Method, device, equipment and medium for evaluating wind resistance of transmission towers based on typhoon path trajectory data

Abstract

The present invention discloses a method, device, equipment, and medium for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data, belonging to the field of information technology. The method comprises: calculating the degree of wind speed attenuation based on the typhoon path trajectory data in combination with time data, terrain data, and turbulence data to obtain a differentiated wind speed distribution map; calculating wind pressure and response amplitude based on the differentiated wind speed distribution map in combination with the transmission tower geometric model, turbulence data, and wind speed attenuation data; determining the distribution consistency of the response amplitude peak and the wind pressure peak position by analyzing the spatiotemporal relationship, adjusting the initial wind speed value distribution in combination with terrain data and turbulence data to obtain wind field distribution data; and generating a wind resistance performance evaluation index for the transmission tower under typhoon path trajectory data based on the wind field distribution data. Therefore, by implementing the present invention, the problem of inaccurate evaluation of the wind resistance performance of transmission towers under typhoon conditions in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method, device, equipment and medium for evaluating the wind resistance of a transmission tower based on typhoon path trajectory data. Background Art

[0002] Evaluating the wind resistance of transmission towers is a core research area crucial to the safe operation of power systems, and its importance is undeniable. In coastal areas prone to typhoons, the wind resistance of transmission towers is directly linked to the stability of the power grid and the smooth functioning of the socio-economic system. Typhoons, as extreme natural disasters, can inflict significant damage to transmission facilities through their high wind loads. Scientifically evaluating the wind resistance of transmission towers is crucial for ensuring reliable power supply.

[0003] However, existing methods have significant shortcomings when evaluating the wind resistance of transmission towers. First, the degree of wind speed decay over time and distance after a typhoon makes landfall is affected by factors such as surface roughness, making it difficult for existing methods to accurately describe this dynamic process. Second, existing methods lack differentiation in the design wind speed values ​​between typhoon-affected areas and non-typhoon areas, resulting in return period wind speeds that deviate from reality. Finally, existing methods fail to fully consider the impact of the typhoon's strong turbulence characteristics on the wind pressure distribution and vibration response of transmission towers. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for evaluating the wind resistance performance of transmission towers based on typhoon path trajectory data, which can solve the problem that the existing technology is not accurate enough in evaluating the wind resistance performance of transmission towers under typhoon conditions.

[0005] An embodiment of the present invention provides a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data, comprising:

[0006] Based on typhoon path data, combined with time data, terrain data and turbulence data, the wind speed attenuation degree is calculated to obtain a differentiated wind speed distribution map;

[0007] Based on the differentiated wind speed distribution map, wind pressure and response amplitude are calculated by combining the transmission tower geometric model, turbulence data and wind speed attenuation data;

[0008] By analyzing the temporal and spatial relationship, the distribution consistency of the response amplitude peak and the wind pressure peak position is determined, and the initial wind speed value distribution is adjusted in combination with the terrain data and turbulence data to obtain the wind field distribution data;

[0009] Based on the wind field distribution data, a wind resistance performance evaluation index of the transmission tower under the typhoon path trajectory data is generated.

[0010] This embodiment of the present invention combines typhoon path data with terrain and turbulence data to simulate and analyze the spatiotemporal relationships of wind speed decay, wind pressure distribution, and vibration response. This method generates an evaluation index for transmission tower wind resistance, fully accounting for the impact of a typhoon's spatiotemporal decay characteristics and strong turbulence on the tower's wind resistance. Compared to existing technologies, this application can more accurately assess the wind resistance of transmission towers under typhoon conditions.

[0011] Furthermore, the wind speed attenuation degree is calculated based on the typhoon path trajectory data in combination with the time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map, including:

[0012] Based on typhoon path data, combined with wind speed time series variation data, surface roughness factor and time sampling interval, numerical simulation was performed to obtain the initial wind field distribution map and initial wind speed value distribution;

[0013] Based on the initial wind field distribution map, wind speed pulsation frequency simulation is performed in combination with the surface roughness factor and the time sampling interval to obtain the initial wind speed adjustment distribution.

[0014] The embodiment of the present invention accurately simulates the influence of terrain factors on typhoon wind speed through typhoon path trajectory data, wind speed time series change data, surface roughness factor and time sampling interval.

[0015] Furthermore, the calculation of the wind speed attenuation degree based on the typhoon path trajectory data in combination with the terrain data and turbulence data to obtain a differentiated wind speed distribution map also includes:

[0016] Based on the initial wind speed adjustment distribution, the wind speed attenuation coefficient is calculated in combination with the surface roughness factor to obtain the wind speed gradient accuracy;

[0017] The full life cycle load records in the regional historical typhoon samples are accurately extracted according to the wind speed gradient, and the wind field data are calculated in combination with the initial wind field uniformity and turbulence intensity distribution to obtain a differentiated wind speed distribution map.

[0018] The embodiment of the present invention realizes spatial differentiation modeling of wind speed by combining historical typhoon samples and terrain factors.

[0019] Furthermore, the calculation of wind pressure and response amplitude based on the differentiated wind speed distribution diagram, combined with the transmission tower geometric model, turbulence data and wind speed attenuation data, includes:

[0020] Based on the differentiated wind speed distribution map, the wind pressure spatial gradient is loaded by the turbulence scale factor, and the incremental effect of the turbulence frequency range on the wind pressure fluctuation amplitude is calculated in combination with the wind speed attenuation coefficient and the turbulence increment step size to obtain the wind pressure adjustment coefficient threshold;

[0021] Based on the wind pressure adjustment coefficient threshold, the wind pressure peak position change is simulated in combination with the transmission tower geometric model and turbulence intensity distribution to obtain the wind pressure distribution field;

[0022] The wind pressure distribution field is loaded with turbulence frequency range constraints to perform calculations and obtain the structural vibration response amplitude.

[0023] The embodiment of the present invention quantifies the impact of turbulence on wind pressure and vibration response by combining differentiated wind speed distribution diagrams with turbulence data.

[0024] Furthermore, the distribution consistency of the response amplitude peak and the wind pressure peak position is determined by analyzing the spatiotemporal relationship, and the initial wind speed value distribution is adjusted in combination with the terrain data and turbulence data to obtain the wind field distribution data, including:

[0025] Based on the spatial gradient distribution of wind pressure, the spatial heterogeneity of wind pressure peak position and time step dynamics is analyzed to obtain the regional consistency trend of wind pressure distribution;

[0026] Based on the regional consistency trend, combined with the turbulence scale factor and the interpolation error range, the spatiotemporal coupling relationship between the wind speed attenuation amplitude and the wind pressure fluctuation amplitude was analyzed, and the distribution consistency of the response amplitude peak and the wind pressure peak position was obtained.

[0027] The embodiment of the present invention determines the distribution consistency of the response amplitude peak value and the wind pressure peak value position by analyzing the spatiotemporal relationship.

[0028] Furthermore, the method further comprises: determining the distribution consistency of the response amplitude peak value and the wind pressure peak position by analyzing the spatiotemporal relationship, adjusting the initial wind speed value distribution in combination with the terrain data and turbulence data, and obtaining the wind field distribution data;

[0029] Based on the distribution consistency, the wind speed distribution grid after adjusting the grid space density by the turbulence increment step is compared with the wind pressure adjustment coefficient threshold to obtain an optimized wind resistance distribution map.

[0030] The embodiment of the present invention responds to the distribution consistency of the peak amplitude and the peak wind pressure position and the influence of turbulence.

[0031] Optimize the wind resistance distribution map.

[0032] Furthermore, the method further comprises: determining the distribution consistency of the response amplitude peak value and the wind pressure peak position by analyzing the spatiotemporal relationship, adjusting the initial wind speed value distribution in combination with the terrain data and turbulence data, and obtaining the wind field distribution data;

[0033] Based on the wind resistance distribution map, the initial wind speed value distribution is adjusted in combination with the surface roughness factor to obtain wind field distribution data.

[0034] The embodiment of the present invention outputs refined wind field distribution data through the optimized wind resistance distribution map.

[0035] Another embodiment of the present invention further provides a transmission tower wind resistance performance evaluation device based on typhoon path trajectory data, comprising: a differentiated wind speed distribution diagram module, a wind pressure and response amplitude module, a wind field distribution data module, and a wind resistance performance evaluation module;

[0036] The differentiated wind speed distribution map module is used to calculate the wind speed attenuation degree based on the typhoon path trajectory data in combination with time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map;

[0037] The wind pressure and response amplitude module is used to calculate the wind pressure and response amplitude based on the differentiated wind speed distribution map in combination with the transmission tower geometric model, turbulence data and wind speed attenuation data;

[0038] The wind field distribution data module is used to determine the distribution consistency of the response amplitude peak and the wind pressure peak position by analyzing the spatiotemporal relationship, and adjust the initial wind speed value distribution in combination with the terrain data and turbulence data to obtain the wind field distribution data;

[0039] The wind resistance performance evaluation module is used to generate a wind resistance performance evaluation index of the transmission tower under typhoon path trajectory data based on the wind field distribution data.

[0040] Another embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data as described in the present invention.

[0041] Another embodiment of the present invention further provides a computer-readable storage medium item, comprising: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a method for evaluating the wind resistance of transmission towers based on typhoon path trajectory data provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the structure of a transmission tower wind resistance performance evaluation device based on typhoon path trajectory data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0047] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0049] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0050] See also Figure 1 To address the problem that the existing technology is not accurate enough in evaluating the wind resistance performance of transmission towers under typhoon conditions, an embodiment of the present invention provides a method for evaluating the wind resistance performance of transmission towers based on typhoon path trajectory data, including steps S101 to S104:

[0051] Step S101 : Based on the typhoon path trajectory data, the wind speed attenuation degree is calculated in combination with the time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map.

[0052] Furthermore, step S101 includes steps S101_1 to S101_4:

[0053] Step S101_1 , based on the typhoon path trajectory data, combined with the wind speed time series change data, the surface roughness factor and the time sampling interval, a numerical simulation is performed to obtain the initial wind field distribution map and the initial wind speed value distribution.

[0054] In step S101_1, the central pressure value and timestamp record in the typhoon path trajectory data, as well as the wind direction angle and monitoring station location identifier in the wind speed time series change are extracted from the meteorological database, and the path trajectory information with time stamp is obtained from the real-time monitoring station. The time stamp of the path trajectory information is aligned with the timestamp record to generate a typhoon movement sequence.

[0055] For example, the central air pressure of a typhoon at a certain moment is 950hPa, the timestamp record is 2025-03-27-08:00:00, the wind direction angle is 45 degrees northeast, and the monitoring station is located at longitude 120.5 and latitude 23.5; when obtaining time-stamped path trajectory information from the real-time monitoring station, the position of the typhoon eye can be tracked by radar or satellite, for example, it is located at a certain point at 08:00:00 and moves 50 kilometers forward at 09:00:00; aligning the time stamp of the path trajectory information with the timestamp record can ensure the time consistency of the typhoon movement sequence. If the deviation is within 30 seconds (assuming the acquisition frequency is 1 minute), it can be determined as time alignment, thereby generating a continuous typhoon movement sequence. This alignment method improves data accuracy.

[0056] In step S101_1, the region is divided based on the typhoon movement sequence, and the terrain height data in the surface roughness factor and the time synchronization flag in the time sampling interval are integrated. The time synchronization flag is generated by the GPS clock. If the deviation between the timestamp record and the time synchronization flag is less than 50% of the acquisition frequency, it is determined to be time aligned.

[0057] For example, based on the typhoon movement sequence, the area is divided into a near-center area and a peripheral area, where the wind speed is higher in the near-center area and milder in the peripheral area; when integrating the terrain height data in the surface roughness factor and the time synchronization mark in the time sampling interval, assuming that a monitoring station is 200 meters above sea level, the time synchronization mark generated by the GPS clock is 08:00:00, and the deviation from the timestamp record is only 10 seconds, which is less than 50% of the acquisition frequency, then the alignment is valid.

[0058] In step S101_1, the time synchronization flag and the acquisition frequency in the time sampling interval are fused according to the regional classification identifier in the surface roughness factor and the terrain height data; based on the logarithmic wind profile formula, the wind speed time series is adjusted according to the terrain height data and the surface roughness, and the wind speed time series after the surface roughness adjustment is generated, and the initial wind speed value distribution is determined.

[0059] For example, the roughness of the flat sea surface is low, and the wind speed decays slowly, while the roughness of the mountain is high, and the wind speed decreases significantly. If the initial wind speed on the sea surface is 30m / s, it may drop to 20m / s after adjustment in the mountain. This adjusted wind speed time series is closer to reality.

[0060] In step S101_1 , if the timestamp record is aligned with the time synchronization mark, the distribution of the initial wind speed value is time interpolated according to the acquisition frequency to obtain a time-synchronized wind speed sequence.

[0061] For example, the time-synchronized wind speed sequence is generated by interpolating every 15 minutes from 25 m / s at 08:00:00 to 28 m / s at 08:15:00.

[0062] In step S101_1, spatial interpolation processing is performed on the wind field distribution according to the grid density constraint to generate an initial wind field distribution map with uniform resolution, thereby obtaining a high-resolution wind field distribution.

[0063] Exemplarily, the wind field is divided into 1 km resolution grids to make the initial wind field distribution map more uniform.

[0064] In step S101_1, based on the high-resolution wind field distribution, combined with the wind direction angle and the monitoring station location identifier, the wind speed direction is adjusted through numerical simulation to obtain a direction-corrected wind field.

[0065] For example, the monitoring station shows that the wind direction is easterly. Through numerical simulation, the wind speed direction is corrected to 60 degrees to obtain a direction-corrected wind field. This correction improves the authenticity of the wind field simulation.

[0066] In step S101_1, the direction-corrected wind field, central air pressure value, and path trajectory information are input into the random forest algorithm. The input features include longitude, latitude, central air pressure value, and terrain height. The output target is the wind speed value to obtain the initial wind speed distribution.

[0067] For example, the input features include longitude 120.5, latitude 23.5, central air pressure value 950hPa and terrain height 200 meters, and the output wind speed value is 27m / s, and the initial wind speed distribution is obtained.

[0068] In step S101_1, if the deviation between the initial wind speed distribution and the terrain height and surface roughness exceeds a preset threshold, a gradient descent algorithm is used to iteratively adjust the result with the root mean square error as the loss function to obtain an optimized wind speed distribution.

[0069] For example, if the deviation between the initial wind speed distribution and the terrain height and surface roughness exceeds a threshold of 5 m / s, the gradient descent algorithm can be used for iterative optimization. After adjusting the loss function with the root mean square error, the optimized wind speed distribution is adjusted from 27 m / s to 25 m / s, which is more in line with the actual terrain influence.

[0070] For example, optimizing wind speed distribution can significantly improve wind speed prediction accuracy and provide reliable support for typhoon disaster warning.

[0071] It should be noted that the fusion of time synchronization and spatial interpolation combined with random forest can effectively balance computational efficiency and result accuracy, and is suitable for real-time monitoring scenarios.

[0072] Step S101_2: Based on the initial wind field distribution map, wind speed pulsation frequency simulation is performed in combination with the surface roughness factor and the time sampling interval to obtain an initial wind speed adjustment distribution.

[0073] In step S101_2, the initial wind field distribution map is processed by the WRF numerical weather forecast model, the surface roughness factor is integrated, and the dynamic adjustment weight is calculated to obtain the wind field distribution after weight adjustment.

[0074] For example, in a typhoon simulation, the initial wind field distribution map shows that the wind speed in a certain area is 32m / s, covering an area of ​​100 kilometers. When integrating the surface roughness factor, the wind speed can be adjusted according to regional characteristics; the roughness of the coastal flat area is low, and the wind speed remains above 30m / s, while the roughness of the inland forest area is high, and the wind speed may drop to 25m / s. When calculating the dynamic adjustment weight, the weight value can be assigned based on the surface type. The weight of the coastal flat area is 0.9, and the weight of the inland forest area is 0.6, reflecting the impact of the terrain on the wind speed.

[0075] In step S101_2, a time series analysis is performed on the wind field distribution after the weight adjustment according to the time resolution in the time sampling interval, the wind speed pulsation frequency is extracted, and the pulsation frequency sequence is determined.

[0076] For example, wind speed data at 15-minute intervals is extracted. The wind speed is 28 m / s at 08:00:00 and rises to 30 m / s at 08:15:00. The pulsation frequency fluctuates slightly every minute. When determining the pulsation frequency sequence, it can be observed that the frequency is concentrated around 0.1 Hz, reflecting the periodic changes in wind speed.

[0077] In step S101_2, according to the grid density constraint, spatial interpolation processing is performed on the pulsation frequency sequence to generate a spatial distribution map containing turbulence frequencies.

[0078] For example, the wind field is divided into 2-kilometer grids, and the frequency of a certain grid point is 0.12 Hz through interpolation of surrounding data. After generating a spatial distribution map including turbulence frequency, the turbulent area is concentrated near the center of the typhoon.

[0079] In step S101_2, spatial consistency features are extracted through the spatial distribution map, turbulence frequency data are integrated, and the wind field distribution is adjusted to obtain a consistent wind field distribution.

[0080] For example, the frequency changes smoothly in the near-center area, while the frequency jumps significantly in the peripheral area. The wind speed in the near-center area is adjusted from 30m / s to 32m / s, reflecting the turbulence enhancement effect.

[0081] In step S101_2, if the deviation between the consistent wind field distribution and the simulation result exceeds a preset threshold, a gradient descent algorithm is used for iterative optimization to output the optimized wind field distribution.

[0082] For example, if the deviation between the consistent wind field distribution and the simulation exceeds a threshold of 5 m / s, the gradient descent algorithm can be used for iterative optimization, and the initial wind speed of 32 m / s is adjusted to 29 m / s after optimization, which is closer to the measured data.

[0083] In step S101_2, based on the optimized wind field distribution and combined with the typhoon landing path data, the wind speed pulsation frequency is adjusted to obtain the landing-affected wind field distribution.

[0084] For example, the frequency was 0.15 Hz before the typhoon made landfall, and dropped to 0.08 Hz after landing due to terrain obstruction.

[0085] In step S101_2, the WRF numerical weather forecast model is used to verify the wind field distribution affected by landfall, and the dynamic adjustment weights are integrated to determine the final initial wind speed adjustment distribution.

[0086] For example, through verification by the WRF numerical weather forecast model, the wind speed in a certain area was adjusted from the simulated 28m / s to the measured 26m / s; after landing, the weight was adjusted from 0.8 to 0.7, and the final wind speed distribution was optimized from 27m / s to 25m / s. This method can improve the adaptability of wind field forecasts and provide a more reliable basis for typhoon impact assessment.

[0087] In one embodiment, the spatial interpolation process can be further refined by combining terrain height.

[0088] For example, the frequency in the area 100 meters above sea level is slightly higher, and the distribution is more even after interpolation.

[0089] It is understandable that the extraction of pulsation frequency sequences helps to identify the law of wind speed variation, and the optimized wind field distribution is more practical in dynamic scenarios.

[0090] In one embodiment, the adjusted wind speed direction can also be aligned with the frequency distribution.

[0091] For example, the wind direction was adjusted from 45 degrees to 50 degrees to enhance wind field consistency. This multi-dimensional adjustment can effectively support the real-time typhoon monitoring.

[0092] Step S101_3: Based on the initial wind speed adjustment distribution, the wind speed attenuation coefficient is calculated in combination with the surface roughness factor to obtain the wind speed gradient accuracy.

[0093] In step S101_3, the distribution of the initial wind speed is adjusted and the surface roughness factor is integrated, the boundary transition characteristics are integrated, and the time series is processed using the data smoothing coefficient to obtain a smooth series of wind speed changes over time.

[0094] For example, the initial wind speed in a coastal area is 35 m / s. When entering the inland shrub area with higher roughness, the wind speed gradually decreases. A smoothing factor of 0.85 is set to make the wind speed slowly transition from 35 m / s to 28 m / s, avoiding sudden changes and improving the naturalness of the distribution.

[0095] For example, the wind speed sequence at a 15-minute interval is 32 m / s, 34 m / s, and 31 m / s. After being processed with a smoothing coefficient of 0.9, it becomes 32.2 m / s, 33.1 m / s, and 31.8 m / s, with smoother fluctuations. This smoothed sequence can more clearly reflect the wind speed change trend and facilitate subsequent analysis.

[0096] In step S101_3, the attenuation amplitude is calculated by smoothing the sequence, and the change trend is analyzed in combination with the time step to determine the dynamic characteristics of the wind speed attenuation.

[0097] For example, the initial wind speed is 30m / s, which decays to 26m / s after 1 hour, with an attenuation of 4m / s. Combined with the time step, sampling every 5 minutes, it can be found that the attenuation trend is fast at first and then slow, with a decrease of 3m / s in the first 20 minutes and only 1m / s in the next 40 minutes, reflecting the nonlinear changes of the dynamic characteristics.

[0098] In step S101_3, the boundary constraint weight is used to process the change trend, generate the wind speed attenuation coefficient, and obtain the quantitative parameter of the wind speed attenuation.

[0099] For example, the weight of the coastal area is set to 0.95, and the weight of the forest area is set to 0.65 to generate the wind speed attenuation coefficient; the coastal wind speed attenuation coefficient is 0.1, and the forest area wind speed attenuation coefficient is 0.3, reflecting the differentiated impact of terrain on attenuation, which helps to quantify the law of wind speed reduction.

[0100] In step S101_3, based on the wind speed attenuation coefficient and the initial wind speed adjustment distribution, the wind speed gradient accuracy is calculated to obtain the spatial characteristics of the gradient distribution.

[0101] For example, the wind speed in a certain area drops from 30m / s to 25m / s at a distance of 10 kilometers, and the wind speed gradient accuracy is 0.5m / s / km.

[0102] In step S101_3, if the deviation between the wind speed gradient accuracy and the distribution characteristic exceeds a preset threshold, the wind speed attenuation coefficient is iteratively adjusted through a gradient descent algorithm to obtain an optimized gradient distribution.

[0103] For example, if the deviation between the wind speed gradient accuracy and distribution characteristics exceeds the threshold of 2m / s / km, the gradient descent algorithm can be used to adjust the wind speed attenuation coefficient from 0.2 to 0.18 to make the gradient distribution closer to reality.

[0104] In step S101_3, based on the optimized gradient distribution, the time series data is integrated, the wind speed distribution is adjusted, and the final wind speed gradient accuracy is determined; the final wind speed gradient accuracy is verified by the WRF numerical weather forecast model, the boundary constraint weights are integrated, and the adjusted distribution characteristics are output.

[0105] For example, through verification by the WRF numerical weather forecast model, the output wind speed distribution is adjusted from the simulated 26 m / s to the measured 25.5 m / s. This adjustment can enhance the reliability of the prediction.

[0106] In one embodiment, the dynamic adjustment of the boundary constraint weights can also be updated based on real-time surface data.

[0107] For example, after the typhoon made landfall, the weight of the forest area dropped from 0.7 to 0.6, and the wind speed distribution became more accurate.

[0108] It is understandable that the improvement in wind speed gradient accuracy helps capture wind field details and provides support for typhoon impact analysis.

[0109] Step S101_4: extract the full life cycle load records in the regional historical typhoon samples according to the wind speed gradient accuracy, calculate the wind field data in combination with the initial wind field uniformity and turbulence intensity distribution, and obtain a differentiated wind speed distribution map.

[0110] In step S101_4, based on the wind speed gradient accuracy, the central pressure and maximum wind speed radius are extracted from the regional historical typhoon samples to form a full life cycle load record, and the initial wind field uniformity data is integrated to obtain the preliminary wind field distribution characteristics.

[0111] For example, the typhoon records of a coastal area over the past 10 years show that the central air pressure changed from 950hPa to 980hPa, and the maximum wind speed radius expanded from 50km to 80km. This full life cycle load record reflects the changing pattern of typhoon intensity and impact range, and provides a basic basis for wind field distribution; the initial wind field was a uniform 25m / s. After adjustment based on historical typhoon samples, it was found that the wind speed in the offshore area was slightly higher, reaching 27m / s, reflecting the preliminary distribution characteristics.

[0112] In step S101_4, the turbulence intensity distribution is used to adjust the preliminary wind field distribution characteristics, and the differentiated wind speed distribution trend is calculated in combination with the terrain factors.

[0113] For example, the turbulence intensity in the offshore area is 0.15, while that in the inland shrub area is 0.25; after turbulence adjustment, the initial wind speed of 25m / s becomes 26m / s in the offshore area and 23m / s in the inland shrub area. This adjustment makes the wind field closer to the actual fluctuation characteristics; the flat terrain factor is set to 1.0, and the hilly terrain factor is reduced to 0.8. After terrain adjustment, the wind speed in a certain area drops from 25m / s to 20m / s in the hilly terrain, reflecting the weakening effect of terrain on wind speed.

[0114] In step S101_4, the grid space is divided based on the differentiated wind speed distribution trend, the density change characteristics of the spatial distribution are obtained, and the grid space is adjusted according to the density change characteristics to obtain optimized wind speed distribution data.

[0115] For example, within a 10km×10km grid, the wind speed density is higher in coastal grids and gradually becomes sparser inland; the average wind speed in coastal grids is 26m / s, and the average wind speed inland drops to 22m / s. After adjusting the grid through density changes, the optimized wind speed distribution is more continuous.

[0116] In step S101_4, if the deviation between the optimized wind speed distribution data and the initial wind field uniformity exceeds a preset threshold, a gradient descent algorithm is used to iteratively adjust the density change characteristics to obtain updated wind field data.

[0117] For example, if the deviation of the optimized wind speed distribution from the initial uniformity exceeds a threshold of 5 m / s, the gradient descent algorithm is used for iterative adjustment, and the density change characteristic is optimized from 0.2 to 0.18, making the wind field data smoother and the deviation reduced to within 3 m / s.

[0118] In step S101_4, the updated wind farm data is integrated with the full life cycle load records to determine the final differentiated wind speed distribution diagram.

[0119] For example, when a typhoon makes landfall, the central air pressure is 970hPa and the maximum wind speed radius is 60km. After fusion, the wind speed distribution is adjusted from 25m / s to 24m / s, reflecting dynamic consistency.

[0120] In step S101_4, the final differentiated wind speed distribution map is verified by a numerical simulation tool, and the adjusted wind field data is output.

[0121] For example, the simulated wind speed of 26m / s is adjusted to be close to the measured 25m / s. This verification can improve the reliability of wind field data and provide more accurate support for subsequent typhoon impact assessments.

[0122] Step S102 : calculating wind pressure and response amplitude based on the differentiated wind speed distribution diagram in combination with the transmission tower geometric model, turbulence data, and wind speed attenuation data.

[0123] Furthermore, step S102 includes steps S102_1 to S102_3:

[0124] Step S102_1: Based on the differentiated wind speed distribution map, the wind pressure spatial gradient is loaded by the turbulence scale factor, and the incremental effect of the turbulence frequency range on the wind pressure fluctuation amplitude is calculated in combination with the wind speed attenuation coefficient and the turbulence increment step size to obtain the wind pressure adjustment coefficient threshold.

[0125] In step S102_1 , the turbulence scale factor is loaded through the differentiated wind speed distribution map to obtain wind pressure spatial gradient data.

[0126] For example, in the differentiated wind speed distribution map of a coastal area, the initial wind speed is 20m / s. After loading the turbulence scale factor of 0.2, the wind pressure gradient in the offshore area shows a steeper trend, while that in the inland area is relatively flat. This method can capture the spatial heterogeneity of wind speed.

[0127] In step S102_1, the wind speed attenuation coefficient and the turbulence increment step are integrated to calculate the initial distribution characteristics of the frequency range and obtain preliminary data of the fluctuation amplitude.

[0128] For example, the wind speed attenuation coefficient is 0.9, the turbulence increment step is 0.5m / s, the offshore wind speed fluctuation frequency is concentrated at 0.1Hz, while the inland wind speed fluctuation frequency is reduced to 0.05Hz. Preliminary data show that the fluctuation amplitude is 3m / s offshore and 2m / s inland, reflecting regional differences.

[0129] In step S102_1 , the fluctuation amplitude is adjusted using a boundary rigidity coefficient according to the initial distribution characteristics of the frequency range, and a preliminary threshold value of the adjustment coefficient is determined.

[0130] For example, the boundary stiffness coefficient is 1.2, the offshore fluctuation amplitude is adjusted from 3m / s to 3.5m / s, and the inland amplitude is increased from 2m / s to 2.3m / s. The initial threshold is set at 3m / s. This adjustment can enhance the stability of the wind farm boundary.

[0131] In step S102_1, after obtaining the preliminary threshold value of the adjustment coefficient, the change trend of the wind speed distribution is analyzed through spatial gradient analysis to obtain density adjustment data of the distribution map.

[0132] For example, the average wind speed of coastal grids is 26 m / s, and the average wind speed of inland grids drops to 22 m / s. The density-adjusted data reflects the characteristic that wind speed gradually becomes sparse from the coast to the inland, which helps to refine the distribution map.

[0133] In step S102_1, based on the density adjustment data of the distribution map, the turbulence scale and the incremental step are integrated to calculate the incremental distribution of the fluctuation amplitude and determine the optimization result of the frequency range.

[0134] For example, a turbulence scale of 0.15 is combined with an incremental step of 0.3 m / s, and the offshore fluctuation amplitude increases to 3.8 m / s. The optimized frequency range is closer to the actual wind conditions.

[0135] In step S102_1 , if the deviation between the optimization result of the frequency range and the wind pressure gradient exceeds a preset threshold, the incremental step size is iteratively adjusted using a gradient descent algorithm to obtain updated fluctuation amplitude data.

[0136] For example, if the deviation between the optimization result and the wind pressure gradient exceeds the preset threshold of 2m / s, the gradient descent algorithm is used to iteratively adjust the incremental step size from 0.3m / s to 0.25m / s, the fluctuation amplitude data is updated to 3.6m / s, and the deviation is reduced to within 1m / s, thereby improving the data accuracy.

[0137] In step S102_1, the final distribution characteristics of the wind pressure gradient are calculated based on the updated fluctuation amplitude data, and the rigidity coefficient and the adjustment coefficient are integrated to determine the optimized spatial gradient data.

[0138] For example, a rigidity coefficient of 1.1 is combined with an adjustment coefficient of 0.95, and the offshore wind pressure gradient is adjusted from the initial value to a smoother trend, and the spatial gradient data is more consistent with the terrain influence.

[0139] In step S102_1, the wind speed distribution is adjusted using the optimized spatial gradient data to obtain a final distribution map.

[0140] For example, the coastal wind speed is adjusted from 26m / s to 25.5m / s, and the inland wind speed is fine-tuned from 22m / s to 21.8m / s. This method can make the wind field distribution more consistent and provide reliable support for subsequent wind load analysis.

[0141] Step S102_2: Based on the wind pressure adjustment coefficient threshold, the wind pressure peak position change is simulated in combination with the transmission tower geometric model and turbulence intensity distribution to obtain the wind pressure distribution field.

[0142] In step S102_2, the turbulence intensity distribution is obtained through the transmission tower geometric model. Combined with the wind pressure adjustment coefficient threshold, the finite element analysis method is used to calculate the wind pressure peak position under the change of grid density to generate the initial wind pressure distribution field.

[0143] For example, in a model of a 50m high transmission tower, the tower base is 5m wide and narrows to 2m at the top. The turbulence intensity gradually decreases from 0.15 at the bottom to 0.1 at the top. When the threshold is set to 2.5, the finite element analysis method is used to divide the grid, the bottom grid density is set to 0.5m, and the top is relaxed to 1m. The calculated wind pressure peak position is concentrated at a tower height of 30m, and the wind pressure value is about 1.2kPa. This method can preliminarily locate the force concentration point.

[0144] In step S102_2, the fluctuation amplitude data is extracted according to the initial wind pressure distribution field, and the turbulence intensity and spatial variation characteristics are integrated to determine the preliminary distribution of the stress area.

[0145] For example, the fluctuation amplitude near the tower base reaches 0.8m / s, and drops to 0.5m / s at the top. The preliminary distribution of the stress areas shows that the middle part of the tower is most affected by wind pressure.

[0146] In step S102_2, the grid density is adjusted by initially distributing the load in the stress area to obtain the spatial variation trend of the wind pressure distribution and obtain an optimized distribution field map.

[0147] For example, the middle grid is encrypted to 0.3m. The optimized distribution field map shows that the wind pressure gradually decreases from the base to the top of the tower, and the spatial variation trend is smoother, which helps to improve the analysis accuracy.

[0148] In step S102_2, the peak position data is fused according to the optimized distribution field map, and the support vector machine algorithm is used to analyze the correlation between the wind pressure distribution and the stress area to determine the distribution of key stress points.

[0149] For example, through training with historical wind pressure data, it was identified that the tower height range of 25m to 35m is the area where key stress points are concentrated. When the wind speed is 15m / s, the peak wind pressure is highly correlated with the turbulence intensity of 0.12.

[0150] In step S102_2, if the deviation between the distribution of key stress points and the turbulence intensity exceeds a preset threshold, the grid density is adjusted by a gradient descent algorithm to obtain an updated wind pressure distribution field.

[0151] For example, if the deviation between the distribution of key stress points and the turbulence intensity exceeds the preset threshold of 0.2, the grid density is adjusted through the gradient descent algorithm, and the grid density is optimized from 0.3m to 0.25m. The updated wind pressure distribution field shows that the fluctuation amplitude is reduced to 0.7m / s in the middle, the deviation is reduced, and the data is closer to reality.

[0152] In step S102_2, the fluctuation amplitude change is extracted according to the updated wind pressure distribution field, and the final distribution of the stress concentration area on the tower surface is determined in combination with the geometric model characteristics.

[0153] For example, the wind pressure is concentrated on the tower surface due to the cross-arm structure, and the fluctuation amplitude at the cross-arm increases to 0.9m / s, which is judged to be a stress concentration area.

[0154] In step S102_2, the optimized wind pressure distribution field map is generated by integrating the adjustment coefficient and the spatial variation data of the final stress area distribution, and the surface stress characteristics of the transmission tower are determined.

[0155] For example, after integrating the adjustment coefficient of 0.9 with the spatial variation data, an optimized wind pressure distribution field map is generated. The wind pressure at the tower base is fine-tuned from 1.1 kPa to 1.0 kPa, and the top is stabilized at 0.8 kPa, making the surface force characteristics clearer.

[0156] In one embodiment, the determination of the surface stress characteristics of the transmission tower can provide a basis for subsequent structural optimization.

[0157] For example, the crossarms, where concentrated forces are present, can be reinforced, while the tower base can be angled appropriately. This approach, through multi-level analysis, from turbulence distribution to grid optimization and algorithm adjustment, forms a complete technical chain, ensuring accurate wind pressure distribution and providing reliable support for engineering design.

[0158] Step S102_3: Calculate the wind pressure distribution field and apply turbulence frequency range constraints to obtain the structural vibration response amplitude.

[0159] In step S102_3, the turbulence frequency range constraint is loaded through the wind pressure distribution field, the structural vibration response amplitude is calculated using the dynamic equation, and the boundary stiffness coefficient and wind speed gradient accuracy are integrated to obtain the vibration amplitude distribution.

[0160] For example, on a 60m high transmission tower, the turbulence frequency range is set to 0.1Hz to 2Hz. When the wind speed is 15m / s, the dynamic equation is used to calculate the vibration response amplitude; the frequency at the base of the tower is higher, about 1.5Hz, and drops to 0.5Hz at the top. The vibration amplitude gradually decreases from 0.03m at the bottom to 0.01m at the top. This method can preliminarily reflect the driving effect of frequency on vibration; the tower base stiffness coefficient is set to 0.9, and 0.6 at the top cantilever. The wind speed gradient increases from 10m / s on the ground to 18m / s at high altitude. The calculated vibration amplitude distribution shows that the amplitude reaches a peak of 0.04m at a height of 40m in the middle of the tower, which helps to capture the amplification effect of wind speed changes on vibration.

[0161] Step S103 , by analyzing the spatiotemporal relationship, determining the distribution consistency of the response amplitude peak and the wind pressure peak position, adjusting the initial wind speed distribution in combination with the terrain data and turbulence data, and obtaining wind field distribution data.

[0162] Furthermore, step S103 includes steps S103_1 to S103_4:

[0163] Step S103_1 : Based on the spatial gradient distribution of wind pressure, a dynamic spatial heterogeneity analysis of the wind pressure peak position and time step is performed to obtain a regional consistency trend of the wind pressure distribution.

[0164] In step S103_1, the spatial gradient features are extracted from the vibration amplitude distribution, and combined with the wind pressure spatial gradient distribution in the full life cycle load record to obtain preliminary spatial heterogeneity data.

[0165] For example, the full life cycle load records show that the spatial gradient of wind pressure is 0.02 kPa / m near the tower base and increases to 0.05 kPa / m in the middle. The extraction results show that the gradient in the middle of the tower changes significantly, and preliminary data on spatial heterogeneity point to the middle as the key area.

[0166] In step S103_1, the time step constraint is loaded according to the preliminary data of spatial heterogeneity, the dynamic change trend of the peak position is analyzed, and the preliminary distribution of regional consistency is obtained.

[0167] For example, the initial time step is set to 0.1s, and the dynamic change trend of the peak position is analyzed. When the wind speed suddenly changes to 20m / s, the peak position moves from 40m in the middle to 35m. The preliminary distribution of regional consistency shows that the change trend in the middle is more obvious. This dynamic adjustment can improve the timeliness of the analysis.

[0168] In step S103_1, the wind speed gradient and turbulence frequency data are fused through the regional consistency preliminary distribution, and the support vector machine algorithm is used to analyze the change pattern of spatial heterogeneity to determine the consistency trend distribution.

[0169] For example, the training data includes samples with wind speeds of 15 m / s to 25 m / s and frequencies of 0.5 Hz to 1.5 Hz, and it is concluded that the tower height of 30 m to 40 m is the key interval for the consistent trend distribution.

[0170] In step S103_1, if the deviation between the consistency trend distribution and the spatial gradient exceeds a preset threshold, the time step parameter is adjusted by the gradient descent algorithm to obtain an updated regional consistency distribution.

[0171] For example, if the deviation between the consistency trend distribution and the spatial gradient exceeds a threshold of 0.15, the time step is adjusted from 0.1s to 0.05s through the gradient descent algorithm. The updated distribution is more accurate and the peak position is stabilized at 38m.

[0172] In step S103_1, the spatial variation characteristics of the peak position are extracted according to the updated regional consistency distribution, and the degree of heterogeneity of the wind pressure distribution on the transmission tower surface is determined in combination with the boundary rigidity coefficient.

[0173] For example, a tower base stiffness of 0.9 stabilizes the bottom amplitude, while a top stiffness of 0.6 results in smaller amplitude fluctuations; the heterogeneity in the middle is the strongest, with an amplitude gradient of 0.03m / m.

[0174] In step S103_1, the turbulence frequency and wind pressure distribution data are fused by the degree of heterogeneity to generate an optimized regional consistency trend map and determine the wind pressure distribution characteristics on the transmission tower surface.

[0175] For example, the wind pressure at the tower base is 1.0 kPa, the wind pressure at the top is 0.7 kPa, the peak wind pressure in the middle is 1.3 kPa, and when the turbulence frequency is 1 Hz, the trend graph shows that the characteristics in the middle are prominent. This optimization can provide a clearer basis for subsequent design.

[0176] Step S103_2: Based on the regional consistency trend, combined with the turbulence scale factor and the interpolation error range, the spatiotemporal coupling relationship between the wind speed attenuation amplitude and the wind pressure fluctuation amplitude is analyzed to obtain the distribution consistency of the response amplitude peak and the wind pressure peak position.

[0177] In step S103_2, the regional consistency trend is obtained through the wind pressure distribution field. Combined with the turbulence scale factor and the interpolation error range, the spatiotemporal coupling characteristics of the wind speed attenuation amplitude and the wind pressure fluctuation amplitude are calculated to obtain preliminary distribution consistency data.

[0178] For example, on a 50-meter-high transmission tower, the wind pressure distribution field shows that the wind pressure at the bottom is 1.2 kPa and at the top is 0.8 kPa; the turbulence scale factor is 0.8, and the interpolation error range is controlled within ±0.05 kPa. The calculated wind speed attenuation amplitude decreases from 15 m / s on the ground to 12 m / s at the top, and the wind pressure fluctuation amplitude is 0.1 kPa at the bottom and 0.06 kPa at the top. This spatiotemporal coupling feature indicates that there is a correlation between wind speed attenuation and wind pressure fluctuation, which helps to preliminarily judge the distribution consistency.

[0179] In step S103_2, the correlation between the turbulence scale and the wind pressure fluctuation is analyzed using the spatiotemporal coupling characteristics, the distribution characteristics of the response amplitude and the peak position are extracted, and the consistency trend distribution is determined.

[0180] For example, the turbulence scale is reduced from 5m at the bottom to 2m at the top. Combined with the wind pressure fluctuation data, the response amplitude distribution is extracted. The bottom is 0.02m, the top is 0.01m, and the peak position occurs at a tower height of 35m with an amplitude of 0.03m. This analysis can clearly reflect the driving effect of the turbulence scale on vibration and determine the consistent trend distribution.

[0181] In step S103_2, a scale factor constraint is applied to the consistency trend distribution to obtain the coupled variation characteristics of the wind speed attenuation amplitude and the error range, and obtain the optimized distribution consistency data.

[0182] For example, the scale factor increases from 0.8 to 1.0, the wind speed attenuation range is adjusted from 14m / s on the ground to 11m / s at the top, and the error range is reduced to ±0.03kPa. The optimized distribution consistency data shows that the wind pressure fluctuation in the middle is more stable, which helps to improve the reliability of the analysis.

[0183] In step S103_2, the wind pressure distribution and peak position are fused through the optimized distribution consistency data, and the support vector machine algorithm is used to analyze the dynamic changes of the spatiotemporal coupling relationship to determine the distribution law of the response amplitude.

[0184] For example, the input wind pressure data is 1.0kPa to 1.3kPa, and the peak position varies between 30m and 40m. The response amplitude distribution law is obtained, and the amplitude fluctuation in the middle is small. This method can dynamically capture the wind pressure change trend.

[0185] In step S103_2, if the deviation between the response amplitude distribution law and the wind pressure fluctuation exceeds a preset threshold, the scale factor parameter is adjusted by the gradient descent algorithm to obtain an updated distribution consistency feature.

[0186] For example, if the deviation between the response amplitude distribution law and the wind pressure fluctuation exceeds the preset threshold of 0.1m, the scale factor is adjusted from 1.0 to 0.9 through the gradient descent algorithm. The updated distribution consistency feature shows that the peak position is stable at 33m and the amplitude fluctuation is reduced. This adjustment can improve the distribution consistency accuracy.

[0187] In step S103_2, the correlation distribution between the peak position and the turbulence scale is extracted according to the updated distribution consistency feature, and the degree of heterogeneity of the wind pressure distribution field is determined in combination with the error range analysis.

[0188] For example, the peak position is relatively low when the turbulence scale is 4m at the bottom, and relatively high when it is 2m at the top; the heterogeneity in the middle is relatively strong, and the wind pressure gradient reaches 0.04kPa / m. This analysis can provide a basis for design optimization.

[0189] In step S103_2, the wind speed attenuation amplitude and spatiotemporal coupling data are fused by the degree of heterogeneity to generate an optimized distribution consistency trend graph and determine the wind pressure characteristics on the transmission tower surface.

[0190] For example, the wind pressure at the bottom is 1.1kPa, the wind pressure in the middle is 1.4kPa, and the wind pressure at the top is 0.9kPa. The trend chart shows that the characteristics in the middle are prominent. This trend chart can intuitively reflect the wind pressure distribution pattern on the surface of the transmission tower and provide a reference for subsequent maintenance.

[0191] Step S103_3 : Based on the distribution consistency, the wind speed distribution grid after adjusting the grid space density by the turbulence increment step is compared with the wind pressure adjustment coefficient threshold to obtain an optimized wind resistance distribution map.

[0192] In step S103_3, the grid space density is adjusted by the turbulence increment step size to obtain the wind speed distribution grid and the wind pressure adjustment coefficient threshold, and obtain preliminary wind speed distribution data.

[0193] For example, on a 50-meter-high transmission tower, the initial grid density is set to one node every 5 meters. If the turbulence increment step is adjusted from 1 meter to 0.5 meters, the grid density increases, the node spacing is reduced to 2.5 meters, and the wind speed distribution data changes more delicately from 15 meters / s at the bottom to 10 meters / s at the top, capturing the transition characteristics of 12 meters / s in the middle. This adjustment can improve the accuracy of the wind speed distribution; the bottom wind speed of 15 meters / s corresponds to a wind pressure of 1.2 kPa, and the top wind speed of 10 meters / s corresponds to 0.7 kPa. The adjustment coefficient threshold is set to 0.9. The preliminary wind speed distribution data reflects the trend of wind pressure decreasing with height.

[0194] In step S103_3, the comparison relationship between the turbulence frequency range and the preset threshold is analyzed based on the wind speed distribution data. If the frequency range is lower than the preset threshold, a data smoothing coefficient is introduced to adjust the safety margin parameter to obtain smoothed distribution data.

[0195] For example, if the frequency range is 0.5 Hz to 2 Hz, which is lower than the preset threshold of 3 Hz, a data smoothing coefficient of 0.8 is introduced, and the safety margin parameter is adjusted to reduce the wind pressure fluctuation from 0.1 kPa to 0.08 kPa, so that the smoothed distribution data is more stable.

[0196] In step S103_3, the smoothed distribution data is used to fuse the distribution grid and spatial density characteristics to obtain an optimized wind speed distribution grid structure.

[0197] For example, the number of grid nodes increases from 20 to 40, and the wind speed distribution in the central area is refined from a single 12 m / s to a gradual change from 11.8 m / s to 12.2 m / s, reflecting the local differences in the impact of turbulence.

[0198] In step S103_3, the coupling relationship between the wind pressure adjustment coefficient and the adjustment threshold is calculated through the optimized wind speed distribution grid structure to obtain the adjustment parameter of the wind pressure distribution.

[0199] For example, the adjustment coefficient increases from 0.9 to 1.1, and the wind pressure distribution adjustment parameters show that the wind pressure in the middle increases from 1.0 kPa to 1.1 kPa, and slightly decreases to 0.6 kPa at the top, indicating that the adjusted distribution is closer to the actual stress characteristics.

[0200] In step S103_3, the safety margin constraint is loaded according to the adjustment parameters of the wind pressure distribution, and the support vector machine algorithm is used to analyze the correlation characteristics of the wind speed distribution and the wind resistance distribution to obtain the preliminary characteristics of the wind resistance distribution.

[0201] For example, the input wind speed is 10m / s to 15m / s and the wind pressure is 0.7kPa to 1.2kPa, and the preliminary characteristics of the wind resistance distribution are obtained, with stronger wind resistance at the bottom and weaker at the top.

[0202] In step S103_3, the variation trend of the smoothing coefficient and the spatial density is extracted according to the preliminary characteristics of the wind resistance distribution, and the turbulence increment step size is adjusted by the gradient descent algorithm to obtain the optimized wind resistance distribution map.

[0203] For example, the smoothing coefficient is reduced from 0.8 to 0.6, the spatial density is increased to one node per 1m, and the gradient descent algorithm adjusts the turbulence increment step to 0.3m. The optimized wind resistance distribution map shows that the wind resistance in the central part is improved and the fluctuation range is reduced.

[0204] In step S103_3, the wind speed distribution and the wind pressure adjustment coefficient are integrated with the optimized wind resistance distribution map to determine the degree of heterogeneity of the wind resistance distribution and obtain the final distribution characteristic data.

[0205] For example, the wind speed at the bottom is 14m / s and the wind pressure is 1.1kPa, the wind speed in the middle is 12m / s and the wind pressure is 1.0kPa, and the wind speed at the top is 9m / s and the wind pressure is 0.6kPa. The heterogeneity is manifested in a large wind pressure gradient in the middle, reaching 0.03kPa / m. This analysis can provide a reference for the wind-resistant design of transmission towers. If the degree of heterogeneity is high, the grid density can be further adjusted to one node every 0.5m. The final distribution characteristic data reflects a more uniform distribution of wind resistance, which helps to improve structural safety.

[0206] For example, multiple aspects of the analysis revealed that the initial grid density for wind speed distribution may have neglected local turbulence effects, while the optimized grid structure, combined with a smoothing coefficient, more accurately captured the changing trends in wind pressure. This multi-level analysis ensured the reliability of the distribution characteristic data and provided a basis for subsequent optimization.

[0207] Step S103_4: Based on the wind resistance distribution map, the initial wind speed value distribution is adjusted in combination with the surface roughness factor to obtain wind field distribution data.

[0208] In step S103_4, the surface roughness factor is integrated with the optimized wind resistance distribution map to extract the boundary transition characteristics, generate the boundary transition adjustment factor, and obtain the adjusted wind speed initial value distribution.

[0209] For example, near a 50-meter-high transmission tower, the surface roughness factor increases from 0.03 for flat terrain to 0.2 for urban environment, and the initial wind speed decreases from 15 m / s at the bottom to 13 m / s, reflecting the attenuation effect of roughness on wind speed. This fusion can more realistically reflect the characteristics of the wind field; in the area with a surface roughness factor of 0.2, the wind speed at the bottom 5 m is 13 m / s, the wind speed at the top 50 m is 10 m / s, and the wind speed at the middle 30 m is 11.5 m / s. By analyzing the transition slope, an adjustment factor of 1.05 is generated to correct the initial wind speed distribution. This method ensures the continuity of data in the boundary area.

[0210] In step S103_4, according to the adjusted initial wind speed value distribution and in combination with the spatial consistency characteristic, a linear interpolation method is used to process the distribution data to obtain verified wind field distribution data.

[0211] For example, the central discrete points such as 11m / s and 12m / s are interpolated into gradient values ​​such as 11.2m / s and 11.4m / s; the node spacing is reduced from 5m to 2.5m. After interpolation, the wind field distribution is smoother, and the verified data can reflect spatial consistency and avoid errors caused by mutations.

[0212] In step S103_4, the roughness factor and the transition characteristic are integrated with the verified wind field distribution data, the change trend of the adjustment factor is calculated, and the optimized initial value distribution is obtained.

[0213] For example, the bottom wind speed of 13 m / s is adjusted to 14.3 m / s, and the top wind speed of 10 m / s is adjusted to 9.5 m / s. The trend shows that the roughness effect weakens with height. This adjustment optimizes the rationality of the initial value distribution.

[0214] In step S103_4, the spatial consistency of the wind field distribution is determined by combining the optimized initial value distribution with the wind resistance distribution characteristics to obtain consistency verification data.

[0215] For example, the difference between the central wind speeds of 11.5 m / s and 11.4 m / s is 0.1 m / s, which is lower than the threshold of 0.3 m / s, indicating a uniform distribution. This verification helps to verify the reliability of the data.

[0216] In step S103_4, based on the consistency check data, the coupling relationship between the initial wind speed value and the distribution data is extracted, and the support vector machine algorithm is used to analyze the change trend to obtain the adjusted wind field distribution.

[0217] For example, input sample data such as 13m / s at the bottom corresponds to a wind pressure of 1.1kPa, and 10m / s at the top corresponds to 0.6kPa, and the model outputs the adjusted wind field distribution; the middle wind speed is optimized from 11.5m / s to 11.6m / s, and the wind pressure is fine-tuned to 0.95kPa, reflecting the algorithm's accurate capture of changing trends.

[0218] In step S103_4, the spatial heterogeneity of the wind resistance distribution is determined by integrating the boundary transition and verification data through the adjusted wind field distribution to obtain the final distribution data.

[0219] For example, when the boundary transition and verification data are integrated to determine the spatial heterogeneity of wind resistance distribution, the wind speed at the bottom is 14m / s and the wind pressure is 1.1kPa, the wind speed in the middle is 11.6m / s and the wind pressure is 0.95kPa, and the wind speed at the top is 9.5m / s and the wind pressure is 0.6kPa. The heterogeneity is manifested as a wind pressure gradient of 0.02kPa / m in the middle. This analysis can reveal the differences in local wind resistance and provide a basis for design optimization.

[0220] It is understandable that the final distribution data is verified through multiple aspects, such as the adjustment of the roughness factor, the smoothing processing of the interpolation method and the trend analysis of the algorithm, which jointly support the accuracy of the wind field distribution.

[0221] For example, after optimization, the wind resistance distribution in the middle is more uniform, and the wind pressure fluctuation is reduced from 0.1kPa to 0.07kPa, which helps to improve the safety of the transmission tower.

[0222] Step S104: generating a wind resistance performance evaluation index of the transmission tower under typhoon path trajectory data based on the wind field distribution data.

[0223] In step S104, the wind field distribution data is processed using a linear interpolation method in combination with spatial consistency characteristics to obtain a smooth wind field distribution result.

[0224] For example, near a transmission tower, the wind speed is 14 m / s at the bottom, 12 m / s at a height of 30 m in the middle, and 10 m / s at a height of 50 m. Through linear interpolation, smooth values ​​of 13 m / s and 11 m / s can be obtained at heights of 15 m and 45 m in the middle, respectively. This method ensures the continuity of wind speed changes with height and avoids mutation points that affect subsequent analysis.

[0225] In step S104, the wind speed gradient distribution of the transmission tower under the path trajectory is obtained by integrating the smoothed wind field distribution result with the typhoon path trajectory data.

[0226] For example, when the typhoon path passes 500m away from the transmission tower, the wind speed at the bottom increases from 14m / s to 16m / s, and the middle and top parts are adjusted to 13.5m / s and 11m / s respectively. The wind speed gradient shows a greater increase at the bottom. This distribution reflects the spatial variation characteristics of wind speed when the typhoon approaches, providing a basis for subsequent wind pressure analysis.

[0227] In step S104, based on the wind speed gradient distribution and combined with the wind pressure spatial characteristics, a support vector machine algorithm is used to analyze the change trend of the wind pressure space to obtain the wind pressure spatial distribution.

[0228] For example, a wind speed of 16 m / s at the bottom corresponds to a wind pressure of 1.2 kPa, 13.5 m / s in the middle corresponds to a wind pressure of 0.9 kPa, and 11 m / s at the top corresponds to a wind pressure of 0.65 kPa. The algorithm captures the nonlinear relationship between wind speed and wind pressure to generate a smooth spatial distribution of wind pressure, which facilitates the disclosure of local pressure changes.

[0229] In step S104, the response amplitude data is fused according to the spatial distribution of wind pressure, the spatiotemporal evolution characteristics of the response amplitude peak are calculated, and the spatiotemporal evolution distribution is obtained.

[0230] For example, the response amplitude is 5mm when the wind pressure at the bottom is 1.2kPa, the response amplitude is 3mm when the wind pressure in the middle is 0.9kPa, and the response amplitude is 2mm when the wind pressure at the top is 0.65kPa. Through time series analysis, the peak value may appear when the typhoon is closest. This distribution reflects the response law of the transmission tower under dynamic wind loads.

[0231] In step S104, the wind resistance performance distribution of the transmission tower under the typhoon path is determined by combining the temporal and spatial evolution distribution with the wind resistance performance index to obtain performance distribution data.

[0232] For example, the wind pressure at the bottom of 1.2 kPa is lower than the threshold, and the middle and top parts are also safe, indicating that the overall wind resistance of the transmission tower is strong. This analysis can provide a reliable reference for structural optimization.

[0233] In step S104, based on the performance distribution data, a data visualization curve is used to draw the evolution curve of the wind speed gradient and wind pressure space to determine the visualization distribution characteristics.

[0234] For example, the bottom wind speed increases from 14m / s to 16m / s, and the wind pressure increases from 1.0kPa to 1.2kPa. The changes in the middle and top are relatively gentle. The curve shows that the gradient gradually becomes steeper as the typhoon approaches. This visualization feature intuitively reflects the evolution trend of wind load.

[0235] In step S104, the comprehensive distribution data of the wind resistance performance of the transmission tower is obtained based on the visual distribution characteristics and the fusion evaluation indicators.

[0236] For example, the wind speed at the bottom is 16m / s, the wind pressure is 1.2kPa, and the amplitude is 5mm; the wind speed in the middle is 13.5m / s, the wind pressure is 0.9kPa, and the amplitude is 3mm; the wind speed at the top is 11m / s, the wind pressure is 0.65kPa, and the amplitude is 2mm. Through weighted analysis, it is concluded that the wind pressure at the bottom of the transmission tower is greater but still within the safe range. This multi-faceted integration improves the comprehensiveness of the performance evaluation.

[0237] It can be understood that the above method gradually builds a complete data chain from wind field to wind resistance performance through smoothing, path fusion and algorithm analysis.

[0238] For example, the dynamic changes in bottom wind speed adjustment and wind pressure distribution can effectively guide the design optimization of transmission towers under typhoon conditions and enhance the adaptability of the structure.

[0239] This embodiment of the present invention combines typhoon path data with terrain and turbulence data to simulate and analyze the spatiotemporal relationships of wind speed decay, wind pressure distribution, and vibration response. This method generates an evaluation index for transmission tower wind resistance, fully accounting for the impact of a typhoon's spatiotemporal decay characteristics and strong turbulence on the tower's wind resistance. Compared to existing technologies, this application can more accurately assess the wind resistance of transmission towers under typhoon conditions.

[0240] like Figure 2 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0241] An embodiment of the present invention provides a transmission tower wind resistance performance evaluation device based on typhoon path trajectory data, comprising: a differentiated wind speed distribution diagram module 201, a wind pressure and response amplitude module 202, a wind field distribution data module 203, and a wind resistance performance evaluation module 204;

[0242] The differentiated wind speed distribution map module 201 is used to calculate the wind speed attenuation degree based on the typhoon path trajectory data in combination with the time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map;

[0243] The wind pressure and response amplitude module 202 is used to calculate the wind pressure and response amplitude based on the differentiated wind speed distribution map in combination with the transmission tower geometric model, turbulence data and wind speed attenuation data;

[0244] The wind field distribution data module 203 is used to determine the distribution consistency of the response amplitude peak and the wind pressure peak position by analyzing the spatiotemporal relationship, and adjust the initial wind speed value distribution in combination with the terrain data and turbulence data to obtain wind field distribution data;

[0245] The wind resistance performance evaluation module 204 is used to generate a wind resistance performance evaluation index of the transmission tower under the typhoon path trajectory data based on the wind field distribution data.

[0246] In the embodiment of the present invention, the differentiated wind speed distribution diagram module 201 includes: an initial wind field distribution diagram submodule and an initial wind speed adjustment distribution submodule:

[0247] The initial wind field distribution map submodule is used to perform numerical simulation based on typhoon path trajectory data, combined with wind speed time series change data, surface roughness factor and time sampling interval to obtain the initial wind field distribution map and wind speed initial value distribution;

[0248] The initial wind speed adjustment distribution submodule is used to simulate the wind speed pulsation frequency based on the initial wind field distribution map in combination with the surface roughness factor and the time sampling interval to obtain the initial wind speed adjustment distribution.

[0249] The embodiment of the present invention accurately simulates the influence of terrain factors on typhoon wind speed through typhoon path trajectory data, wind speed time series change data, surface roughness factor and time sampling interval.

[0250] In the embodiment of the present invention, the differentiated wind speed distribution map module 201 further includes: a wind speed gradient accuracy submodule and a differentiated wind speed distribution map submodule:

[0251] The wind speed gradient accuracy submodule is used to calculate the wind speed attenuation coefficient based on the initial wind speed adjustment distribution and the surface roughness factor to obtain the wind speed gradient accuracy;

[0252] The differentiated wind speed distribution map submodule is used to extract the full life cycle load records in the regional historical typhoon samples based on the wind speed gradient accuracy, calculate the wind field data in combination with the initial wind field uniformity and turbulence intensity distribution, and obtain a differentiated wind speed distribution map.

[0253] The embodiment of the present invention realizes spatial differentiation modeling of wind speed by combining historical typhoon samples and terrain factors.

[0254] In the embodiment of the present invention, the wind pressure and response amplitude module 202 includes: a wind pressure adjustment coefficient threshold submodule, a wind pressure distribution field submodule, and a structural vibration response amplitude submodule:

[0255] The wind pressure adjustment coefficient threshold submodule is used to calculate the incremental effect of the turbulence frequency range on the wind pressure fluctuation amplitude based on the differentiated wind speed distribution map by loading the wind pressure spatial gradient through the turbulence scale factor, combining the wind speed attenuation coefficient and the turbulence increment step size, and obtain the wind pressure adjustment coefficient threshold;

[0256] The wind pressure distribution field submodule is used to simulate the change of the wind pressure peak position based on the wind pressure adjustment coefficient threshold in combination with the transmission tower geometric model and turbulence intensity distribution to obtain the wind pressure distribution field;

[0257] The structural vibration response amplitude submodule is used to calculate the structural vibration response amplitude by loading the turbulence frequency range constraint on the wind pressure distribution field.

[0258] The embodiment of the present invention quantifies the impact of turbulence on wind pressure and vibration response by combining differentiated wind speed distribution diagrams with turbulence data.

[0259] In the embodiment of the present invention, the wind field distribution data module 203 includes: a regional consistency trend submodule and a distribution consistency submodule:

[0260] The regional consistency trend submodule is used to perform spatial heterogeneity analysis of wind pressure peak position and time step dynamics based on the wind pressure spatial gradient distribution to obtain the regional consistency trend of wind pressure distribution;

[0261] The distribution consistency submodule is used to analyze the spatiotemporal coupling relationship between the wind speed attenuation amplitude and the wind pressure fluctuation amplitude based on the regional consistency trend, combined with the turbulence scale factor and the interpolation error range, to obtain the distribution consistency of the response amplitude peak and the wind pressure peak position.

[0262] The embodiment of the present invention determines the distribution consistency of the response amplitude peak value and the wind pressure peak value position by analyzing the spatiotemporal relationship.

[0263] In the embodiment of the present invention, the wind field distribution data module 203 further includes: a wind resistance distribution map submodule:

[0264] The wind resistance distribution map submodule is used to compare the wind speed distribution grid after adjusting the grid space density by the turbulence increment step size based on the distribution consistency with the wind pressure adjustment coefficient threshold to obtain an optimized wind resistance distribution map.

[0265] The embodiment of the present invention responds to the distribution consistency of the peak amplitude and the peak wind pressure position and the influence of turbulence.

[0266] Optimize the wind resistance distribution map.

[0267] In the embodiment of the present invention, the wind field distribution data module 203 further includes: a wind field distribution data submodule:

[0268] The wind field distribution data submodule is used to adjust the initial wind speed value distribution based on the wind resistance distribution map in combination with the surface roughness factor to obtain wind field distribution data.

[0269] The embodiment of the present invention outputs refined wind field distribution data through the optimized wind resistance distribution map.

[0270] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement any one of the above-mentioned method embodiments of the present invention to provide a method for evaluating the wind resistance performance of transmission towers based on typhoon path trajectory data.

[0271] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Furthermore, in the drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which may be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement the present invention without inventive effort.

[0272] Based on the above-mentioned embodiment of a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data according to any embodiment of the present invention.

[0273] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0274] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0275] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0276] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data as described in any of the above-mentioned method embodiments of the present invention.

[0277] If the module / unit integrated into the device / terminal equipment is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0278] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the wind resistance of transmission towers based on typhoon path trajectory data, characterized in that: include: Based on typhoon path data, combined with time data, terrain data and turbulence data, the wind speed attenuation degree is calculated to obtain a differentiated wind speed distribution map; Based on the differentiated wind speed distribution map, wind pressure and response amplitude are calculated by combining the transmission tower geometric model, turbulence data and wind speed attenuation data; Based on the spatial gradient distribution of wind pressure, the spatial heterogeneity of wind pressure peak position and time step dynamics is analyzed to obtain the regional consistency trend of wind pressure distribution; Based on the regional consistency trend, combined with the turbulence scale factor and the interpolation error range, the spatiotemporal coupling relationship between the wind speed attenuation amplitude and the wind pressure fluctuation amplitude was analyzed to obtain the distribution consistency of the response amplitude peak and the wind pressure peak position. The initial wind speed value distribution was adjusted by combining the terrain data and turbulence data to obtain the wind field distribution data; Based on the wind field distribution data, a wind resistance performance evaluation index of the transmission tower under the typhoon path trajectory data is generated.

2. The method for evaluating the wind resistance of a transmission tower based on typhoon path trajectory data according to claim 1, characterized in that: The wind speed attenuation degree is calculated based on the typhoon path trajectory data, combined with time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map, including: Based on typhoon path data, combined with wind speed time series variation data, surface roughness factor and time sampling interval, numerical simulation was performed to obtain the initial wind field distribution map and initial wind speed value distribution; Based on the initial wind field distribution map, wind speed pulsation frequency simulation is performed in combination with the surface roughness factor and the time sampling interval to obtain the initial wind speed adjustment distribution.

3. The method for evaluating the wind resistance of transmission towers based on typhoon path trajectory data according to claim 2, characterized in that: The method of calculating the wind speed attenuation degree based on the typhoon path trajectory data in combination with the terrain data and turbulence data to obtain a differentiated wind speed distribution map further includes: Based on the initial wind speed adjustment distribution, the wind speed attenuation coefficient is calculated in combination with the surface roughness factor to obtain the wind speed gradient accuracy; The full life cycle load records in the regional historical typhoon samples are accurately extracted according to the wind speed gradient, and the wind field data are calculated in combination with the initial wind field uniformity and turbulence intensity distribution to obtain a differentiated wind speed distribution map.

4. The method for evaluating the wind resistance of a transmission tower based on typhoon path trajectory data according to claim 1, wherein: The calculating of wind pressure and response amplitude based on the differentiated wind speed distribution diagram and combining the transmission tower geometric model, turbulence data and wind speed attenuation data includes: Based on the differentiated wind speed distribution map, the wind pressure spatial gradient is loaded by the turbulence scale factor, and the incremental effect of the turbulence frequency range on the wind pressure fluctuation amplitude is calculated in combination with the wind speed attenuation coefficient and the turbulence increment step size to obtain the wind pressure adjustment coefficient threshold; Based on the wind pressure adjustment coefficient threshold, the wind pressure peak position change is simulated in combination with the transmission tower geometric model and turbulence intensity distribution to obtain the wind pressure distribution field; The wind pressure distribution field is loaded with turbulence frequency range constraints to perform calculations and obtain the structural vibration response amplitude.

5. The method for evaluating the wind resistance of a transmission tower based on typhoon path trajectory data according to claim 1, wherein: The adjusting of the initial wind speed distribution value by combining the terrain data and the turbulence data to obtain the wind field distribution data includes: Based on the distribution consistency, the wind speed distribution grid after adjusting the grid space density by the turbulence increment step is compared with the wind pressure adjustment coefficient threshold to obtain an optimized wind resistance distribution map.

6. The method for evaluating the wind resistance of a transmission tower based on typhoon path trajectory data according to claim 5, characterized in that: The adjusting of the initial wind speed distribution value by combining the terrain data and the turbulence data to obtain the wind field distribution data further includes: Based on the wind resistance distribution map, the initial wind speed value distribution is adjusted in combination with the surface roughness factor to obtain wind field distribution data.

7. A device for evaluating the wind resistance of transmission towers based on typhoon path data, characterized in that: include: Differentiated wind speed distribution module, wind pressure and response amplitude module, wind field distribution data module, and wind resistance performance evaluation module; The differentiated wind speed distribution map module is used to calculate the wind speed attenuation degree based on the typhoon path trajectory data in combination with time data, terrain data and turbulence data to obtain a differentiated wind speed distribution map; The wind pressure and response amplitude module is used to calculate the wind pressure and response amplitude based on the differentiated wind speed distribution map in combination with the transmission tower geometric model, turbulence data and wind speed attenuation data; The wind field distribution data module is used to perform spatial heterogeneity analysis of wind pressure peak position and time step dynamics based on the wind pressure spatial gradient distribution to obtain the regional consistency trend of wind pressure distribution; Based on the regional consistency trend, combined with the turbulence scale factor and the interpolation error range, the spatiotemporal coupling relationship between the wind speed attenuation amplitude and the wind pressure fluctuation amplitude was analyzed to obtain the distribution consistency of the response amplitude peak and the wind pressure peak position. The initial wind speed value distribution was adjusted by combining the terrain data and turbulence data to obtain the wind field distribution data; The wind resistance performance evaluation module is used to generate a wind resistance performance evaluation index of the transmission tower under typhoon path trajectory data based on the wind field distribution data.

8. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that include: A stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for evaluating the wind resistance performance of a transmission tower based on typhoon path trajectory data as described in any one of claims 1 to 6.

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