Optimization Method for Calculating Steel Consumption Requirements of Offshore Photovoltaic Bracket

Abstract

The invention discloses an optimization method for calculating the steel quantity requirement of an offshore photovoltaic support, which relates to the technical field of offshore photovoltaic supports and aims to solve the problem of inaccurate acquisition of the steel quantity of the support during the installation of the offshore photovoltaic support. Through a variety of finite element analysis methods, the invention can comprehensively evaluate the performance of the offshore photovoltaic support under different working conditions. Through buckling analysis and fatigue analysis, the bearing capacity and fatigue life of the support under extreme conditions can be evaluated, so as to avoid safety problems such as buckling instability or fatigue failure of the support during use. By accurately analyzing the performance of the support under different working conditions, the steel quantity can be more reasonably determined, avoiding cost waste caused by over-design. Setting the material properties in the geometric model according to the material data can truly reflect the mechanical properties of the support material. Dividing the geometric model into hexahedral elements can better capture the deformation and stress distribution of the structure under the action of loads.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore photovoltaic brackets, and specifically to an optimization method for calculating the steel consumption requirements of offshore photovoltaic brackets. Background Art

[0002] An offshore photovoltaic bracket refers to a structural system used to support and fix photovoltaic modules (such as solar panels) in a marine environment.

[0003] The patent application with the publication number CN118094665A discloses an optimization method for reducing the structure of a photovoltaic bracket. It mainly optimizes the purlins, diagonal beams, columns, and diagonal braces respectively in the first round, further optimizes the connecting parts and the column layout form in the second round, and also adjusts the calculation load by adjusting the value of the structure importance coefficient in the third round, so as to reduce the amount of the photovoltaic direct structure. The optimization is hierarchical, step-by-step, and focused. After optimization, the column layout form, stress structure, and component selection of the photovoltaic bracket are more reasonable. While meeting the stress structure of the bracket, it can maximize the control of the steel consumption of the photovoltaic bracket, thus saving the material cost of the photovoltaic bracket. Although the above patent solves the problem of reduction optimization, there are still the following problems in actual operation:

[0004] 1. It does not accurately obtain the material data and offshore data of the photovoltaic bracket, resulting in inaccurate acquisition of the steel amount of the photovoltaic bracket.

[0005] 2. It does not accurately construct a model based on the data of the photovoltaic bracket and the marine environment data, resulting in uneven stress distribution after the installation of the photovoltaic bracket.

[0006] 3. It does not simulate the installation process of the photovoltaic bracket, so it is impossible to understand the performance of the photovoltaic bracket installation under different conditions, resulting in inaccurate acquisition of the steel amount of the photovoltaic bracket. Summary of the Invention

[0007] The purpose of the present invention is to provide an optimization method for calculating the steel consumption requirements of offshore photovoltaic brackets. Through a variety of finite element analysis methods, it can comprehensively evaluate the performance of offshore photovoltaic brackets under different working conditions. Through buckling analysis and fatigue analysis, it can evaluate the bearing capacity and fatigue life of the bracket under extreme conditions, thus avoiding safety problems such as buckling instability or fatigue failure during the use of the bracket. By accurately analyzing the performance of the bracket under different working conditions, it can more reasonably determine the amount of steel used, avoid cost waste caused by over-design, set the material properties in the geometric model according to the material data, which can truly reflect the mechanical properties of the bracket material, and divide the geometric model into hexahedron elements, which can better capture the deformation and stress distribution of the structure under the action of load, and can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An optimization method for calculating the steel consumption requirement of an offshore photovoltaic support, comprising the following steps:

[0010] S1: Confirmation of photovoltaic support data: Confirm the basic data and load data of the photovoltaic support, and obtain the support data after confirmation. Among them, the basic data and load data of the photovoltaic support are retrieved from the database;

[0011] S2: Confirmation of offshore environment data: Confirm the sea area location where the photovoltaic support is installed, and confirm the offshore environment data according to the sea area location, and obtain the environmental data after confirmation;

[0012] S3: Construction of a simulation structure model: Construct a structure model for the installation of an offshore photovoltaic support according to the support data and environmental data, and obtain a simulation structure model after construction;

[0013] S4: Structural model analysis: Perform finite element analysis on the simulation structure model, and obtain the load data of the support in the simulation structure model. Optimize the steel consumption according to the finite element analysis and bearing data, and obtain the steel quantity data of the offshore photovoltaic support after optimization;

[0014] S5: Output of steel quantity data: Perform demand summary optimization according to the steel quantity data of the offshore photovoltaic support, and transmit the summary optimization to the display terminal for display after summary optimization.

[0015] Preferably, for the confirmation of the basic data and load data of the photovoltaic support in S1, it includes:

[0016] The photovoltaic support is a steel pipe column and a steel truss;

[0017] The steel pipe column adopts a stepped steel pipe column, with a diameter of 1m below the mud surface at the bottom, a wall thickness of 10-20mm, a diameter of 0.4m at the stepped end above the mud surface, and a wall thickness of 10-20mm;

[0018] The basic data of the photovoltaic support includes dimension data, material data, connection method, and surface treatment data;

[0019] Among them, the dimension data is the structural dimension and floating body dimension of the support; the material data is the material type, specification, and mechanical properties of the support; the connection method is bolt connection and welding connection; the surface treatment data is the anti-corrosion treatment and anti-corrosion layer thickness data of the support.

[0020] Preferably, for the confirmation of the basic data and load data of the photovoltaic support in S1, it further includes:

[0021] The load data of the photovoltaic support includes dead load, live load, special load, and other loads;

[0022] Dead loads include self-weight loads and static loads; live loads include wind loads, wave loads, current loads, and tidal loads; special loads include sea ice loads, seismic loads, and temperature loads; other loads are biological attachment loads and drift loads;

[0023] Label the obtained load data and basic data as support data.

[0024] Preferably, confirm the sea area location where the PV support is installed in S2, and confirm the offshore environmental data according to the sea area location, including:

[0025] Obtain the installation location of the PV support from the database, where the installation location of the PV support is located by GPS;

[0026] Use sonar to conduct topographic mapping of the installation location and conduct simulation installation verification, and confirm the installation location of the PV support according to the verification results.

[0027] Preferably, confirm the sea area location where the PV support is installed in S2, and confirm the offshore environmental data according to the sea area location, and also include:

[0028] Confirm the offshore environmental data according to the installation location of the PV support;

[0029] Offshore environmental data includes meteorological data, ocean data, ecological data, engineering-related data, geological and topographic data;

[0030] Among them, the offshore environmental data is obtained by using satellite remote sensing technology;

[0031] And label the offshore environmental data as environmental data.

[0032] Preferably, for the construction of the structural model of the offshore PV support according to the support data and environmental data in S3, it includes:

[0033] Confirm the support data and environmental data, and perform data preprocessing after confirmation;

[0034] After data preprocessing, establish a geometric model according to the support dimensions in the support data;

[0035] Set the material properties in the geometric model according to the material data;

[0036] And define the connection conditions of the geometric model according to the geological, topographic data and connection methods.

[0037] Preferably, the data preprocessing of the environmental data includes:

[0038] Monitor the invalid numerical values of the collected environmental data and obtain the data collection time corresponding to the invalid data;

[0039] Extract the valid data closest to the adjacent data collection times before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data;

[0040] Obtain the replacement data corresponding to the invalid data by using the valid data closest to the adjacent data collection times before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data;

[0041] Among them, the replacement data corresponding to the invalid data is obtained through the following formula:

[0042]

[0043] Among them, X t represents the replacement data corresponding to the invalid data; n represents the number of valid data collections for valid data collection before and after the data collection time corresponding to the adjacent data of the data type of the invalid data, and the value range of the number of valid data collections is 3 - 5; X 01 represents the value of the valid data with the earlier collection time among the adjacent two data corresponding to the data type of the invalid data; X 02 represents the data value of the data with the later collection time among the adjacent two data corresponding to the data type of the invalid data; X 01i represents the data value of the i-th valid data corresponding to the n consecutive collection data before the collection time of the valid data with the earlier collection time among the adjacent two data corresponding to the data type of the invalid data; X 02i represents the data value of the i-th valid data corresponding to the n consecutive collection data after the collection time of the data with the later collection time among the adjacent two data corresponding to the data type of the invalid data;

[0044] Replace the invalid data with the replacement data corresponding to the invalid data.

[0045] Preferably, for the data preprocessing of environmental data, it further includes:

[0046] Monitor the missing data of the collected environmental data to obtain the data collection time corresponding to the missing data;

[0047] Extract the valid data closest to the adjacent data collection times before and after the data collection time corresponding to the missing data;

[0048] Obtain the first replacement data corresponding to the missing data by using the valid data closest to the adjacent data collection times before and after the data collection time corresponding to the missing data;

[0049] Among them, the first replacement data corresponding to the missing data is obtained through the following formula:

[0050]

[0051] Among them, W t01 represents the first replacement data corresponding to the missing data; m represents the number of valid data acquisitions for the adjacent front and rear data corresponding to the data type of the missing data, both before and after the data acquisition time. Moreover, the value range of the number of valid data acquisitions is 3 - 5; W 01 represents the value of the valid data with an earlier acquisition time among the adjacent front and rear data corresponding to the data type of the missing data; W 02 represents the data value of the data with a later acquisition time among the adjacent front and rear data corresponding to the data type of the missing data; W 01i represents the data value of the i-th valid data among the m consecutive acquisition data with earlier times corresponding to the valid data with an earlier acquisition time among the adjacent front and rear data corresponding to the data type of the missing data; W 02i represents the data value of the i-th valid data among the m consecutive acquisition data with later times corresponding to the data with a later acquisition time among the adjacent front and rear data corresponding to the data type of the missing data;

[0052] Compensate and adjust the first replacement data by using all the valid data corresponding to the data type of the missing data in the environmental data to obtain the second replacement data;

[0053] Among them, the second replacement data corresponding to the missing data is obtained through the following formula:

[0054]

[0055] Among them, W t02 represents the second replacement data corresponding to the missing data; W t01 represents the first replacement data corresponding to the missing data; W 01 represents the value of the valid data with an earlier acquisition time among the adjacent front and rear data corresponding to the data type of the missing data; W 02 represents the data value of the data with a later acquisition time among the adjacent front and rear data corresponding to the data type of the missing data; k represents the total number of all valid data corresponding to the data type of the missing data; W i represents the data value of the i-th valid data among all the valid data corresponding to the data type of the missing data; W z represents the data median among all the valid data corresponding to the data type of the missing data;

[0056] Replace the missing data with the second replacement data corresponding to the missing data.

[0057] Preferably, for the construction of the structural model for the installation of the offshore photovoltaic support according to the support data and environmental data in S3, it further includes:

[0058] After the connection conditions of the geometric model are defined, the load is applied.

[0059] Among them, the geometric model is divided into hexahedral elements by model mesh division.

[0060] After the model mesh division is completed, the load is applied to the model mesh by the distributed load application method.

[0061] After the load application is completed, the simulated structural model for the installation of the offshore photovoltaic support is obtained.

[0062] Preferably, for the finite element analysis of the simulated structural model in S4, and obtaining the load data of the support in the simulated structural model, and optimizing the steel consumption according to the finite element analysis and bearing data, it includes:

[0063] The simulated structural model is subjected to static analysis, modal analysis, buckling analysis, and fatigue analysis using finite element software.

[0064] Among them, the static analysis is to calculate the static equilibrium data according to the connection and load data between the support and the floating body in the simulated structural model, and after calculation, the displacement, stress, strain, reaction force, and deformation shape data of the simulated structural model are obtained.

[0065] The modal analysis is to perform an operation analysis according to the connection between the support and the floating body in the simulated structural model. After the operation analysis, the natural frequency, vibration mode, damping ratio, and mass participation coefficient of the simulated structural model are obtained.

[0066] The buckling analysis is to perform an operation analysis according to the connection and load data between the support and the floating body in the simulated structural model. After the operation analysis, the buckling load factor, buckling mode, critical stress, and buckling load of the simulated structural model are obtained.

[0067] The fatigue analysis is to perform a fatigue analysis according to the static analysis results of the simulated structural model. After the fatigue analysis, the stress range, stress amplitude, mean stress, fatigue life, damage accumulation, fatigue safety factor, and S-N curve of the simulated structural model are obtained.

[0068] Preferably, for the finite element analysis of the simulated structural model in S4, and obtaining the load data of the support in the simulated structural model, and optimizing the steel consumption according to the finite element analysis and bearing data, it further includes:

[0069] Judge whether the types of steel pipe columns and steel trusses meet the standards according to the load data and finite element analysis data. If they do not meet the standards, reselect the steel pipe columns and steel trusses.

[0070] Adjust the cross-sectional dimensions of the steel pipe columns and steel trusses according to the finite element analysis data, and perform structural stiffness adjustment after the cross-sectional dimension adjustment is completed;

[0071] After the structural stiffness adjustment is completed, optimize the supports and connection design through the load data;

[0072] After the support optimization and connection design are completed, confirm the volume data of the steel pipe columns and steel trusses, and multiply the density of the steel pipe columns and steel trusses by the volume data to obtain the optimized steel consumption;

[0073] At the same time, the optimized consumption includes cutting losses and welding joint losses;

[0074] Mark the optimized steel consumption of the steel as the steel quantity data of the offshore photovoltaic support.

[0075] Preferably, for the demand summary optimization according to the steel quantity data of the offshore photovoltaic support in S5, after the summary optimization, it is transmitted to the display terminal for display, including:

[0076] Summarize the steel quantity demand data, bearing capacity data, and constructed model data in the steel quantity data of the offshore photovoltaic support respectively;

[0077] Generate text data and image data after summarization, and perform visual conversion on the generated text data and image data;

[0078] After the visual conversion, it is transmitted to the display terminal for display.

[0079] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0080] 1. The steel quantity demand calculation and optimization method for offshore photovoltaic supports provided by the present invention fully considers various load conditions, including special loads under extreme conditions, such as sea ice load, earthquake load, etc., which can improve the anti-risk ability of the supports, reduce damage caused by natural disasters and other reasons, and comprehensively considers environmental factors such as meteorological data, ocean data, ecological data, engineering-related data, geological and topographical data, etc. These data are important bases for evaluating the design and material requirements of photovoltaic supports.

[0081] 2. The steel quantity demand calculation and optimization method for offshore photovoltaic supports provided by the present invention sets the material properties in the geometric model according to the material data, which can truly reflect the mechanical properties of the support materials. The geometric model is divided into hexahedron elements. This mesh division method can not only improve the calculation efficiency, but also better capture the deformation and stress distribution of the structure under the action of loads, thereby improving the accuracy of the simulation results.

[0082] 3. The steel consumption requirement calculation optimization method for offshore photovoltaic supports provided by the present invention can comprehensively evaluate the performance of offshore photovoltaic supports under different working conditions through various finite element analysis methods. Through buckling analysis and fatigue analysis, the bearing capacity and fatigue life of the supports under extreme conditions can be evaluated, thereby avoiding safety problems such as buckling instability or fatigue failure during the use of the supports. By accurately analyzing the performance of the supports under different working conditions, the steel consumption can be more reasonably determined, avoiding cost waste caused by over-design, and ensuring the safety and stability of the supports at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the calculation steps for the steel consumption requirement of the offshore photovoltaic support of the present invention;

[0084] Figure 2 It is a schematic diagram of the calculation process for the steel consumption requirement of the offshore photovoltaic support of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] In order to solve the problem in the prior art that the accurate acquisition is not carried out according to the material data and offshore data of the photovoltaic support, resulting in inaccurate acquisition of the steel quantity of the photovoltaic support, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0087] The steel consumption requirement calculation optimization method for offshore photovoltaic supports includes the following steps:

[0088] S1: Confirmation of photovoltaic support data: Confirm the basic data and load data of the photovoltaic support, and obtain the support data after confirmation. Among them, the basic data and load data of the photovoltaic support are retrieved from the database;

[0089] Among them, the use of steel is reduced through optimized design, and energy consumption and carbon emissions are reduced;

[0090] S2: Confirmation of offshore environment data: Confirm the sea area location where the photovoltaic support is installed, and confirm the offshore environment data according to the sea area location, and obtain the environmental data after confirmation;

[0091] Among them, on the basis of accurately understanding the installation location of the photovoltaic support and comprehensively mastering the offshore environment data, the steel consumption requirement of the photovoltaic support can be calculated more accurately;

[0092] S3: Simulation Structure Model Construction: Construct a structural model for the installation of the offshore PV support based on the support data and environmental data. After construction, a simulation structure model is obtained;

[0093] Among them, based on the constructed simulation structure model, structural analysis and optimization can be further carried out to reduce unnecessary steel usage;

[0094] S4: Structural Model Analysis: Perform finite element analysis on the simulation structure model, and obtain the load data of the supports in the simulation structure model. Optimize the steel consumption according to the finite element analysis and bearing data. After optimization, the steel quantity data of the offshore PV support is obtained;

[0095] Among them, by accurately analyzing the performance of the support under different working conditions, the steel consumption can be determined more reasonably;

[0096] S5: Steel Quantity Data Output: Summarize and optimize the requirements according to the steel quantity data of the offshore PV support, and transmit the summarized and optimized data to the display terminal for display;

[0097] Among them, publicly displaying key data such as steel quantity requirements in a visual form helps to enhance the transparency and credibility of the project.

[0098] Regarding the confirmation of the basic data and load data of the PV support in S1, it includes:

[0099] The PV support is a steel pipe column and a steel truss;

[0100] The steel pipe column adopts a variable-diameter steel pipe column, with a diameter of 1m below the mud surface at the bottom, a wall thickness of 10 - 20mm, a diameter of 0.4m at the variable-diameter end above the mud surface, and a wall thickness of 10 - 20mm;

[0101] The basic data of the PV support includes dimension data, material data, connection method, and surface treatment data;

[0102] Among them, the dimension data is the structural dimension and floating body dimension of the support; the material data is the material type, specification, and mechanical properties of the support; the connection method is bolt connection and welding connection; the surface treatment data is the anti-corrosion treatment and anti-corrosion layer thickness data of the support.

[0103] The load data of the PV support includes dead load, live load, special load, and other loads;

[0104] The dead load includes self-weight load and static load; the live load includes wind load, wave load, current load, and tidal load; the special load includes sea ice load, earthquake load, and temperature load; the other load is biological attachment load and drift load;

[0105] Mark the obtained load data and basic data as support data.

[0106] Specifically, by carefully confirming the dimensional data of the bracket (including structural dimensions and floating body dimensions), it is possible to ensure the accuracy of the data used in the calculation, thereby improving the accuracy and reliability of the entire calculation process. The accuracy of material data (such as material type, specifications, and mechanical properties) directly affects the bearing capacity and safety of the bracket. Carefully confirming these data can avoid safety issues caused by improper material selection or incorrect specifications. Understanding the connection method and surface treatment data helps optimize the bracket structure during the design phase, select the appropriate connection method and anti-corrosion treatment to improve the stability and durability of the bracket. Accurate load data (including dead load, live load, special load, and other loads) provides comprehensive and accurate load conditions for the design, helping to design an economical and safe bracket structure. Fully considering various load conditions, including special loads under extreme conditions such as sea ice load and earthquake load, can improve the risk resistance of the bracket and reduce damage caused by natural disasters, etc. By optimizing the design to reduce the use of steel, lower energy consumption and carbon emissions, it helps to achieve the goals of green building and sustainable development.

[0107] Confirm the sea area location where the photovoltaic bracket is installed in S2, and confirm the offshore environmental data according to the sea area location, including:

[0108] Obtain the installation location of the photovoltaic bracket from the database, where the installation location of the photovoltaic bracket is positioned by GPS;

[0109] Use sonar to conduct topographic mapping of the installation location and conduct simulated installation verification, and confirm the installation location of the photovoltaic bracket according to the verification results.

[0110] Confirm the offshore environmental data according to the installation location of the photovoltaic bracket;

[0111] The offshore environmental data includes meteorological data, ocean data, ecological data, engineering-related data, geological and topographic data;

[0112] Among them, the offshore environmental data is obtained by using satellite remote sensing technology;

[0113] And label the offshore environmental data as environmental data.

[0114] Specifically, GPS is used for precise positioning of the installation location of the photovoltaic support, ensuring the accuracy of the installation location and avoiding subsequent problems caused by position errors. Sonar is used for topographic mapping of the installation location, enabling a more detailed understanding of the seabed topography, which is crucial for the stable installation of the photovoltaic support. Through simulation installation verification, the rationality and safety of the installation location are further ensured. By comprehensively considering various environmental factors such as meteorological data, ocean data, ecological data, engineering-related data, geological and topographic data, these data are important bases for evaluating the design and material requirements of the photovoltaic support. Based on accurately understanding the installation location of the photovoltaic support and comprehensively mastering the offshore environmental data, the steel consumption requirements of the photovoltaic support can be calculated more precisely. This helps to avoid material waste and cost overruns, and improve the economic benefits of the project.

[0115] To solve the problem in the prior art that an accurate model is not constructed based on the data of the photovoltaic support and the ocean environment data, resulting in uneven stress distribution after the installation of the photovoltaic support, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0116] Regarding the construction of the structural model for the installation of the offshore photovoltaic support according to the support data and environmental data in S3, it includes:

[0117] Confirm the support data and environmental data, and perform data preprocessing after confirmation;

[0118] After data preprocessing, establish a geometric model according to the support dimensions in the support data;

[0119] Set the material properties in the geometric model according to the material data;

[0120] And define the connection conditions of the geometric model according to the geological, topographic data and connection methods.

[0121] After the connection conditions of the geometric model are defined, apply loads;

[0122] Among them, divide the geometric model into hexahedron elements for model mesh division;

[0123] After the model mesh division is completed, apply the loads to the model mesh through the distributed load application method;

[0124] After the load application is completed, obtain the simulated structural model for the installation of the offshore photovoltaic support.

[0125] Specifically, by validating and preprocessing the support data and environmental data, the accuracy and integrity of the input data for the model are ensured. This helps reduce the deviation of simulation results caused by data errors, thereby improving the accuracy of the entire simulation process. Based on the support dimensions, an accurate geometric model is established, which can accurately reflect the actual shape and size of the photovoltaic support. This refined modeling method is crucial for subsequent structural analysis and optimization. By setting the material properties in the geometric model according to the material data, the mechanical properties of the support material (such as elastic modulus, yield strength, etc.) can be truly reflected, which plays a key role in simulating the response of the structure under loads. The geometric model is divided into hexahedron elements. This mesh division method can not only improve the calculation efficiency but also better capture the deformation and stress distribution of the structure under loads, thereby enhancing the accuracy of the simulation results. By applying the distributed load method, the loads are accurately applied to the model grid, enabling the simulation of various loads (such as wind loads, wave loads, etc.) acting on the photovoltaic support in the actual environment and ensuring the accuracy of the simulation results. Based on the constructed simulation structure model, further structural analysis and optimization can be carried out to reduce unnecessary steel usage and lower the construction cost. For example, by simulating the structural response under different steel usage amounts, a steel configuration plan that meets the safety requirements and has the lowest cost can be found.

[0126] Specifically, the data preprocessing of the environmental data includes:

[0127] Monitoring the invalid numerical values of the collected environmental data to obtain the data collection time corresponding to the invalid data;

[0128] Extracting the valid data adjacent to the data collection time before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data;

[0129] Using the valid data adjacent to the data collection time before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data to obtain the replacement data corresponding to the invalid data;

[0130] Among them, the replacement data corresponding to the invalid data is obtained through the following formula:

[0131]

[0132] Among them, X t represents the replacement data corresponding to the invalid data; n represents the number of valid data collections for the valid data corresponding to the two adjacent data before and after the data type corresponding to the invalid data in the forward and backward directions of the data collection time, and the value range of the number of valid data collections is 3 - 5; X 01 represents the numerical value of the valid data with the earlier collection time among the two adjacent data before and after the data type corresponding to the invalid data; X 02The data type representing invalid data corresponds to the data value of the data with a later acquisition time among the adjacent front and rear two data; X 01i The data type representing invalid data corresponds to the data value of the i-th valid data among the n consecutive acquisition data with earlier acquisition times corresponding to the valid data with an earlier acquisition time among the adjacent front and rear two data; X 02i The data type representing invalid data corresponds to the data value of the i-th valid data among the n consecutive acquisition data with later acquisition times corresponding to the data with a later acquisition time among the adjacent front and rear two data;

[0133] Replace the invalid data with the replacement data corresponding to the invalid data.

[0134] The technical effect of the above technical solution is that by monitoring the invalid numerical values of the collected environmental data and identifying the invalid data and their corresponding data acquisition times, the abnormal or invalid data generated due to various reasons (such as sensor failures, data transmission errors, etc.) can be effectively eliminated or replaced. This significantly improves the accuracy and reliability of the data, providing a more solid foundation for subsequent data analysis and decision-making.

[0135] The above technical solution extracts the valid data adjacent to the front and rear of the data acquisition time corresponding to the invalid data and of the same data type as the invalid data, and uses these data to estimate the replacement value of the invalid data, thereby maintaining the continuity of the data. This is particularly important for application scenarios such as time series analysis and trend prediction that require continuous data support. The replacement data calculation formula proposed in the solution takes into account the information of multiple valid data points before and after the invalid data (n consecutive acquisition data with earlier acquisition times and n consecutive acquisition data with later acquisition times), and allows the value range of the number of valid data acquisitions n (3 - 5) to be adjusted according to the data type and actual situation. This flexibility enables the replacement data to more accurately reflect the true situation of the invalid data point, reducing the deviation caused by a single data point.

[0136] The entire data preprocessing process, including the monitoring of invalid data, the extraction of valid data, the calculation of replacement data, and the replacement of invalid data, can be realized through programming for automated processing. This not only improves the processing efficiency but also reduces the errors caused by human intervention, making the data preprocessing process more intelligent and standardized. Since the acquisition and analysis of environmental data are widely applied in many fields such as environmental monitoring, climate change research, agricultural production, and urban planning, this technical solution has wide applicability. By improving the data quality and continuity, it can provide more accurate and reliable data support for these fields, promoting the development and progress of related fields.

[0137] In summary, through a series of scientific and reasonable data preprocessing steps, this technical solution effectively improves the quality and continuity of environmental data, providing strong data support for research and applications in related fields.

[0138] Specifically, data preprocessing of environmental data also includes:

[0139] Monitoring the missing data in the collected environmental data to obtain the data collection time corresponding to the missing data;

[0140] Extracting the nearest adjacent valid data before and after the data collection time corresponding to the missing data;

[0141] Using the nearest adjacent valid data before and after the data collection time corresponding to the missing data to obtain the first replacement data corresponding to the missing data;

[0142] Among them, the first replacement data corresponding to the missing data is obtained through the following formula:

[0143]

[0144] Among them, W t01 represents the first replacement data corresponding to the missing data; m represents the number of valid data collections for the adjacent front and back two data corresponding to the data type of the missing data in the forward and backward directions of the data collection time, and the value range of the number of valid data collections is 3 - 5; W 01 represents the value of the valid data with the earlier collection time among the adjacent front and back two data corresponding to the data type of the missing data; W 02 represents the data value of the data with the later collection time among the adjacent front and back two data corresponding to the data type of the missing data; W 01i represents the data value of the i-th valid data corresponding to the m consecutive time earlier collection data before the valid data with the earlier collection time among the adjacent front and back two data corresponding to the data type of the missing data; W 02i represents the data value of the i-th valid data corresponding to the m consecutive time later collection data after the data with the later collection time among the adjacent front and back two data corresponding to the data type of the missing data;

[0145] Using all the valid data corresponding to the data type of the missing data in the environmental data to compensate and adjust the first replacement data to obtain the second replacement data;

[0146] Among them, the second replacement data corresponding to the missing data is obtained through the following formula:

[0147]

[0148] Among them, Wt02 Represents the second replacement data corresponding to the missing data; W t01 Represents the first replacement data corresponding to the missing data; W 01 Represents the valid data value with an earlier acquisition time among the two adjacent data corresponding to the data type of the missing data; W 02 Represents the data value of the data with a later acquisition time among the two adjacent data corresponding to the data type of the missing data; k represents the total number of all valid data corresponding to the data type of the missing data; W i Represents the data value of the i-th valid data among all valid data corresponding to the data type of the missing data; W z Represents the data median among all valid data corresponding to the data type of the missing data;

[0149] Replace the missing data with the second replacement data corresponding to the missing data.

[0150] The technical effects of the above technical solution are as follows: The above technical solution not only considers the processing of invalid data, but also adds the monitoring and processing of missing data, thereby realizing the comprehensive preprocessing of environmental data. This ensures the integrity and availability of the data set, providing a more solid foundation for subsequent data analysis and decision-making. By extracting the nearest adjacent valid data before and after the data acquisition time corresponding to the missing data, and using these data to calculate the first replacement data through the above formula, the above technical solution can more accurately estimate the value of the missing data. In addition, by further compensating and adjusting the first replacement data using all valid data corresponding to the data type of the missing data to obtain the second replacement data, the accuracy of the replacement data can be further improved. This dual replacement strategy significantly reduces the bias caused by missing data. At the same time, the value ranges of the number of valid data acquisitions m and k in the above solution have strong flexibility and can be adjusted according to the data type and actual situation. This flexibility enables the replacement data to more accurately reflect the true situation of the missing data points, improving the adaptability and accuracy of data processing. Through reasonable replacement data calculation and adjustment strategies, the above technical solution can maintain the consistency and continuity of the data. This is particularly important for application scenarios such as time series analysis and trend prediction that require continuous data support, and helps to reduce analysis errors caused by data missing or anomalies.

[0151] The entire data preprocessing process, including the monitoring of missing data, the extraction of valid data, the calculation of replacement data, and the replacement of missing data, can be automated through programming. This not only improves the processing efficiency but also reduces the errors caused by human intervention, making the data preprocessing process more intelligent and standardized. Since the collection and analysis of environmental data are widely applied in multiple fields, such as environmental monitoring, climate change research, agricultural production, urban planning, etc., this technical solution has wide applicability. By improving the data quality and continuity, it can provide more accurate and reliable data support for these fields, promoting the development and progress of related fields.

[0152] In summary, through a series of scientific and reasonable data preprocessing steps, this technical solution effectively improves the quality and integrity of environmental data, providing strong data support for the research and application in related fields.

[0153] To solve the problem in the existing technology that the installation process of the photovoltaic support is not simulated, so that it is impossible to understand the performance of the photovoltaic support installation under different conditions, resulting in inaccurate acquisition of the steel quantity of the photovoltaic support, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0154] Regarding the finite element analysis of the simulated structural model in S4 and obtaining the load data of the supports in the simulated structural model, and optimizing the steel usage according to the finite element analysis and bearing data, including:

[0155] Performing static analysis, modal analysis, buckling analysis, and fatigue analysis on the simulated structural model using finite element software;

[0156] Among them, the static analysis is to calculate the static equilibrium data according to the connection and load data of the supports and the floating body in the simulated structural model, and obtain the displacement, stress, strain, reaction force, and deformation shape data of the simulated structural model after calculation;

[0157] The modal analysis is to perform an operation analysis according to the connection of the supports and the floating body in the simulated structural model. After the operation analysis, the natural frequency, vibration mode, damping ratio, and mass participation coefficient of the simulated structural model are obtained;

[0158] The buckling analysis is to perform an operation analysis according to the connection and load data of the supports and the floating body in the simulated structural model. After the operation analysis, the buckling load factor, buckling mode, critical stress, and buckling load of the simulated structural model are obtained;

[0159] The fatigue analysis is to perform a fatigue analysis according to the static analysis results of the simulated structural model. After the fatigue analysis, the stress range, stress amplitude, mean stress, fatigue life, damage accumulation, fatigue safety factor, and S-N curve of the simulated structural model are obtained.

[0160] Specifically, through a variety of finite element analysis methods (static analysis, modal analysis, buckling analysis, and fatigue analysis), the performance of the offshore PV support can be comprehensively evaluated under different working conditions. These analyses not only consider the behavior of the support under static loads but also dynamic responses (such as vibration characteristics), stability (buckling), and fatigue life under long-term use, thus greatly improving the comprehensiveness and accuracy of the analysis. The detailed data obtained (such as displacement, stress, strain, reaction force, natural frequency, vibration mode, damping ratio, buckling load factor, critical stress, fatigue life, etc.) provides solid data support for the optimization of steel consumption. These data can be directly used to evaluate the bearing capacity and safety of the support, providing a scientific basis for reducing unnecessary steel use or strengthening weak areas. By accurately analyzing the performance of the support under different working conditions, the steel consumption can be more reasonably determined, avoiding cost waste caused by overdesign while ensuring the safety and stability of the support. This helps to improve the overall economy of the project. Through buckling analysis and fatigue analysis, the bearing capacity and fatigue life of the support under extreme conditions can be evaluated, thus avoiding safety problems such as buckling instability or fatigue failure during the use of the support. This improves the overall safety and reliability of the PV support and extends its service life.

[0161] Judge whether the types of steel pipe columns and steel trusses meet the standards according to the load data and finite element analysis data. If they do not meet the standards, reselect the steel pipe columns and steel trusses.

[0162] Adjust the cross-sectional dimensions of the steel pipe columns and steel trusses according to the finite element analysis data. After the cross-sectional dimension adjustment is completed, perform structural stiffness adjustment.

[0163] After the structural stiffness adjustment is completed, optimize the supports and conduct connection design through the load data.

[0164] After the support optimization and connection design are completed, confirm the volume data of the steel pipe columns and steel trusses, multiply the density of the steel pipe columns and steel trusses by the volume data to obtain the optimized steel consumption.

[0165] At the same time, the optimized consumption includes cutting losses and welding joint losses.

[0166] Mark the optimized steel consumption as the steel quantity data of the offshore PV support.

[0167] Specifically, by combining load data and finite element analysis data, it is possible to accurately determine whether the types of steel pipe columns and steel trusses meet the design standards, thereby ensuring the safety and stability of the structure. The adjustment of cross-sectional dimensions and structural stiffness is also based on detailed data analysis, avoiding blind design and waste of resources. Considering losses in actual production such as cutting losses and welding joint losses, the optimized steel consumption is closer to the actual situation, reducing waste. By optimizing the steel consumption, resource consumption and waste generation are reduced, which is in line with the concept of sustainable development.

[0168] Regarding the demand summary and optimization based on the steel quantity data of the offshore PV support in S5, after the summary and optimization, it is transmitted to the display terminal for display, including:

[0169] Respectively summarize the steel quantity demand data, bearing capacity data and constructed model data in the steel quantity data of the offshore PV support;

[0170] Generate text data and image data after summarization, and perform visual conversion on the generated text data and image data;

[0171] After visual conversion, it is transmitted to the display terminal for display.

[0172] Specifically, multi-dimensional information such as steel quantity demand data, bearing capacity data and constructed model data is integrated and summarized, providing comprehensive and integrated information support for decision-makers. By performing visual conversion on text data and image data and displaying them on the display terminal, the originally complex and abstract data becomes intuitive and easy to understand. Publicly displaying key data such as steel quantity demand in a visual form helps enhance the transparency and credibility of the project.

[0173] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0174] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. An optimized method for calculating the steel consumption requirement of an offshore photovoltaic support, characterized in that, It includes the following steps: S1: Confirmation of PV support data: Confirm the basic data and load data of the PV support. After confirmation, support data is obtained. Among them, the basic data and load data of the PV support are retrieved from the database; S2: Confirmation of offshore environment data: Confirm the sea area location where the PV support is installed, and confirm the offshore environment data according to the sea area location. After confirmation, environmental data is obtained; S3: Construction of simulation structure model: Construct the structure model for the installation of the offshore PV support according to the support data and environmental data. After construction, a simulation structure model is obtained; S4: Structural model analysis: Conduct finite element analysis on the simulation structure model, and obtain the load data of the support in the simulation structure model. Optimize the steel consumption according to the finite element analysis and bearing data. After optimization, the steel quantity data of the offshore PV support is obtained; S5: Output of steel quantity data: Conduct demand summary and optimization according to the steel quantity data of the offshore PV support, and transmit it to the display terminal for display after summary and optimization; Regarding the confirmation of the basic data and load data of the PV support in S1, it includes: The PV support is a steel pipe column and a steel truss; The steel pipe column is a variable-diameter steel pipe column, with a diameter of 1m below the mud surface at the bottom and a wall thickness of 10 - 20mm. The diameter of the variable-diameter end above the mud surface is 0.4m, and the wall thickness is 10 - 20mm; The basic data of the PV support includes dimension data, material data, connection method, and surface treatment data; Among them, the dimension data is the structural dimension and floating body dimension of the support; the material data is the material type, specification, and mechanical properties of the support; the connection method is bolt connection and welding connection; the surface treatment data is the anti-corrosion treatment and anti-corrosion layer thickness data of the support; Regarding the construction of the structure model for the installation of the offshore PV support according to the support data and environmental data in S3, it includes: Confirm the support data and environmental data, and conduct data preprocessing after confirmation; Establish a geometric model according to the support dimensions in the support data after data preprocessing; Set the material properties in the geometric model according to the material data; Define the connection conditions of the geometric model according to the geological, terrain data, and connection method; The data preprocessing of the environmental data includes: Monitor the invalid numerical values of the collected environmental data, and obtain the data collection time corresponding to the invalid data; Extract the valid data adjacent to the data collection time before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data; Obtain the replacement data corresponding to the invalid data by using the valid data adjacent to the data collection time before and after the data collection time corresponding to the invalid data and corresponding to the data type of the invalid data; Among them, the replacement data corresponding to the invalid data is obtained through the following formula: Among them, X t represents the replacement data corresponding to the invalid data; n represents the number of valid data acquisitions for the valid data acquisitions before and after the adjacent front and back data corresponding to the data type of the invalid data, and the value range of the number of valid data acquisitions is 3 to 5; X 01 represents the value of the valid data with the earlier acquisition time among the adjacent front and back data corresponding to the data type of the invalid data; X 02 represents the data value of the data with the later acquisition time among the adjacent front and back data corresponding to the data type of the invalid data; X 01i represents the data value of the i-th valid data corresponding to the n consecutive data acquisitions before the acquisition time of the valid data with the earlier acquisition time among the adjacent front and back data corresponding to the data type of the invalid data; X 02i represents the data value of the i-th valid data corresponding to the n consecutive data acquisitions after the acquisition time of the data with the later acquisition time among the adjacent front and back data corresponding to the data type of the invalid data; Replace the invalid data with the replacement data corresponding to the invalid data.

2. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 1, characterized in that Regarding the confirmation of the basic data and load data of the PV support in S1, it also includes: The load data of the PV support includes dead load, live load, special load, and other loads; The dead load includes self-weight load and static load; the live load includes wind load, wave load, current load, and tidal load; the special load includes sea ice load, earthquake load, and temperature load; other loads are biological attachment load and drift load; Label the acquired load data and basic data as support data.

3. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 2, wherein, Confirm the sea area location where the PV support is installed in S2, and confirm the offshore environmental data according to the sea area location, including: Obtain the installation location of the PV support from the database, where the installation location of the PV support is located by GPS; Use sonar to conduct topographic survey of the installation location and conduct simulated installation verification, and confirm the installation location of the PV support according to the verification results.

4. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 3, characterized in that Confirm the sea area location where the PV support is installed in S2, and confirm the offshore environmental data according to the sea area location, which also includes: Confirm the offshore environmental data according to the installation location of the PV support; The offshore environmental data includes meteorological data, ocean data, ecological data, engineering-related data, geological and topographic data; Among them, the offshore environmental data is obtained by satellite remote sensing technology; And label the offshore environmental data as environmental data.

5. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 4, wherein, For data preprocessing of the environmental data, it also includes: Monitor the missing data of the collected environmental data and obtain the data collection time corresponding to the missing data; Extract the nearest valid data adjacent to the data collection time before and after the data collection time corresponding to the missing data; Use the nearest valid data adjacent to the data collection time before and after the data collection time corresponding to the missing data to obtain the first replacement data corresponding to the missing data; Among them, the first replacement data corresponding to the missing data is obtained through the following formula: Among them, W t01 represents the first replacement data corresponding to the missing data; m represents the number of valid data acquisitions for the adjacent front and rear data corresponding to the data type of the missing data, for valid data acquisitions before and after the data acquisition time, and the value range of the number of valid data acquisitions is 3 to 5; W 01 represents the value of the valid data with the earlier acquisition time among the adjacent front and rear two data corresponding to the data type of the missing data; W 02 represents the data value of the data with the later acquisition time among the adjacent front and rear two data corresponding to the data type of the missing data; W 01i represents the data value of the i-th valid data corresponding to the m consecutive acquisition data with earlier time among the adjacent front and rear two data corresponding to the data type of the missing data; W 02i represents the data value of the i-th valid data corresponding to the m consecutive acquisition data with later time among the adjacent front and rear two data corresponding to the data type of the missing data; Use all valid data corresponding to the data type of the missing data in the environmental data to compensate and adjust the first replacement data to obtain the second replacement data; Among them, the second replacement data corresponding to the missing data is obtained through the following formula: Among them, W t02 represents the second replacement data corresponding to the missing data; W t01 represents the first replacement data corresponding to the missing data; W 01 represents the valid data value with an earlier acquisition time among the adjacent front and back two data corresponding to the data type of the missing data; W 02 represents the data value of the data with a later acquisition time among the adjacent front and back two data corresponding to the data type of the missing data; k represents the total number of all valid data corresponding to the data type of the missing data; W i represents the data value of the i-th valid data among all valid data corresponding to the data type of the missing data; W z represents the data median among all valid data corresponding to the data type of the missing data; Use the second replacement data corresponding to the missing data to replace the missing data.

6. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 5, characterized in that For the construction of the structural model for the installation of the offshore PV support according to the support data and environmental data in S3, it also includes: Apply loads after defining the connection conditions of the geometric model; Among them, divide the geometric model into hexahedral elements for model mesh division; After the model mesh division is completed, apply the loads to the model mesh by the distributed load application method; After the load application is completed, obtain the simulated structural model for the installation of the offshore PV support.

7. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 6, wherein For the finite element analysis of the simulated structural model in S4 and obtain the load data of the supports in the simulated structural model, and optimize the steel consumption according to the finite element analysis and bearing data, including: Conduct static analysis, modal analysis, buckling analysis and fatigue analysis on the simulated structural model using finite element software; Among them, the static analysis is to calculate the static balance data according to the connection and load data between the supports and the floating bodies in the simulated structural model, and obtain the displacement, stress, strain, reaction force and deformation shape data of the simulated structural model after calculation; The modal analysis is to conduct an operation analysis according to the connection between the supports and the floating bodies in the simulated structural model, and obtain the natural frequency, vibration mode, damping ratio and mass participation coefficient of the simulated structural model after the operation analysis; The buckling analysis is to conduct an operation analysis according to the connection and load data between the supports and the floating bodies in the simulated structural model, and obtain the buckling load factor, buckling mode, critical stress and buckling load of the simulated structural model after the operation analysis; The fatigue analysis is to perform fatigue analysis based on the static analysis results of the simulated structural model. After the fatigue analysis, the stress range, stress amplitude, mean stress, fatigue life, damage accumulation, fatigue safety factor, and S-N curve of the simulated structural model are obtained.

8. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 7, wherein, For S4, the simulated structural model is subjected to finite element analysis, and the load data of the brackets in the simulated structural model are obtained. The steel consumption is optimized based on the finite element analysis and the bearing data, and it also includes: Judging whether the types of steel pipe columns and steel trusses meet the standards according to the load data and finite element analysis data. If they do not meet the standards, the steel pipe columns and steel trusses are reselected; Adjusting the cross-sectional dimensions of the steel pipe columns and steel trusses according to the finite element analysis data. After the cross-sectional dimension adjustment is completed, the structural stiffness is adjusted; After the structural stiffness adjustment is completed, the support optimization and connection design are carried out through the load data; After the support optimization and connection design are completed, the volume data of the steel pipe columns and steel trusses are confirmed, and the density of the steel pipe columns and steel trusses is multiplied by the volume data to obtain the optimized steel consumption; At the same time, the optimized consumption includes cutting loss and welding joint loss; The optimized steel consumption is marked as the steel quantity data of the offshore PV bracket.

9. The steel consumption requirement calculation and optimization method for the offshore photovoltaic support according to claim 8, wherein For S5, the demand summary optimization is carried out according to the steel quantity data of the offshore PV bracket. After the summary optimization, it is transmitted to the display terminal for display, and it includes: Respectively summarizing the steel quantity demand data, bearing capacity data, and constructed model data in the steel quantity data of the offshore PV bracket; Generating text data and image data after summarization, and performing visual conversion on the generated text data and image data; After the visual conversion, it is transmitted to the display terminal for display.

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