anemometers, this method can directly sense the stress state, resonance effect, and gust impact of the support structure itself, effectively solving the problem that simple wind speed monitoring cannot truly reflect the structural instability risk. This allows for timely warnings before substantial structural damage occurs, significantly improving the safety of facilities under severe weather conditions. 3. This system establishes a dynamic balance strategy that prioritizes safety while also considering growth through an adaptive collaborative control module. When the structure is in a safe range, the system prioritizes adjusting the support to the critical height for unobstructed views to increase crop yield. When wind load risk is detected entering the danger zone, the system forcibly adjusts to the minimum height for safety to minimize the wind-exposed area. This design successfully resolves the contradiction between pursuing light efficiency and ensuring structural safety in existing technologies, thus ensuring both the survival rate of the facility and the benefits of agricultural production. 4. This system utilizes data from different frequency bands of the same tension sensor to provide early warning of risks through high-frequency components and monitor the physical slack of the cable through low-frequency components. Combined with a tension compensation unit, the system can automatically tighten the cable when insufficient tension is detected, preventing a decrease in stiffness due to structural slack. At the same time, with the help of a logic interlocking unit, human error is shielded in dangerous situations. This design, which has self-healing capabilities and safety redundancy, significantly ensures the long-term mechanical stability of the support system and extends the service life of the equipment. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A smart control system for crop growth environment based on photovoltaic support, comprising: The multidimensional data acquisition module is used to acquire the biological growth parameters of the target crop, the real-time tension data of the load-bearing cables of the photovoltaic support, and the light geometry parameters of the external environment. The light and shadow geometry solution module is used to construct a light and shadow coupling model based on biological growth parameters and light geometry parameters, and to calculate the unobstructed critical height that meets the photosynthetic needs of the target crop at the current moment. The wind load risk assessment module is used to extract high-frequency vibration components from real-time tension data and compare them with preset structural stability thresholds to generate the structural wind load risk level. The adaptive collaborative control module is used to generate height adjustment instructions for the photovoltaic support based on the structural wind load risk level and the critical height without shading. The height adjustment command is configured as follows: when the structural wind load risk level is in the safe range, adjust the photovoltaic support to the critical height of no shading; when the structural wind load risk level is in the dangerous range, forcibly adjust the photovoltaic support to the preset minimum height for risk avoidance.

[0015] This embodiment details the core architecture and operating logic of the system, aiming to solve the core technical problems of the disconnect between mechanical structure adjustment and crop biological needs in existing photovoltaic agricultural facilities, and the neglect of structural wind load safety when pursuing light efficiency. The system constructs a comprehensive mapping from the physical world to the digital control system through a multi-dimensional data acquisition module. This module not only collects conventional environmental parameters, but more importantly, it simultaneously acquires key indicators that determine crop photosynthetic efficiency and physical resistance, namely the biological growth parameters of the target crop, as well as time-series data, namely real-time tension data, obtained at a high sampling rate by tension sensors installed on the support traction cable. At the same time, it acquires light geometric parameters including solar radiation intensity PAR and the geometric position of the sun relative to the ground. The light and shadow geometry calculation module constructs a light and shadow coupling model based on the above parameters, and reverse-engineers the critical unshaded height that must be reached at the current moment to ensure that the crop canopy is not shaded by the photovoltaic panel. Meanwhile, the wind load risk assessment module directly monitors the load-bearing structure, using cables as sensitive elements for wind-induced vibrations to capture structural resonances or gust impacts that anemometers cannot detect. The adaptive collaborative control module acts as the decision-making center, executing a control strategy that prioritizes safety while also considering growth. When the structural wind load risk level is within the safe range, the system determines that the current wind load will not damage the support, prioritizing the needs of crop photosynthesis and adjusting the photovoltaic support to the critical height without shading. When the structural wind load risk level is within the dangerous range, meaning that the structure faces the risk of instability or collapse, the system immediately lowers the photovoltaic support to the preset minimum height for risk avoidance through forced adjustment, usually the lowest position of the mechanical structure, to minimize the wind-exposed area and lever arm. This embodiment achieves a dynamic balance strategy by constructing a coupled control mechanism between light and shadow and wind load. In agricultural production scenarios, this system breaks the rigid mode of fixed-level adjustment of traditional photovoltaic brackets. It can maximize crop photosynthetic yield through precise height adjustment when weather conditions are good, and automatically trigger a protection mechanism based on structural ontology perception under severe weather conditions. This mechanism does not rely on indirect wind speed prediction, but is based on direct feedback of structural forces, thereby significantly reducing operation and maintenance costs while ensuring the survival rate of the facility.

[0016] Example 2: The multidimensional data acquisition module obtains the biological growth parameters of the target crop, including: Identify the current gear position information of the physical encoder disk set on the photovoltaic bracket adjustment mechanism; Based on the current gear information, the corresponding biological growth parameters are mapped from the preset biological database; Among them, biological growth parameters include at least the minimum light requirement threshold, the crop canopy growth curve, and the maximum allowable wind speed threshold.

[0017] This embodiment details the process of the multidimensional data acquisition module acquiring parameters. This module does not rely solely on cloud downloads but incorporates on-site physical interaction hardware. The system identifies the current gear information of the physical encoding disk set on the photovoltaic support adjustment mechanism through Hall sensors or photoelectric switches. The physical encoding disk originates from a mechanical device installed at the manual or electric adjustment knob, and its physical meaning is to represent the preset gear scale for different crop types such as fruit trees, leafy vegetables, and shade-loving medicinal materials. Each gear corresponds to a unique digital code. The system uses the current gear information as an index key to map the corresponding biological growth parameters from a pre-set biological database in memory; this parameter set includes multi-dimensional indicators: minimum light requirement threshold. Derived from crop photosynthetic physiological characteristics research data, its physical meaning is the minimum photosynthetically active radiation intensity required for crops to carry out effective photosynthesis; crop canopy growth curve. Derived from long-term agronomic observations and statistics, its physical meaning is a functional relationship describing the change in crop height with planting time or accumulated temperature; maximum permissible wind speed threshold. The value is derived from crop lodging resistance mechanical tests and its physical meaning is to help determine whether it is necessary to lower the wind speed in advance to protect the crop itself. This embodiment uses a mapping technology between a physical encoder and digital parameters to encapsulate complex agronomic knowledge within a simple mechanical knob operation. In actual farmer operation scenarios, users only need to turn the knob to the corresponding crop setting, and the system can automatically load the growth model and environmental requirements parameters for the entire life cycle. This design greatly reduces the barrier to entry for the system, effectively avoids control failures caused by incorrect parameter input, and ensures accurate matching between the system strategy and the actual crop being planted.

[0018] Example 3: The light and shadow geometry calculation module calculates the unobstructed critical height that meets the photosynthetic requirements of the target crop, including: Use the current latitude, longitude, and time data to calculate the solar altitude angle and solar azimuth angle; By combining crop canopy height, a biological growth parameter, the geometric boundary of the shadow cast by the photovoltaic panel on the ground is calculated. Using the constraints of the geometric boundary not covering the crop canopy and the photovoltaic panel height being within a preset mechanical travel range, the minimum height value satisfying the constraints is solved, and this minimum height value is defined as the unshaded critical height. This embodiment elaborates on the specific algorithm flow for the unshaded critical height, which includes rigorous geometric derivation steps; the system calls the current latitude and longitude data obtained by the GPS module and the time data provided by the RTC clock, and uses solar position algorithms such as the SPA algorithm to calculate the current solar altitude angle. and solar azimuth Combine biological growth parameters and substitute the current system time into the crop canopy growth curve. The current crop canopy height was calculated. Specifically, crop canopy growth curve The Logistic Growth Model is used for construction, and the calculation formula is as follows:

[0019] in, This is the maximum plant height constant of the crop at maturity. The intrinsic growth rate coefficient is preset. This represents the current number of planting days. This refers to parameters indicating the inflection point of growth; the system reads data from the biological database specific to this crop. , and The parameters are substituted into the above formula to solve for the current time step. ; In this process, a projection geometric model based on the panel dimensions is constructed: taking the vertical projection point of the support column as the origin of the coordinate system, and assuming the width of the photovoltaic panel is... Real-time tilt angle is Select the main projection edge of the photovoltaic panel, i.e. the edge with the higher geometric position, and calculate the initial offset of the photovoltaic panel's projection on the ground relative to the origin. Simultaneously, considering the vertical rise of the edges caused by the tilt angle of the photovoltaic panel, the vertical height increment of the upper edge of the photovoltaic panel relative to the central axis is calculated. To ensure the validity of the geometric model in three-dimensional space, the system introduces azimuth correction logic: the azimuth of the normal to the crop planting row is defined as... Calculate the angle between the solar azimuth and the crop row normal. To implement the shadow overshoot avoidance strategy, which controls the shadows cast onto the gaps between crop rows, a target shadow projection distance, corrected for azimuth angle, is set relative to the origin. Before performing calculations, the system... Perform singularity check: If ,in, For the preset angle dead zone, such as This indicates that the sunlight is approximately parallel to the crop rows, at which point the projection distance approaches infinity, and the system forces a setting. The preset maximum physical protection distance Skip subsequent division operations; if the verification result indicates that it is not within the angle dead zone, then perform the following calculation: This distance is determined by the physical farthest boundary of the crop-growing area. and safety margin The exported calculation formula is as follows: ,in, Defined as the horizontal projection distance of the farthest edge of the crop planting area relative to the origin of the coordinate system, i.e., the vertical projection point of the support column; thus, the light and shadow constraints of the three-dimensional space are projected onto the two-dimensional calculation plane; the system constructs optimization equations with the dual constraint conditions that the geometric boundary does not cover the crop canopy, i.e., the shadow projection distance exceeds the target projection distance, and the height of the photovoltaic panel is within the preset mechanical travel range; set up Given the current vertical height of the photovoltaic support, the system solves the inequality. ,Right now

[0020] Deformation The system calculates the boundary values ​​of this inequality and defines the lowest height value that satisfies the above constraints as the unobstructed critical height. Specifically, regarding the solar altitude angle In extreme angle situations, the system has built-in logic for determining the effective angle domain: when During sunrise and sunset, the distance of shadows cast tends to infinity, or During the midday direct sunlight period, When it approaches 0, directly determine Upper limit of mechanical stroke To prevent control commands from overflowing and to ensure that crops receive the maximum possible diffused light at low light angles; This embodiment uses real-time calculation. Combined with azimuth correction, the system enables on-demand allocation of light resources. During crop growth, the system can accurately find the height point where there is no shading. Compared with the extensive control of directly raising to the highest point, this on-demand adjustment strategy can reduce the wear and tear of mechanical mechanisms and the energy consumption of the stroke.

[0021] Example 4: The wind load risk assessment module generates a structural wind load risk level and is configured to perform the following steps: Spectral analysis was performed on real-time tension data to separate the low-frequency component characterizing cable slack and the high-frequency component characterizing wind-induced vibration. Calculate the root mean square value of the amplitude of the high-frequency component as an indicator of the current wind load intensity. The maximum permissible wind speed threshold among the biological growth parameters is obtained, and the maximum permissible wind speed threshold is converted into the corresponding tension amplitude limit using a preset wind speed-tension conversion model, which serves as the structural stability threshold. The wind load intensity index is compared with the structural stability threshold: if the wind load intensity index is greater than the structural stability threshold, the structural wind load risk level is determined to be in the dangerous range; if the wind load intensity index is less than or equal to the structural stability threshold, the structural wind load risk level is determined to be in the safe range.

[0022] This embodiment discloses in detail the signal processing logic of the wind load risk assessment module, aiming to quantify hidden wind load risks; the system processes the collected real-time tension data. Perform a Fast Fourier Transform (FFT) to separate the signal components in different frequency bands; among them, the low-frequency components... Reflects the static tension of the cable, high-frequency component The system reflects wind-induced vibration; a transition stopband of 0.5Hz-2Hz is set to effectively isolate quasi-static tension changes caused by steady-state wind pressure and dynamic flutter caused by turbulence, ensuring that subsequent indicators purely reflect dynamic risks; the system calculates high-frequency components. root mean square value of amplitude Define it as the current wind load intensity index ; The system uses a preset wind speed-tension conversion model to calculate the structural stability threshold. The model consists of two sub-steps: aerodynamic load calculation: calculating the equivalent wind resistance experienced by the photovoltaic panel based on aerodynamic formulas. ,in, For standard air density, take the value. , Let be the dimensionless drag coefficient of the photovoltaic panel. The projected area against the wind at the current tilt angle, in units of , The maximum permissible wind speed threshold, in units Here, it is assumed that the wind load calculation is based on a two-dimensional cross-sectional model, that is, it is assumed that the wind direction vector is perpendicular to the rotation axis of the photovoltaic support to simulate the most unfavorable stress conditions. The calculation formula is ,in, The current tilt angle of the photovoltaic panel. The preset wind direction vector is the angle of incidence of the most unfavorable wind direction relative to the horizontal plane. For example, 90 degrees represents a vertically downward gust. Mechanical transfer mapping: Based on the cable structure mechanical model, to simplify calculations, this embodiment uses wind resistance... Approximately equivalent to a concentrated load acting at the mid-span of the cable, wind resistance Mapped to cable tension increments; structural geometric factors introduced. Its sag-to-span ratio relative to the initial static design of the cable The relevant derivation formula is as follows: This formula is derived from the mechanics of flexible cable structures; it assumes that the sag of the cable under its own weight and wind load is small, i.e., it satisfies... The assumption of small verticality, and where The cable span length between the two columns of the photovoltaic support structure, i.e., the span diameter of the cable structure, is then the cable mid-span tension. With mid-span concentrated load The relation approximately satisfies ; using the definition ,Right now Substituting these values, the transfer coefficient of load into tension can be derived. This achieves a mechanical mapping from aerodynamic force to axial tension in the cable; Cable mid-span tension With mid-span concentrated load The relation approximately satisfies ; using the definition ,Right now Substituting these values, the transfer coefficient of load into tension can be derived. This achieves a mechanical mapping from aerodynamic force to cable axial tension; assuming the cable's sag under its own weight and wind load is small, thus satisfying... The small verticality assumption, where, This represents the cable span length between the two columns of the photovoltaic support, i.e., the span of the cable structure; The final calculated threshold for structural stability, used to measure the allowable vibration amplitude, is obtained. Due to the measured index The root mean square (RMS) value of the high-frequency components represents the dynamic alternating stress, while To determine quasi-static aerodynamic drag, a structural dynamic response factor needs to be introduced. Perform physical dimension alignment; the calculation formula is:

[0023] in, The preset empirical coefficient has a range of values. Its physical meaning is: at the maximum permissible wind speed Under the influence of wind speed, the upper limit of the allowable ratio of the dynamic high-frequency flutter component generated by the structure to the static tension increment caused by that wind speed; if the vibration is monitored in real time... If the value exceeds the threshold calculated by this ratio, it is determined that there is a risk of fatigue failure. This step clarifies the physical conversion process from wind speed (velocity dimension) to tension (mechanical dimension), and by mapping the biological limit wind speed to the structural threshold, it ensures that the system initiates structural defense before the environment becomes too harsh to threaten crops, thus achieving the unity of biological safety and structural safety. The system executes risk assessment logic: in response to Greater than If a structural instability risk is identified, the structural wind load risk level is marked as a dangerous zone; otherwise, it is marked as a safe zone.

[0024] Example 5: The adaptive cooperative control module also includes a logic locking unit, which is used for: In response to the structural wind load risk level being in the dangerous range, the logic lockout state is activated. In the logic-locked state, all external human adjustment signals except for the height reduction and risk avoidance command are blocked, and the photovoltaic support is kept locked at the lowest risk avoidance height until the wind load intensity index corresponding to the real-time tension data returns to the safe range and continues for the preset observation period.

[0025] This embodiment introduces a logic locking unit as a safety redundancy mechanism; the system monitors the wind load risk level of the structure, and in response to the level being determined to be in a dangerous zone, i.e., strong wind-induced vibration is detected, the logic locking state is immediately activated; in the logic locking state, the system executes the highest priority safety strategy, actively blocking all external human adjustment signals except for the height reduction and risk avoidance command, such as the height increase command sent by farmers through mobile APP or physical button, and forcibly keeping the photovoltaic support locked at the lowest risk avoidance height; The system continuously monitors real-time tension data, and only when the wind load intensity index corresponding to the real-time tension data is reached... The system returns to a safe range, and this safe state is maintained for a preset observation period, such as 10 minutes, to prevent misjudgments caused by brief pauses in wind force. Only after this period is the logic lockout state released, and normal adjustment functions are restored. This embodiment solves the safety hazards caused by human error through a logic lockout mechanism. In extreme weather scenarios, farmers may forcibly raise the support frame due to concerns about insufficient sunlight. This embodiment eliminates structural collapse accidents caused by human intervention by forcibly depriving farmers of human control during dangerous moments, demonstrating the superiority of machine intelligence in critical safety decisions and ensuring the absolute survival safety of the system under extreme conditions.

[0026] Example 6: It also includes an execution feedback module, which is used for: Real-time monitoring of the height position feedback signal after the photovoltaic support executes the height adjustment command; Calculate the deviation between the height position feedback signal and the unobstructed critical height; If the deviation exceeds the preset control dead zone range, and the current structural wind load risk level is within the safe range, a fine-tuning compensation command will be generated to correct the height of the photovoltaic support.

[0027] This embodiment constructs a closed-loop control system by adding an execution feedback module; the system uses an encoder or laser rangefinder to read the height position feedback signal of the photovoltaic support after executing the height adjustment command in real time. ; Calculate the feedback signal and the unobstructed critical height calculated by the system. Deviation between The system introduces a control dead zone range. For example, a tolerance of ±5cm is designed to avoid frequent micro-movements caused by sensor noise; the system executes fine-tuning logic in response to the deviation value. If the system exceeds the control dead zone and the current structural wind load risk level is within the safe range, it indicates that there is an error or slippage in the actuator. The system generates a fine-tuning compensation command to correct the height of the photovoltaic support, bringing it closer to the target height. This embodiment effectively eliminates positioning errors caused by physical factors such as mechanical transmission gaps and cable elastic elongation by introducing a position feedback mechanism. In long-term operation scenarios, this mechanism ensures the consistency between the theoretical calculated height and the actual physical height, thereby guaranteeing the control accuracy of the light and shadow model and avoiding crop shading or excessive extension of the support due to execution errors.

[0028] Example 7: The system also includes a tension compensation unit for: Determining the physical slack state of cables based on low-frequency components; When the low-frequency component shows that the tension is lower than the preset minimum tension threshold, a tension tightening command is generated to drive the reel mechanism to move until the real-time tension data is restored to the preset reference tension range.

[0029] This embodiment, based on the spectrum analysis results of Embodiment 4, further utilizes low-frequency components for maintenance control; the system is based on the separated low-frequency components. To determine the physical slack state of the cable, since the low-frequency component filters out wind-induced vibrations, its value accurately reflects the static tension of the cable; the system compares this low-frequency component with a preset minimum tension threshold. Comparison was performed; the response to the low-frequency component indicated a tension value lower than [previous value]. This indicates that the cable has become loose, which may cause the bracket to shake more. The system generates a tension tightening command to drive the reel mechanism, such as a motor-driven winch, to move. The reel mechanism continues to move until the low-frequency component of the real-time tension data returns to the preset reference tension range. This embodiment realizes self-diagnosis and self-healing of cable status; in long-term service scenarios, the system uses different frequency bands of the same sensor data to realize both high-frequency wind prevention and early warning and low-frequency mechanical maintenance. This reuse design significantly extends the service life of the equipment, prevents the reduction of structural stiffness caused by cable slack, and ensures the long-term mechanical stability of the entire support system.

[0030] Example 8: When generating height adjustment commands, the adaptive cooperative control module also executes the following constraint logic: Obtain the current mechanical travel limit parameters of the photovoltaic support; Compare the unobstructed critical height with the mechanical travel limit parameters; If the unobstructed critical height exceeds the upper limit of the mechanical travel limit parameter, the target value of the height adjustment command will be corrected to the upper limit value of the mechanical travel limit parameter.

[0031] This embodiment adds physical constraint logic when the adaptive cooperative control module generates instructions to ensure the executability of the control instructions; the system reads the mechanical travel limit parameters of the current photovoltaic support from non-volatile memory, including the upper mechanical limit. and mechanical lower limit The unobstructed critical height calculated by the lighting and shadow resolution module. The system compares the parameters with the mechanical travel limits; the system executes boundary correction logic in response to... If the limit of the mechanical travel parameter is exceeded, for example, if the theoretical calculation requires the support to be raised to 6 meters to achieve complete unobstructed lighting, but the mechanical limit is only 4.5 meters, the system determines that the physical lighting requirements cannot be fully met. In this case, the system executes a saturation limit, forcibly correcting the target value of the height adjustment command to the upper limit value of the mechanical travel parameter. ; This embodiment constructs a hardware protection barrier for the system through physical constraint verification at the software level. In actual operation, this feature prevents the motor from stalling or burning out due to attempting to drive the bracket beyond the mechanical limit, ensuring that the algorithm output is always within the envelope of the hardware capabilities, thereby ensuring the hardware security and reliability of the system.

[0032] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart control system for crop growth environment based on photovoltaic support, characterized in that, include: The multidimensional data acquisition module is used to acquire the biological growth parameters of the target crop, the real-time tension data of the load-bearing cables of the photovoltaic support, and the light geometry parameters of the external environment. The light and shadow geometry calculation module is used to construct a light and shadow coupling model based on the biological growth parameters and the light geometry parameters, and calculate the unobstructed critical height that meets the photosynthetic requirements of the target crop at the current moment. The wind load risk assessment module is used to extract the high-frequency vibration component from the real-time tension data and compare it with the preset structural stability threshold to generate the structural wind load risk level. An adaptive collaborative control module is used to generate height adjustment instructions for the photovoltaic support based on the structural wind load risk level and the unshaded critical height. The height adjustment command is configured to: when the structural wind load risk level is in the safe range, adjust the photovoltaic support to the unobstructed critical height; when the structural wind load risk level is in the dangerous range, forcibly adjust the photovoltaic support to the preset minimum height for risk avoidance.

2. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, The multidimensional data acquisition module obtains the biological growth parameters of the target crop, including: Identify the current gear position information of the physical encoder disk set on the photovoltaic bracket adjustment mechanism; Based on the current gear information, the corresponding biological growth parameters are mapped from a preset biological database; The biological growth parameters include at least the minimum light requirement threshold, the crop canopy growth curve, and the maximum permissible wind speed threshold.

3. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, The light and shadow geometry calculation module calculates the unobstructed critical height that meets the photosynthetic requirements of the target crop, including: Use the current latitude, longitude, and time data to calculate the solar altitude angle and solar azimuth angle; Based on the crop canopy height in the biological growth parameters, the geometric boundary of the shadow cast by the photovoltaic panel on the ground is calculated; Using the constraint that the geometric boundary does not cover the crop canopy and the height of the photovoltaic panel is within a preset mechanical travel range, the minimum height value that satisfies the constraint is solved, and the minimum height value is defined as the unshaded critical height.

4. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, The wind load risk assessment module generates a structural wind load risk level and is configured to perform the following steps: Spectral analysis was performed on the real-time tension data to separate the low-frequency component characterizing cable slack and the high-frequency component characterizing wind-induced vibration. Calculate the root mean square value of the amplitude of the high-frequency component as the current wind load intensity index; The maximum permissible wind speed threshold among the biological growth parameters is obtained, and the maximum permissible wind speed threshold is converted into the corresponding tension amplitude limit using a preset wind speed-tension conversion model, which is used as the structural stability threshold. The wind load intensity index is compared with the structural stability threshold: if the wind load intensity index is greater than the structural stability threshold, the wind load risk level of the structure is determined to be in the dangerous range; if the wind load intensity index is less than or equal to the structural stability threshold, the wind load risk level of the structure is determined to be in the safe range.

5. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, The adaptive cooperative control module further includes a logic locking unit, which is used for: In response to the structural wind load risk level being in the dangerous range, the logic lockout state is activated; In the logic-locked state, all external human adjustment signals except for the height reduction and risk avoidance command are blocked, and the photovoltaic support is kept locked at the lowest risk avoidance height until the wind load intensity index corresponding to the real-time tension data returns to the safe range and continues for the preset observation period.

6. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, It also includes an execution feedback module, which is used for: Real-time monitoring of the height position feedback signal of the photovoltaic support after executing the height adjustment command; Calculate the deviation between the height position feedback signal and the unobstructed critical height; If the deviation value exceeds the preset control dead zone range, and the current structural wind load risk level is within the safe range, a fine-tuning compensation command is generated to correct the height of the photovoltaic support.

7. The intelligent control system for crop growth environment based on photovoltaic support according to claim 4, characterized in that, The system also includes a tension compensation unit for: The physical relaxation state of the cable is determined based on the low-frequency components. When the low-frequency component shows tension below the preset minimum tension threshold, a tension tightening command is generated to drive the reel mechanism to operate until the real-time tension data is restored to the preset reference tension range.

8. The intelligent control system for crop growth environment based on photovoltaic support according to claim 1, characterized in that, When generating height adjustment commands, the adaptive cooperative control module also executes the following constraint logic: Obtain the current mechanical travel limit parameters of the photovoltaic support; Compare the unobstructed critical height with the mechanical travel limit parameter; If the unobstructed critical height exceeds the upper limit of the mechanical travel limit parameter, then the target value of the height adjustment command is corrected to the upper limit value of the mechanical travel limit parameter.

Images