Method and device for evaluating wind resistance of tower structure of power distribution network, terminal equipment and computer readable storage medium

Abstract

The invention discloses a power distribution network tower structure wind resistance capability assessment method and device, terminal equipment and a computer readable storage medium. The method comprises the following steps: acquiring meteorological data and topographic data of a power distribution network tower to be assessed; the meteorological data comprises temperature, humidity, wind speed and rainfall intensity; the topographic data comprises a land type, a gradient, a windward slope or a leeward slope, and an included angle between a prevailing wind direction in winter and a line trend; inputting the meteorological data and the topographic data into a neural network model, so that the model generates soil shear strength according to the meteorological data and the topographic data; according to the soil shear strength, the limit overturning moment and the corresponding critical wind speed are determined; and according to the critical wind speed, determining the wind resistance of the to-be-evaluated power distribution network tower. By implementing the method, the influence of rainfall on soil can be considered, and the accuracy and reliability of evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the field of power engineering performance evaluation, and in particular to a method, device, terminal equipment and computer-readable storage medium for evaluating the wind resistance of a distribution network tower structure. Background Art

[0002] Distribution network towers are an important part of the transmission line, supporting the transmission line and bearing the load of the cables. In severe weather conditions, wind is one of the main external loads. If the wind resistance of the tower structure is insufficient, it may lead to tower collapse, line breakage or cable damage, which in turn may cause power outages and have a serious impact on users and grid operations. Therefore, evaluating and improving the wind resistance of distribution network towers is crucial to ensuring power supply reliability.

[0003] At present, there are the following methods for evaluating the wind resistance of distribution network tower structures: (1) using a probabilistic framework to calculate the vulnerability of distribution towers under wind loads, obtain the probability of different damage levels of the tower structure, and evaluate the wind resistance of the tower structure; (2) using the JC method to calculate the component reliability index of the tower structure under different wind load design conditions, and use the reliability of the structure under different wind loads to evaluate the wind resistance of the tower structure; (3) evaluating the wind vibration response of the tower structure based on the collapse ratio and wind vibration coefficient of the risk point, so as to quantify the wind resistance of the tower structure. These methods are based on the structural characteristics of the distribution network tower structure itself and the influence of wind loads to evaluate its wind resistance, while ignoring the influence of other external environmental factors. However, soil characteristics and conditions are crucial to the stability and bearing capacity of the tower foundation, which in turn affects the wind resistance of the entire tower structure. Rainfall will deteriorate the soil at the foundation of the distribution tower and reduce its strength. The existing evaluation methods ignore the influence of rainwater infiltration and soil erosion by rainwater. Therefore, it is urgent to further improve the existing evaluation methods and consider the impact of rainfall on soil. Summary of the invention

[0004] The embodiments of the present invention provide a method, an apparatus, a terminal device and a computer-readable storage medium for evaluating the wind resistance of a distribution network tower structure, which can take into account the impact of rainfall on soil and improve the accuracy and reliability of the evaluation.

[0005] An embodiment of the present invention provides a method for evaluating the wind resistance of a distribution network tower structure, comprising:

[0006] Obtain meteorological and topographic data of the distribution network towers to be evaluated; meteorological data, including temperature, humidity, wind speed and rainfall intensity; topographic data, including land type, slope, windward slope or leeward slope, and the angle between the main wind direction and the line direction in winter;

[0007] inputting meteorological data and topographic data into the neural network model so that the neural network model generates soil shear strength according to the meteorological data and topographic data;

[0008] According to the soil shear strength, determine the ultimate overturning moment and the corresponding critical wind speed;

[0009] According to the critical wind speed, the wind resistance capacity of the distribution network tower to be evaluated is determined.

[0010] Furthermore, before the meteorological data and terrain data are input into the neural network model, it also includes:

[0011] The meteorological data and terrain data are standardized and missing values ​​are supplemented to obtain preprocessed meteorological data and terrain data.

[0012] Furthermore, the meteorological data and terrain data are standardized and missing values ​​are supplemented to obtain preprocessed meteorological data and terrain data, including:

[0013] Z-Score standardization was used to transform the meteorological and topographic data into a normal distribution with a mean of 0 and a standard deviation of 1;

[0014] Linear interpolation method is used to supplement the missing values ​​of normal distribution and obtain the preprocessed meteorological and topographic data.

[0015] Furthermore, the neural network model is obtained in the following way:

[0016] Obtain historical meteorological data and historical terrain data of distribution network towers at several historical moments and the soil shear strength at the corresponding historical moments;

[0017] For the historical meteorological data and historical terrain data at each historical moment, standardization processing and missing value supplementation are performed on the historical meteorological data and historical terrain data to obtain pre-processed historical meteorological data and historical terrain data;

[0018] The preprocessed historical meteorological data and historical terrain data at several historical moments and their corresponding soil shear strength at historical moments are respectively input into the initial physical information neural network model for iterative training until the loss function converges to obtain a trained neural network model.

[0019] Furthermore, the loss function includes: a data error term and a physical information error term;

[0020] Data error terms, specifically: mean square error;

[0021] The physical information error term is determined as follows:

[0022] According to the pre-processed historical meteorological data and historical terrain data at the current historical moment input into the physical information neural network model, the mathematical shear strength of the soil is calculated by the Moore-Coulomb criterion;

[0023] Calculate the residual of soil strength based on the predicted shear strength generated during the current iterative training of the physical information neural network model and the corresponding mathematical shear strength;

[0024] The residual of soil strength is taken as the physical information error term in the current iteration process of the physical information neural network model.

[0025] Furthermore, the ultimate overturning moment and the corresponding critical wind speed are determined based on the soil shear strength, including:

[0026] The soil erosion thickness is calculated according to the universal soil loss equation; the soil erodibility factor in the universal soil loss equation is determined according to the soil shear strength;

[0027] According to the structure of distribution network tower, soil shear strength and soil scour thickness, a numerical model of distribution network tower is constructed based on artificial boundary method.

[0028] The numerical model of distribution network tower is solved by equivalent strain wedge method, and the ultimate overturning moment and corresponding critical wind speed of distribution network tower are obtained.

[0029] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: an environmental data acquisition module, a soil shear strength determination module, a critical wind speed determination module and a wind resistance assessment module;

[0030] Environmental data acquisition module, used to obtain meteorological data and terrain data of the distribution network tower to be evaluated; meteorological data, including: temperature, humidity, wind speed and rainfall intensity; terrain data, including: land type, slope, windward slope or leeward slope, and the angle between the main wind direction and the line direction in winter;

[0031] A soil shear strength determination module is used to input meteorological data and terrain data into the neural network model so that the neural network model generates soil shear strength according to the meteorological data and terrain data;

[0032] A critical wind speed determination module is used to determine the ultimate overturning moment and the corresponding critical wind speed according to the soil shear strength;

[0033] The wind resistance evaluation module is used to determine the wind resistance of the distribution network tower to be evaluated according to the critical wind speed.

[0034] Furthermore, the device for evaluating the wind resistance of a distribution network tower structure further includes: a preprocessing module;

[0035] The preprocessing module includes: standardizing the meteorological data and terrain data and supplementing the missing values ​​to obtain the preprocessed meteorological data and terrain data.

[0036] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, 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, the steps of the distribution network tower structure wind resistance assessment method as described in the present invention are implemented.

[0037] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the distribution network tower structure wind resistance assessment method as described in the present invention when the computer program is running.

[0038] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0039] The present invention obtains meteorological data and topographic data of a distribution network tower to be evaluated, wherein the meteorological data includes: temperature, humidity, wind speed and rainfall intensity, and the topographic data includes: land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the line direction; then, the meteorological data and topographic data are standardized and missing values ​​are supplemented to obtain pre-processed meteorological data and topographic data; the pre-processed meteorological data and topographic data are input into a neural network model, so that the neural network generates soil moisture content and shear strength according to the pre-processed meteorological data and topographic data, and the strength of the soil after rainfall scouring and infiltration is predicted through the neural network model, so as to consider the influence of rainwater infiltrating the soil and soil being scoured by rainwater; then, according to the soil moisture content and shear strength, the ultimate overturning moment and the corresponding critical wind speed are determined; according to the critical wind speed, the wind resistance of the distribution network tower to be evaluated is determined, so as to realize the evaluation of the wind resistance of the distribution network tower through the soil conditions.

[0040] In summary, the present invention considers the impact of rainfall on the soil environment, utilizes the meteorological data and terrain data of the distribution network tower, calculates the moisture content and shear strength of the soil, and determines the wind resistance of the distribution network tower accordingly, thereby improving the accuracy and reliability of the assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a method for evaluating the wind resistance of a distribution network tower structure provided by an embodiment of the present invention;

[0042] Figure 2 is a schematic diagram of linear interpolation calculation provided by an embodiment of the present invention;

[0043] Figure 3 It is a flowchart of a neural network model training process provided by an embodiment of the present invention;

[0044] Figure 4 is a schematic structural diagram of a strain wedge provided by an embodiment of the present invention;

[0045] Figure 5 Schematic diagram of a cross section of a distribution network tower flushing pit provided by an embodiment of the present invention

[0046] Figure 6 It is a structural schematic diagram of a distribution network tower structure wind resistance assessment device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the wind resistance of a distribution network tower structure, the method comprising at least the following steps:

[0049] Step S1: Acquire meteorological data and terrain data of the distribution network tower to be evaluated; meteorological data, including: temperature, humidity, wind speed and rainfall intensity; terrain data, including: land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the line direction;

[0050] For step S1, meteorological data and terrain data of the distribution network tower to be evaluated are collected from the meteorological station, wherein the meteorological data include but are not limited to temperature, humidity, wind speed and rainfall intensity, and the terrain data include but are not limited to land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the line direction.

[0051] Step S2: inputting meteorological data and terrain data into the neural network model, so that the neural network model generates soil shear strength according to the meteorological data and terrain data;

[0052] In a preferred embodiment, before the meteorological data and terrain data are input into the neural network model, the method further comprises:

[0053] The meteorological data and terrain data are standardized and missing values ​​are supplemented to obtain preprocessed meteorological data and terrain data.

[0054] In a preferred embodiment, the meteorological data and the terrain data are standardized and missing values ​​are supplemented to obtain pre-processed meteorological data and terrain data, including:

[0055] Z-Score standardization was used to transform the meteorological and topographic data into a normal distribution with a mean of 0 and a standard deviation of 1;

[0056] Linear interpolation method is used to supplement the missing values ​​of normal distribution and obtain the preprocessed meteorological and topographic data.

[0057] For step S2, in order to improve data quality and prediction accuracy, the collected meteorological data and terrain data are preprocessed before being input into the neural network model, and the inconsistency and scale difference of the data are solved by methods such as standardization and missing value processing. Specifically, the Z-Score standardization method is first used to standardize the meteorological data and terrain data, and the data is converted into a normal distribution with a mean of 0 and a standard deviation of 1. The mathematical expression is as follows:

[0058]

[0059] Among them, z represents the mean, x represents the value of the data, μ represents the expected value, σ represents the standard deviation, and N represents the number of data in each group. Data standardization can eliminate the scale differences between different data and make them comparable.

[0060] Secondly, for the treatment of missing values, such as Figure 2 As shown, linear interpolation is used for completion. The principle of linear interpolation is to infer the linear relationship between known data points. Assuming that the point to be completed for the missing value is x, and the two known data points are (x0, y0) and (x1, y1), according to the linear interpolation formula:

[0061]

[0062] You can get:

[0063]

[0064] Filling missing values ​​by linear interpolation can preserve the integrity and accuracy of the data to the greatest extent. Through the preprocessing steps, preprocessed meteorological data and terrain data are obtained to ensure the consistency and availability of the data.

[0065] Next, the preprocessed meteorological data and terrain data are input into the neural network model. In the neural network model, meteorological data and terrain data are used as input features to describe the environmental conditions and terrain characteristics of the soil. Meteorological data such as temperature, humidity, wind speed and rainfall intensity can reflect the climate environment of the soil and affect the shear strength of the soil. Terrain data such as land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the line direction can describe the physical characteristics and stress of the soil. These data are input into the input layer of the neural network, and after calculation and learning of the hidden layer, an output layer is finally obtained, which can represent the prediction result of the shear strength of the soil.

[0066] The following is a detailed description of the training process of the neural network model:

[0067] like Figure 3 As shown, the training process of the neural network model includes the following steps:

[0068] Step S201: Obtain historical meteorological data and historical terrain data of distribution network towers at several historical moments and the soil shear strength at the corresponding historical moments;

[0069] For step S201, historical meteorological data and historical terrain data of the distribution network tower at several historical moments are obtained from the meteorological database, wherein the historical meteorological data include: temperature, humidity, wind speed and rainfall intensity, and the historical terrain data include: land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the line direction. Next, the soil shear strength data at the historical moments are obtained through field observation, soil sampling test or historical records.

[0070] After collecting meteorological data, topographic data, and soil shear strength data, a data set is established. Each data sample contains meteorological data, topographic data, and corresponding soil shear strength at a historical moment. Such a data set can be used to train and test prediction models.

[0071] Step S202: for each historical moment in time, the historical meteorological data and historical terrain data are standardized and missing values ​​are supplemented to obtain pre-processed historical meteorological data and historical terrain data;

[0072] For step S202, the historical meteorological data and historical terrain data at each historical moment are standardized using the Z-Score standardization method. By calculating the difference between each data point and the mean of the feature in which it is located and dividing it by the standard deviation of the feature, the data can be converted into a normal distribution with a mean of 0 and a standard deviation of 1. For the missing values ​​in the historical meteorological data and historical terrain data, linear interpolation is performed to make the data set more complete, improve the accuracy of subsequent modeling and prediction, and finally obtain the preprocessed data set.

[0073] Step S203: The pre-processed historical meteorological data and historical terrain data at several historical moments and the soil shear strength at the corresponding historical moments are respectively input into the initial physical information neural network model for iterative training until the loss function converges to obtain a trained neural network model.

[0074] In a preferred embodiment, the loss function includes: a data error term and a physical information error term;

[0075] Data error terms, specifically: mean square error;

[0076] The physical information error term is determined as follows:

[0077] According to the pre-processed historical meteorological data and historical terrain data at the current historical moment input into the physical information neural network model, the mathematical shear strength of the soil is calculated by the Moore-Coulomb criterion;

[0078] Calculate the residual of soil strength based on the predicted shear strength generated during the current iterative training of the physical information neural network model and the corresponding mathematical shear strength;

[0079] The residual of soil strength is taken as the physical information error term in the current iteration process of the physical information neural network model.

[0080] For step S203, first, a physical information neural network (Physics-Informed Neural Networks, PINN) model is constructed, and then the preprocessed data set obtained in step S202 is input into the initial physical information neural network model for iterative training until the loss function converges to obtain a trained neural network model.

[0081] The PINN architecture consists of an input layer, a hidden layer, and an output layer. The input layer receives the preprocessed data set, the output layer represents the soil shear strength, and the hidden layer is the core of the neural network, where each neuron processes the input signal through a series of linear layers with a standard tanh activation function. Through these hidden layers, the model can learn the complex nonlinear relationship between the input data and the soil shear strength.

[0082] It should be noted that PINN is a new network structure that combines traditional mathematical physics models with modern deep learning technology. It is similar to the traditional fully connected neural network structure, but it differs from the traditional fully connected neural network structure in the implementation of the model's loss function. The PINN model embeds physical information in the definition of the loss function, which consists of two main terms: data-fidelity loss and physics-informed loss.

[0083] The data error term is used to measure the difference between the network prediction output and the actual observation data, with the purpose of making the network fit the data as much as possible. In this embodiment, the mean square error is used as the data error term to optimize the model by minimizing the gap between the network prediction value and the true observation value; the physical information error term is used to evaluate whether the prediction result meets the physical law. The residual calculated by substituting the physical quantity predicted by the network into the corresponding physical law constitutes this part of the loss function, thereby ensuring that the prediction result follows the physical law. PINN introduces physical knowledge as a priori, so it can also have good generalization ability when facing new problems beyond the distribution of training data, and even in an environment with scarce data, it can maintain a reasonable prediction of physical phenomena. In this embodiment, the mathematical shear strength of the soil is calculated using the Moore-Coulomb criterion, and then the predicted shear strength is compared with the mathematical shear strength, and the residual of the soil strength is calculated and used as the physical information error term.

[0084] After obtaining the trained neural network model, the test set can be used to cross-validate the predicted data and the corresponding experimental data. Specifically, the data in the test set is input into the neural network model for prediction to obtain the model prediction results. Then, these prediction results are compared with the corresponding experimental data. By calculating the error between them, the accuracy of the model can be evaluated. If the error between the two is within 10%, it can be considered that the prediction results of the model have good credibility.

[0085] Step S3: determining the ultimate overturning moment and the corresponding critical wind speed according to the soil shear strength;

[0086] In a preferred embodiment, determining the ultimate overturning moment and the corresponding critical wind speed according to the soil shear strength includes:

[0087] The soil erosion thickness is calculated according to the universal soil loss equation; the soil erodibility factor in the universal soil loss equation is determined according to the soil shear strength;

[0088] According to the structure of distribution network tower, soil shear strength and soil scour thickness, a numerical model of distribution network tower is constructed based on artificial boundary method.

[0089] The numerical model of distribution network tower is solved by equivalent strain wedge method, and the ultimate overturning moment and corresponding critical wind speed of distribution network tower are obtained.

[0090] For step S3, the universal soil loss equation is a mathematical model established based on the physical laws of the soil scouring process and the principles of hydrodynamics. In this equation, the soil erodibility factor is considered to be one of the key factors determining the soil scouring thickness. The larger the value of the soil erodibility factor, the lower the shear strength of the soil, and the more prone to scouring. The following is the mathematical expression of the universal soil loss equation:

[0091] A=R×K×L×S×C×P

[0092] Where A represents the average annual soil loss per unit area (t / (hm 2 ·a)), R represents the rainfall erosivity factor (MJ·mm / (hm 2 h), K represents soil erodibility factor, L and S represent slope length factor and slope gradient factor, LS is also called terrain factor, C represents vegetation coverage and management factor, P represents soil and water conservation measures factor. When no soil and water conservation measures are taken, P = 1.

[0093] The rainfall erosivity factor R can be calculated according to the rainfall data obtained in step S1 by the following formula:

[0094] R r =α(P r ) β

[0095] α=21.586β -7.182

[0096]

[0097] Among them, R r represents the rainfall erosivity factor on day r (MJ·mm / (hm 2 h)), P r represents the daily rainfall on day r (mm), when P r <12mm, P r The value is 0, P d12 Indicates daily rainfall ≥ 12 mm (mm), P y12 It represents the annual rainfall (mm) with daily rainfall ≥ 12 mm. α and β represent the statistical coefficients of rainfall erosion factor, which are dimensionless.

[0098] The soil erodibility factor K can be determined according to the soil shear strength obtained in step S2. The soil shear strength refers to the maximum resistance that the soil can withstand when subjected to shearing. It is one of the important indicators of soil mechanical properties and reflects the stability and anti-scouring ability of the soil. Specifically, the soil shear strength can be combined with other parameters (such as rainfall intensity, slope, soil type, etc.) to calculate the value of the soil erodibility factor K through an empirical formula or a statistical model.

[0099] The terrain factor LS can be calculated according to the terrain data obtained in step S1 by the following formula:

[0100]

[0101] Among them, λ represents the projected slope length of the slope, τ represents the ratio of rill erosion to surface erosion, and θ represents the slope.

[0102] The vegetation coverage factor C can be calculated according to the vegetation coverage by the following formula:

[0103]

[0104] Among them, FVC stands for Fractional Vegetation Cover, NDVI stands for Normalized Difference Vegetation Index, and NDVI stands for max and NDVI min Indicates the maximum and minimum values ​​of NDVI during vegetation growth. For distribution network towers, NDVI can be used. max =1, NDVI min = 0. NDVI values ​​can be directly obtained from domestic data platforms such as the Geospatial Data Cloud.

[0105] After calculating the soil loss amount using the universal soil loss equation, the soil erosion thickness S under rainfall erosion can be calculated using the following formula: d :

[0106]

[0107] Among them, S d It represents the thickness of soil washed away by rainfall, and A represents the annual average soil loss per unit area (t / (hm 2 a)), ρ represents the density of soil, which can generally be taken as 1.35kg / m 3 , or use local measured data, T represents the duration of rainfall.

[0108] According to the structure of the distribution network tower, the shear strength of the soil and the scouring thickness of the soil, the numerical model of the distribution network tower is constructed based on the artificial boundary method using ANSYS software. Specifically, a certain range of soil can be considered, and artificial bottom boundaries and artificial side boundaries can be introduced to divide the soil structure model into finite domains and infinite domains. For non-rigid foundations, a viscous boundary can be selected as the artificial bottom boundary. The viscous boundary can simulate the interaction between the soil and the structure, and consider the stress transfer and deformation of the soil. For rigid foundations, a fixed boundary can be selected as the artificial bottom boundary. The fixed boundary can simulate the constraint of the rigid foundation on the structure, and consider the stress transfer and deformation of the soil. The artificial side boundary can select the binding boundary and the vertical displacement constraint boundary. Both boundary conditions can accurately simulate the free field response of the soil. In the dynamic response analysis of soil-structure interaction, the artificial boundary method shows good performance in simulating soil-structure interaction.

[0109] Then, the numerical model of the distribution network tower is solved by the equivalent strain wedge method. The strain wedge method is an extension of the one-dimensional elastic foundation reaction method (Beam on elastic foundation method, BEF) in three-dimensional space. Figure 4 As shown in the figure, in the strain wedge method, it is assumed that the soil in front of the pile forms a three-dimensional wedge in the compression area. As the horizontal force on the structure increases, the size of the wedge and the stress-strain curve of the soil will change. When the pile top is subjected to horizontal loads, the soil around the pile will resist the pile body. The soil around the pile is equivalent to a nonlinear spring on the foundation beam. The control equation of the pile-soil system is:

[0110]

[0111] Where EI represents the bending stiffness of the pile, y represents the horizontal displacement of the pile, z represents the depth below the ground surface, k represents the foundation reaction modulus, k = p / y, p represents the soil resistance provided by the unit pile length (N / m). Under the action of rainfall erosion, the foundation soil of the distribution network tower will form scour pits, such as Figure 5 As shown in the figure, S tw =S w +S d / tanθ.

[0112] It should be noted that the strain wedge method takes into account the continuity and constitutive model of the soil, and can also consider pile characteristics such as pile top constraints, pile body materials, cross-sectional shapes and flexural stiffness. This method does not rely on empirical parameters based on field tests and can provide more accurate soil-structure interaction analysis results.

[0113] The pile-soil system control equation is used to solve the stress-strain curve of the excavated body, and the overturning moment of the distribution network tower is solved by the following formula:

[0114]

[0115] Among them, M u represents the ultimate overturning moment of the distribution network tower, h represents the foundation depth of the pole, b0 represents the calculated width of the pole, t represents the height of the pole rotation point from the ground, p z represents the earth pressure at depth z.

[0116] The finite element model under different wind speeds is subjected to dynamic analysis to obtain the bending moment at the bottom of the pole, so as to establish the corresponding relationship between the critical wind speed and the ultimate overturning moment. Specifically, the pole and the surrounding soil layer are established as a finite element model using finite element software. The model should include the geometric shape of the pole, material properties and properties of the soil layer. For different wind speeds, the corresponding wind load is applied to the pole, and a dynamic analysis is performed. The stress-strain curve of the soil body is solved using the pile-soil system control equation, and the overturning moment of the distribution network tower is solved. By gradually increasing the wind speed, the ultimate overturning moment under different wind speeds is obtained. Then, based on the ultimate overturning moment obtained and the corresponding wind speed, the corresponding relationship between the critical wind speed and the ultimate overturning moment is established.

[0117] Further explanation: the wind load applied in the dynamic analysis is regarded as the superposition of average wind and fluctuating wind. Fluctuating wind is simulated by Davenport wind speed spectrum. Average wind is the average value of wind direction and wind speed in a certain period of time, which can be calculated by the following formula:

[0118]

[0119] Where, v represents the average wind speed, v 10 It represents the average wind speed at a height of 10m for 10 minutes, z represents the height, and γ represents the power exponent related to the ground roughness.

[0120] Finally, according to the corresponding relationship between critical wind speed and ultimate overturning moment, the critical wind speed of distribution network tower is obtained.

[0121] Step S4: Determine the wind resistance of the distribution network tower to be evaluated according to the critical wind speed.

[0122] For step S4, according to the critical wind speed obtained in step S3, the corresponding wind resistance is determined according to a preset range. In this embodiment, when the critical wind speed does not exceed the first preset wind speed threshold, the corresponding wind resistance is determined to be the third level wind resistance level; when the critical wind speed exceeds the first preset wind speed threshold, and the critical wind speed does not exceed the second preset wind speed threshold, the corresponding wind resistance is determined to be the second level wind resistance level; when the critical wind speed exceeds the second preset wind speed threshold, the corresponding wind resistance is determined to be the first level wind resistance level. The distribution network tower corresponding to the first level wind resistance level can withstand stronger wind forces and provide higher structural stability and reliability.

[0123] The present invention predicts the strength of soil after rainfall erosion and infiltration through the PINN neural network model, thereby considering the influence of rainwater infiltrating soil and soil being eroded by rainwater. Unlike the traditional neural network model, the PINN model embeds physical information in the loss function to ensure that the predicted data meets the physical laws. This approach greatly reduces the error and greatly improves the accuracy of the prediction. In addition, the present invention also uses the equivalent strain wedge method to solve the ultimate overturning moment and wind speed tolerance of the structure. Unlike the traditional method, this method does not rely on the empirical parameters of the field experiment and has better applicability. By using the equivalent strain wedge method, the wind resistance of the structure can be more accurately evaluated, providing a more reliable basis for engineering design and decision-making.

[0124] like Figure 6 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0125] An embodiment of the present invention provides a device for evaluating the wind resistance of a distribution network tower structure, comprising: an environmental data acquisition module, a soil shear strength determination module, a critical wind speed determination module, and a wind resistance evaluation module;

[0126] Environmental data acquisition module, used to obtain meteorological data and terrain data of the distribution network tower to be evaluated; meteorological data, including: temperature, humidity, wind speed and rainfall intensity; terrain data, including: land type, slope, windward slope or leeward slope, and the angle between the main wind direction and the line direction in winter;

[0127] A soil shear strength determination module is used to input meteorological data and terrain data into the neural network model so that the neural network model generates soil shear strength according to the meteorological data and terrain data;

[0128] A critical wind speed determination module is used to determine the ultimate overturning moment and the corresponding critical wind speed according to the soil shear strength;

[0129] The wind resistance evaluation module is used to determine the wind resistance of the distribution network tower to be evaluated according to the critical wind speed.

[0130] In a preferred embodiment, the device for evaluating the wind resistance of a distribution network tower structure further includes: a pre-processing module;

[0131] The preprocessing module includes: standardizing the meteorological data and terrain data and supplementing the missing values ​​to obtain the preprocessed meteorological data and terrain data.

[0132] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention, and can implement the method for evaluating the wind resistance of the distribution network tower structure provided by any one of the above-mentioned method item embodiments of the present invention.

[0133] 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. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0134] Based on the above-mentioned embodiment of the method for evaluating the wind resistance of distribution network tower structures, 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, the method for evaluating the wind resistance of distribution network tower structures according to any embodiment of the present invention is implemented.

[0135] Exemplarily, 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 implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0136] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0137] 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) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0138] Based on the above method embodiment, another embodiment is provided: 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 the method for evaluating the wind resistance of the distribution network tower structure described in any one of the above method embodiments of the present invention.

[0139] Wherein, the module / unit integrated in the distribution network pole tower structure wind resistance assessment device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0140] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle 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 a distribution network tower structure, characterized in that: include: Obtain meteorological and topographic data of the distribution network towers to be evaluated; The meteorological data include: temperature, humidity, wind speed and rainfall intensity; the terrain data include: land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the route direction; Inputting meteorological data and topographic data into the neural network model so that the neural network model generates soil shear strength according to the meteorological data and topographic data; According to the soil shear strength, determine the ultimate overturning moment and the corresponding critical wind speed; The wind resistance of the distribution network tower to be evaluated is determined according to the critical wind speed.

2. The method for evaluating the wind resistance of a distribution network tower structure according to claim 1, characterized in that: Before feeding the meteorological and topographic data into the neural network model, it also includes: The meteorological data and terrain data are standardized and missing values ​​are supplemented to obtain preprocessed meteorological data and terrain data.

3. The method for evaluating the wind resistance of a distribution network tower structure according to claim 2, characterized in that: The meteorological data and terrain data are standardized and missing values ​​are supplemented to obtain pre-processed meteorological data and terrain data, including: The meteorological data and topographic data are converted into a normal distribution with a mean of 0 and a standard deviation of 1 by using Z-Score standardization; The missing values ​​of the normal distribution are supplemented by linear interpolation method to obtain preprocessed meteorological data and terrain data.

4. The method for evaluating the wind resistance of a distribution network tower structure according to claim 3 is characterized in that: The neural network model is obtained in the following way: Obtain historical meteorological data and historical terrain data of distribution network towers at several historical moments and the soil shear strength at the corresponding historical moments; For the historical meteorological data and historical terrain data at each historical moment, standardization processing and missing value supplementation are performed on the historical meteorological data and historical terrain data to obtain pre-processed historical meteorological data and historical terrain data; The preprocessed historical meteorological data and historical terrain data at several historical moments and their corresponding soil shear strength at historical moments are respectively input into the initial physical information neural network model for iterative training until the loss function converges to obtain a trained neural network model.

5. The method for evaluating the wind resistance of a distribution network tower structure according to claim 4 is characterized in that: The loss function includes: a data error term and a physical information error term; The data error term is specifically: mean square error; The physical information error term is determined by: Calculate the mathematical shear strength of the soil by the Moore-Coulomb criterion based on the preprocessed historical meteorological data and historical terrain data at the historical moment currently inputted by the physical information neural network model; Calculate the residual of soil strength based on the predicted shear strength generated during the current iterative training of the physical information neural network model and the corresponding mathematical shear strength; The residual of the soil strength is used as the physical information error term in the current iteration process of the physical information neural network model.

6. The method for evaluating the wind resistance of a distribution network tower structure according to claim 5, characterized in that: According to the soil shear strength, determine the ultimate overturning moment and the corresponding critical wind speed, including: Calculate the scour thickness of the soil according to the universal soil loss equation; wherein the soil erodibility factor in the universal soil loss equation is determined according to the soil shear strength; According to the structure of distribution network tower, soil shear strength and soil scour thickness, a numerical model of distribution network tower is constructed based on artificial boundary method. The numerical model of distribution network tower is solved by equivalent strain wedge method, and the ultimate overturning moment and corresponding critical wind speed of distribution network tower are obtained.

7. A device for evaluating the wind resistance of a distribution network tower structure, characterized in that: include: Environmental data acquisition module, soil shear strength determination module, critical wind speed determination module and wind resistance assessment module; The environmental data acquisition module is used to acquire meteorological data and terrain data of the distribution network tower to be evaluated; The meteorological data include: temperature, humidity, wind speed and rainfall intensity; the terrain data include: land type, slope, windward slope or leeward slope, and the angle between the main wind direction in winter and the route direction; The soil shear strength determination module is used to input meteorological data and terrain data into the neural network model so that the neural network model generates soil shear strength according to the meteorological data and terrain data; The critical wind speed determination module is used to determine the ultimate overturning moment and the corresponding critical wind speed according to the soil shear strength; The wind resistance evaluation module is used to determine the wind resistance of the distribution network tower to be evaluated according to the critical wind speed.

8. The device for evaluating the wind resistance of a distribution network tower structure according to claim 7, characterized in that: Also includes: Preprocessing module; The preprocessing module includes: performing standardization processing and missing value supplementation on the meteorological data and terrain data to obtain preprocessed meteorological data and terrain data.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the wind resistance of a distribution network tower structure according to any one of claims 1 to 6 is implemented.

10. 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 the method for evaluating the wind resistance of the distribution network tower structure according to any one of claims 1 to 6.

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