Wind tunnel test analysis method and system for large-span warehouse structure

Abstract

The invention relates to the technical field of data analysis, in particular to a wind tunnel test analysis method and system for a large-span warehouse structure. The method comprises the following steps: executing a wind tunnel simulation test operation, and collecting wind tunnel test data of the wind tunnel simulation test operation; performing wind pressure quantitative distribution simulation analysis of multi-scale wind direction angles based on wind tunnel test data to generate multi-scale wind pressure quantitative distribution data; designing a wind tunnel test large-span warehouse model according to the wind tunnel simulation test operation; based on the multi-scale wind pressure quantitative distribution data, carrying out wind load analysis on each node of the warehouse on the wind tunnel test large-span warehouse model to generate warehouse wind load data; based on the warehouse wind load data, large-span warehouse structure design parameter analysis is carried out, and large-span warehouse structure design parameters are generated; and transmitting the large-span warehouse structure design parameters to a terminal to execute large-span warehouse structure design parameter feedback operation. According to the method, optimization design of large-span warehouse structure parameters is realized through wind tunnel simulation test analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a wind tunnel testing and analysis method and system for a large-span warehouse structure. Background Art

[0002] With the rapid development of the logistics industry, large warehouses are increasingly used in ports, logistics centers, factories and other fields. In particular, large-span warehouse structures have become an important part of modern buildings due to their high space utilization and strong adaptability. Large-span warehouse structures have the characteristics of large span and high flexibility. However, such buildings will be significantly affected by natural environmental factors such as wind loads during actual use. Wind loads are a factor that needs to be considered in the design of large-span structures, because they cause deformation, fatigue and even failure of the structure. However, in the wind load analysis and structural design of existing large-span warehouse structures, theoretical calculations or numerical simulations are often used, which cannot accurately simulate the effects of complex wind environments. In particular, wind tunnel tests lack accurate analysis of local wind pressure distribution, and large-span warehouse structures are different in geometry and wind direction, which leads to incomplete structural design and difficulty in achieving high-precision quantification of wind pressure distribution at multiple wind directions and different wind speeds, affecting the long-term stability and safety of the warehouse. Summary of the invention

[0003] Based on this, the present invention provides a wind tunnel testing and analysis method and system for a large-span warehouse structure to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a wind tunnel test analysis method for a large-span warehouse structure includes the following steps: Step S1: performing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data acquisition and processing on the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; performing numerical quantitative distribution analysis of wind speed according to the gradient wind height data and the gradient wind height wind speed data to generate quantitative distribution data of wind speed; Step S2: obtaining wind tunnel reference static pressure monitoring data; performing wind pressure quantitative distribution simulation analysis processing of the gradient wind height wind pressure data at multi-scale wind direction angles based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, and generating multi-scale wind pressure quantitative distribution data; Step S3: design a large-span warehouse model; perform wind tunnel test simulation and update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; perform wind load analysis and processing on each node of the wind tunnel test large-span warehouse model based on multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; perform warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data; Step S4: performing block warehouse wind load analysis processing on the warehouse wind load data to generate block warehouse wind load data; performing block warehouse wind load extreme value characteristic analysis processing on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; Step S5: Based on the warehouse wind load overall effect data and the block warehouse wind load extreme characteristic data, the large-span warehouse structure design parameter analysis is performed to generate the large-span warehouse structure design parameters, and the large-span warehouse structure design parameters are transmitted to the terminal to perform the large-span warehouse structure design parameter feedback operation.

[0005] Furthermore, step S1 includes the following steps: Step S11: executing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data collection and processing on the wind tunnel simulation test operation; Step S12: performing wind tunnel average wind speed distribution power index analysis on the wind tunnel simulation test operation to generate a wind tunnel average wind speed distribution power index; Step S13: performing wind speed numerical quantitative distribution analysis on the gradient wind height data and the gradient wind height wind speed data based on the wind tunnel average wind speed distribution power index to generate wind speed distribution quantitative data.

[0006] Further, step S2 includes the following steps: Step S21: Acquire wind tunnel reference static pressure monitoring data; Step S22: performing dimensionless wind pressure calculation on the gradient wind height wind pressure data based on the wind speed distribution quantified data and the wind tunnel reference static pressure monitoring data to obtain dimensionless wind pressure data; Step S23: performing a wind pressure quantitative distribution analysis on the dimensionless wind pressure data based on the gradient wind height data to generate wind pressure quantitative distribution data; Step S24: performing a multi-scale wind pressure quantitative distribution simulation analysis on the wind pressure quantitative distribution data based on a preset multi-scale wind direction angle threshold value to generate multi-scale wind pressure quantitative distribution data.

[0007] Further, step S3 includes the following steps: Step S31: Obtaining geometric design parameters of a large-span warehouse; Step S32: performing three-dimensional geometric modeling of the large-span warehouse according to the geometric design parameters of the large-span warehouse to generate a large-span warehouse model; Step S33: performing wind tunnel test large-span warehouse model simulation update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; Step S34: performing wind load analysis and processing on each node of the warehouse for the large-span warehouse model tested in the wind tunnel based on the multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; Step S35: Perform warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data.

[0008] Further, step S34 includes the following steps: Step S341: performing a wind pressure point shape coefficient analysis according to the multi-scale wind pressure quantitative distribution data to generate a wind pressure point shape coefficient; Step S342: Perform statistical analysis on the wind pressure height variation coefficient according to the multi-scale wind pressure quantitative distribution data to generate the wind pressure height variation coefficient; Step S343: Based on the wind pressure point body coefficient and the wind pressure height variation coefficient, the wind load analysis and processing of each node of the large-span warehouse model in the wind tunnel test is performed to generate warehouse wind load data.

[0009] Further, step S4 includes the following steps: Step S41: performing model block processing on the large-span warehouse model for wind tunnel testing to generate a block test warehouse model; Step S42: analyzing the shape and structure characteristics of the block test warehouse model according to the block test warehouse model to generate shape and structure characteristic data of the block test warehouse model; Step S43: performing block warehouse wind load analysis processing on the warehouse wind load data by testing the warehouse model shape and structure characteristic data in blocks to generate block warehouse wind load data; Step S44: using the block warehouse wind load data as the wind load extreme value correlation factor of each block test warehouse model; performing block wind load extreme value probability analysis processing on the wind load extreme value correlation factor to generate block wind load extreme value probability data; Step S45: performing wind load extreme value characteristic analysis and processing on the block warehouse according to the wind load data of the block warehouse and the wind load extreme value probability, and generating wind load extreme value characteristic data on the block warehouse.

[0010] Further, step S5 includes the following steps: Step S51: performing a large-span warehouse structure design feature analysis based on the warehouse wind load overall effect data to generate large-span warehouse structure design feature data; Step S52: performing a large-span warehouse local enclosure design characteristic analysis based on the block warehouse wind load extreme value characteristic data, and generating large-span warehouse local enclosure design characteristic data; Step S53: Perform large-span warehouse structure design parameter analysis based on large-span warehouse structure design feature data and large-span warehouse local enclosure design feature data, generate large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to execute the large-span warehouse structure design parameter feedback operation.

[0011] Further, step S51 includes the following steps: According to the overall effect data of warehouse wind load, the overall structure wind resistance demand analysis of large-span warehouse is carried out to generate the overall structure wind resistance demand data of large-span warehouse; The design characteristics of the large-span warehouse structure are analyzed based on the wind resistance demand data of the large-span warehouse structure as a whole, and the design characteristics data of the large-span warehouse structure are generated.

[0012] Further, step S52 includes the following steps: According to the extreme characteristic data of wind load of block warehouses, the local enclosure wind resistance demand analysis of large-span warehouses is carried out to generate the local enclosure wind resistance demand data of large-span warehouses; According to the wind resistance demand data of the local enclosure of large-span warehouses, the design characteristics of the local enclosure of large-span warehouses are analyzed to generate the design characteristics data of the local enclosure of large-span warehouses.

[0013] This specification provides a wind tunnel test and analysis system for a large-span warehouse structure, which is used to perform the wind tunnel test and analysis method for a large-span warehouse structure as described above. The wind tunnel test and analysis system for a large-span warehouse structure includes: A wind speed distribution quantitative analysis module is used to perform a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and to collect and process wind tunnel test data for the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; and to perform a wind speed numerical quantitative distribution analysis based on the gradient wind height data and the gradient wind height wind speed data to generate wind speed distribution quantitative data; The wind pressure distribution quantitative analysis module is used to obtain the wind tunnel reference static pressure monitoring data; based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, the gradient wind height wind pressure data is simulated and analyzed for the wind pressure quantitative distribution of multi-scale wind direction angles to generate multi-scale wind pressure quantitative distribution data; The warehouse wind load overall effect analysis module is used to design a large-span warehouse model; based on the wind tunnel simulation test operation, the large-span warehouse model is simulated and updated by the wind tunnel test to generate a large-span warehouse model for the wind tunnel test; based on the multi-scale wind pressure quantitative distribution data, the wind load analysis and processing of each node of the large-span warehouse model for the wind tunnel test is performed to generate warehouse wind load data; based on the warehouse wind load data, the overall effect analysis of the warehouse wind load is performed to generate the overall effect data of the warehouse wind load; The block warehouse wind load extreme value characteristic analysis module is used to perform block warehouse wind load analysis on the warehouse wind load data to generate block warehouse wind load data; perform block warehouse wind load extreme value characteristic analysis on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; The large-span warehouse structure design feedback module is used to analyze the large-span warehouse structure design parameters based on the overall effect data of the warehouse wind load and the extreme characteristic data of the block warehouse wind load, generate the large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to perform the large-span warehouse structure design parameter feedback operation.

[0014] The beneficial effect of the present application is that by performing wind tunnel simulation operations based on preset wind tunnel simulation test parameters, the wind load conditions in the actual environment can be effectively simulated, and combined with data acquisition and processing technology, key wind tunnel test data can be obtained in real time to ensure the integrity and accuracy of the data. The average wind speed distribution power index analysis is performed on the simulation test operation, so that the complex wind field characteristics are accurately described by a mathematical model. Further based on the generated wind speed distribution power index, combined with the gradient wind height data and wind speed data, quantitative distribution analysis is performed to generate accurate wind speed distribution quantitative data, which improves the accuracy and effectiveness of the wind tunnel test results, provides a scientific basis for the subsequent optimization design of the warehouse structure, and ensures the safety and reliability of the structural design under wind load. Obtaining wind tunnel reference static pressure monitoring data ensures the reliability and reference value of pressure data in the test. Combining wind speed distribution quantitative data and wind tunnel reference static pressure monitoring data, and using dimensionless wind pressure calculation method to analyze dimensionless wind pressure data, dimensionless processing of wind pressure data at different heights is achieved, simplifying complex wind pressure calculations. Based on the gradient wind height data, the dimensionless wind pressure data is quantitatively analyzed and the wind pressure quantitative distribution data is generated, making the wind pressure distribution more intuitive and operable. The simulation analysis is performed based on the preset multi-scale wind direction angle threshold, which makes the simulation of wind pressure distribution more refined and can cover the wind pressure changes at different scales. The geometric design parameters of the large-span warehouse are obtained to provide an accurate data basis for subsequent modeling and ensure the authenticity of the model. These parameters are used for three-dimensional geometric modeling, and the model is updated based on wind tunnel simulation tests to ensure that the model reflects the actual wind load conditions, making the simulation results closer to the actual situation. Combined with the multi-scale wind pressure quantitative distribution data, the wind load distribution of each node of the warehouse is carefully analyzed to ensure the refined analysis of each part of the structure, so that the wind load data can fully cover each key node of the warehouse, ensuring the safety and stability of the structure under different wind pressure conditions. The overall effect of the wind load of the warehouse is comprehensively analyzed to generate overall effect data to help designers fully understand the stress of the structure under wind load. By dividing the large-span warehouse model into blocks, the complex overall structure is disassembled into multiple small units, and a block test warehouse model is generated, laying the foundation for subsequent analysis. The shape and structure characteristics are analyzed according to the block model, and the structural characteristic data is generated to ensure that the morphological characteristics of each block can accurately reflect the actual structural characteristics of the warehouse. Using these shape and structure characteristic data, the wind load of each block is analyzed, and the analysis of wind load distribution is further refined, making the wind load distribution more accurate.By analyzing the wind load extreme value related factors of each block, the probability analysis of the extreme conditions of wind load is carried out, and the wind load extreme value conditions are provided, which provides a basis for safety design. Based on these extreme value probability data, the wind load extreme values ​​of each block are analyzed, and the block warehouse wind load extreme value characteristic data is generated, which can more comprehensively predict the stress conditions of the warehouse under extreme wind loads and ensure that the design scheme can cope with various extreme wind pressure conditions, thereby greatly improving the wind resistance safety and reliability of large-span warehouse structures. Structural design characteristic analysis based on the overall effect data of warehouse wind load can fully reflect the overall structural characteristics of the entire warehouse under wind loads and ensure that the structural design meets the wind resistance requirements. Combined with the extreme value characteristic data of block warehouse wind loads, the design characteristic analysis of the local enclosure structure of the warehouse is carried out. The generated local enclosure design characteristic data provides a targeted reference for the wind resistance design of the local area and ensures the safety of the local structure under extreme wind loads. By combining the overall and local design feature data, a comprehensive structural design parameter analysis is performed, the final structural design parameters are generated, and these parameters are fed back to the terminal system so as to adjust and optimize the design scheme of the long-span warehouse. This feedback mechanism ensures that the design scheme meets the wind load bearing requirements of the long-span warehouse and achieves the optimal structural design. Therefore, a wind tunnel testing method for a long-span warehouse structure of the present invention simulates the actual wind environment through wind tunnel testing, and combined with multi-scale quantitative analysis of wind pressure distribution, it can more accurately capture the wind load distribution of the long-span warehouse structure under different wind speeds and wind directions, making the wind load distribution more comprehensive and detailed, especially for the accurate simulation of local wind pressure distribution. By dividing the long-span warehouse structure into blocks for wind load analysis and extreme value probability analysis, the required stress conditions of each part of the structure can be refined, the wind-resistant design effect of the local area of ​​the structure is improved, and more reliable data support is provided for the overall safety of the structure. The dual analysis of the overall effect of wind loads and local enclosure structures can adjust the stress capacity of large-span warehouse structures under multiple wind directions and wind pressure conditions, effectively improving the structural stability and wind resistance of large-span warehouses in long-term use, and avoiding potential structural safety hazards caused by incomplete wind load analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of the steps of a wind tunnel test and analysis method for a large-span warehouse structure according to the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a wind tunnel test analysis method for a large-span warehouse structure. In the embodiments of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of a process flow of a wind tunnel test analysis method for a large-span warehouse structure according to the present invention. The wind tunnel test analysis method for a large-span warehouse structure comprises the following steps: Step S1: performing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data acquisition and processing on the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; performing numerical quantitative distribution analysis of wind speed according to the gradient wind height data and the gradient wind height wind speed data to generate quantitative distribution data of wind speed; In the embodiment of the present invention, the warehouse wind tunnel test is carried out in the high-speed test section of the atmospheric boundary layer wind tunnel of the wind tunnel test system. Using precise wind tunnel test equipment, by setting a reduced model that is consistent with the actual warehouse size ratio, the test parameters such as wind speed range, wind direction angle, simulated atmospheric boundary layer conditions, etc. are determined. The wind tunnel is driven by six DC motors with a maximum power of 335 kW, and the wind speed is regulated and controlled by a thyristor DC stepless speed regulation system centrally controlled by a computer terminal. The wind tunnel is equipped with an automatic speed regulation, control and data acquisition system and an automatic turntable system for building structure model tests. The turntable has a diameter of 2.5m and good flow field performance. The velocity non-uniformity of the flow field in the test area is less than 1%, the turbulence is less than 0.2%, and the average airflow angle is less than 0.5, so as to simulate the wind speed and wind pressure with a return period of 50 years and a return period of 100 years. For example, the basic wind pressure averaged for 10 minutes at a height of 10 meters (corresponding to the corresponding height of the wind tunnel test system in proportion to the return period of 50 years) is w0,50=0.40 kN / m2, and the corresponding basic wind speed is 25.30 m / s; corresponding to the return period of 100 years, w0,100=0.45 kN / m2, and the corresponding basic wind speed is 26.83 m / s. The environmental variables of the simulation test, such as the wind speed level and the fluid mechanics conditions in the wind tunnel, are set through the experimental control system. Subsequently, the wind tunnel equipment is started to simulate the real wind environment. During the wind tunnel simulation test, high-precision sensors placed on the surface and around the warehouse model are used to collect relevant data such as wind speed, wind pressure, vortex, and gradient wind height in real time during the test. In order to obtain wind tunnel test data, including gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data. The wind tunnel average wind speed distribution power exponent analysis is performed on the wind tunnel simulation test operation, and the wind speed data collected in the wind tunnel experiment is analyzed using data processing algorithms, especially the distribution of average wind speed. Through numerical analysis tools (such as MATLAB or Python's numerical calculation library), the experimental data is curve-fitted to solve the power exponent value of the wind speed distribution. This power exponent value can be used for the subsequent accurate description of the wind speed distribution to improve the authenticity and accuracy of the simulation model. Based on the power exponent of the wind tunnel average wind speed distribution, the wind speed numerical quantitative distribution analysis of the gradient wind height data and the gradient wind height wind speed data is performed, and the wind speed data at different heights are extracted from the experimental data. According to the generated power exponent, the change trend of wind speed with height is further numerically processed. Using numerical analysis tools, the wind speed distribution at different heights is quantified into an accurate numerical model to generate quantitative data of wind speed distribution.

[0020] Step S2: obtaining wind tunnel reference static pressure monitoring data; performing wind pressure quantitative distribution simulation analysis processing of the gradient wind height wind pressure data at multi-scale wind direction angles based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, and generating multi-scale wind pressure quantitative distribution data; In an embodiment of the present invention, a reference static pressure monitoring data is obtained during a wind tunnel experiment by using a precise pressure sensor system. Multi-point static pressure sensors are arranged at appropriate positions of the wind tunnel experimental device, and the static pressure data collected by each sensor is transmitted to the main control system in real time through the data acquisition system and recorded in the database. This monitoring data serves as a reference for subsequent wind pressure calculations. Through the dimensionless wind pressure calculation formula, the dimensionless wind pressure, the pressure value at a certain point, the static pressure value, the air density, and the wind speed are calculated to calculate the dimensionless wind pressure data at different heights. According to the gradient wind height data, the wind pressure at different heights is dimensionlessly processed using the quantified wind speed distribution data and static pressure monitoring data. During the calculation process, a numerical processing tool (such as MATLAB) will convert the wind pressure data at each height into dimensionless wind pressure data to ensure that the wind pressure data at different heights can be dimensionlessly compared and analyzed. The dimensionless wind pressure data is classified and processed according to different gradient heights to ensure that the wind pressure change trend at each height can be accurately reflected in the analysis process. Using numerical statistical analysis tools such as Matlab or Python for multidimensional curve fitting and distribution statistics processing, the dimensionless wind pressure data at different heights are distributed and quantified, the wind pressure change law at each height level is clarified, and complete wind pressure quantitative distribution data is generated. This data will be used to describe the wind pressure distribution at each gradient height and provide a basis for subsequent multi-scale analysis. Based on the preset multi-scale wind direction angle threshold, such as taking the wind direction angle at an interval of 15° (a total of 24 working conditions), where the wind direction angle increases in a clockwise direction, to set the multi-scale wind direction angle threshold. According to different wind direction angles combined with multi-scale wind pressure distribution data, CFD (computational fluid dynamics) software such as ANSYS Fluent or OpenFOAM is used to simulate and analyze the wind pressure distribution under different wind directions to obtain the average wind pressure coefficient corresponding to each measuring point at each wind direction angle, and finally generate multi-scale wind pressure quantitative distribution data. This analysis ensures that under different wind direction conditions, the wind load analysis of the warehouse structure can cover a wider range of wind pressure action and improve the accuracy and applicability of wind pressure distribution.

[0021] Step S3: design a large-span warehouse model; perform wind tunnel test simulation and update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; perform wind load analysis and processing on each node of the wind tunnel test large-span warehouse model based on multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; perform warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data; In an embodiment of the present invention, the geometric design parameters of the large-span warehouse are obtained, and key geometric parameters such as the length, width, height, span, roof slope, facade shape, etc. of the warehouse are usually extracted from architectural design drawings or digital models. These geometric parameters are read and extracted using a building information model (BIM) tool or other three-dimensional design software, and it is ensured that these parameters are consistent with the actual construction requirements to provide basic data for subsequent three-dimensional modeling. According to the obtained geometric design parameters, a three-dimensional modeling software (such as AutoCAD, Revit or 3ds Max) is used to perform three-dimensional geometric modeling of the large-span warehouse. During the modeling process, it is ensured that the geometric dimensions, structural form, facade features, etc. of the warehouse are consistent with the actual design parameters. This step generates a three-dimensional warehouse model that conforms to the actual structure, and the model should include the main structural components of the warehouse, such as the roof, columns, walls, etc. Each detail of the model is refined to ensure that it can be used for subsequent wind tunnel simulation tests and wind load analysis. Based on the wind tunnel simulation test work conducted in the early stage, the three-dimensional warehouse model is updated by wind tunnel test simulation simulation using computational fluid dynamics (CFD) software. The wind pressure, wind speed and other data collected in the wind tunnel experiment are input into the simulation system to accurately update the surface wind load of the warehouse model so that the model reflects the wind load in the real environment. The updated model should include the pressure distribution under different wind speed and wind direction conditions, and reflect the response of the warehouse under the wind load, and generate a large-span warehouse model for wind tunnel testing. Based on the multi-scale wind pressure quantitative distribution data, the wind load analysis of the updated large-span warehouse model for wind tunnel testing is carried out. In the specific implementation, the generated multi-scale wind pressure quantitative distribution data is combined with the geometric characteristics of each node of the warehouse. The wind load of each node of the warehouse is calculated in detail through finite element analysis software (such as ABAQUS or ANSYS), considering various wind pressure distribution conditions and the stress state of different nodes, and the wind load data of each node is generated, and the stress conditions of each part of the structure under the wind pressure are recorded. Based on the wind load data, the overall effect analysis of the warehouse wind load is carried out using structural analysis software (such as SAP2000 or ETABS). The wind load data of each node is summarized, the distribution and transmission path of wind load on the overall structure of the warehouse are analyzed, the stress state of the overall structure is evaluated, and the overall effect data of the warehouse wind load is generated, including the stress distribution of the warehouse under different wind speeds and wind directions and the stress concentration areas of key nodes. These data provide a basis for structural design and ensure the stability and safety of the warehouse under various wind load conditions.

[0022] Step S4: performing block warehouse wind load analysis processing on the warehouse wind load data to generate block warehouse wind load data; performing block warehouse wind load extreme value characteristic analysis processing on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; In the embodiment of the present invention, the expression form based on a large amount of test data (the point shape coefficient or pressure coefficient of each measuring point) is very complex, which is not convenient for analyzing the most unfavorable wind direction and its corresponding wind load. In order to facilitate analysis, the warehouse is divided into several block parts during data processing, and the overall model is divided into several block areas, so as to simulate and analyze the wind load of the local area more finely. The block division standard is based on the geometric complexity and stress conditions of the structure, and CAD or other three-dimensional modeling software is used for block processing to ensure that each block can accurately reflect the local characteristics of the warehouse structure and generate a block test warehouse model. The shape and structure characteristics of the block test warehouse model are analyzed. The shape and structure characteristics of each block are analyzed by geometric analysis tools such as AutoCAD or Revit. By inputting the geometric parameters of the warehouse model, the shape characteristics of the block model, etc., the shape characteristics of the block model are analyzed to generate shape and structure characteristic data. This characteristic data includes information such as the surface area, central node, surface curvature, and node connection method of each block, ensuring that the block model can accurately reflect the response of the actual warehouse structure under wind load and provide reliable basic data for subsequent wind load analysis. Based on the shape and structural characteristic data of the block test warehouse model, the wind load of each block is analyzed and processed using finite element analysis software (such as ANSYS or Abaqus). Each block is regarded as an independent force unit, and after inputting the structural characteristic data of each block, the wind load distribution analysis is performed on it. By calculating the wind pressure, wind speed and other forces of each block, the wind load data of the block warehouse is generated, including the stress state, stress concentration and displacement of each block under wind, ensuring that the wind load data of each block can reflect the distribution characteristics under the actual wind load. According to the wind load data of the block warehouse, the key force information of each block is extracted, including the distribution of wind load on the surface of each block, wind pressure peak and stress state. For each block, its wind load extreme value related factors are determined, which usually include the maximum wind pressure, maximum wind speed, and structural deformation stress under extreme wind load conditions. For these extreme value related factors, statistical methods are used for probability analysis to evaluate the possibility of extreme values ​​occurring in each block under different extreme wind conditions. The extreme value distribution model of each block is obtained by fitting the wind load data obtained in the wind tunnel experiment or numerical simulation. For example, suppose that the maximum wind pressure value of a block is 2000 Pa when the wind speed reaches 50 m / s. By inputting this wind load extreme value data into the Gumbel extreme value distribution model, the probability of wind pressure extreme value of the block at a higher wind speed (such as 60 m / s or 70 m / s) is calculated. The extreme value distribution model is used to fit the maximum wind load data in the experiment or simulation to determine the distribution parameters (such as location parameters and scale parameters). According to the fitted distribution model, the probability of extreme value conditions occurring in each block under different wind speeds and wind direction angles is calculated to generate the wind load extreme value probability data of each block.The wind load data and extreme value probability data of the blocks are input into the structural analysis software (such as finite element analysis tools such as ANSYS and ABAQUS). Assume that the extreme value probability data of the wind load of a certain block indicates that the probability of extreme wind pressure is 0.02 (2%) when the wind speed is 70 m / s. At this time, this extreme wind load condition is applied to the finite element model of the block to perform stress characteristic analysis and output the maximum displacement and overall deformation force mode of each block. Extract the stress and other characteristic data of each block to comprehensively form the extreme value characteristic data of the wind load of the block. Determine the maximum stress point of each block under the extreme wind load, record the stress value and its specific location. Through the extraction of these data, the complete extreme value characteristic data of the wind load of the block warehouse is generated and used to judge the wind resistance of the warehouse structure under extreme wind load. These extreme value characteristic data are directly applied to the subsequent structural design optimization, especially for strengthening the design of those areas with stress concentration or excessive deformation.

[0023] Step S5: Based on the warehouse wind load overall effect data and the block warehouse wind load extreme characteristic data, the large-span warehouse structure design parameter analysis is performed to generate the large-span warehouse structure design parameters, and the large-span warehouse structure design parameters are transmitted to the terminal to perform the large-span warehouse structure design parameter feedback operation.

[0024] In an embodiment of the present invention, based on the previously generated warehouse wind load overall effect data, the overall design characteristics of the long-span warehouse structure are analyzed in detail. The wind load effect data of the overall structure of the warehouse, including stress distribution and load transfer path of key nodes, are input through structural analysis software (such as SAP2000 or ETABS). Based on these data, the bearing capacity, stiffness and stability of the entire long-span warehouse are evaluated. The generated structural design characteristic data will provide a basis for subsequent structural optimization, such as reinforcing the structure or adjusting the design of certain key nodes. Based on the extreme characteristic data of the block warehouse wind load obtained in the previous analysis, the local enclosure structure (such as walls, roofs, etc.) of the warehouse is focused on for characteristic analysis. Finite element analysis software (such as ANSYS or ABAQUS) is used to analyze the stress and stability of the local enclosure structure under wind load in detail. The extreme characteristic data of each block is input into the finite element model of the local enclosure structure to evaluate the stress of these local enclosure structures under extreme wind loads. Combined with this value, it is further evaluated whether the bearing capacity of the local warehouse support is sufficient. Identify areas that need to be strengthened or optimized, such as adding support structures or choosing higher-strength materials. The final generated local enclosure design feature data of the large-span warehouse will reflect the specific stress characteristics and design improvement suggestions of the local enclosure structure under wind loads. Combine the structural design feature data of the large-span warehouse and the local enclosure design feature data to conduct a comprehensive analysis of the structural design parameters. Compare the design feature data of the overall structure with the design features of the local enclosure structure to ensure the coordination of the global and local designs. Through professional structural design software (such as MIDAS or STAAD.Pro), the various design parameters of the warehouse are calculated and optimized in detail to generate the final structural design parameters. These design parameters include the main structural dimensions of the warehouse (such as the cross-sectional dimensions of beams and columns), material selection (such as the strength grade of steel), the connection method of key nodes, and the reinforcement measures of the local enclosure structure. Transmit these parameters to the terminal system. Feedback these parameters to the design and construction team through the building information model (BIM) or other design platforms for design adjustment and optimization. In the terminal system, the automated design feedback workflow will ensure that these optimized design parameters are correctly applied during the actual construction phase, thereby improving the wind resistance and overall safety of large-span warehouse structures.

[0025] Furthermore, step S1 includes the following steps: Step S11: executing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data collection and processing on the wind tunnel simulation test operation; In the embodiment of the present invention, the warehouse wind tunnel test is carried out in the high-speed test section of the atmospheric boundary layer wind tunnel of the wind tunnel test system. Using precise wind tunnel test equipment, by setting a reduced model that is consistent with the actual warehouse size ratio, the test parameters such as wind speed range, wind direction angle, simulated atmospheric boundary layer conditions, etc. are determined. The wind tunnel is driven by six DC motors with a maximum power of 335 kW, and the wind speed is regulated and controlled by a thyristor DC stepless speed regulation system centrally controlled by a computer terminal. The wind tunnel is equipped with an automatic speed regulation, control and data acquisition system and an automatic turntable system for building structure model tests. The turntable has a diameter of 2.5m and good flow field performance. The velocity non-uniformity of the flow field in the test area is less than 1%, the turbulence is less than 0.2%, and the average airflow angle is less than 0.5, so as to simulate the wind speed and wind pressure with a return period of 50 years and a return period of 100 years. For example, the basic wind pressure averaged for 10 minutes at a height of 10 meters (corresponding to the corresponding height of the wind tunnel test system in proportion to the return period of 50 years) is w0,50=0.40 kN / m2, and the corresponding basic wind speed is 25.30 m / s; corresponding to the return period of 100 years, w0,100=0.45 kN / m2, and the corresponding basic wind speed is 26.83 m / s. The environmental variables of the simulation test, such as the wind speed level and the fluid mechanics conditions in the wind tunnel, are set through the experimental control system. Subsequently, the wind tunnel equipment is started to simulate the real wind environment. During the wind tunnel simulation test, high-precision sensors placed on the surface and surroundings of the warehouse model are used to collect real-time data on wind speed, wind pressure, eddy current, gradient wind height, and other related data during the test. After data collection is completed, all raw data are saved in a computer database using a data acquisition and processing system to ensure that the basic data for subsequent analysis and processing is complete and correct, so as to obtain wind tunnel test data, including gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data.

[0026] Step S12: performing wind tunnel average wind speed distribution power index analysis on the wind tunnel simulation test operation to generate a wind tunnel average wind speed distribution power index; In an embodiment of the present invention, a power exponent analysis of the wind tunnel average wind speed distribution is performed on the wind tunnel simulation test operation, and the wind speed data collected in the wind tunnel experiment is analyzed using a data processing algorithm, especially the distribution of the average wind speed. By comprehensively analyzing the test results of multiple wind directions and different heights, the power exponent calculation formula is used to fit the distribution characteristics of the wind speed changing with height, and the power exponent of the wind tunnel average wind speed distribution is generated. Specifically, the experimental data is curve-fitted through numerical analysis tools (such as MATLAB or Python's numerical calculation library) to solve the power exponent value of the wind speed distribution. The power exponent value can be used for the subsequent accurate description of the wind speed distribution to improve the authenticity and accuracy of the simulation model.

[0027] Step S13: performing wind speed numerical quantitative distribution analysis on the gradient wind height data and the gradient wind height wind speed data based on the wind tunnel average wind speed distribution power index to generate wind speed distribution quantitative data.

[0028] In the embodiment of the present invention, the wind speed numerical quantification distribution analysis of the gradient wind height data and the gradient wind height wind speed data is performed based on the wind tunnel average wind speed distribution power index, the wind speed data at different heights are extracted from the experimental data, and the wind speed change trend with height is further numerically processed according to the generated power index. The wind speed distribution at different heights is quantified into an accurate numerical model by using numerical analysis tools to generate wind speed distribution quantitative data, firstly numerically calculate through the set mathematical model, and then segment the wind speed data according to the height gradient to ensure the meticulousness of the data distribution. The wind speed distribution quantitative data will be used for the subsequent calculation and analysis of wind loads at each node of the warehouse structure to ensure the reliability and accuracy of the test.

[0029] Further, step S2 includes the following steps: Step S21: Acquire wind tunnel reference static pressure monitoring data; In an embodiment of the present invention, a precise pressure sensor system is used to obtain reference static pressure monitoring data during a wind tunnel experiment. First, multiple static pressure sensors are arranged at appropriate locations of the wind tunnel experimental device. These sensors are distributed according to the size of the warehouse model and the test range of the wind tunnel. The sensors monitor the static pressure changes inside the wind tunnel in real time. During the experiment, the static pressure data collected by each sensor is transmitted to the main control system in real time through the data acquisition system and recorded in the database. This monitoring data serves as a benchmark for subsequent wind pressure calculations to ensure that the wind pressure calculations at each measuring point have a basis to rely on.

[0030] Step S22: performing dimensionless wind pressure calculation on the gradient wind height wind pressure data based on the wind speed distribution quantified data and the wind tunnel reference static pressure monitoring data to obtain dimensionless wind pressure data; In an embodiment of the present invention, a dimensionless wind pressure calculation is performed based on the wind tunnel reference static pressure monitoring data and wind speed distribution quantified data collected in the previous step. The dimensionless wind pressure calculation formula is used to calculate the dimensionless wind pressure, the pressure value at a certain point, the static pressure value, the air density, and the wind speed, and the dimensionless wind pressure data at different heights are calculated. According to the gradient wind height data, the wind pressure at different heights is dimensionlessly processed using the quantified wind speed distribution data and static pressure monitoring data. During the calculation process, a numerical processing tool (such as MATLAB) converts the wind pressure data at each height into dimensionless wind pressure data to ensure that the wind pressure data at different heights can be dimensionlessly compared and analyzed.

[0031] Step S23: performing a wind pressure quantitative distribution analysis on the dimensionless wind pressure data based on the gradient wind height data to generate wind pressure quantitative distribution data; In an embodiment of the present invention, the dimensionless wind pressure data is classified and processed according to different gradient heights to ensure that the wind pressure change trend at each height can be accurately reflected in the analysis process. Multidimensional curve fitting and distribution statistics processing are performed using numerical statistical analysis tools such as Matlab or Python, and the dimensionless wind pressure data at different heights are distributed and quantified to clarify the wind pressure change law at each height level and generate complete wind pressure quantitative distribution data. This data will be used to describe the wind pressure distribution at each gradient height and provide a basis for subsequent multi-scale analysis.

[0032] Step S24: performing a multi-scale wind pressure quantitative distribution simulation analysis on the wind pressure quantitative distribution data based on a preset multi-scale wind direction angle threshold value to generate multi-scale wind pressure quantitative distribution data.

[0033] In an embodiment of the present invention, based on a preset multi-scale wind direction angle threshold, such as taking the wind direction angle at intervals of 15° (a total of 24 working conditions), the wind direction angle increases in a clockwise direction to set the multi-scale wind direction angle threshold. According to different wind direction angles (e.g., 15°, 30°, 45°, etc.) combined with multi-scale wind pressure distribution data, CFD (computational fluid dynamics) software such as ANSYS Fluent or OpenFOAM is used to simulate and analyze the wind pressure distribution under different wind directions. By simulating the distribution of wind pressure data under multiple wind direction angles, since the wind pressure in the turbulent field is a random variable, in order to obtain the average wind pressure coefficient, the recorded data must be statistically analyzed to obtain the average wind pressure coefficient corresponding to each measuring point at each wind direction angle, and finally generate multi-scale wind pressure quantitative distribution data. This analysis ensures that under different wind direction conditions, the wind load analysis of the warehouse structure can cover a wider range of wind pressure action, thereby improving the accuracy and applicability of wind pressure distribution.

[0034] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps: Step S31: Obtaining geometric design parameters of a large-span warehouse; In the embodiment of the present invention, the geometric design parameters of the long-span warehouse are obtained. This process usually extracts key geometric parameters from architectural design drawings or digital models, such as the length, width, height, span, roof slope, facade shape, etc. of the warehouse. Using building information modeling (BIM) tools or other 3D design software, these geometric parameters are read and extracted, and it is ensured that these parameters are consistent with the actual construction requirements, so as to provide basic data for subsequent 3D modeling.

[0035] Step S32: performing three-dimensional geometric modeling of the large-span warehouse according to the geometric design parameters of the large-span warehouse to generate a large-span warehouse model; In the embodiment of the present invention, according to the obtained geometric design parameters, a 3D modeling software (such as AutoCAD, Revit or 3ds Max) is used to perform 3D geometric modeling of a large-span warehouse. During the modeling process, it is ensured that the geometric dimensions, structural form, facade features, etc. of the warehouse are consistent with the actual design parameters. This step generates a 3D warehouse model that conforms to the actual structure. The model should include the main structural components of the warehouse, such as the roof, columns, walls, etc. Each detail of the model is refined to ensure that it can be used for subsequent wind tunnel simulation tests and wind load analysis.

[0036] Step S33: performing wind tunnel test large-span warehouse model simulation update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; In the embodiment of the present invention, based on the wind tunnel simulation test work conducted in the early stage, the three-dimensional warehouse model is simulated and updated by using computational fluid dynamics (CFD) software. The wind pressure, wind speed and other data collected in the wind tunnel experiment are input into the simulation system, and the surface wind load of the warehouse model is accurately updated so that the model reflects the wind load effect in the real environment. The updated model should include the pressure distribution under different wind speed and wind direction conditions, and reflect the response of the warehouse under the wind load, so as to generate a large-span warehouse model for wind tunnel testing.

[0037] Step S34: performing wind load analysis and processing on each node of the warehouse for the large-span warehouse model tested in the wind tunnel based on the multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; In the embodiment of the present invention, the wind load analysis is performed on the updated wind tunnel test large-span warehouse model based on the multi-scale wind pressure quantitative distribution data. In the specific implementation, the generated multi-scale wind pressure quantitative distribution data is combined with the geometric characteristics of each node of the warehouse, and the wind load of each node of the warehouse is calculated in detail through finite element analysis software (such as ABAQUS or ANSYS), taking into account various wind pressure distribution conditions and the stress state of different nodes, and generating wind load data for each node, and recording the stress conditions of each part of the structure under the action of wind pressure.

[0038] Step S35: Perform warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data.

[0039] In an embodiment of the present invention, based on the generated wind load data, a structural analysis software (such as SAP2000 or ETABS) is used to analyze the overall effect of the warehouse wind load, and the stress condition of the entire warehouse model is calculated. At this time, the software will calculate the effect of the wind load on the overall structure by summing and transferring the wind load of each node. The stress distribution of the main structure of the warehouse (such as beams, columns, roof supports, etc.) under the action of wind load is calculated, especially the stress concentration points. The wind load data of each node is summarized, the distribution and transmission path of the wind load on the overall structure of the warehouse are analyzed, the stress state of the structure as a whole is evaluated, and the overall effect data of the warehouse wind load is generated, including the stress distribution of the warehouse under different wind speeds and wind directions and the stress concentration areas of key nodes. These data provide a basis for structural design to ensure the stability and safety of the warehouse under various wind load conditions.

[0040] Further, step S34 includes the following steps: Step S341: performing a wind pressure point shape coefficient analysis according to the multi-scale wind pressure quantitative distribution data to generate a wind pressure point shape coefficient; In an embodiment of the present invention, based on the previous wind tunnel test data, the wind pressure quantitative distribution data is classified according to the wind pressure value of each measuring point on the warehouse surface. The wind pressure point shape coefficient of each measuring point is calculated by using the shape coefficient analysis formula, and using the measured wind pressure, air density, wind speed, and wind-receiving area. These coefficients reflect the wind pressure distribution characteristics at various positions on the warehouse surface, especially the local stress conditions under different wind direction angles. For example, at a certain wind speed, the wind pressure at a node on the roof of the warehouse is 1500 Pa, the air density is 1.225 kg / m³, and the wind speed is 25 m / s. The wind pressure point shape coefficient of the node is calculated by the formula. This coefficient is used to describe the wind load characteristics of the node and reflect the wind pressure distribution law in the local area of ​​the building surface. Through this analysis, the wind pressure point shape coefficient of each measuring point is generated to ensure that the wind load distribution of each part of the warehouse is accurately reflected.

[0041] Step S342: Perform statistical analysis on the wind pressure height variation coefficient according to the multi-scale wind pressure quantitative distribution data to generate the wind pressure height variation coefficient; In an embodiment of the present invention, a statistical analysis of the wind pressure height variation coefficient is performed based on the multi-scale wind pressure quantitative distribution data. First, the warehouse model is divided into multiple horizontal layers according to different heights. By extracting the wind pressure data at each height layer, the height variation coefficient is calculated by the wind pressure at a specific height and the wind pressure value at the reference height. At a height of 20 meters, the wind pressure is measured to be 2000 Pa, and the wind pressure is measured at the ground (reference height) as 1000 Pa, then the height variation coefficient is 2. Through this analysis, the wind pressure variation coefficient at different heights of the warehouse structure is generated to provide support for subsequent wind load analysis. Analyze the wind pressure changes at each height layer, generate the wind pressure height variation coefficient, grasp the law of wind pressure changes with height, and ensure accurate evaluation of the stress conditions of the warehouse structure at different heights.

[0042] Step S343: Based on the wind pressure point body coefficient and the wind pressure height variation coefficient, the wind load analysis and processing of each node of the large-span warehouse model in the wind tunnel test is performed to generate warehouse wind load data.

[0043] In an embodiment of the present invention, a finite element analysis software (such as ANSYS or Abaqus) is used to input the wind pressure point body coefficient and the height variation coefficient into the finite element model of the warehouse structure, and the wind load of each node is numerically analyzed according to different wind directions and wind speeds. The action position and direction of the wind load are defined in the model, and then the stress of each node under the action of the wind load is calculated by the software. For example, under the condition of a wind speed of 30 m / s, the wind pressure point body coefficient of a node is 1.2, the wind pressure height variation coefficient is 1.5, the air density is 1.225 kg / m³, and the node area is 10 m², then the wind load of the node can be calculated. After this series of wind load analysis and processing, the wind load data of each node of the complete warehouse is generated. These data will be used to evaluate the stress of the warehouse structure under different wind load conditions to ensure that the wind resistance performance of the design meets the safety requirements. After the wind load analysis is completed, the wind load data of each node is generated, including the wind load size, direction and stress distribution of the action point. The execution of this step provides detailed wind load distribution data for warehouse structural design, which helps to optimize structural design and improve wind resistance.

[0044] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S4 in FIG. 1 , in this embodiment, step S4 includes the following steps: Step S41: performing model block processing on the large-span warehouse model for wind tunnel testing to generate a block test warehouse model; In the embodiment of the present invention, the expression form based on a large amount of test data (the point shape coefficient or pressure coefficient of each measuring point) is very complex, which is not convenient for analyzing the most unfavorable wind direction and its corresponding wind load. In order to facilitate analysis, the warehouse is divided into several blocks during data processing, and the overall model is divided into several block areas, so as to simulate and analyze the wind load of the local area more finely. The block division standard is based on the geometric complexity and stress conditions of the structure, and CAD or other three-dimensional modeling software is used for block processing to ensure that each block can accurately reflect the local characteristics of the warehouse structure and generate a block test warehouse model.

[0045] Step S42: analyzing the shape and structure characteristics of the block test warehouse model according to the block test warehouse model to generate shape and structure characteristic data of the block test warehouse model; In an embodiment of the present invention, a shape and structure feature analysis is performed on a block test warehouse model. The shape and structure features of each block are analyzed using a geometric analysis tool such as AutoCAD or Revit. By inputting the geometric parameters of the warehouse model, the shape features of the block model, etc., the shape feature analysis of the block model is performed to generate shape and structure feature data. This feature data includes information such as the surface area, center node, surface curvature, and node connection method of each block, ensuring that the block model can accurately reflect the response of the actual warehouse structure under wind load, and provide reliable basic data for subsequent wind load analysis.

[0046] Step S43: performing block warehouse wind load analysis processing on the warehouse wind load data by testing the warehouse model shape and structure characteristic data in blocks to generate block warehouse wind load data; In an embodiment of the present invention, based on the shape and structure characteristic data of the block test warehouse model, the wind load of each block is analyzed and processed using finite element analysis software (such as ANSYS or Abaqus). Each block is regarded as an independent force unit, and after the structural characteristic data of each block is input, the wind load distribution analysis is performed on it. By calculating the wind pressure, wind speed and other forces of each block, the wind load data of the block warehouse is generated, including the stress state, stress concentration and displacement of each block under the action of wind, to ensure that the wind load data of each block can reflect the distribution characteristics under the actual wind load.

[0047] Step S44: using the block warehouse wind load data as the wind load extreme value correlation factor of each block test warehouse model; performing block wind load extreme value probability analysis processing on the wind load extreme value correlation factor to generate block wind load extreme value probability data; In an embodiment of the present invention, according to the wind load data of the block warehouse generated in the early stage, the key force information of each block is extracted, including the distribution of wind load on the surface of each block, the peak wind pressure and the force state. For each block, its wind load extreme value related factors are determined, which usually include the maximum wind pressure, the maximum wind speed and the structural deformation stress under extreme wind load conditions. For these extreme value related factors, statistical methods are used for probability analysis to evaluate the possibility of extreme values ​​occurring in each block under different extreme wind conditions. The wind load extreme value related factors of each block are input into a statistical analysis tool, such as the statistical toolbox of MATLAB or the probability analysis package of R language. Assuming that the maximum wind speed and wind pressure values ​​recorded in the wind tunnel experiment follow the extreme value distribution model (such as Weibull distribution or Gumbel distribution), these distribution functions are used for parameter fitting. By fitting the wind load data obtained in the wind tunnel experiment or numerical simulation, the extreme value distribution model of each block is obtained. For example, it is assumed that the maximum wind pressure value of a certain block is 2000 Pa when the wind speed reaches 50 m / s. By inputting this wind load extreme value data into the Gumbel extreme value distribution model, the probability of the wind pressure extreme value of the block at a higher wind speed (such as 60 m / s or 70 m / s) is calculated. The extreme value distribution model is used to fit the maximum wind load data in the experiment or simulation to determine the distribution parameters (such as location parameters and scale parameters). According to the fitted distribution model, the probability of extreme value conditions occurring in each block under different wind speed and wind direction angle conditions is calculated. For example, the probability that the wind load of a block exceeds a certain extreme value under specific wind direction and wind speed conditions is calculated. Based on the combination of different wind speeds and wind directions, the wind load extreme value probability data of each block is generated. This data is used to describe the possibility of extreme wind loads occurring in each block under extreme conditions. The generated block wind load extreme value probability data can be used to evaluate the stability and safety of the entire warehouse structure under extreme wind conditions, providing a basis for further structural optimization and wind resistance design. For example, through probabilistic analysis, it is possible to identify block areas with a higher probability of extreme wind loads. These areas need to be strengthened in the actual design to ensure the safety and stability of the overall warehouse structure under extreme wind load conditions.

[0048] Step S45: performing wind load extreme value characteristic analysis and processing on the block warehouse according to the wind load data of the block warehouse and the wind load extreme value probability, and generating wind load extreme value characteristic data on the block warehouse.

[0049] In an embodiment of the present invention, the wind load data and extreme value probability data of the blocks are input into the structural analysis software (such as finite element analysis tools such as ANSYS and ABAQUS). The wind load data of each block is assigned to its corresponding structural node or surface, and the model is set according to the wind pressure distribution of the actual wind tunnel experiment or numerical simulation. For example, assuming that the extreme value probability data of the wind load of a certain block indicates that the probability of extreme wind pressure is 0.02 (2%) when the wind speed is 70 m / s. At this time, this extreme wind load condition is applied to the finite element model of the block to perform stress characteristic analysis. After the data input and model setting are completed, the force characteristics of each block under extreme wind load are calculated using finite element analysis software. Calculate the maximum stress and stress concentration area of ​​each block under the action of the extreme wind load. For example, stress concentration will occur at certain nodes of the roof or wall, which is usually a structural weak point. According to the wind load distribution, the deformation effect of each block on the block warehouse under the action of the extreme wind load, such as the roof or the towering structural parts, is calculated. The software will output the maximum displacement and overall deformation force mode of each block. Assume that the roof block of a warehouse is under a wind speed of 70 m / s, and the maximum stress at the stress concentration point reaches 90% of the structural yield limit. These analysis results will be used to further evaluate the wind resistance of the block. After the finite element analysis is completed, the stress and other characteristic data of each block are extracted to form the extreme characteristic data of the block wind load. Determine the maximum stress point of each block under the extreme wind load, record the stress value and its specific location. Through the extraction of these data, the complete extreme characteristic data of the block warehouse wind load is generated, and it is used to judge the wind resistance of the warehouse structure under extreme wind load. These extreme characteristic data can be directly applied to the subsequent structural design optimization, especially for strengthening the design of those areas with stress concentration or excessive deformation.

[0050] Further, step S5 includes the following steps: Step S51: performing a large-span warehouse structure design feature analysis based on the warehouse wind load overall effect data to generate large-span warehouse structure design feature data; In an embodiment of the present invention, based on the previously generated warehouse wind load overall effect data, the overall design features of the large-span warehouse structure are analyzed in detail. The wind load effect data of the overall structure of the warehouse, including stress distribution and load transfer paths of key nodes, are input through structural analysis software (such as SAP2000 or ETABS). Based on these data, the bearing capacity, stiffness and stability of the entire large-span warehouse are evaluated. For example, the wind load borne by the main frame of the warehouse may be mainly concentrated at the connection between the roof and the wall. The analysis software will simulate these stress concentration areas and generate design feature data of the large-span warehouse structure according to the stress state, including the stress characteristics of the entire frame, the stress distribution of key nodes, and the deformation mode of the overall structure. Ultimately, the generated structural design feature data will provide a basis for subsequent structural optimization, such as strengthening the structure or adjusting the design of certain key nodes.

[0051] Step S52: performing a large-span warehouse local enclosure design characteristic analysis based on the block warehouse wind load extreme value characteristic data, and generating large-span warehouse local enclosure design characteristic data; In an embodiment of the present invention, based on the extreme characteristic data of wind loads of block warehouses obtained by previous analysis, the characteristic analysis is focused on the local enclosure structures (such as walls, roofs, etc.) of the warehouse. Finite element analysis software (such as ANSYS or ABAQUS) is used to perform a detailed analysis of the stress and stability of the local enclosure structure under wind loads. The extreme characteristic data of each block is input into the finite element model of the local enclosure structure to evaluate the stress conditions of these local enclosure structures under extreme wind loads. Combined with this value, it is further evaluated whether the bearing capacity of the local warehouse support is sufficient. Through this analysis, it is possible to identify areas that need to be strengthened or optimized in design, such as adding support structures or selecting higher strength materials. The final generated local enclosure design characteristic data of large-span warehouses will reflect the specific stress characteristics and design improvement suggestions of the local enclosure structure under wind loads.

[0052] Step S53: Perform large-span warehouse structure design parameter analysis based on large-span warehouse structure design feature data and large-span warehouse local enclosure design feature data, generate large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to execute the large-span warehouse structure design parameter feedback operation.

[0053] In the embodiment of the present invention, a comprehensive analysis of the structural design parameters is performed in combination with the structural design feature data of the large-span warehouse and the design feature data of the local enclosure. The design feature data of the overall structure is compared with the design features of the local enclosure structure to ensure the coordination of the global and local designs. Through professional structural design software (such as MIDAS or STAAD.Pro), the various design parameters of the warehouse are calculated and optimized in detail to generate the final structural design parameters. These design parameters include the main structural dimensions of the warehouse (such as the cross-sectional dimensions of beams and columns), material selection (such as the strength grade of steel), the connection method of key nodes, and the reinforcement measures of the local enclosure structure. For example, if the wind load analysis results show that the bearing capacity of some wall support systems is insufficient under extreme wind loads, the design parameters will adjust the size or material of these supports to ensure safety. After the optimized design parameters are generated, these parameters are transmitted to the terminal system. Through the building information model (BIM) or other design platforms, these parameters are fed back to the design and construction team for design adjustment and optimization. In the terminal system, the automated design feedback workflow will ensure that these optimized design parameters are correctly applied during the actual construction phase, thereby improving the wind resistance and overall safety of large-span warehouse structures.

[0054] Further, step S51 includes the following steps: According to the overall effect data of warehouse wind load, the overall structure wind resistance demand analysis of large-span warehouse is carried out to generate the overall structure wind resistance demand data of large-span warehouse; The design characteristics of the large-span warehouse structure are analyzed based on the wind resistance demand data of the large-span warehouse structure as a whole, and the design characteristics data of the large-span warehouse structure are generated.

[0055] In an embodiment of the present invention, the overall effect data of the warehouse wind load generated by wind tunnel testing or simulation in the early stage (including stress, deformation and wind load distribution under different wind speed and wind direction conditions) are input into the structural analysis software (such as ETABS or SAP2000). According to the data, the force characteristics of the warehouse under different wind loads are analyzed. For example, under the condition of a wind speed of 70 m / s, the top of the main frame of the warehouse bears the maximum wind pressure, and the connection between the wall and the foundation will also be subject to large shear force and bending moment. On this basis, the wind resistance of the overall structure of the warehouse is evaluated, and special attention is paid to the key parts of the warehouse, such as the main beam, column, local warehouse support, etc., to determine whether these structures have sufficient rigidity and stability under the action of wind load. Through analysis, the wind resistance demand data of the overall structure of the large-span warehouse is generated, including the maximum wind load, maximum stress, deformation limit and other information borne by the warehouse structure. These wind resistance demand data will clearly indicate in which areas of the warehouse the wind resistance performance needs to be enhanced, especially when the load borne by certain structural parts is close to the design limit, it is recommended to further optimize the design. The wind resistance demand data is input into the structural design software (such as MIDAS or STAAD.Pro), and the bearing capacity, material properties and cross-sectional dimensions of each key structural component are calculated and analyzed in detail according to the wind resistance demand. For example, if the maximum stress of the warehouse main beam under wind load is close to the yield limit of the material, the design parameters may require increasing the cross-sectional dimensions of the main beam or using higher-strength steel. Similarly, if there is a large shear force at the connection between the wall and the foundation, it is necessary to add reinforcement measures or optimize the connection design at these nodes. In addition, the design feature analysis should also consider the stiffness and stability of the overall structure to ensure that the warehouse will not produce excessive deformation or local instability due to wind loads. Through these analyses, the structural design feature data of the large-span warehouse is generated. The data includes specific design parameters, such as the size and material selection of the main beam and column, the connection method of the node, and the reinforcement recommendations for the local enclosure structure.

[0056] Further, step S52 includes the following steps: According to the extreme characteristic data of wind load of block warehouses, the local enclosure wind resistance demand analysis of large-span warehouses is carried out to generate the local enclosure wind resistance demand data of large-span warehouses; According to the wind resistance demand data of the local enclosure of large-span warehouses, the design characteristics of the local enclosure of large-span warehouses are analyzed to generate the design characteristics data of the local enclosure of large-span warehouses.

[0057] In an embodiment of the present invention, the block wind load extreme value data is used to focus on analyzing the stress conditions of the warehouse enclosure structure (such as walls, roofs, etc.) under extreme wind load conditions. The block extreme value characteristic data (such as maximum wind pressure, maximum deformation, etc.) is input into the structural analysis software (such as ANSYS or ABAQUS). For example, it is assumed that the maximum wind pressure borne by the wall of a certain block is 2500 Pa when the wind speed is 80 m / s. By analyzing these data, the stability, strength and deformation of these local enclosure structures under extreme wind loads are evaluated. The goal of this wind resistance demand analysis is to identify the weak points in the enclosure structure, for example, the bearing capacity of some enclosure structures is close to or exceeds the design limit, or the deformation is too large and may cause structural failure. After the analysis is completed, the wind resistance demand data of the local enclosure of the large-span warehouse is generated. Including the maximum bearing capacity of the enclosure structure under extreme wind loads, wind resistance strength requirements and the maximum allowable deformation value of the enclosure structure. According to the local enclosure wind resistance demand data generated earlier, the design feature analysis of the local enclosure structure of the large-span warehouse is further carried out to ensure that the enclosure structure meets the wind resistance demand while optimizing its design and enhancing its performance under extreme wind loads. Using structural design software (such as STAAD.Pro or MIDAS), the wind resistance demand data of the local enclosure is used as input to perform design feature analysis. For example, assuming that in the wind resistance demand analysis, the wind load borne by a certain wall is too large and its wind resistance needs to be improved, the design feature analysis will recommend increasing the thickness of the wall or using a higher strength material. In addition, if the deformation of some roof parts exceeds the design standard, it may be necessary to recommend adding a support structure or using a higher stiffness material in the design feature. Through these design feature analyses, the design feature data of the local enclosure structure of the large-span warehouse is finally generated. The data includes specific design suggestions and parameters, such as material selection, thickness, connection method, and additional reinforcement measures required for enclosure structures such as walls and roofs.

[0058] This specification provides a wind tunnel test and analysis system for a large-span warehouse structure, which is used to perform the wind tunnel test and analysis method for a large-span warehouse structure as described above. The wind tunnel test and analysis system for a large-span warehouse structure includes: A wind speed distribution quantitative analysis module is used to perform a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and to collect and process wind tunnel test data for the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; and to perform a wind speed numerical quantitative distribution analysis based on the gradient wind height data and the gradient wind height wind speed data to generate wind speed distribution quantitative data; The wind pressure distribution quantitative analysis module is used to obtain the wind tunnel reference static pressure monitoring data; based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, the gradient wind height wind pressure data is simulated and analyzed for the wind pressure quantitative distribution of multi-scale wind direction angles to generate multi-scale wind pressure quantitative distribution data; The warehouse wind load overall effect analysis module is used to design a large-span warehouse model; based on the wind tunnel simulation test operation, the large-span warehouse model is simulated and updated by the wind tunnel test to generate a large-span warehouse model for the wind tunnel test; based on the multi-scale wind pressure quantitative distribution data, the wind load analysis and processing of each node of the large-span warehouse model for the wind tunnel test is performed to generate warehouse wind load data; based on the warehouse wind load data, the overall effect analysis of the warehouse wind load is performed to generate the overall effect data of the warehouse wind load; The block warehouse wind load extreme value characteristic analysis module is used to perform block warehouse wind load analysis on the warehouse wind load data to generate block warehouse wind load data; perform block warehouse wind load extreme value characteristic analysis on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; The large-span warehouse structure design feedback module is used to analyze the large-span warehouse structure design parameters based on the overall effect data of the warehouse wind load and the extreme characteristic data of the block warehouse wind load, generate the large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to perform the large-span warehouse structure design parameter feedback operation.

[0059] The beneficial effect of the present application is that by performing wind tunnel simulation operations based on preset wind tunnel simulation test parameters, the wind load conditions in the actual environment can be effectively simulated, and combined with data acquisition and processing technology, key wind tunnel test data can be obtained in real time to ensure the integrity and accuracy of the data. The average wind speed distribution power index analysis is performed on the simulation test operation, so that the complex wind field characteristics are accurately described by a mathematical model. Further based on the generated wind speed distribution power index, combined with the gradient wind height data and wind speed data, quantitative distribution analysis is performed to generate accurate wind speed distribution quantitative data, which improves the accuracy and effectiveness of the wind tunnel test results, provides a scientific basis for the subsequent optimization design of the warehouse structure, and ensures the safety and reliability of the structural design under wind load. Obtaining wind tunnel reference static pressure monitoring data ensures the reliability and reference value of pressure data in the test. Combining wind speed distribution quantitative data and wind tunnel reference static pressure monitoring data, and using dimensionless wind pressure calculation method to analyze dimensionless wind pressure data, dimensionless processing of wind pressure data at different heights is achieved, simplifying complex wind pressure calculations. Based on the gradient wind height data, the dimensionless wind pressure data is quantitatively analyzed and the wind pressure quantitative distribution data is generated, making the wind pressure distribution more intuitive and operable. The simulation analysis is performed based on the preset multi-scale wind direction angle threshold, which makes the simulation of wind pressure distribution more refined and can cover the wind pressure changes at different scales. The geometric design parameters of the large-span warehouse are obtained to provide an accurate data basis for subsequent modeling and ensure the authenticity of the model. These parameters are used for three-dimensional geometric modeling, and the model is updated based on wind tunnel simulation tests to ensure that the model reflects the actual wind load conditions, making the simulation results closer to the actual situation. Combined with the multi-scale wind pressure quantitative distribution data, the wind load distribution of each node of the warehouse is carefully analyzed to ensure the refined analysis of each part of the structure, so that the wind load data can fully cover each key node of the warehouse, ensuring the safety and stability of the structure under different wind pressure conditions. The overall effect of the wind load of the warehouse is comprehensively analyzed to generate overall effect data to help designers fully understand the stress of the structure under wind load. By dividing the large-span warehouse model into blocks, the complex overall structure is disassembled into multiple small units, and a block test warehouse model is generated, laying the foundation for subsequent analysis. The shape and structure characteristics are analyzed according to the block model, and the structural characteristic data is generated to ensure that the morphological characteristics of each block can accurately reflect the actual structural characteristics of the warehouse. Using these shape and structure characteristic data, the wind load of each block is analyzed, and the analysis of wind load distribution is further refined, making the wind load distribution more accurate.By analyzing the wind load extreme value related factors of each block, the probability analysis of the extreme conditions of wind load is carried out, and the wind load extreme value conditions are provided, which provides a basis for safety design. Based on these extreme value probability data, the wind load extreme values ​​of each block are analyzed, and the block warehouse wind load extreme value characteristic data is generated, which can more comprehensively predict the stress conditions of the warehouse under extreme wind loads and ensure that the design scheme can cope with various extreme wind pressure conditions, thereby greatly improving the wind resistance safety and reliability of large-span warehouse structures. Structural design characteristic analysis based on the overall effect data of warehouse wind load can fully reflect the overall structural characteristics of the entire warehouse under wind loads and ensure that the structural design meets the wind resistance requirements. Combined with the extreme value characteristic data of block warehouse wind loads, the design characteristic analysis of the local enclosure structure of the warehouse is carried out. The generated local enclosure design characteristic data provides a targeted reference for the wind resistance design of the local area and ensures the safety of the local structure under extreme wind loads. By combining the overall and local design feature data, a comprehensive structural design parameter analysis is performed, the final structural design parameters are generated, and these parameters are fed back to the terminal system so as to adjust and optimize the design scheme of the long-span warehouse. This feedback mechanism ensures that the design scheme meets the wind load bearing requirements of the long-span warehouse and achieves the optimal structural design. Therefore, a wind tunnel testing method for a long-span warehouse structure of the present invention simulates the actual wind environment through wind tunnel testing, and combined with multi-scale quantitative analysis of wind pressure distribution, it can more accurately capture the wind load distribution of the long-span warehouse structure under different wind speeds and wind directions, making the wind load distribution more comprehensive and detailed, especially for the accurate simulation of local wind pressure distribution. By dividing the long-span warehouse structure into blocks for wind load analysis and extreme value probability analysis, the required stress conditions of each part of the structure can be refined, the wind-resistant design effect of the local area of ​​the structure is improved, and more reliable data support is provided for the overall safety of the structure. The dual analysis of the overall effect of wind loads and local enclosure structures can adjust the stress capacity of large-span warehouse structures under multiple wind directions and wind pressure conditions, effectively improving the structural stability and wind resistance of large-span warehouses in long-term use, and avoiding potential structural safety hazards caused by incomplete wind load analysis.

[0060] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0061] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A wind tunnel test analysis method for a large-span warehouse structure, characterized in that: The following steps are involved: Step S1: performing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data acquisition and processing on the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; performing numerical quantitative distribution analysis of wind speed according to the gradient wind height data and the gradient wind height wind speed data to generate quantitative distribution data of wind speed; Step S2: obtaining wind tunnel reference static pressure monitoring data; performing wind pressure quantitative distribution simulation analysis processing of the gradient wind height wind pressure data at multi-scale wind direction angles based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, and generating multi-scale wind pressure quantitative distribution data; Step S3: design a large-span warehouse model; perform wind tunnel test simulation and update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; perform wind load analysis and processing on each node of the wind tunnel test large-span warehouse model based on multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; perform warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data; Step S4: performing block warehouse wind load analysis processing on the warehouse wind load data to generate block warehouse wind load data; performing block warehouse wind load extreme value characteristic analysis processing on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; Step S5: Based on the warehouse wind load overall effect data and the block warehouse wind load extreme characteristic data, the large-span warehouse structure design parameter analysis is performed to generate the large-span warehouse structure design parameters, and the large-span warehouse structure design parameters are transmitted to the terminal to perform the large-span warehouse structure design parameter feedback operation.

2. The wind tunnel test analysis method for a large-span warehouse structure according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: executing a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and performing wind tunnel test data collection and processing on the wind tunnel simulation test operation; Step S12: performing wind tunnel average wind speed distribution power index analysis on the wind tunnel simulation test operation to generate a wind tunnel average wind speed distribution power index; Step S13: performing wind speed numerical quantitative distribution analysis on the gradient wind height data and the gradient wind height wind speed data based on the wind tunnel average wind speed distribution power index to generate wind speed distribution quantitative data.

3. The wind tunnel test analysis method for a large-span warehouse structure according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Acquire wind tunnel reference static pressure monitoring data; Step S22: performing dimensionless wind pressure calculation on the gradient wind height wind pressure data based on the wind speed distribution quantified data and the wind tunnel reference static pressure monitoring data to obtain dimensionless wind pressure data; Step S23: performing a wind pressure quantitative distribution analysis on the dimensionless wind pressure data based on the gradient wind height data to generate wind pressure quantitative distribution data; Step S24: performing a multi-scale wind pressure quantitative distribution simulation analysis on the wind pressure quantitative distribution data based on a preset multi-scale wind direction angle threshold value to generate multi-scale wind pressure quantitative distribution data.

4. The wind tunnel test analysis method for a large-span warehouse structure according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Obtaining geometric design parameters of a large-span warehouse; Step S32: performing three-dimensional geometric modeling of the large-span warehouse according to the geometric design parameters of the large-span warehouse to generate a large-span warehouse model; Step S33: performing wind tunnel test large-span warehouse model simulation update processing on the large-span warehouse model based on the wind tunnel simulation test operation to generate a wind tunnel test large-span warehouse model; Step S34: performing wind load analysis and processing on each node of the warehouse for the large-span warehouse model tested in the wind tunnel based on the multi-scale wind pressure quantitative distribution data to generate warehouse wind load data; Step S35: performing warehouse wind load overall effect analysis based on the warehouse wind load data to generate warehouse wind load overall effect data.

5. The wind tunnel test analysis method for a large-span warehouse structure according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: performing a wind pressure point shape coefficient analysis according to the multi-scale wind pressure quantitative distribution data to generate a wind pressure point shape coefficient; Step S342: Perform statistical analysis on the wind pressure height variation coefficient according to the multi-scale wind pressure quantitative distribution data to generate the wind pressure height variation coefficient; Step S343: Based on the wind pressure point body coefficient and the wind pressure height variation coefficient, the wind load analysis and processing of each node of the large-span warehouse model in the wind tunnel test is performed to generate warehouse wind load data.

6. The wind tunnel test analysis method for a large-span warehouse structure according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: performing model block processing on the large-span warehouse model for wind tunnel testing to generate a block test warehouse model; Step S42: analyzing the shape and structure characteristics of the block test warehouse model according to the block test warehouse model to generate shape and structure characteristic data of the block test warehouse model; Step S43: performing block warehouse wind load analysis on the warehouse wind load data by testing the warehouse model shape and structure characteristic data in blocks to generate block warehouse wind load data; Step S44: using the block warehouse wind load data as the wind load extreme value correlation factor of each block test warehouse model; performing block wind load extreme value probability analysis processing on the wind load extreme value correlation factor to generate block wind load extreme value probability data; Step S45: performing block warehouse wind load extreme value characteristic analysis and processing according to the block warehouse wind load data and wind load extreme value probability, and generating block warehouse wind load extreme value characteristic data.

7. The wind tunnel test analysis method for a large-span warehouse structure according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: performing a large-span warehouse structure design feature analysis based on the warehouse wind load overall effect data to generate large-span warehouse structure design feature data; Step S52: performing a large-span warehouse local enclosure design characteristic analysis based on the block warehouse wind load extreme value characteristic data, and generating large-span warehouse local enclosure design characteristic data; Step S53: Perform large-span warehouse structure design parameter analysis based on large-span warehouse structure design feature data and large-span warehouse local enclosure design feature data, generate large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to execute the large-span warehouse structure design parameter feedback operation.

8. The wind tunnel test analysis method for a large-span warehouse structure according to claim 7 is characterized in that: Step S51 includes the following steps: According to the overall effect data of warehouse wind load, the overall structure wind resistance demand analysis of large-span warehouse is carried out to generate the overall structure wind resistance demand data of large-span warehouse; The design characteristics of the large-span warehouse structure are analyzed based on the wind resistance demand data of the large-span warehouse structure as a whole, and the design characteristics data of the large-span warehouse structure are generated.

9. The wind tunnel test analysis method for a large-span warehouse structure according to claim 7 is characterized in that: Step S52 includes the following steps: According to the extreme characteristic data of wind load of block warehouses, the local enclosure wind resistance demand analysis of large-span warehouses is carried out to generate the local enclosure wind resistance demand data of large-span warehouses; According to the wind resistance demand data of the local enclosure of large-span warehouses, the design characteristics of the local enclosure of large-span warehouses are analyzed to generate the design characteristics data of the local enclosure of large-span warehouses.

10. A wind tunnel test and analysis system for a large-span warehouse structure, characterized in that: For executing the wind tunnel test analysis method of the long-span warehouse structure according to claim 1, the wind tunnel test analysis system of the long-span warehouse structure comprises: A wind speed distribution quantitative analysis module is used to perform a wind tunnel simulation test operation based on preset wind tunnel simulation test parameters, and to collect and process wind tunnel test data for the wind tunnel simulation test operation to generate wind tunnel test data, wherein the wind tunnel test data includes gradient wind height data, gradient wind height wind speed data, and gradient wind height wind pressure data; and to perform a wind speed numerical quantitative distribution analysis based on the gradient wind height data and the gradient wind height wind speed data to generate wind speed distribution quantitative data; The wind pressure distribution quantitative analysis module is used to obtain the wind tunnel reference static pressure monitoring data; based on the wind speed distribution quantitative data, the wind tunnel reference static pressure monitoring data and the gradient wind height data, the gradient wind height wind pressure data is simulated and analyzed for the wind pressure quantitative distribution of multi-scale wind direction angles to generate multi-scale wind pressure quantitative distribution data; The warehouse wind load overall effect analysis module is used to design a large-span warehouse model; based on the wind tunnel simulation test operation, the large-span warehouse model is simulated and updated by the wind tunnel test to generate a large-span warehouse model for the wind tunnel test; based on the multi-scale wind pressure quantitative distribution data, the wind load analysis and processing of each node of the large-span warehouse model for the wind tunnel test is performed to generate warehouse wind load data; based on the warehouse wind load data, the overall effect analysis of the warehouse wind load is performed to generate the overall effect data of the warehouse wind load; The block warehouse wind load extreme value characteristic analysis module is used to perform block warehouse wind load analysis on the warehouse wind load data to generate block warehouse wind load data; perform block warehouse wind load extreme value characteristic analysis on the block warehouse wind load data to generate block warehouse wind load extreme value characteristic data; The large-span warehouse structure design feedback module is used to analyze the large-span warehouse structure design parameters based on the overall effect data of the warehouse wind load and the extreme characteristic data of the block warehouse wind load, generate the large-span warehouse structure design parameters, and transmit the large-span warehouse structure design parameters to the terminal to perform the large-span warehouse structure design parameter feedback operation.

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