Sectional type main beam coaxiality detection method and system in photovoltaic tracking system

Abstract

The invention relates to the technical field of new energy, and discloses a sectional type main beam coaxiality detection method and system in a photovoltaic tracking system, and the method comprises the steps: carrying out the static reference calibration, measuring the three-dimensional coordinates of the end points of a head main beam and a tail main beam through a total station, and constructing a theoretical axis parameter equation L; dynamic deformation monitoring is carried out, the inclination angle of each main beam section, the distance between adjacent sections, the temperature and the wind speed data are collected in real time, and a deformation prediction model is input to calculate the real-time offset delta D; intelligent error correction is carried out, when delta D exceeds a preset threshold value, an alarm is triggered, and a compensation adjustment scheme is output; wherein the deformation prediction model is delta D = alpha * T + beta * V2 + gamma * sin (theta); t is temperature variation, V is wind speed, theta is a main beam inclination angle, and alpha, beta and gamma are calibration coefficients. According to the invention, through cooperation of static reference calibration, dynamic deformation monitoring and intelligent error correction, efficient and accurate detection of the coaxiality of the sectional type main beam is realized.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method and system for detecting the coaxiality of segmented main beams in photovoltaic tracking systems. Background Technology

[0002] As the photovoltaic industry develops towards higher efficiency and larger scale, tracking systems have become the mainstream configuration for large-scale ground-mounted power plants due to their significant advantage in increasing power generation. Among them, the segmented main beam, as the core load-bearing and transmission structure of the tracking system, directly determines the light-gathering efficiency of photovoltaic modules and the operational stability of the system through its coaxiality accuracy. Excessive deviation of the main beam axis can lead to uneven drive load, increased risk of microcracks in modules, and in extreme cases, even safety accidents such as loosening and breakage of connecting flange bolts and overturning of tracking brackets.

[0003] Currently, the coaxiality detection of segmented main beams still relies on traditional manual methods, which presents significant technical bottlenecks.

[0004] First, the testing efficiency is low. Existing technologies use straight lines, micrometer measurements, or laser levels for calibration. Testing a single 150-meter main beam takes more than 1.5 hours, and the accuracy is significantly affected by the operator's experience, making it difficult to meet the efficiency requirements of batch installation and operation in large power plants.

[0005] Secondly, there is a lack of full life-cycle monitoring. After the photovoltaic modules are installed, traditional methods cannot measure the deviation of the internal main beam of the module array, making it difficult to detect deformation problems during the operation period. More importantly, during the operation phase, the real-time coaxiality status of the main beam under dynamic conditions such as tracking rotation, wind load impact, and temperature changes cannot be captured. If the system is in a "sub-healthy" operating state for a long time, the power generation efficiency will be implicitly reduced.

[0006] Third, the environmental adaptability is insufficient. Existing testing methods do not consider the coupled effects of multiple working conditions: factors such as thermal expansion and contraction of the main beam due to temperature changes, wind-induced vibration under strong winds, and center of gravity shift during tilt adjustment can all cause dynamic deformation. However, traditional static test results cannot reflect the real deviation in actual operation, resulting in a disconnect between calibration accuracy and actual needs.

[0007] Therefore, in response to the problems of low detection efficiency, lack of dynamic monitoring, and insufficient environmental adaptability in existing technologies, there is an urgent need for an integrated and intelligent coaxiality detection solution to achieve high-precision and high-efficiency detection of segmented main beams throughout their entire life cycle, thereby ensuring the safe and stable operation of photovoltaic tracking systems and maximizing power generation benefits. Summary of the Invention

[0008] In view of this, the present invention provides a method and system for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system, in order to solve the problems of low detection efficiency, lack of dynamic monitoring, and insufficient environmental adaptability in the prior art.

[0009] In a first aspect, the present invention provides a method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system, comprising:

[0010] Static benchmark calibration involves measuring the three-dimensional coordinates of the endpoints of the first and last main beams using a total station to construct the theoretical axis parameter equation L.

[0011] Dynamic deformation monitoring collects in real time data on the inclination angle, distance between adjacent segments, temperature and wind speed of each main beam segment, and inputs it into the deformation prediction model to calculate the real-time offset δD;

[0012] Intelligent error correction: When δD exceeds a preset threshold, an alarm is triggered and a compensation adjustment plan is output.

[0013] The deformation prediction model is as follows:

[0014] δD=α·T+β·V 2 +γ·sin(θ);

[0015] T represents the temperature change, V represents the wind speed, θ represents the inclination angle of the main beam, and α, β, and γ represent the calibration coefficients.

[0016] The present invention provides a method for detecting the coaxiality of segmented main beams in photovoltaic tracking systems. Through the coordinated use of static benchmark calibration, dynamic deformation monitoring, and intelligent error correction, this method achieves efficient and accurate detection of the coaxiality of segmented main beams. The method constructs the theoretical axis parameter equation L using total station measurements. Compared to traditional methods such as manual stringing, this significantly improves the accuracy of establishing the benchmark axis, providing a reliable reference standard for subsequent testing and reducing the impact of benchmark errors on the overall test results. The dynamic deformation monitoring stage, by collecting multi-dimensional data in real time and inputting it into the model calculation, can accurately capture the real-time offset δD of the main beam caused by factors such as temperature changes, wind force, and tilt angle adjustments during operation. This overcomes the limitation of traditional methods that can only perform static detection, enabling coaxiality monitoring under all operating conditions. The intelligent error correction mechanism triggers alarms promptly through threshold judgment and outputs compensation schemes. It can quickly respond when coaxiality deviation exceeds limits, avoiding problems such as uneven drive load and structural stress concentration caused by excessive deviation. This reduces safety risks such as loose or broken main beam connecting bolts, damage to supports and tracking systems, and ensures the stable operation of the photovoltaic tracking system.

[0017] In one optional implementation, the theoretical axis parameter equation L is:

[0018] ax + by + cz + d = 0;

[0019] Where a, b, and c are the components of the normal vector in the plane along the X-axis, Y-axis, and Z-axis, respectively; d is the offset of the plane from the origin; and x, y, and z are the three-dimensional coordinates of the center point of the main beam.

[0020] The theoretical axis parametric equation L adopts the form of the plane equation ax + by + cz + d = 0, precisely defining the ideal axis reference of the segmented main beam through a three-dimensional coordinate system. a, b, and c correspond to the components of the plane normal vector in the X, Y, and Z axes, respectively, determining the spatial orientation of the plane containing the theoretical axis through the direction of the normal vector; the value of d represents the offset distance between this plane and the origin of the coordinate system, together forming a uniquely determined spatial plane. In the equation, x, y, and z are the three-dimensional coordinates of each center point of the segmented main beam. When the main beam is in an ideal coaxial state, the coordinates of all center points satisfy this equation, meaning that the actual axis of each segment completely conforms to the theoretical plane. In dynamic deformation monitoring, the deviation value of the real-time acquired segment position coordinates of the main beam, after being substituted into the equation, directly reflects the degree of deviation between the actual axis and the theoretical axis, providing basic data for calculating the real-time offset δD.

[0021] In one alternative implementation, the construction of the theoretical axis parameter equations in the static reference calibration includes:

[0022] Measure the first endpoint P1(x1,y1,z1) and the last endpoint P2(x2,y2,z2);

[0023] Calculate the direction vectors: Δx = x2 - x1, Δy = y2 - y1, Δz = z2 - z1;

[0024] Generating equation:

[0025] x = x1 + t·Δx

[0026] y = y1 + t·Δy

[0027] z = z1 + t·Δz

[0028] Where t∈[0,1] is the scaling factor.

[0029] This process is based on the three-dimensional coordinates of the first endpoint P1 (x1, y1, z1) and the last endpoint P2 (x2, y2, z2) (precisely measured by a total station). By calculating the differences between the two points in the X, Y, and Z axes, Δx = x2 - x1, Δy = y2 - y1, and Δz = z2 - z1, a direction vector representing the orientation of the main beam axis is obtained. Based on this vector, parametric equations with a scaling factor t (t ∈ [0, 1]) are generated: x = x1 + t·Δx, y = y1 + t·Δy, z = z1 + t·Δz. When t = 0, the equation corresponds to the first endpoint P1; when t = 1, it corresponds to the last endpoint P2; when t varies in the interval 0-1, the equation can describe the three-dimensional coordinates of any position between the first and last endpoints, that is, the ideal axis trajectory that theoretically should coincide for each segment of the main beam.

[0030] In one optional implementation, the dynamic deformation monitoring step acquires data through a detection ring pre-installed at the main beam connecting flange; the detection ring includes an inclinometer, a laser rangefinder, a temperature and humidity sensor, and a magnetic interface.

[0031] In the dynamic deformation monitoring step, the detection ring pre-installed at the main beam connecting flange integrates multiple types of sensors to achieve real-time acquisition of key parameters of the main beam segments. The detection ring is quickly fixed to the preset positions of each main beam connecting flange via a magnetic interface, ensuring a tight fit with the main beam structure to improve data acquisition accuracy. Specifically, the inclinometer monitors the tilt angle θ of the corresponding main beam segment in real time, providing attitude parameters for the deformation prediction model; the laser rangefinder measures the distance between adjacent main beam segments by emitting a laser beam, capturing the inter-segment displacement caused by deformation; the temperature and humidity sensor synchronously collects the temperature change T of the main beam surface and surrounding environment, providing environmental parameters for deformation caused by thermal expansion and contraction; the data collected by each sensor is integrated through a built-in module and transmitted in real time to the central processing system as the raw input for dynamic deformation monitoring. The detection ring is quickly installed via a magnetic interface and is adaptable to different main beam cross-sectional dimensions.

[0032] In one alternative embodiment, the detection ring further includes an opening and closing hinge and a locking device adapted to a circular or polygonal main beam cross section.

[0033] A hinge is located on one side of the detection ring, allowing the ring to rotate and open along the hinge axis, forming a "C"-shaped opening. A locking device, consisting of a snap-fit ​​and a latch structure, is located on the other side of the detection ring. During installation, the detection ring is opened via the hinge, wrapped around the outer circumference of the connecting flange of the circular or polygonal main beam, and then the ring is closed and the locking device is engaged. The mechanical locking force of the latch ensures that no displacement occurs during rotation or vibration of the main beam. For a circular cross-section, the ring can achieve full circumferential fit through uniform force; for a polygonal cross-section, the flexible rotation of the hinge and the fine-tuning function of the locking device allow the ring to adapt to the zigzag shape at the corners.

[0034] In one alternative implementation, the calibration coefficients α, β, and γ are determined in any of the following ways:

[0035] Fitting was performed by applying a single variable in the wind tunnel and temperature-controlled laboratory.

[0036] The solution is obtained through multiple linear regression based on on-site monitoring data.

[0037] In one optional implementation, the alarm mechanism in the intelligent error correction step is a dynamic multi-level early warning:

[0038] When δD≥3mm and V≥the first threshold, a level one alarm is triggered;

[0039] When δD≥5mm and V≥the second threshold, a level 2 alarm is triggered and the tracking system is controlled to enter the protection state;

[0040] When δD≥8mm and V≥the third threshold, a level 3 alarm is triggered and an emergency shutdown is executed;

[0041] Among them, the first threshold < the second threshold < the third threshold.

[0042] In one optional implementation, in the intelligent error correction step, the compensation adjustment scheme includes adjusting the height of the support pile, the torque value M of the connecting flange bolts, or increasing the thickness of the gasket; wherein the bolt torque value M satisfies the following functional relationship:

[0043] M = f(δD,L);

[0044] Where L is the total length of the main beam.

[0045] The compensation and adjustment scheme provides three specific correction methods based on the magnitude and distribution characteristics of the real-time offset δD: When there is a linear offset in the main beam as a whole, the foundation support position is changed by adjusting the height of the support piles to restore the overall axis of the main beam; when there is significant local inter-segment offset, the target torque value is calculated based on the functional relationship M = f(δD,L) between the bolt torque value M, the real-time offset δD, and the total length L of the main beam, and the tightness of the connecting flange bolts is adjusted, using the deformation generated by the bolt preload to correct the inter-segment deviation; for small angular offsets (such as δD < 2mm), the flange face gap is compensated by increasing the gasket thickness to achieve fine calibration. The functional relationship M = f(δD,L) is established based on material mechanics calculations and field experimental data. For example, for a steel structure main beam, it can be expressed as M = k·δD·L (k is the material coefficient), ensuring a quantitative match between torque adjustment and offset correction values.

[0046] In one alternative implementation, the method for detecting the coaxiality of the segmented main beam in a photovoltaic tracking system further includes an edge computing and cloud collaboration step:

[0047] Local processing latency <50ms, real-time output of correction instructions;

[0048] Historical data is stored in the cloud and deformation trend reports are generated to optimize early warning thresholds.

[0049] Secondly, the present invention also provides a system for implementing the segmented main beam coaxiality detection method in the photovoltaic tracking system, comprising:

[0050] Multiple detection rings are provided at the main beam connecting flange, and each detection ring includes an inclinometer, a laser rangefinder, and a temperature and humidity sensor.

[0051] The central processing terminal has the deformation prediction model built-in.

[0052] The communication module enables data synchronization between the detection ring and the terminal, as well as cloud transmission.

[0053] In this system, multiple detection rings are installed at the connecting flanges of each main beam. An integrated inclinometer collects the inclination angle θ of the main beam segment in real time, a laser rangefinder measures the spacing change between adjacent segments, and a temperature and humidity sensor records the ambient temperature change T. The sensor data is preprocessed within the detection rings to form standardized data frames, providing raw input for subsequent analysis. The central processing terminal receives the data transmitted from the detection rings and calls the built-in deformation prediction model δD=α·T+β·V. 2 The real-time offset δD is calculated using +γ·sin(θ) and combined with wind speed data. Simultaneously, the terminal executes intelligent error correction logic, triggering dynamic multi-level early warnings by comparing δD with a preset threshold. Based on the deviation value, a compensation adjustment scheme is generated, including the adjustment amount of the support pile, the bolt torque value M (based on M=f(δD,L)), or the shim thickness. The communication module uses wireless or wired methods to achieve real-time data synchronization between the detection loop and the central processing terminal. Simultaneously, the processed structured data (such as δD, early warning records, and correction instructions) is uploaded to the cloud platform, supporting historical data storage and trend analysis. Attached Figure Description

[0054] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating a method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to an embodiment of the present invention.

[0056] Figure 2 This is a schematic diagram of the detection ring in the segmented main beam coaxiality detection system of a photovoltaic tracking system.

[0057] Explanation of reference numerals in the attached figures:

[0058] 1. Inclinometer;

[0059] 2. Opening and closing hinges;

[0060] 3. Temperature and humidity sensor;

[0061] 4. Laser rangefinder;

[0062] 5. Magnetic connector;

[0063] 6. Locking device. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] As the photovoltaic industry develops towards higher efficiency and larger scale, tracking systems have become the mainstream configuration for large-scale ground-mounted power plants due to their significant advantage in increasing power generation. Among them, the segmented main beam, as the core load-bearing and transmission structure of the tracking system, directly determines the power generation efficiency of photovoltaic modules and the operational stability of the system through its coaxiality accuracy. Excessive deviation of the main beam axis can lead to uneven drive load, increased risk of microcracks in modules, and in extreme cases, even safety accidents such as loosening and breakage of connecting flange bolts, damage to tracking brackets, and overturning.

[0066] Currently, the coaxiality detection of segmented main beams still relies on traditional manual methods, which presents significant technical bottlenecks.

[0067] First, the testing efficiency is low. Existing technologies use straight lines, micrometer measurements, or laser levels for calibration. Testing a single 150-meter main beam takes more than 1.5 hours, and the accuracy is significantly affected by the operator's experience, making it difficult to meet the efficiency requirements of batch installation and operation of large power plants.

[0068] Secondly, there is a lack of full life-cycle monitoring. After the photovoltaic modules are installed, traditional methods cannot meet the requirements for measuring the deviation of the internal main beam of the module array, making it difficult to detect deformation problems in a timely manner during the operation period. More importantly, during the operation phase, the real-time coaxiality deviation of the main beam under dynamic conditions such as tracking rotation, wind load impact, and temperature changes cannot be captured, causing the system to be in a "sub-healthy" operating state for a long time, thus implicitly reducing power generation efficiency.

[0069] Third, the environmental adaptability is insufficient. Existing testing methods do not consider the coupled effects of multiple working conditions: factors such as thermal expansion and contraction of the main beam due to temperature changes, wind-induced vibration under strong winds, and center of gravity shift during tilt adjustment can all cause dynamic deformation. However, traditional static test results cannot reflect the real deviation in actual operation, resulting in a disconnect between calibration accuracy and actual needs.

[0070] Therefore, in view of the problems of low detection efficiency, lack of dynamic monitoring and insufficient environmental adaptability in the existing technology, there is an urgent need for an integrated and intelligent coaxiality detection solution to achieve high-precision and high-efficiency detection of segmented main beams throughout their entire life cycle, so as to ensure the safe and stable operation of photovoltaic brackets and tracking systems and maximize power generation benefits.

[0071] In view of this, this embodiment provides a method and system for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system, in order to solve the problems of low detection efficiency, lack of dynamic monitoring, and insufficient environmental adaptability in the prior art.

[0072] The following is combined Figures 1 to 2 The following describes embodiments of the present invention.

[0073] According to an embodiment of the present invention, in one aspect, a method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system is provided, comprising:

[0074] Static benchmark calibration involves measuring the three-dimensional coordinates of the endpoints of the first and last main beams using a total station to construct the theoretical axis parameter equation L.

[0075] Dynamic deformation monitoring collects in real time data on the inclination angle, distance between adjacent segments, temperature and wind speed of each main beam segment, and inputs it into the deformation prediction model to calculate the real-time offset δD;

[0076] Intelligent error correction: When δD exceeds a preset threshold, an alarm is triggered and a compensation adjustment plan is output.

[0077] The deformation prediction model is as follows:

[0078] δD=α·T+β·V 2 +γ·sin(θ);

[0079] T represents the temperature change, V represents the wind speed, θ represents the inclination angle of the main beam, and α, β, and γ represent the calibration coefficients.

[0080] The method for detecting the coaxiality of segmented main beams in the photovoltaic tracking system provided in this embodiment achieves efficient and accurate detection of the coaxiality of segmented main beams through the coordinated use of static benchmark calibration, dynamic deformation monitoring, and intelligent error correction.

[0081] For static benchmark calibration, a total station was used to accurately measure the three-dimensional coordinates of the endpoints at both ends of the segmented main beam. The collected coordinate data was then substituted into the calculations to construct the theoretical axis parameter equation L. This equation serves as the benchmark standard for the main beam axis, clarifying the spatial position trajectory that each segment of the main beam should follow under ideal conditions, and providing a reference benchmark for deviation comparison in subsequent dynamic monitoring.

[0082] For dynamic deformation monitoring, the inclination angle of each main beam segment, the spacing between adjacent segments, ambient temperature, and wind speed data are collected in real time. These parameters are then input into the preset deformation prediction model δD=α·T+β·V 2+γ·sin(θ) where T is the temperature change, V is the wind speed, θ is the girder inclination angle, and α, β, and γ are coefficients calibrated through experiments or data regression. The model calculates the real-time offset δD of the actual axis of the main girder relative to the theoretical axis parametric equation L at each moment. This offset directly reflects the coaxiality deviation of the main girder under dynamic conditions.

[0083] The system compares the calculated real-time offset δD with a preset threshold. When δD exceeds the threshold, an alarm mechanism is immediately triggered to indicate a risk of coaxiality deviation. At the same time, based on the magnitude, distribution and related parameters of δD, a targeted compensation and adjustment scheme is generated to provide specific operational guidance for subsequent main beam calibration.

[0084] This method constructs the theoretical axis parameter equation L through total station measurements, significantly improving the accuracy of establishing the benchmark axis compared to traditional methods such as manual stringing. This provides a reliable reference standard for subsequent testing and reduces the impact of benchmark errors on the overall testing results. The dynamic deformation monitoring stage, by collecting multi-dimensional data in real time and inputting it into the model calculation, can accurately capture the real-time offset δD of the main beam caused by factors such as temperature changes, wind force, and tilt angle adjustments during operation. This overcomes the limitation of traditional methods that can only perform static testing, achieving coaxiality monitoring under all working conditions. The intelligent error correction mechanism triggers alarms promptly through threshold judgment and outputs compensation schemes. It can quickly respond when coaxiality deviation exceeds the limit, avoiding problems such as uneven drive load and structural stress concentration caused by excessive deviation. This reduces safety risks such as loose or broken main beam connecting bolts, and damage to supports and drive systems, ensuring the stable operation of the photovoltaic system as a whole.

[0085] In one embodiment, the theoretical axis parameter equation L is:

[0086] ax + by + cz + d = 0;

[0087] Where a, b, and c are the components of the normal vector in the plane along the X-axis, Y-axis, and Z-axis, respectively; d is the offset of the plane from the origin; and x, y, and z are the three-dimensional coordinates of the center point of the main beam.

[0088] The theoretical axis parametric equation L adopts the form of the plane equation ax + by + cz + d = 0, precisely defining the ideal axis reference of the segmented main beam through a three-dimensional coordinate system. a, b, and c correspond to the components of the plane normal vector in the X, Y, and Z axes, respectively, determining the spatial orientation of the plane containing the theoretical axis through the direction of the normal vector; the value of d represents the offset distance between this plane and the origin of the coordinate system, together forming a uniquely determined spatial plane. In the equation, x, y, and z are the three-dimensional coordinates of each center point of the segmented main beam. When the main beam is in an ideal coaxial state, the coordinates of all center points satisfy this equation, meaning that the actual axis of each segment completely conforms to the theoretical plane. In dynamic deformation monitoring, the deviation value of the real-time acquired segment position coordinates of the main beam, after being substituted into the equation, directly reflects the degree of deviation between the actual axis and the theoretical axis, providing basic data for calculating the real-time offset δD.

[0089] In one embodiment, the construction of the theoretical axis parameter equations in static benchmark calibration includes:

[0090] Measure the first endpoint P1(x1,y1,z1) and the last endpoint P2(x2,y2,z2);

[0091] Calculate the direction vectors: Δx = x2 - x1, Δy = y2 - y1, Δz = z2 - z1;

[0092] Generating equation:

[0093] x = x1 + t·Δx

[0094] y = y1 + t·Δy

[0095] z = z1 + t·Δz

[0096] Where t∈[0,1] is the scaling factor.

[0097] Specifically, this process is based on the three-dimensional coordinates of the first endpoint P1 (x1, y1, z1) and the last endpoint P2 (x2, y2, z2) (precisely measured by a total station). By calculating the differences between the two points in the X, Y, and Z axes, Δx = x2 - x1, Δy = y2 - y1, and Δz = z2 - z1, a direction vector representing the orientation of the main beam axis is obtained. Based on this vector, parametric equations with a scaling factor t (t ∈ [0, 1]) are generated: x = x1 + t·Δx, y = y1 + t·Δy, z = z1 + t·Δz. When t = 0, the equation corresponds to the first endpoint P1; when t = 1, it corresponds to the last endpoint P2; when t varies within the interval 0-1, the equation can describe the three-dimensional coordinates of any position between the first and last endpoints, that is, the ideal axis trajectory that theoretically should coincide for each segment of the main beam.

[0098] In one embodiment, the dynamic deformation monitoring step achieves data acquisition through a detection ring pre-installed at the main beam connecting flange; the detection ring includes an inclinometer 1, a laser rangefinder 4, a temperature and humidity sensor 3, and a magnetic interface 5.

[0099] In the dynamic deformation monitoring step, the detection ring pre-installed at the main beam connecting flange integrates multiple types of sensors to achieve real-time acquisition of key parameters of the main beam segments. The detection ring is quickly fixed to the preset positions of each main beam connecting flange via magnetic interface 5, ensuring a tight fit with the main beam structure to improve data acquisition accuracy. Specifically, the inclinometer 1 monitors the tilt angle θ of the corresponding main beam segment in real time, providing attitude parameters for the deformation prediction model; the laser rangefinder 4 measures the distance between adjacent main beam segments by emitting a laser beam, capturing the inter-segment displacement caused by deformation; the temperature and humidity sensor 3 synchronously collects the temperature change T of the main beam surface and surrounding environment, providing environmental parameters for deformation caused by thermal expansion and contraction; the data collected by each sensor is integrated through a built-in module and transmitted in real time to the central processing system as the raw input for dynamic deformation monitoring. The detection ring is quickly installed via magnetic interface 5 and adapts to different main beam cross-sectional dimensions.

[0100] In one embodiment, the detection ring further includes an opening and closing hinge 2 and a locking device 6, adapted to a circular or polygonal main beam cross section.

[0101] A hinge 2 is located on one side of the detection ring, allowing the ring to rotate and open along the hinge axis, forming a "C"-shaped opening. A locking device 6, comprising a snap-fit ​​and a latch structure, is located on the other side of the detection ring. During installation, the detection ring is opened via the hinge 2, wrapped around the outer circumference of the connecting flange of the circular or polygonal main beam, and then the ring is closed and the locking device 6 is fastened. The mechanical locking force of the latch ensures that no displacement occurs when the main beam rotates or vibrates. For a circular cross-section, the ring can achieve full circumferential fit through uniform force; for a polygonal cross-section, the flexible rotation of the hinge 2 and the fine-tuning function of the locking device 6 allow the ring to adapt to the zigzag shape at the corners.

[0102] In one embodiment, the calibration coefficients α, β, and γ are determined by fitting a single variable individually in a wind tunnel and a temperature-controlled laboratory.

[0103] The process involves independent testing of three variables—temperature, wind speed, and tilt angle—in a controlled environment: In a temperature-controlled laboratory, with wind speed maintained at 0 and main beam tilt angle fixed, the main beam undergoes a preset temperature change T by adjusting the ambient temperature. The main beam offset at different temperatures is measured in real time, and the temperature influence coefficient α is obtained by fitting using the least squares method. In a wind tunnel experiment, with temperature and tilt angle fixed, different wind speeds V are simulated in the wind tunnel, and the main beam offset data at the corresponding wind speeds is collected. The wind force influence coefficient β is obtained by fitting. Similarly, in a wind tunnel or dedicated testing platform, with temperature and wind speed kept constant, the main beam tilt angle θ is adjusted, and the gravity deformation offset at different tilt angles is measured. The tilt angle influence coefficient γ is obtained by fitting. The fitting results of these three single-variable tests together constitute the calibration coefficients of the deformation prediction model.

[0104] In one embodiment, the calibration coefficients α, β, and γ are determined by solving multiple linear regression based on field monitoring data.

[0105] This method directly utilizes on-site monitoring data from the actual operation of the photovoltaic tracking system: During different operational stages of the main beam (e.g., initial installation and long-term operation), a large amount of on-site data on temperature changes (T), wind speed (V), main beam tilt angle (θ), and corresponding real-time offset (δD) are continuously collected through the detection ring, forming a multivariate sample dataset. This dataset is then substituted into a multiple linear regression model, with δD as the dependent variable and T and V as the dependent variables. 2 sin(θ) are the independent variables. The coefficients α, β, and γ that minimize the deviation between the predicted value and the actual δD are determined by solving the least squares method.

[0106] In one embodiment, the alarm mechanism in the intelligent error correction step is a dynamic multi-level early warning:

[0107] When δD≥3mm and V≥the first threshold, a level one alarm is triggered;

[0108] When δD≥5mm and V≥the second threshold, a level 2 alarm is triggered and the tracking system is controlled to enter the protection state;

[0109] When δD≥8mm and V≥the third threshold, a level 3 alarm is triggered and an emergency shutdown is executed;

[0110] Among them, the first threshold < the second threshold < the third threshold.

[0111] For example, the first threshold is 15 m / s, the second threshold is 20 m / s, and the third threshold is 25 m / s.

[0112] The system compares the real-time offset δD calculated by the system with the preset three-level thresholds (3mm, 5mm, 8mm), and combines the current wind speed V with the corresponding wind speed conditions (15m / s, 20m / s, 25m / s) to form multi-dimensional triggering conditions: when δD≥3mm and wind speedV≥15m / s, a level one alarm is triggered, and maintenance personnel are notified to pay attention to the main beam status through audible and visual prompts or remote messages; when δD≥5mm and V≥20m / s, a level two alarm is triggered, and in addition to alarm prompts, the automatic control tracking system enters a protection state (such as stopping angle adjustment, maintaining horizontality, or setting a preset safe tilt angle); when δD≥8mm and V≥25m / s, a level three alarm is triggered, and an emergency shutdown procedure is immediately executed to cut off the tracking system drive power supply to prevent the deviation from further expanding.

[0113] In one embodiment, the compensation adjustment scheme in the intelligent error correction step includes adjusting the height of the support pile, the torque value M of the connecting flange bolts, or increasing the thickness of the gasket; wherein the bolt torque value M satisfies the following functional relationship:

[0114] M = f(δD,L);

[0115] Where L is the total length of the main beam.

[0116] The compensation and adjustment scheme provides three specific correction methods based on the magnitude and distribution characteristics of the real-time offset δD: When there is a linear offset in the main beam as a whole, the foundation support position is changed by adjusting the height of the support piles to restore the overall axis of the main beam; when there is significant local inter-segment offset, the target torque value is calculated based on the functional relationship M = f(δD,L) between the bolt torque value M, the real-time offset δD, and the total length L of the main beam, and the tightness of the connecting flange bolts is adjusted, using the deformation generated by the bolt preload to correct the inter-segment deviation; for small angular offsets (such as δD < 2mm), the flange face gap is compensated by increasing the gasket thickness to achieve fine calibration. The functional relationship M = f(δD,L) is established based on material mechanics calculations and field experimental data. For example, for a steel structure main beam, it can be expressed as M = k·δD·L (k is the material coefficient), ensuring a quantitative match between torque adjustment and offset correction values.

[0117] In one embodiment, the segmented main beam coaxiality detection method in a photovoltaic tracking system further includes an edge computing and cloud collaboration step: local processing latency <50ms, real-time output of correction instructions; cloud storage of historical data and generation of deformation trend reports, optimization of early warning thresholds.

[0118] Edge computing nodes are deployed in the local control cabinet of the photovoltaic tracking system, communicating directly with the sensors of the detection ring to receive real-time data such as tilt angle, temperature, wind speed, and offset δD. Local processing employs lightweight algorithms, completing data verification, offset calculation, and early warning condition judgment within a delay of <50ms. When an alarm threshold is triggered, compensation adjustment commands (such as bolt torque value M, support pile adjustment, etc.) are immediately generated and sent to the actuator, achieving millisecond-level response. Simultaneously, the edge nodes upload the processed structured data (including δD, V, θ, and correction records) to the cloud platform at preset intervals (e.g., every 5 minutes). The cloud system stores historical data, generates main beam deformation trend reports (such as monthly / quarterly offset change curves and temperature-deformation correlation analysis) through big data analysis, and dynamically optimizes the three-level early warning threshold based on the trend data.

[0119] According to an embodiment of the present invention, in another aspect, a system for implementing a method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system is also provided, comprising:

[0120] Multiple detection rings are installed at the main beam connecting flange. The detection rings include an inclinometer 1, a laser rangefinder 4, and a temperature and humidity sensor 3.

[0121] Central processing terminal with built-in deformation prediction model;

[0122] The communication module enables data synchronization between the detection ring and the terminal, as well as cloud transmission.

[0123] In this system, multiple detection rings are installed at the connecting flanges of each main beam. An integrated inclinometer 1 collects the inclination angle θ of the main beam segment in real time, a laser rangefinder 4 measures the spacing change between adjacent segments, and a temperature and humidity sensor 3 records the ambient temperature change T. The data from each sensor is preprocessed within the detection rings to form standardized data frames, providing raw input for subsequent analysis. The central processing terminal receives the data transmitted from the detection rings and calls the built-in deformation prediction model δD=α·T+β·V. 2 The real-time offset δD is calculated using +γ·sin(θ) and combined with wind speed data. Simultaneously, the terminal executes intelligent error correction logic, triggering dynamic multi-level early warnings by comparing δD with a preset threshold. Based on the deviation value, a compensation adjustment scheme is generated, including the adjustment amount of the support pile, the bolt torque value M (based on M=f(δD,L)), or the shim thickness. The communication module uses wireless or wired methods to achieve real-time data synchronization between the detection loop and the central processing terminal. Simultaneously, the processed structured data (such as δD, early warning records, and correction instructions) is uploaded to the cloud platform, supporting historical data storage and trend analysis.

[0124] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system, characterized in that, include: Static benchmark calibration involves measuring the three-dimensional coordinates of the endpoints of the first and last main beams using a total station to construct the theoretical axis parameter equation L. Dynamic deformation monitoring collects in real time data on the inclination angle, distance between adjacent segments, temperature and wind speed of each main beam segment, and inputs it into the deformation prediction model to calculate the real-time offset δD; Intelligent error correction: When δD exceeds a preset threshold, an alarm is triggered and a compensation adjustment plan is output. The deformation prediction model is as follows: δD=α·T+β·V 2 +γ·sin(θ); T represents the temperature change, V represents the wind speed, θ represents the inclination angle of the main beam, and α, β, and γ represent the calibration coefficients.

2. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, The theoretical axis parameter equation L is: ax + by + cz + d = 0; Where a, b, and c are the components of the normal vector in the plane along the X-axis, Y-axis, and Z-axis, respectively; d is the offset of the plane from the origin; and x, y, and z are the three-dimensional coordinates of the center point of the main beam.

3. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 2, characterized in that, In the static reference calibration, the construction of the theoretical axis parameter equations includes: Measure the first endpoint P1(x1,y1,z1) and the last endpoint P2(x2,y2,z2); Calculate the direction vectors: Δx = x2 - x1, Δy = y2 - y1, Δz = z2 - z1; Generating equation: x = x1 + t·Δx y = y1 + t·Δy z = z1 + t·Δz Where t∈[0,1] is the scaling factor.

4. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, The dynamic deformation monitoring step achieves data acquisition through a detection ring pre-installed at the main beam connecting flange; the detection ring includes an inclinometer (1), a laser rangefinder (4), a temperature and humidity sensor (3), and a magnetic interface (5).

5. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 4, characterized in that, The detection ring also includes an opening and closing hinge (2) and a locking device (6), which are adapted to a main beam with a circular or polygonal cross section.

6. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, The calibration coefficients α, β, and γ are determined in any of the following ways: Fitting was performed by applying a single variable in the wind tunnel and temperature-controlled laboratory. The solution is obtained through multiple linear regression based on on-site monitoring data.

7. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, In the intelligent error correction step, the alarm mechanism is a dynamic multi-level early warning: When δD≥3mm and V≥the first threshold, a level one alarm is triggered; When δD≥5mm and V≥the second threshold, a level 2 alarm is triggered and the tracking system is controlled to enter the protection state; When δD≥8mm and V≥the third threshold, a level 3 alarm is triggered and an emergency shutdown is executed; Among them, the first threshold < the second threshold < the third threshold.

8. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, In the intelligent error correction step, the compensation adjustment scheme includes adjusting the height of the support pile, the torque value M of the connecting flange bolts, or increasing the thickness of the gasket.

9. The method for detecting the coaxiality of a segmented main beam in a photovoltaic tracking system according to claim 1, characterized in that, It also includes steps for edge computing and cloud collaboration: Local processing latency <50ms, real-time output of correction instructions; Historical data is stored in the cloud and deformation trend reports are generated to optimize early warning thresholds.

10. A system for implementing the segmented main beam coaxiality detection method in any one of claims 1-9 photovoltaic tracking systems, characterized in that, include: Multiple detection rings are provided at the main beam connecting flange. The detection rings include an inclinometer (1), a laser rangefinder (4), and a temperature and humidity sensor (3). The central processing terminal has the deformation prediction model built-in. The communication module enables data synchronization between the detection ring and the terminal, as well as cloud transmission.

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