A simulation analysis method for seismic performance of a photovoltaic support

Abstract

This invention relates to the field of simulation analysis, and more particularly to a simulation analysis method for the seismic performance of photovoltaic (PV) brackets. The method includes: determining whether to adjust the benchmark acquisition granularity corresponding to the target time sequence based on smoothing difference reference values ​​and point category difference reference values; acquiring the target time sequence based on the finally determined acquisition granularity, and determining whether it is in the first influence stage based on the ambient temperature and snow thickness corresponding to the collected data at each time point within the target time sequence; performing single-matrix adjustment by introducing a first adjustment matrix for time points in the first influence stage; determining whether to determine the second influence stage based on the effective fluctuation value of ambient temperature, the cumulative freeze-thaw reference value, and the ice shell reference thickness for time points not in the first influence stage; and performing dual-matrix adjustment by introducing both a first adjustment matrix and a second adjustment matrix for time points in the second influence stage. This invention improves the accuracy of simulation analysis of the seismic performance of PV brackets.

Description

Technical Field

[0001] This invention relates to the field of simulation analysis, and in particular to a simulation analysis method for the seismic performance of photovoltaic brackets. Background Technology

[0002] As a crucial component of photovoltaic (PV) power plants, the structural safety of PV support structures directly impacts the long-term stable operation of the power plant. In earthquake-prone areas, the seismic performance of PV support structures is a critical factor that must be considered during the design phase. Existing seismic simulation analysis methods for PV support structures typically employ the finite element method, which mainly includes the following steps: establishing a finite element model of the support structure, defining material properties and boundary conditions; performing modal analysis to obtain the structure's natural frequencies and mode shapes; applying seismic loads using the response spectrum method or time history analysis; and extracting the analysis results and performing strength and deformation verification. These methods can effectively evaluate the seismic performance of support structures under normal operating conditions and have been widely applied in engineering design. However, for PV support structures in winter snow-covered scenarios, existing simulation analysis methods have significant limitations. Snow is not statically present on the surface of the PV panels but undergoes complex physical changes under the influence of environmental factors such as temperature, solar radiation, and wind speed. Snow accumulation can exist as dry snow, only increasing structural mass; or it can undergo freeze-thaw cycles to form an ice crust, increasing both mass and additional stiffness. Current technologies typically simplify snow loads as static loads uniformly applied to the photovoltaic panel surface, failing to consider the dynamic evolution of snow accumulation and unable to differentiate the varying impacts of different snow accumulation stages on structural dynamic characteristics, thus lacking effective quantification methods. Therefore, simulation analysis of the seismic performance of photovoltaic supports that adaptively adjust based on environmental factors is a significant concern for those skilled in the art. Summary of the Invention

[0003] To address this, the present invention provides a simulation analysis method for the seismic performance of photovoltaic supports, which overcomes the problems in the existing technology of lacking effective quantitative means for the seismic performance of photovoltaic supports in winter snow scenes, being unable to dynamically adjust the analysis strategy according to environmental factors and actual snow characteristics, and having analysis accuracy that is difficult to meet user needs.

[0004] Therefore, this invention provides a simulation analysis method for the seismic performance of photovoltaic supports, comprising: The granularity of benchmark acquisition corresponding to the target time series is determined based on the smoothing difference reference value and the point category difference reference value. Based on the final determined acquisition granularity, the target time sequence is acquired, and the ambient temperature and snow thickness corresponding to the collected data at each time point in the target time sequence are used to determine whether it is in the first influence stage. A single-matrix adjustment is performed at the moment point in the first influence phase, introducing the first adjustment matrix; For time points not in the first impact stage, the determination of whether to use the cumulative freeze-thaw reference value and the ice shell reference thickness is based on the effective fluctuation value of ambient temperature to determine whether to use the second impact stage; For the moment point in the second influence phase, a dual-matrix adjustment is implemented by introducing a first adjustment matrix and a second adjustment matrix.

[0005] Preferably, for sequence difference states where the smooth difference reference value is greater than the preset smooth difference reference value or the point category difference reference value is greater than the preset point category difference reference value, the benchmark acquisition granularity corresponding to the target time sequence is adjusted to a high acquisition granularity.

[0006] Preferably, for a point in time where the ambient temperature is within a first temperature threshold range and the snow thickness is greater than a preset snow thickness, the point in time is determined to be in the first influence stage.

[0007] Preferably, when the first adjustment matrix is ​​introduced, the wind speed reference value and the wind speed stability value are detected. If the wind speed reference value is greater than the preset wind speed reference value or the wind speed stability value is less than or equal to the preset wind speed stability value, the first adjustment matrix is ​​adjusted based on the wind disturbance coefficient.

[0008] Preferably, the initial first adjustment matrix is: ; ; in, for Snow thickness on solar panels at any given time for Snow density at any given time For the preset shape function matrix, The surface area of ​​the photovoltaic panel; Wind disturbance coefficient is ; in, As a wind-driven snow-driving factor, The azimuth angle of the photovoltaic panel. For reference wind direction.

[0009] Preferably, the determination of the second impact stage is made at the time point when the effective fluctuation value of the ambient temperature is greater than the preset effective fluctuation value of the ambient temperature; Specifically, for the moment when the cumulative freeze-thaw reference value is greater than the preset cumulative freeze-thaw reference value and the ice shell reference thickness is greater than the preset ice shell reference thickness, the moment is determined to be in the second influence stage.

[0010] Preferably, the initial second adjustment matrix is: ; in, For reference strain-displacement matrix, For the elasticity matrix, The unit area is denoted as .

[0011] Preferably, based on the interference conditions that the frequency of tilt angle change is greater than the preset frequency of tilt angle change or the radiation intensity deviation value is greater than the preset radiation intensity deviation value, uniformity interference adjustment is determined for the second adjustment matrix.

[0012] Preferably, when the time point of the second influence stage is a preset analysis point, the determination of whether to adjust the integration step size is based on the continuous reference value of the ice shell; If the continuous reference value of the ice shell is less than or equal to the preset continuous reference value of the ice shell, then no adjustment is needed for the integration step size; If the continuous reference value of the ice shell is greater than the preset continuous reference value of the ice shell, the integral step size will be reduced.

[0013] Compared with the prior art, the beneficial effects of the present invention are: by adjusting and determining the benchmark acquisition granularity through smoothing difference reference value and point category difference reference value, the fixed acquisition granularity is avoided from failing to acquire data effectively, which would make it difficult for the data accuracy of the target time sequence used subsequently to meet the actual use requirements, thereby further improving the simulation analysis accuracy of the present invention.

[0014] Furthermore, different impact stages are determined for the target time series. Through targeted analysis of ambient temperature, snow thickness, cumulative freeze-thaw reference values, and ice shell reference thickness, accurate determination of different impact stages is achieved, thereby enabling subsequent targeted matrix adjustment determination analysis and improving the determination accuracy of the corresponding matrix adjustment.

[0015] Furthermore, the first and second adjustment matrices are respectively equipped with adjustment based on wind disturbance coefficient and uniformity disturbance adjustment. The corresponding adjustment operation is determined according to the snowfall characteristics and environmental factors in the actual scenario, so that the structural model at the time point is more accurate and conforms to the dynamic changes of the actual scenario, thereby improving the accuracy of simulation analysis. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the simulation analysis method for the seismic performance of photovoltaic brackets according to the present invention; Figure 2 This is a flowchart illustrating whether a given moment in the present invention is in the first influence phase. Figure 3 This is a flowchart illustrating whether a given point in the present invention is in the second influence phase. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0018] Please see Figures 1 to 3 As shown, this invention provides a simulation analysis method for the seismic performance of photovoltaic brackets, comprising: The granularity of benchmark acquisition corresponding to the target time series is determined based on the smoothing difference reference value and the point category difference reference value. Based on the final determined acquisition granularity, the target time sequence is acquired, and the ambient temperature and snow thickness corresponding to the collected data at each time point in the target time sequence are used to determine whether it is in the first influence stage. A single-matrix adjustment is performed at the moment point in the first influence phase, introducing the first adjustment matrix; For time points not in the first impact stage, the determination of whether to use the cumulative freeze-thaw reference value and the ice shell reference thickness is based on the effective fluctuation value of ambient temperature to determine whether to use the second impact stage; For the moment point in the second influence phase, a dual-matrix adjustment is implemented by introducing a first adjustment matrix and a second adjustment matrix.

[0019] The target time series includes several time points, and the target time series is {t1, t2, ..., ti, ..., tn}, where n is the total number of time points, i = 1, 2, 3, ..., n. For each time point ti, corresponding data is collected, including but not limited to ambient temperature, snow thickness, wind speed reference value, wind speed stability value, cumulative freeze-thaw reference value, and ice shell reference thickness.

[0020] Specifically, this invention modifies the mass matrix and stiffness matrix of the photovoltaic support finite element model to obtain a high-precision photovoltaic support finite element model that conforms to the dynamic characteristics of the actual scene, thereby improving the accuracy of subsequent simulation analysis based on the photovoltaic support finite element model. How to perform modal analysis based on the photovoltaic support finite element model and apply seismic loads through response spectrum method or time history analysis method, extract analysis results and perform strength and deformation verification are all contents that have been mastered by those skilled in the art, and will not be elaborated here.

[0021] The uploaded data refers to the data collected by the user during a single snowfall process at the photovoltaic station at a fixed collection frequency. If the collection frequency is once every 5 minutes, then the time interval between recorded moments is 5 minutes, which will not be elaborated here.

[0022] For sequence difference states where the smooth difference reference value is greater than the preset smooth difference reference value or the point category difference reference value is greater than the preset point category difference reference value, the benchmark acquisition granularity corresponding to the target time sequence is adjusted to a high acquisition granularity.

[0023] The granularity of acquisition refers to the number of time points extracted from the uploaded data. Preferably, the baseline acquisition granularity is 50% of the number of recorded time points corresponding to the uploaded data, the high acquisition granularity is 80% of the number of recorded time points corresponding to the uploaded data, the preset smoothing difference reference value is 5, and the preset point category difference reference value is 50%. Smoothing difference reference value = α1 × first smoothing difference sub-value + α2 × second smoothing difference sub-value; First smoothing difference subvalue ; Second smoothing difference subvalue ; in, for The ambient temperature at that moment for The ambient temperature at that moment This is a preset temperature smoothing comparison value. for Wind speed reference value at any given time for Wind speed reference value at any given time, L This is the preset wind speed smoothing comparison value.

[0024] Preferably, the preset temperature smoothing comparison value is 40% of the average ambient temperature at each time point, the preset wind speed smoothing comparison value is 30% of the average wind speed reference value at each time point, α1 is the first weighting coefficient, α1=0.5, and α2 is the second weighting coefficient, α2=0.5.

[0025] Point category difference reference value = total number of time points in the first influence stage and the second influence stage / total number of time points.

[0026] For a given moment when the ambient temperature is within the first temperature threshold range and the snow thickness is greater than the preset snow thickness, that moment is determined to be in the first influence stage.

[0027] Preferably, the snow thickness is the same as the snow thickness on the photovoltaic panel. The values ​​within the first temperature threshold range are all less than 0℃. The preset snow thickness is 2cm. It can be understood that the first impact stage reflects the current stage of dry snow accumulation on the photovoltaic panel. Therefore, the values ​​within the first temperature threshold range are all less than 0℃ to ensure that there is no melting. The greater the snow thickness, the greater the impact of snow on the seismic resistance of the photovoltaic support. Therefore, the higher the user's sensitivity to the impact of snow thickness on the accuracy of seismic analysis, the larger the preset snow thickness value.

[0028] Preferably, for a single point in time, the corresponding ambient temperature is collected by measuring the temperature at the site of the photovoltaic support, and the corresponding snow thickness is measured manually, using a laser snow depth sensor, or by measuring the snow load using a weight sensor, and then calculating the snow thickness by combining the snow load with the density. No specific method is specified here. When the first adjustment matrix is ​​introduced, the wind speed reference value and the wind speed stability value are detected. If the wind speed reference value is greater than the preset wind speed reference value or the wind speed stability value is less than or equal to the preset wind speed stability value, the first adjustment matrix is ​​adjusted based on the wind disturbance coefficient.

[0029] For a single point in time, the corresponding wind speed reference value is the average environmental wind speed obtained by the wind speed sensor up to 1 minute before that point in time. The wind speed stability value is the absolute value of the maximum difference between the wind speed reference values ​​at that point in time and the three points in time before that point in time.

[0030] Preferably, the preset wind speed reference value is 5 m / s and the preset wind speed stability value is 3 m / s. The user can obtain the snow thickness change per unit time under different wind speed reference values ​​in historical working conditions, and record the minimum wind speed reference value where the snow thickness change exceeds the user's tolerance as the preset wind speed reference value. The principle of determining the preset wind speed stability value is the same, and will not be repeated here.

[0031] The initial first adjustment matrix is ; ; in, for Snow thickness on photovoltaic panels at a given time point, in meters (m). for Snow density at any given time For the preset shape function matrix, Let g be the surface area of ​​the photovoltaic panel, and g be the acceleration due to gravity, g = 9.81 m / s². 2 Analysis of snowfall data from multiple locations shows that the average unit mass of snow ranges from 19.6 N / m³. 2 / cm to 29.4 N / m2 / cm, which translates to a density value ranging from 200 to 500 kg / m³. 3 In this invention, the snow density is fixed at 300 kg / m³. 3 ; In this invention, the finite element method is used for discretization. When constructing the element mass matrix, a preset shape function matrix [N] is required. The function of the preset shape function matrix is ​​to interpolate the physical quantities (such as displacement and mass) at any point within the element using the physical quantities of the element nodes. Its specific form depends on the selected element type and will not be elaborated here.

[0032] In the embodiments, a four-node quadrilateral shell is preferred, whose preset shape function is in the natural coordinate system ( The following can be represented as: ; ; ; ; Then the pre-defined shape function matrix for: ; It is understood that the above-mentioned four-node quadrilateral element and its shape function are only a specific example, and the present invention is not limited thereto. Those skilled in the art can select other element types (such as three-node triangular elements, eight-node quadrilateral elements, etc.) and construct corresponding shape functions according to actual needs.

[0033] Wind disturbance coefficient is ; in, As a wind-driven snow-driving factor, The azimuth angle of the photovoltaic panel. For reference wind direction.

[0034] Preferred, =0.7, which allows users to adjust according to the wind speed in the environment. The greater the wind speed in the environment, The azimuth angle of a photovoltaic panel refers to the angle between the projection of the normal line (i.e., the straight line perpendicular to the panel) of the tilted surface of the photovoltaic panel onto the horizontal plane and the due south direction. The reference direction is obtained by real-time data collection from the wind direction sensor of the automatic weather station deployed in the photovoltaic power station. The sampling frequency is no less than once per minute. The wind direction with the highest frequency obtained from continuous observations over the past year is used as the reference wind direction. Both the azimuth angle of the photovoltaic panel and the reference wind direction are based on due south as 0°. Positive values ​​represent westward deviation, and negative values ​​represent eastward deviation. The range of values ​​is [-90°, 90°].

[0035] The first adjustment matrix after adjusting the first adjustment matrix based on the wind disturbance coefficient is: .

[0036] For the point in time when the effective fluctuation value of the ambient temperature exceeds the preset effective fluctuation value of the ambient temperature, the determination of the second impact stage is made. Specifically, for the moment when the cumulative freeze-thaw reference value is greater than the preset cumulative freeze-thaw reference value and the ice shell reference thickness is greater than the preset ice shell reference thickness, the moment is determined to be in the second influence stage.

[0037] For a single point in time, the effective fluctuation value of the corresponding ambient temperature is obtained by extracting other points in the previous 24 hours and detecting the ambient temperature corresponding to each adjacent point in time. The effective fluctuation value of ambient temperature = the total number of points in time and other points in the previous 24 hours - |the number of points in time where the ambient temperature is within the first temperature threshold range - the number of points in time where the ambient temperature is within the second temperature threshold range|. Preferably, the preset effective fluctuation value of ambient temperature is 70% of the total number of time points within the previous 24 hours. The values ​​within the second temperature threshold range are all greater than or equal to 0℃ and less than 5℃. It can be seen that the second influence stage is characterized by the freezing-thaw cycle of environmental snowfall. Therefore, the absolute value of the difference between the number of time points where the ambient temperature is within the first temperature threshold range and the number of time points where the ambient temperature is within the second temperature threshold range reflects whether the fluctuation of the ambient temperature at the time point meets the requirements of the second influence stage.

[0038] The cumulative freeze-thaw reference value is the number of zero-time points before this time point. The ambient temperature corresponding to the zero-time point and the ambient temperature corresponding to the time point after it in time sequence have only one positive ambient temperature before it.

[0039] For the ice shell reference thickness is ; in, The freezing coefficient is... The melting coefficient is... for The solar radiation intensity at that moment. The solar radiation intensity is expressed as watts per square meter (W / m²), representing the solar radiation power received per unit area. This represents the time interval between two points in time. The value range is [0.1, 0.3]. The value range is [0.05, 0.15], preferably... =0.2, =0.1.

[0040] Preferably, the preset cumulative freeze-thaw reference value is 3, and the preset ice shell reference thickness is 2.5mm. It can be understood that the larger the cumulative freeze-thaw reference value and the ice shell reference thickness, the greater the impact of freeze-thaw cycles and ice shell formation on the seismic resistance of photovoltaic supports. Therefore, the greater the user's sensitivity to freeze-thaw cycles and ice shell formation, the smaller the preset cumulative freeze-thaw reference value and the preset ice shell reference thickness. A value selection method is provided, which extracts the average values ​​of the cumulative freeze-thaw reference value and the ice shell reference thickness in historical working conditions where the impact of ice shell formation on seismic resistance simulation exceeds the user's tolerance level, and records them as the preset cumulative freeze-thaw reference value and the preset ice shell reference thickness, respectively.

[0041] Historical operating conditions are the time points and corresponding data collected during historical snowfall. Simulations are performed based on the original finite element model and the dual-matrix finite element model with the introduction of the first and second adjustment matrices. The degree of deviation between the simulation results (e.g., the absolute value of the difference in the maximum displacement of the same point of the photovoltaic support corresponding to the two simulation results) is used to determine whether the impact of ice shell formation on the simulation of seismic resistance exceeds the user's tolerance. This is a technical method that is easy for those skilled in the art to understand and will not be elaborated here.

[0042] The initial second adjustment matrix is ; in, For reference strain-displacement matrix, For the elasticity matrix, The unit area is denoted as .

[0043] The second adjustment matrix after uniform disturbance adjustment is: ; ; Determined based on the partial derivatives of the shape function with respect to physical coordinates of the selected element type (e.g., shell element or solid element). , The radiation non-uniformity factor ranges from [0.2, 0.6]. Since the sun is a point source, the radiation intensity received at different locations on the photovoltaic panel surface varies. Areas directly facing the sun receive stronger radiation (melting faster), while areas away from the sun receive weaker radiation (melting slower). Furthermore, the distribution of scattered radiation from the sky also affects the non-uniformity. Users can adjust the non-uniformity based on cloud cover. The determination is δ=δ0×(1-0.5×C), where C is the cloud cover coefficient (0 for sunny days, 1 for cloudy days). Let be the elastic modulus of ice. =9 GPa, Poisson's ratio, =0.33, The array position attenuation factor, , The number of the photovoltaic panel (starting from 1, with 1 being the first row). This represents the total number of rows. The occlusion attenuation coefficient has a value range of [0.2, 0.5], preferably... The value is set to 0.3, which users can adjust based on the spacing and tilt angle of the photovoltaic panels. A larger spacing results in a higher value. The smaller the angle, the larger the tilt angle. The larger the value, the better. It can be understood that photovoltaic panels are typically arranged in an array, with the ROWs numbered according to the order in which they receive solar radiation. The row that receives direct sunlight first when the sun is incident at its principal angle is the front row (ROW=1), and so on, increasing sequentially.

[0044] Based on the interference conditions that the frequency of tilt angle changes is greater than the preset frequency of tilt angle changes or the radiation intensity deviation value is greater than the preset radiation intensity deviation value, uniformity interference adjustment is determined for the second adjustment matrix.

[0045] For a single point in time, the frequency of tilt angle change is the variance of the azimuth angle of the photovoltaic panel corresponding to the 10 points in time preceding that point, and the radiation intensity deviation is the variance of the solar radiation power corresponding to the 10 points in time preceding that point.

[0046] Preferably, the preset tilt angle change frequency is 30% of the average azimuth angle of the photovoltaic panel corresponding to the 10 time points prior to the current time point, and the preset radiation intensity deviation value is 15% of the solar radiation power corresponding to the 10 time points prior to the current time point. It is understood that the tilt angle change frequency and radiation intensity deviation value will affect the uniformity of the ice shell morphology. Therefore, the greater the user's sensitivity to the influence of the tilt angle change frequency and radiation intensity deviation value, the smaller the preset tilt angle change frequency and preset radiation intensity deviation value should be.

[0047] When the time point of the second influence stage is the preset analysis point, it is determined whether to adjust the integration step size based on the continuous reference value of the ice shell. If the continuous reference value of the ice shell is less than or equal to the preset continuous reference value of the ice shell, then no adjustment is needed for the integration step size; If the continuous reference value of the ice shell is greater than the preset continuous reference value of the ice shell, the integral step size will be reduced.

[0048] As is known to those skilled in the art, after obtaining the finite element model of the photovoltaic support, modal analysis and seismic load application through time history analysis are required to extract the analysis results and perform strength and deformation verification. In the embodiments of the present invention, nonlinear time history analysis is used. The preset analysis point is the time point within the time period during which the user applies the seismic load; the continuous reference value of the ice shell is the variance of the ice shell reference thickness corresponding to the 5 time points before the current time point.

[0049] Preferably, the preset continuous reference value of the ice shell is 20% of the average value of the ice shell reference thickness corresponding to the 5 time points before the current time point. It can be understood that the larger the continuous reference value of the ice shell, the more unstable the ice shell thickness is. Therefore, the higher the requirement for the integration step size is to achieve accurate data analysis, the higher the user's requirement for simulation accuracy, the smaller the value of the preset continuous reference value of the ice shell.

[0050] The initial integration step size is set by the user; this is a standard parameter in this field and will not be elaborated further. When adjusting the integration step size, the adjusted integration step size = initial integration step size × (preset continuous ice shell reference value / continuous ice shell reference value). Nonlinear time history analysis is a direct dynamic analysis method that uses seismic acceleration time history curves as input and integrates the structural motion equations step by step to solve for the displacement, velocity, and acceleration response of the structure at each moment. The integration step size is the time interval between two adjacent calculation moments during numerical integration. For example, if a continuous seismic time history (e.g., 30 seconds) is divided into countless tiny segments, the length of each segment is the integration step size. The program calculates the structural response at the beginning and end of each step size.

[0051] In this invention, for a single point in time, the first adjustment matrix is ​​introduced, which is the sum of the first adjustment matrix and its original mass matrix. The second adjustment matrix is ​​introduced, which is the sum of the second adjustment matrix and its original stiffness matrix. If a single adjustment matrix has undergone adjustment based on wind disturbance coefficient or uniformity disturbance adjustment, the adjusted adjustment matrices are added together accordingly. Furthermore, how to obtain the mass matrix and stiffness matrix of the photovoltaic support is a subject already known to those skilled in the art and will not be elaborated further.

[0052] For time points that are not in the first or second influence phase, there is no need to introduce adjustment matrices for the corresponding mass and stiffness matrices.

[0053] The terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation analysis method for the seismic performance of photovoltaic brackets, characterized in that, include: The granularity of benchmark acquisition corresponding to the target time series is determined based on the smoothing difference reference value and the point category difference reference value. Based on the final determined acquisition granularity, the target time sequence is acquired, and the ambient temperature and snow thickness corresponding to the collected data at each time point in the target time sequence are used to determine whether it is in the first influence stage. A single-matrix adjustment is performed at the moment point in the first influence phase, introducing the first adjustment matrix; For time points not in the first impact stage, the determination of whether to use the cumulative freeze-thaw reference value and the ice shell reference thickness is based on the effective fluctuation value of ambient temperature to determine whether to use the second impact stage; For the moment point in the second influence phase, a dual-matrix adjustment is implemented by introducing a first adjustment matrix and a second adjustment matrix.

2. The simulation analysis method for the seismic performance of photovoltaic brackets according to claim 1, characterized in that, For sequence difference states where the smooth difference reference value is greater than the preset smooth difference reference value or the point category difference reference value is greater than the preset point category difference reference value, the benchmark acquisition granularity corresponding to the target time sequence is adjusted to a high acquisition granularity.

3. The simulation analysis method for the seismic performance of photovoltaic supports according to claim 2, characterized in that, For a given moment when the ambient temperature is within the first temperature threshold range and the snow thickness is greater than the preset snow thickness, that moment is determined to be in the first influence stage.

4. The simulation analysis method for the seismic performance of photovoltaic supports according to claim 3, characterized in that, When the first adjustment matrix is ​​introduced, the wind speed reference value and the wind speed stability value are detected. If the wind speed reference value is greater than the preset wind speed reference value or the wind speed stability value is less than or equal to the preset wind speed stability value, the first adjustment matrix is ​​adjusted based on the wind disturbance coefficient.

5. The simulation analysis method for the seismic performance of photovoltaic supports according to claim 4, characterized in that, The initial first adjustment matrix is ; ； in, for Snow thickness on solar panels at any given time for Snow density at any given time For the preset shape function matrix, The surface area of ​​the photovoltaic panel; Wind disturbance coefficient is ; in, As a wind-driven snow-driving factor, The azimuth angle of the photovoltaic panel. For reference wind direction.

6. The simulation analysis method for the seismic performance of photovoltaic supports according to claim 1, characterized in that, For the point in time when the effective fluctuation value of the ambient temperature exceeds the preset effective fluctuation value of the ambient temperature, the determination of the second impact stage is made. Specifically, for the moment when the cumulative freeze-thaw reference value is greater than the preset cumulative freeze-thaw reference value and the ice shell reference thickness is greater than the preset ice shell reference thickness, the moment is determined to be in the second influence stage.

7. The simulation analysis method for the seismic performance of photovoltaic supports according to claim 6, characterized in that, The initial second adjustment matrix is ; in, For reference strain-displacement matrix, For the elasticity matrix, The unit area is denoted as .

8. The simulation analysis method for the seismic performance of photovoltaic brackets according to claim 7, characterized in that, Based on the interference conditions that the frequency of tilt angle changes is greater than the preset frequency of tilt angle changes or the radiation intensity deviation value is greater than the preset radiation intensity deviation value, uniformity interference adjustment is determined for the second adjustment matrix.

9. The simulation analysis method for the seismic performance of photovoltaic brackets according to claim 8, characterized in that, When the time point of the second influence stage is the preset analysis point, it is determined whether to adjust the integration step size based on the continuous reference value of the ice shell. If the continuous reference value of the ice shell is less than or equal to the preset continuous reference value of the ice shell, then no adjustment is needed for the integration step size; If the continuous reference value of the ice shell is greater than the preset continuous reference value of the ice shell, the integral step size will be reduced.

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