Earthquake landslide risk assessment method for transmission tower and related equipment

Abstract

The invention relates to the technical field of power systems, and discloses a transmission tower earthquake landslide risk assessment method and related equipment, and the method comprises the steps: building a time-varying anti-disaster capability model based on an aging factor in a structure aging feature and an initial anti-disaster capability model; according to the ground vibration parameters, the landslide displacement field and the time-varying anti-disaster capability model, a structural damage index is generated; adjusting the time-varying anti-disaster capability model according to the structural damage indication and the historical earthquake damage record; outputting a dynamic risk level map based on the adjusted model and the risk assessment model; and when a risk assessment request of a target transmission tower is received, performing earthquake landslide risk assessment based on the dynamic risk grade map. The problem that an existing earthquake landslide risk assessment scheme of the power transmission tower is poor in accuracy is solved.

Description

Technical Field

[0001] This application relates to the field of power system technology, specifically to a method and related equipment for assessing the risk of earthquake landslides on transmission towers. Background Technology

[0002] With the rapid development of the social economy and the continuous expansion of urban infrastructure, transmission towers, as key infrastructure in power transmission networks, are directly related to the reliability of power grid supply due to their safe and stable operation. In earthquake-prone and geologically hazardous areas, earthquakes and their induced landslides and other secondary disasters have become major risk factors threatening the structural safety of transmission towers. Currently, structural health monitoring of transmission towers and earthquake-landslide coupled risk assessment have become research hotspots and technological development directions in the field of power system safety.

[0003] However, current seismic landslide risk assessment schemes for transmission towers neglect the dynamic evolution of tower disaster resistance over time. In actual engineering projects, as the service life of towers increases, environmental erosion intensifies (such as humidity, salt spray, and temperature cycle), and historical earthquake damage accumulates, the structural performance and disaster resistance of towers continuously degrade, manifested as reduced tower material strength, weakened foundation stability, and decreased node stiffness. The currently widely used fixed disaster resistance assessment factors fail to reflect the time-varying characteristics of these performance indicators, leading to a disconnect between risk assessment results and actual engineering changes. This makes it difficult to accurately reflect the true risk level of towers during long-term operation and maintenance. Therefore, current seismic landslide risk assessment schemes for transmission towers suffer from poor accuracy. Summary of the Invention

[0004] This application provides a method and related equipment for assessing the seismic landslide risk of power transmission towers, which can solve the problem of poor accuracy in current seismic landslide risk assessment schemes for power transmission towers.

[0005] In a first aspect, embodiments of this application provide a method for assessing the seismic landslide risk of transmission towers, including: Acquire structural health monitoring data of the target transmission tower; Construct the structural aging characteristics corresponding to the structural health monitoring data; Based on the physical model of the tower structure and the preset disaster resistance indicators, an initial disaster resistance capability model is constructed. Based on the aging factors and initial disaster resistance model in the structural aging characteristics, a time-varying disaster resistance model is constructed. Based on seismic motion parameters, landslide displacement field, and time-varying disaster resistance model, a structural damage index is generated. The time-varying disaster resistance model is adjusted based on the structural damage indications and historical earthquake damage records. Based on the adjusted model and risk assessment model, a dynamic risk level map is output. When a risk assessment request for the target transmission tower is received, an earthquake and landslide risk assessment is performed based on the dynamic risk level map.

[0006] Optionally, in some embodiments of this application, the step of constructing an initial disaster resistance capability model based on the tower structure physics model and preset disaster resistance indicators includes: Determine the preset disaster resistance indicators; Establish physical models corresponding to each disaster resistance index; Establish a mapping relationship between various disaster resistance indicators and disaster resistance capabilities; Based on the physical models corresponding to each disaster resistance index and the mapping relationship between each disaster resistance index and disaster resistance capability, an initial disaster resistance capability model is constructed.

[0007] Optionally, in some embodiments of this application, the step of constructing a time-varying disaster resistance model based on the aging factor and initial disaster resistance model in the structural aging characteristics includes: Extract the aging factors from the structural aging characteristics; To determine the physical influence mechanism of the aging factor on the initial disaster resistance model indicators; Based on the aforementioned physical influence mechanism, a dynamic mapping function between the aging factor and the disaster resistance index is constructed. Based on the dynamic mapping function and the initial disaster resilience model, a time-varying disaster resilience model is constructed.

[0008] Optionally, in some embodiments of this application, generating a structural damage index based on seismic motion parameters, landslide displacement field, and time-varying disaster resistance model includes: A multidimensional dynamic sequence is constructed based on seismic motion parameters and landslide displacement field; Based on the aforementioned multidimensional dynamic sequence and time-varying disaster resistance model, a structural damage index is generated.

[0009] Optionally, in some embodiments of this application, adjusting the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records includes: The structural damage index is standardized with historical earthquake damage records to generate observation benchmark data. The time-varying disaster resilience model is adjusted based on the aforementioned observation baseline data.

[0010] Optionally, in some embodiments of this application, the adjusted model and risk assessment model output a dynamic risk level map, including: The adjusted model is normalized to obtain the normalized model; Obtain the raw data on the susceptibility of earthquake-induced landslides in the target area, and rasterize the raw data to obtain an intensity matrix; Based on the normalization model, intensity matrix, and risk assessment model, a dynamic risk level map is output.

[0011] Optionally, in some embodiments of this application, the step of performing an earthquake and landslide risk assessment based on the dynamic risk level map when a risk assessment request for the target transmission tower is received includes: When a risk assessment request for a target transmission tower is received, the tower information to be assessed is determined from the risk assessment request; Based on the information of the tower to be evaluated, obtain the basic risk data of the tower to be evaluated; Based on the basic risk data and the dynamic risk level map, an earthquake landslide risk assessment is conducted on the tower to be evaluated.

[0012] Secondly, embodiments of this application provide a seismic landslide risk assessment device for power transmission towers, comprising: The acquisition module is used to acquire structural health monitoring data of the target transmission tower; The first construction module is used to construct the structural aging characteristics corresponding to the structural health monitoring data; The second construction module is used to construct an initial disaster resistance capability model based on the physical model of the tower structure and the preset disaster resistance indicators. The third construction module is used to construct a time-varying disaster resistance model based on the aging factor and the initial disaster resistance model in the structural aging characteristics. The generation module is used to generate a structural damage index based on ground motion parameters, landslide displacement field, and time-varying disaster resistance model. The adjustment module is used to adjust the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records. The output module is used to output a dynamic risk level map based on the adjusted model and the risk assessment model; The assessment module is used to conduct an earthquake and landslide risk assessment based on the dynamic risk level map when a risk assessment request for a target transmission tower is received.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the earthquake landslide risk assessment method for transmission towers as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a storage medium storing a computer program capable of being loaded by a processor and executing the earthquake landslide risk assessment method for transmission towers as described in the first aspect.

[0015] This application provides a method, apparatus, electronic device, and storage medium for seismic landslide risk assessment of transmission towers. After acquiring structural health monitoring data of the target transmission tower, the method constructs structural aging characteristics corresponding to the structural health monitoring data. Then, based on the tower structure physics model and preset disaster resistance indicators, an initial disaster resistance capacity model is constructed. Next, based on the aging factors in the structural aging characteristics and the initial disaster resistance capacity model, a time-varying disaster resistance capacity model is constructed. A structural damage index is generated based on seismic motion parameters, landslide displacement field, and the time-varying disaster resistance capacity model. Subsequently, the time-varying disaster resistance capacity model is adjusted based on the structural damage indication and historical earthquake damage records. Finally, based on the adjusted model and the risk assessment model, a dynamic risk level map is output. When a risk assessment request for the target transmission tower is received, an seismic landslide risk assessment is performed based on the dynamic risk level map. In the earthquake landslide risk assessment scheme for transmission towers provided in this application, an initial disaster resistance model is built by combining the tower's physical model with disaster resistance indicators. An aging factor is incorporated to construct a time-varying disaster resistance model, which replaces the fixed disaster resistance factor and accurately reflects the degradation of tower material strength and foundation stability over time. Subsequently, the time-varying model is used to generate a damage index and adjust the model. Combined with the risk assessment model, the output spectrum is used to respond to assessment requests. Considering the coupled risk response under the time-varying characteristics of the structure, the accuracy of the assessment is greatly improved, closely matches the actual risk level of the tower, and solves the problem of poor accuracy in traditional schemes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of the seismic landslide risk assessment method for transmission towers provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the seismic landslide risk assessment method for transmission towers provided in the embodiments of this application; Figure 3 This is another flowchart illustrating the seismic landslide risk assessment method for transmission towers provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of the earthquake landslide risk assessment device for transmission towers provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0019] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0020] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0021] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0022] To address the aforementioned technical problems and overcome the shortcomings of existing technologies, this application provides a method and related equipment for assessing the seismic landslide risk of transmission towers. This method enables accurate identification and rapid protection of fault areas in active distribution networks, thereby improving the reliability and adaptability of differential protection in active distribution networks.

[0023] Figure 1 This is a diagram illustrating the application environment of a seismic landslide risk assessment method for transmission towers in one embodiment. (Refer to...) Figure 1The seismic landslide risk assessment method for power transmission towers is applied to a seismic landslide risk assessment system for power transmission towers. This system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; a mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers.

[0024] Server 120 is configured to execute the aforementioned earthquake landslide risk assessment method for transmission towers, including: acquiring structural health monitoring data of the target transmission tower; constructing structural aging characteristics corresponding to the structural health monitoring data; constructing an initial disaster resistance capacity model based on the tower structure physics model and preset disaster resistance indicators; constructing a time-varying disaster resistance capacity model based on the aging factors in the structural aging characteristics and the initial disaster resistance capacity model; generating a structural damage index based on seismic motion parameters, landslide displacement field, and the time-varying disaster resistance capacity model; adjusting the time-varying disaster resistance capacity model based on the structural damage indication and historical earthquake damage records; outputting a dynamic risk level map based on the adjusted model and the risk assessment model; and conducting an earthquake landslide risk assessment based on the dynamic risk level map when a risk assessment request for the target transmission tower is received.

[0025] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for assessing the seismic landslide risk of transmission towers according to an embodiment of this application. This embodiment primarily uses the application of this method to a server as an example. Specifically, the method for assessing the seismic landslide risk of transmission towers according to an embodiment of this application may include the following steps: S101. Obtain structural health monitoring data of the target transmission tower.

[0026] Among them, the target transmission towers refer to specific transmission towers that require dynamic risk assessment of earthquakes and landslides. They are the specific objects of full life cycle health monitoring and risk assessment, and it is necessary to collect data on their structural status and environmental effects in a targeted manner.

[0027] Structural health monitoring data refers to multi-source data that can reflect the structural performance, aging status and stress characteristics of the target transmission tower, including service life, tower material strain, foundation displacement, corrosion rate and foundation settlement rate. It may also include environmental erosion factors (such as average annual rainfall, salt spray concentration, number of temperature cycles, and factors that accelerate structural aging) and historical seismic event sequences (such as earthquake occurrence time, magnitude and epicenter distance).

[0028] For example, specifically, a unique identifier for the target transmission tower can be determined, and its design file and commissioning timestamp can be retrieved based on the unique identifier. The service life quantification value T_age can be calculated based on the difference between the commissioning time and the current time, and the time span and sampling frequency of data collection can be determined.

[0029] Distributed fiber optic strain sensors or resistance strain gauges are deployed at stress concentration points on the target tower to synchronously acquire raw strain signals. The Kalman filter algorithm (with process noise covariance Q and measurement noise covariance R set) is used to suppress signal noise. The principal strain direction is extracted through eigenvalue decomposition. The peak strain sequence ε(t) is calculated by combining the peak hold algorithm to generate a feature vector containing the principal strain amplitude and direction.

[0030] Optionally, in some embodiments of this application, positioning measuring points can be set up on the foundation platform of the tower based on a differential GPS positioning system to collect the horizontal displacement and vertical settlement components of the foundation in real time, and the trend can be predicted by combining the displacement rate model to output the foundation displacement vector dbase(t). Optionally, in some embodiments of this application, InSAR remote sensing data and on-site settlement monitoring station data can be fused to generate the ground settlement rate field vsettle(t) around the target tower through spatial interpolation modeling.

[0031] Optionally, in some embodiments of this application, electrochemical sensors can be deployed on the surface of the target tower material, and electrochemical impedance spectroscopy analysis technology can be used to identify corrosion kinetic parameters; combined with humidity and salt spray concentration data from surrounding environmental monitoring stations, a corrosion rate model can be established, and corrosion rate time series data vcorr(t) can be output.

[0032] Optionally, in some embodiments of this application, the annual average rainfall, wind speed, humidity, salt spray concentration and temperature difference cycle number of the target tower area can be obtained from meteorological stations and geological monitoring stations to construct the environmental erosion intensity index Eenv; Optionally, in some embodiments of this application, the seismic events that occurred during the service period of the target tower and the corresponding tower responses can be extracted based on the GIS system and power grid operation and maintenance records to form a historical earthquake damage event database Eeq(t).

[0033] S102. Construct the structural aging characteristics corresponding to the structural health monitoring data.

[0034] Structural aging characteristics refer to a unified time series dataset that accurately characterizes the aging state of a transmission tower structure, formed from multi-source structural health monitoring data of the target tower after preprocessing, time alignment, and feature extraction.

[0035] Since different monitoring data have different sampling frequencies (e.g., tower material strain 200Hz, foundation displacement once per hour), it is necessary to unify the data resolution according to the time dimension. Therefore, the minimum time unit for data sampling can be determined based on the service timestamp of the target transmission tower. Then, time series resampling technology is used to process high-frequency data by down-averaging, such as averaging 200Hz data into 1 data point per hour, and low-frequency data by interpolation and frequency compensation. Finally, all data are unified to the same time resolution to generate a timestamp sequence with fixed time intervals. Next, principal component analysis (PCA) is used to extract the core aging factors that have the greatest impact on disaster resistance from the aligned multi-source data. Specifically, the covariance matrix between various aging indicators can be calculated for the time-aligned standardized data to quantify the correlation between indicators and identify redundant information. Then, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvectors and eigenvalue sequences arranged in descending order of eigenvalues. Next, the cumulative variance contribution rate of the first k eigenvectors is calculated. When the cumulative contribution rate exceeds a preset threshold, k is determined as the optimal number of principal components. Finally, the high-dimensional original data is transformed into k-dimensional key aging factor eigenvectors, and the key aging factors are fused with environmental and earthquake damage data to form complete structural aging characteristics.

[0036] S103. Based on the physical model of the tower structure and the preset disaster resistance indicators, construct an initial disaster resistance capability model.

[0037] Among them, the tower structure physics model refers to the theoretical model describing the stress and performance relationship of the transmission tower structure based on the theories of materials mechanics, structural mechanics, and soil mechanics. The preset disaster resistance index refers to the key disaster resistance performance indicators pre-determined based on the core failure risk of the tower under earthquakes and landslides, including the foundation depth-to-width ratio, tower material strength, and connection node stiffness. The initial disaster resistance capacity model refers to the static disaster resistance capacity assessment model (denoted as R0) constructed solely based on the initial design state of the tower and the physical laws of the preset disaster resistance index, without considering the influence of structural aging factors.

[0038] For example, specifically, based on structural mechanics theory, an independent mechanical calculation model can be constructed for each disaster resistance index to determine the physical relationship between the index and disaster resistance performance. Then, a static mapping relationship between disaster resistance index and disaster resistance capacity can be constructed. Next, the index weights can be allocated based on historical earthquake damage and engineering specifications, combined with earthquakes. Finally, the index contribution coefficients and weights can be integrated to construct a static quantitative model and output the initial disaster resistance capacity value R0.

[0039] Optionally, in some embodiments of this application, the step of "constructing an initial disaster resistance capability model based on the physical model of the tower structure and preset disaster resistance indicators" may specifically include: Determine the preset disaster resistance indicators; Establish physical models corresponding to each disaster resistance index; Establish a mapping relationship between various disaster resistance indicators and disaster resistance capabilities; Based on the physical models corresponding to each disaster resistance index and the mapping relationship between each disaster resistance index and disaster resistance capability, an initial disaster resistance capability model is constructed.

[0040] For example, specifically, based on the theories of materials mechanics and structural mechanics, a calculable mechanical model is constructed for each disaster resistance index. For instance, by combining the characteristic value of the bearing capacity of the foundation soil, the relationship between the foundation depth-to-width ratio and the anti-slip safety factor is established. For the combined stress of tower materials under vertical loads (i.e., self-weight and conductor tension) and horizontal loads (seismic inertial force and landslide thrust), a stress calculation model is established. Furthermore, the mapping relationship of the foundation depth-to-width ratio is constructed with the minimum depth-to-width ratio threshold of 1.2 required by the code and the seismic intensity of 8 degrees as the benchmark, and the mapping relationship between the actual value / benchmark value and the anti-stability contribution coefficient is constructed.

[0041] S104. Based on the aging factor and initial disaster resistance model in the structural aging characteristics, construct a time-varying disaster resistance model.

[0042] The time-varying disaster resistance model (R(t)) refers to a quantitative model that introduces structural aging factors and, based on the initial disaster resistance model, describes the dynamic evolution of the disaster resistance capability of transmission towers over service time.

[0043] Since structural aging characteristics contain multi-dimensional data (service life, tower material strain, etc.), it is necessary to extract core aging factors through principal component analysis (PCA) to eliminate redundancy and focus on the variables with the greatest impact on disaster resistance capability. Specifically, the original aging data in the structural aging characteristics are unified to the [0,1] interval. Then, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvectors and eigenvalues ​​arranged in descending order of contribution. Next, the cumulative variance contribution rate of the first k eigenvectors is calculated. When the cumulative contribution rate is ≥90%, the first k eigenvectors are selected, and the corresponding matrices are constructed. Then, a mapping formula is used to reduce the high-dimensional data (i.e., the matrix) to k-dimensional key aging factor eigenvectors. Next, the physical influence mechanism of aging factors on the initial disaster resistance capability model indicators is analyzed. Then, a dynamic mapping function between aging factors and disaster resistance indicators is constructed. Finally, the dynamic mapping function is integrated with the initial disaster resistance capability model to generate a time-varying disaster resistance capability model.

[0044] Optionally, in some embodiments of this application, the step of "constructing a time-varying disaster resistance model based on the aging factor and initial disaster resistance model in the structural aging characteristics" may include: Extract the aging factors from the structural aging characteristics; To determine the physical influence mechanism of the aging factor on the initial disaster resistance model indicators; Based on the aforementioned physical influence mechanism, a dynamic mapping function between the aging factor and the disaster resistance index is constructed. Based on the dynamic mapping function and the initial disaster resilience model, a time-varying disaster resilience model is constructed.

[0045] For example, specifically, the covariance matrix is ​​calculated for standardized monitoring data, and the correlation between indicators is identified through eigenvalue decomposition, extracting principal components with a cumulative variance contribution rate exceeding 90%. For example, corrosion, settlement, and strain—three core factors—are screened from the five-dimensional raw data to construct key aging factor feature vectors, achieving dimensionality reduction mapping from high-dimensional redundant data to low-dimensional core information, providing efficient input for subsequent modeling. Corrosion rate and service life are core aging factors; both lead to thinning of the tower material cross-section and grain refinement through electrochemical corrosion and fatigue accumulation effects, resulting in an exponential decline in yield strength.

[0046] Furthermore, to address the temporal characteristics of aging factors, a three-layer LSTM network is constructed. For example, the number of nodes in the input layer matches the dimension of the key aging factors (e.g., 3-dimensional), the hidden layer uses 64 memory units to capture long-term dependencies, and the output layer generates the decay coefficients of three disaster resistance indicators through the Sigmoid activation function.

[0047] The training and validation sets are divided using a leave-one-out method. A 5-year aging factor time-series data is input, and the corresponding tower material strength, foundation depth-to-width ratio, and node stiffness attenuation coefficient are output. By comparing LSTM predictions with field measurements, the mapping error (RMSE) is ensured to be below 8%. For example, when a tower has been in service for 10 years, the model predicts a 22% attenuation in tower material strength, while the actual measured attenuation is 20.5%, validating the function's ability to capture nonlinear degradation processes.

[0048] The attenuation coefficients output by the LSTM are substituted into the initial disaster resilience model to generate time-varying disaster resilience indices. A Bayesian optimization algorithm is used to fine-tune the number of hidden layer nodes in the LSTM and the noise covariance of the time-series model. Based on historical tower damage data from earthquakes, the mean absolute error (MAE) between the predicted disaster resilience and the actual disaster response is minimized to below 5%. Finally, the dynamic attenuation coefficients output by the LSTM are substituted into the initial model to transform the static disaster resilience indices into time-varying indices, thus constructing a time-varying disaster resilience model.

[0049] S105. Based on the ground motion parameters, landslide displacement field, and time-varying disaster resistance model, generate the structural damage index.

[0050] Among them, the ground motion parameters (dynamic load) and the landslide displacement field (spatial-dynamic load) are heterogeneous data. It is necessary to first eliminate the differences in dimensions, time series and space to form a multidimensional dynamic input sequence suitable for structural analysis. Then, the standardized coupled load is input into the coupled finite element model of the tower, foundation and ground. Combined with the real-time parameters of the time-varying disaster resistance capacity model (R'(t)), the dynamic response of the structure is solved by numerical calculation: the standard engineering dynamic analysis method is used to numerically integrate the structural dynamic equation. Then, the response data of the core stress-bearing parts of the tower, such as the displacement time history of the tower bottom, the middle of the tower body and the tower head, are extracted to form the basic data for subsequent damage analysis.

[0051] Peak indicators that reflect the "ultimate stress state" of the tower are selected from the dynamic response time history. These indicators are directly related to the occurrence and development of structural damage and are the core basis for damage quantification. Calculate the maximum inter-story drift angle from the inter-story relative displacement time history. Extract the maximum nodal shear force (e.g., horizontal shear force at the tower base) from the nodal shear force time history, the maximum tower bending moment from the tower bending moment time history, and the maximum stress value from the tower material stress time history; compare these peak values ​​with the limit values ​​corresponding to R'(t). Remove abnormal peak values ​​caused by sensor noise, and retain the response peak values ​​corresponding to the peak times of coupled loads to ensure that the indicators can truly reflect the structural stress limit.

[0052] The cumulative energy dissipation E_d(t) of the tower under coupled load is calculated by integrating the dynamic response time history of the structure. (E_d(t) = ∫F(t)) dx(t), F(t) are load vectors, x(t) is displacement vector); then the energy damage coefficient d1 = E_d(t) / E_ult(t) (d1∈[0,1], the closer the energy consumption is to the limit value, the more severe the damage. Considering the sequential effect of earthquake and landslide loads, the Miner linear accumulation criterion is used to correct the energy damage coefficient. Then, the three types of damage coefficients, displacement, force and energy damage coefficient, are fused according to the engineering weight, and the initial structural damage index D(t) is obtained after calculation.

[0053] Optionally, in some embodiments of this application, the step of "generating a structural damage index based on seismic motion parameters, landslide displacement field, and time-varying disaster resistance model" may specifically include: A multidimensional dynamic sequence is constructed based on seismic motion parameters and landslide displacement field; Based on the aforementioned multidimensional dynamic sequence and time-varying disaster resistance model, a structural damage index is generated.

[0054] S106. Adjust the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records.

[0055] The structural damage index (D(t)) is dynamic time-series data, while historical earthquake damage records are discrete event data. It is necessary to first eliminate heterogeneity and fill in deficiencies to form an observational benchmark for the parameter-damage-earthquake damage correlation. Then, the key parameters and prior probability distributions of the time-varying disaster resistance model are determined. Based on engineering principles and historical data, a prior distribution is set. Next, an observational likelihood model of damage-earthquake damage-parameters is constructed (establishing probabilistic correlation logic) to quantify the probabilistic relationship (likelihood function) between the observed data (D(t), historical earthquake damage) and the R(t) parameter. Because the evolution of the R(t) parameter is nonlinear and non-Gaussian, particle filtering is used to approximate the posterior distribution of the parameter, enabling dynamic updates driven by the observed data. The resampled particle set constitutes the posterior probability distribution of the parameter θ. The posterior distribution is updated in real time through a sliding window (such as the particle set of the last 24 hours), dynamically correcting the parameter distribution once for each new set of D(t) observation data, capturing the evolution trend of the parameter with damage and earthquake damage. Finally, the optimal parameters are determined from the posterior distribution to generate the adjusted model R'(t).

[0056] Optionally, in some embodiments of this application, the step of "adjusting the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records" may specifically include: The structural damage index is standardized with historical earthquake damage records to generate observation benchmark data. The time-varying disaster resilience model is adjusted based on the aforementioned observation baseline data.

[0057] S107. Based on the adjusted model and risk assessment model, output a dynamic risk level map.

[0058] The output parameters of the adjusted time-varying disaster resistance capacity model (R'(t)), landslide susceptibility data, and tower spatial data are standardized to eliminate heterogeneity and dimensional differences, providing a unified benchmark for risk calculation. Then, based on the risk assessment model, the ground-landslide coupled risk factor of a single tower is calculated. Combining the engineering early warning requirements and the correlation patterns of historical disaster losses, the coupled risk factor C(t) is mapped to a 5-level risk level to clarify the risk status of each tower. Next, the discrete single-tower risk data needs to be transformed into a continuous spatial risk distribution, while incorporating the time dimension to achieve dynamic risk visualization.

[0059] Optionally, in some embodiments of this application, the step "outputting a dynamic risk level map based on the adjusted model and the risk assessment model" may specifically include: The adjusted model is normalized to obtain the normalized model; Obtain the raw data on the susceptibility of earthquake-induced landslides in the target area, and rasterize the raw data to obtain an intensity matrix; Based on the normalization model, intensity matrix, and risk assessment model, a dynamic risk level map is output.

[0060] S108. When a risk assessment request for the target transmission tower is received, an earthquake and landslide risk assessment is performed based on the dynamic risk level map.

[0061] Upon receiving a risk assessment request, the system parses the unique information of the target tower, including the tower number, precise spatial coordinates, and the line segment it belongs to. Then, confirming the requirement is "current-time risk assessment or short-term future risk prediction," it locates and extracts the basic risk data, creating a dynamic risk level map as a GIS-visualized raster map. Initial risk information for the tower is obtained through spatial matching, supplemented with real-time data, and the basic risk data is corrected to eliminate the lag in the map's periodic updates. The assessment accuracy is optimized by combining real-time data. Then, based on the risk assessment model, the final risk level is recalculated using the corrected parameters. The risk is accurately calculated through the risk assessment model, avoiding the averaging error of the map area. Next, the rationality of the assessment results is verified, ensuring accuracy through historical data and physical logic verification to avoid misjudgments caused by single data points. Finally, the assessment results are output to the operation and maintenance decision-making end, presenting a structured view and providing precise decision support for operation and maintenance.

[0062] Optionally, in some embodiments of this application, the step "when a risk assessment request for the target transmission tower is received, perform an earthquake and landslide risk assessment based on the dynamic risk level map" may specifically include: When a risk assessment request for a target transmission tower is received, the tower information to be assessed is determined from the risk assessment request; Based on the information of the tower to be evaluated, obtain the basic risk data of the tower to be evaluated; Based on the basic risk data and the dynamic risk level map, an earthquake landslide risk assessment is conducted on the tower to be evaluated.

[0063] To further understand the seismic landslide risk assessment scheme for transmission towers in this application, the following will provide further explanation, such as... Figure 3 As shown, the details are as follows: S1: Collect structural health monitoring data of the transmission tower throughout its entire life cycle. The data includes service life, tower material strain, foundation displacement, corrosion rate, and foundation settlement rate, and record environmental erosion factors and historical earthquake damage event sequences.

[0064] S2: Normalize and remove outliers from the collected structural health monitoring data, align the multi-source heterogeneous data based on the service timestamp, and generate a structural aging feature dataset in a unified time series format.

[0065] S3: Based on the physical mechanism and data-driven fusion modeling method, a dynamic mapping function is constructed between the structural aging factor and the disaster resistance capacity index of the tower. The index includes the foundation depth-to-width ratio, tower material strength, and connection node stiffness. The time-varying disaster resistance capacity R(t) model is output.

[0066] S4: Input the ground motion parameters and landslide displacement field into the coupled dynamic response model, and combine the disaster resistance capacity R(t) at the current moment to calculate the peak value of the structural response of the tower under the earthquake-landslide coupling action, and generate the structural damage index D(t).

[0067] S5: Based on the structural damage index D(t) and historical earthquake damage records, a Bayesian online update mechanism is used to correct the parameters of the time-varying disaster resistance capacity R(t) model, so as to realize the dynamic updating and adaptive enhancement of the model over time.

[0068] S6: Substitute the updated disaster resistance capacity R(t) into the risk assessment model, combine the susceptibility layer of earthquake-induced landslide disasters with the spatial distribution of towers, and output a dynamic risk level map to guide the operation and maintenance decisions of transmission lines.

[0069] S7: Determine whether the current risk level exceeds the preset threshold. If it does, generate an early warning signal and activate the risk response plan, including recommendations for tower reinforcement and landslide protection measures.

[0070] S8: Based on historical early warning response data and actual disaster occurrence records, backtesting and performance evaluation of the dynamic risk assessment model are conducted to identify model biases and optimize parameter weight configuration.

[0071] Obtain data on the service life of transmission towers, calculate the cumulative service life based on the difference between the tower commissioning timestamp and the current time, quantify the impact of structural aging on the time scale, and output the quantified service life value T_age.

[0072] Data from tower material strain sensors were collected, and the original strain signal was processed by noise suppression using the Kalman filter algorithm. The principal strain direction and peak strain sequence under structural stress were extracted, and the structural strain feature vector ε(t) was output.

[0073] Based on the input tower material strain sensor signal stream, the raw strain signals from distributed fiber optic strain sensors or resistance strain gauges are acquired to the data buffer module to achieve multi-channel synchronous sampling and timestamp calibration.

[0074] Kalman filter algorithm (initial state mean) With covariance matrix The process noise covariance matrix is ​​set by the sensor calibration parameters. and measurement noise covariance matrix Based on the equipment noise spectral density calibration, the original strain signal is subjected to time-series noise reduction processing to achieve robust suppression of measurement noise and environmental disturbances.

[0075] Furthermore, local peak detection is performed on the filtered strain time history data using a short-time sliding window envelope analysis method, and within each time window, the eigenvalue decomposition method (decomposing the two-dimensional strain tensor to obtain the directions of the maximum and minimum principal strains) is applied to calculate the principal strain direction vector under structural stress. .

[0076] Furthermore, a peak hold algorithm is employed to capture the peak values ​​of the principal strain modulus curves for each time window, using the formula... To calculate the peak strain sequence, where Number of windows This represents the maximum principal strain value within a single window.

[0077] Furthermore, a multidimensional structural strain feature vector is constructed, which includes the principal strain direction, principal strain amplitude, and their time rate of change. The data is then normalized and mapped to a dimensionless feature space for subsequent extraction of structural aging factors and modeling of dynamic disaster resistance capabilities.

[0078] By combining Kalman filtering and eigenvalue decomposition, the original strain signal is transformed into a strain characteristic index with high signal-to-noise ratio, clear directionality, and clear physical meaning, thereby achieving accurate characterization of the stress state evolution of the tower structure.

[0079] For example, a 500kV transmission line steel tower was equipped with four distributed fiber optic strain sensors in its 15th year of service, with a sampling frequency of [missing information]. Hz, the standard deviation of the original strain signal is με, the noise spectral density is calibrated to obtain the measured noise covariance. με², process noise covariance με².

[0080] Initialize the state using Kalman filtering με, με², the signal-to-noise ratio is improved to more than 3 times the original after filtering. Eigenvalue decomposition of the two-dimensional strain tensor is performed within a 1-second sliding window to obtain time-history curves showing continuous variation of the principal strain directions between 0° and 90°, and the mean peak strain value... με, standard deviation is με. The peak sequence and direction sequence are fused to generate a 4-dimensional strain feature vector (amplitude, direction, rate of change of amplitude, rate of change of direction), which is then normalized to the [0,1] interval. In the subsequent calculation of the dynamic disaster resistance capacity R(t) model, this feature vector accurately reflects the stress adaptation change of the tower under the coupled influence of earthquake and wind loads, improving the response accuracy of the time-varying model.

[0081] Obtain tower foundation displacement monitoring data, extract foundation settlement and horizontal displacement components based on differential GPS positioning system, combine with displacement rate model to predict trend, and output foundation displacement vector dbase(t).

[0082] Corrosion rate monitoring data were collected, and corrosion kinetic parameters of the tower material surface were identified based on electrochemical impedance spectroscopy. A corrosion rate model was established by combining environmental humidity and salt spray concentration data, and the corrosion rate time series data vcorr(t) was output.

[0083] We acquired foundation settlement rate monitoring data, and used InSAR remote sensing and field monitoring station fusion data to perform spatial interpolation modeling of foundation deformation, generating a foundation settlement rate field vsettle(t), which serves as a key input for tower stability assessment.

[0084] Record environmental erosion factor data, including annual average rainfall, wind speed, humidity, salt spray concentration and temperature difference cycle number, and construct the environmental erosion intensity index Eenv to quantify the accelerating effect of the external environment on the aging of tower structures.

[0085] Historical earthquake damage event sequence data are collected, and earthquake occurrence time, magnitude, epicenter distance and tower response characteristics are extracted based on GIS system and operation and maintenance records to construct a historical earthquake damage event database and output earthquake damage event sequence Eeq(t).

[0086] Missing values ​​were imputed in the collected structural health monitoring data, including service life, tower material strain, foundation displacement, corrosion rate, and foundation settlement rate. Linear interpolation or K-nearest neighbor interpolation algorithm was used to fill in the missing items in the data to obtain a continuous and complete original data sequence.

[0087] Normalization is performed on the filled structural health monitoring data. The max-min standardization method is used to scale the data of each dimension so that each type of data is within the range of [0,1], so as to eliminate the influence of the difference in dimensions on subsequent modeling, and the normalized structural health feature matrix is ​​output.

[0088] The Z-score method is used to detect and remove outliers from the normalized structural health feature matrix, identifying outlier data points that deviate from the mean by more than three times the standard deviation. A sliding window mean substitution strategy is then used to correct these outliers, thereby improving the robustness of the dataset.

[0089] The corrected structural health monitoring data is time-aligned according to the tower service timestamp. Time series resampling technology is used to unify data with different sampling frequencies to the same time resolution, generating a timestamp sequence based on a fixed time interval.

[0090] By integrating time-aligned structural health monitoring data with environmental erosion factors and historical earthquake damage event sequences, a multi-source heterogeneous structural aging feature dataset containing timestamps, structural parameters, environmental parameters, and earthquake damage records is constructed, and a unified format structural aging feature time series dataset is output.

[0091] Features such as service life, tower material strain, foundation displacement, corrosion rate, and foundation settlement rate in the structural aging feature dataset are extracted. Principal component analysis algorithm is used to extract key aging factors to form a structural aging factor feature vector.

[0092] The time series dataset of structural aging features in a unified format is input into the feature extraction module. The feature standardization matrix is ​​used as the input benchmark to achieve equal weighting of each indicator on the numerical scale.

[0093] A feature covariance matrix calculation method (parameters: number of data samples N, feature dimension M) is used to measure the pairwise correlation between various aging features and obtain the covariance matrix. .

[0094] Furthermore, an eigenvalue decomposition algorithm (parameters: matrix C, precision threshold ε) is used to perform spectral decomposition of the covariance matrix, resulting in a set of eigenvector matrices arranged in descending order of eigenvalues. and eigenvalue sequences .

[0095] Furthermore, the cumulative variance contribution rate of the first k eigenvectors is calculated using the variance contribution rate calculation method: The optimal number of principal components k is determined based on the contribution rate threshold θ.

[0096] Furthermore, through the principal component mapping formula The original aging feature matrix X is transformed into a k-dimensional principal component space to generate a dimension-reduced structural aging factor feature vector F.

[0097] By using the PCA dimensionality reduction method described above, high-dimensional and redundant aging feature data are transformed into low-dimensional and information-concentrated feature vectors, thereby achieving a compact representation of the aging state of the structure.

[0098] For example, in a structural health monitoring dataset of a certain group of transmission towers, the service life (Tage) ranges from 0 to 40 years, the tower material strain (ε(t)) ranges from 0 to 350 μɛ, the foundation displacement (dbase(t)) ranges from -5 to 18 mm, the corrosion rate (vcorr(t)) ranges from 0 to 0.15 mm / a, and the foundation settlement rate (vsettle(t)) ranges from -2 to 6 mm / a. The above five types of normalized time series are used to construct a 10000×5 feature matrix. A 5×5 matrix C is obtained using the covariance matrix calculation method, and the eigenvalue sequence is obtained through eigenvalue decomposition. , , , , The corresponding eigenvector matrix V. Calculate the cumulative contribution rate of the first 3 features. The value is approximately 0.918, which is greater than the set threshold of 0.90, so k=3 is selected. The principal component mapping formula is used to map the original feature matrix to a 3-dimensional space. The resulting F matrix retains 91.8% of the information and achieves the compression of the feature dimension from 5 dimensions to 3 dimensions, which facilitates subsequent coupling with the physical mechanism model and improves computational efficiency and robustness.

[0099] Based on the physical and mechanical model of the tower structure, theoretical models are built for disaster resistance indicators such as foundation depth-to-width ratio, tower material strength, and connection node stiffness. A static mapping relationship between structural performance indicators and physical parameters is established to form an initial disaster resistance model driven by physical mechanisms.

[0100] The structural aging factor feature vector is coupled with the initial disaster resistance capacity model driven by physical mechanisms, and a long short-term memory network (LSTM) is used to train historical time series data to construct a nonlinear dynamic mapping function between structural aging factors and disaster resistance capacity indicators.

[0101] Based on the disaster resistance index output by the dynamic mapping function, the state space model is used to perform time series modeling of the tower disaster resistance capacity R(t) to generate a time-varying disaster resistance capacity function R(t) with time evolution characteristics.

[0102] The parameter sensitivity analysis of the generated time-varying disaster resistance function R(t) was performed, and the model parameters were tuned based on the Bayesian optimization algorithm to improve the generalization ability and stability of the model under different service years and environmental erosion conditions.

[0103] The ground motion parameters and landslide displacement field are aligned in the time domain and matched in the spectrum. Based on the seismic wave propagation model and the landslide kinematic equation, a coupled input excitation function is constructed to form a multidimensional dynamic input sequence under a unified time reference.

[0104] The multidimensional dynamic input sequence is input into the coupled dynamic response model of tower-foundation-soil constructed based on the finite element method. The Newmark-β method is used to solve the time history of the structural dynamic response to obtain the acceleration, velocity and displacement response time history curves of the key nodes of the tower.

[0105] Based on the structural response time history curve, peak structural response indices, including maximum inter-story drift angle, maximum node shear force, and maximum tower bending moment, are extracted as basic characteristic parameters for assessing structural damage status.

[0106] Based on the peak structural response index and the time-varying disaster resistance capacity R(t) at the current moment, an energy dissipation model and a damage accumulation criterion are used to quantitatively assess the damage state of the tower structure under earthquake-landslide coupling to generate the structural damage index D(t).

[0107] The structural damage index D(t) is normalized and mapped to state hierarchy, and a fuzzy comprehensive evaluation method is used to generate... The structural damage index D(t) sequence and historical earthquake damage records were time-aligned and fused to generate an observation dataset for Bayesian updates.

[0108] A parameter update model is constructed based on the Bayesian inference framework. The current observation dataset and the prior disaster resistance capacity R(t) distribution are input, and the posterior probability density function is calculated to optimize the model parameter estimation.

[0109] The particle filter algorithm is used to approximate the posterior distribution and generate an updated sample set of the disaster resistance capability R(t) state space to achieve dynamic correction of model parameters.

[0110] Statistical analysis was performed on the updated disaster resilience R(t) sample set to extract key parameters such as mean, variance and confidence interval, and to generate a dynamic disaster resilience assessment index set.

[0111] The updated disaster resilience R(t) parameter set is fed back into the risk assessment model to drive the adaptive updating of the risk level map and achieve continuous optimization of the model at different service stages.

[0112] The disaster resistance parameter sequence output by the updated time-varying disaster resistance capability R(t) model is normalized to eliminate the dimensional differences of structural parameters between different towers, and a standardized disaster resistance capability vector Rnorm(t) is obtained for subsequent model fusion calculation.

[0113] Based on the susceptibility layer data of earthquake-induced landslide disasters, a spatial interpolation algorithm is used to perform raster modeling of the landslide displacement field, generating a high-resolution landslide intensity distribution matrix S(x,y,t), where (x,y) represents spatial coordinates and t represents the time dimension.

[0114] The standardized disaster resistance capacity vector Rnorm(t) and the landslide intensity distribution matrix S(x,y,t) are spatially superimposed and analyzed. The weighted Euclidean distance method is used to calculate the earthquake-landslide coupling risk factor C(x,y,t) at each tower location, where the weight coefficients are obtained by training based on historical earthquake damage data.

[0115] Spatial matching is performed between the risk factor C(x,y,t) and the vector map layer of tower spatial distribution. The kernel density estimation method is used to perform spatial smoothing of the risk factor to generate a continuously distributed dynamic risk level raster map RISKMAP(t), and the level is labeled.

[0116] The dynamic risk level raster map RISKMAP(t) is visualized and rendered using a geographic information system (GIS) engine to generate an interactive risk level heat map, which is then output to the operation and maintenance decision support platform for power grid operation and maintenance personnel to view and analyze risk warnings in real time.

[0117] Threshold comparison processing is performed on the risk level data of each node along the tower output by the dynamic risk assessment model to identify high-risk areas that exceed the preset risk tolerance.

[0118] Based on the difference between the risk level and the preset threshold, a fuzzy logic control algorithm is used to generate graded early warning signal strengths and output multi-level early warning labels to achieve refined control of the early warning response.

[0119] Based on the identified high-risk areas and their corresponding risk causes, the system calls upon the pre-built tower reinforcement strategy library in the knowledge graph to generate targeted structural reinforcement suggestions.

[0120] By combining the landslide displacement field evolution trend with geological environment monitoring data, a landslide protection engineering recommendation algorithm is used to match corresponding slope stabilization measures and output a landslide protection recommendation list.

[0121] The generated early warning signals and risk response suggestions are integrated into a structured contingency plan instruction, which is transmitted to the operation and maintenance dispatch center through a communication interface to activate the emergency response mechanism and assist in on-site handling decisions.

[0122] For multi-level early warning signals and corresponding tower reinforcement and landslide protection measures, a structured information coding method (parameters: early warning level, geographical location, timestamp, response suggestion category) is adopted to achieve semantic and machine-readable encapsulation of various types of information.

[0123] Furthermore, through the field mapping rule set (parameters: ISO 19115 geographic information metadata standard, IEC61968-9 interface specification), the field standardization and data type unification of early warning signals and response suggestions are achieved, and structured data frames that meet the access protocol of the dispatch center are obtained.

[0124] Furthermore, by using a multi-channel data packetization algorithm (parameters: TCP / IP packet size, CRC-32 redundancy check code, message priority label), the packet transmission of warning and suggestion information is optimized, and a packet sequence with transmission control information is generated.

[0125] Furthermore, by using a secure transmission encryption algorithm (parameters: AES-256 key, SHA-256 digest verification), the structured pre-plan instructions are encrypted and their integrity is verified, and a data packet that can be securely transmitted is generated.

[0126] Furthermore, through the communication interface driver module (parameter: interface type IEC 60870-5-104 or MQTT overTLS protocol), encrypted data packets are transmitted to the operation and maintenance scheduling center via the scheduling data network, and protocol parsing and instruction entry operations are performed at the receiving end.

[0127] Through the aforementioned communication link processing method, the risk warning signals and response suggestions from the previous step are transformed into structured contingency plan instruction data that conforms to the standards of the dispatch center, thereby enabling the automatic triggering of the emergency response module and the issuance of instructions for on-site handling.

[0128] For example, in the assessment of a 220kV transmission line for both earthquake and landslide high risk, the system generates a warning signal with a warning level of 5, which includes the geographical location (longitude). ,latitude ), timestamp Recommended types (tower foundation reinforcement, slope grid retaining) and implementation priorities (urgent). The structured coding phase maps the risk level field to... Geographic coordinates should be encoded in WGS-84 format, and timestamp fields should be formatted as YYYYMMDDhhmmss. Multi-value enumeration encoding is recommended. During the communication packetization phase, the TCP / IP packet size should be set. Bytes, with CRC-32 checksum appended A priority identifier is added to the packet header. The encryption phase uses an AES-256 bit key. Byte length and SHA-256 digest verification ensure data security. Data packets are sent from the field monitoring center to the operation and maintenance dispatch center via the IEC 60870-5-104 interface protocol, with a transmission latency of less than [missing information]. Milliseconds, success rate reaches %. Upon parsing, the receiving end triggers the emergency response module, automatically generating an on-site work order and sending it to the mobile terminal to ensure that... The mobilization of contingency plans for high-risk areas can be completed within minutes.

[0129] Historical early warning response data and actual disaster occurrence records are time-aligned and event-matched to construct a real-event comparison dataset required for model validation, which can then be used for subsequent retrospective validation analysis.

[0130] Based on the constructed real-event comparison dataset, the confusion matrix and ROC curve analysis methods are used to quantitatively evaluate the early warning accuracy and false alarm / false negative rate of the dynamic risk assessment model, so as to identify the classification performance of the model under different risk levels.

[0131] Spatial matching analysis was performed on the dynamic risk level map output by the model and the actual disaster location. The spatial consistency index and Kappa coefficient were used to evaluate the predictive reliability of the model in the geospatial dimension, so as to identify the bias characteristics of the model in the regional risk distribution.

[0132] Based on the classification error and spatial bias indices in the backtracking verification results, a multi-objective optimization algorithm is used to perform sensitivity analysis and global optimization adjustment on the parameter weights in the risk assessment model, so as to improve the generalization ability of the model under different service years and environmental conditions.

[0133] The optimized parameter weights are updated into the dynamic risk assessment model, and the model parameters are adaptively corrected by combining the Bayesian online update mechanism to enhance the robustness and engineering applicability of the model in long-term operation.

[0134] In summary, the earthquake landslide risk assessment method for transmission towers provided in this embodiment, after acquiring the structural health monitoring data of the target transmission tower, constructs the structural aging characteristics corresponding to the structural health monitoring data. Next, based on the tower's structural physics model and preset disaster resistance indicators, an initial disaster resistance capacity model is constructed. Then, based on the aging factors in the structural aging characteristics and the initial disaster resistance capacity model, a time-varying disaster resistance capacity model is constructed. A structural damage index is generated based on seismic motion parameters, landslide displacement field, and the time-varying disaster resistance capacity model. Following this, the time-varying disaster resistance capacity model is adjusted based on the structural damage indication and historical earthquake damage records. Finally, based on the adjusted model and the risk assessment model, a dynamic risk level map is output. When a risk assessment request for the target transmission tower is received, an earthquake landslide risk assessment is performed based on the dynamic risk level map. In the earthquake landslide risk assessment scheme for transmission towers provided in this application, an initial disaster resistance model is built by combining the tower's physical model with disaster resistance indicators. An aging factor is incorporated to construct a time-varying disaster resistance model, which replaces the fixed disaster resistance factor and accurately reflects the degradation of tower material strength and foundation stability over time. Subsequently, the time-varying model is used to generate a damage index and adjust the model. Combined with the risk assessment model, the output spectrum is used to respond to assessment requests. Considering the coupled risk response under the time-varying characteristics of the structure, the accuracy of the assessment is greatly improved, closely matches the actual risk level of the tower, and solves the problem of poor accuracy in traditional schemes.

[0135] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0136] To facilitate better implementation of the seismic landslide risk assessment method for transmission towers according to the embodiments of this application, this application also provides a seismic landslide risk assessment device for transmission towers based on the above-described seismic landslide risk assessment method for transmission towers. The meanings of the terms used are the same as in the above-described seismic landslide risk assessment method for transmission towers, and specific implementation details can be found in the descriptions in the method embodiments.

[0137] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of the earthquake landslide risk assessment device for transmission towers provided in this application embodiment. Specifically, the device may include an acquisition module 201, a first construction module 202, a second construction module 203, a third construction module 204, a generation module 205, an adjustment module 206, an output module 207, and an assessment module 208, as follows: Module 201 is used to acquire structural health monitoring data of the target transmission tower; The first construction module 202 is used to construct the structural aging characteristics corresponding to the structural health monitoring data; The second construction module 203 is used to construct an initial disaster resistance capability model based on the physical model of the tower structure and the preset disaster resistance indicators. The third construction module 204 is used to construct a time-varying disaster resistance model based on the aging factor and the initial disaster resistance model in the structural aging characteristics. The generation module 205 is used to generate a structural damage index based on seismic motion parameters, landslide displacement field and time-varying disaster resistance model. The adjustment module 206 is used to adjust the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records. Output module 207 is used to output a dynamic risk level map based on the adjusted model and risk assessment model; The assessment module 208 is used to conduct an earthquake and landslide risk assessment based on the dynamic risk level map when a risk assessment request for the target transmission tower is received.

[0138] Specific limitations regarding the seismic landslide risk assessment device for transmission towers can be found in the limitations of the seismic landslide risk assessment method for transmission towers mentioned above, and will not be repeated here. Each module in the aforementioned seismic landslide risk assessment device for transmission towers can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0139] The earthquake landslide risk assessment device for transmission towers provided in this embodiment includes the following steps: After the acquisition module 201 acquires the structural health monitoring data of the target transmission tower, the first construction module 202 constructs the structural aging characteristics corresponding to the structural health monitoring data. Then, the second construction module 203 constructs an initial disaster resistance model based on the tower structure physics model and preset disaster resistance indicators. Next, the third construction module 204 constructs a time-varying disaster resistance model based on the aging factors in the structural aging characteristics and the initial disaster resistance model. The generation module 205 generates a structural damage index based on the seismic motion parameters, landslide displacement field, and time-varying disaster resistance model. Then, the adjustment module 206 adjusts the time-varying disaster resistance model based on the structural damage indication and historical earthquake damage records. Finally, the output module 207 outputs a dynamic risk level map based on the adjusted model and the risk assessment model. When the assessment module 208 receives a risk assessment request for the target transmission tower, it performs an earthquake landslide risk assessment based on the dynamic risk level map. In the earthquake landslide risk assessment scheme for transmission towers provided in this application, an initial disaster resistance model is built by combining the tower's physical model with disaster resistance indicators. An aging factor is incorporated to construct a time-varying disaster resistance model, which replaces the fixed disaster resistance factor and accurately reflects the degradation of tower material strength and foundation stability over time. Subsequently, the time-varying model is used to generate a damage index and adjust the model. Combined with the risk assessment model, the output spectrum is used to respond to assessment requests. Considering the coupled risk response under the time-varying characteristics of the structure, the accuracy of the assessment is greatly improved, closely matches the actual risk level of the tower, and solves the problem of poor accuracy in traditional schemes.

[0140] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 5 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0141] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and earthquake landslide risk assessment methods for transmission towers by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0142] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0143] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0144] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows: Acquire structural health monitoring data of the target transmission tower; construct structural aging characteristics corresponding to the structural health monitoring data; construct an initial disaster resistance capacity model based on the tower structure physics model and preset disaster resistance indicators; construct a time-varying disaster resistance capacity model based on the aging factors in the structural aging characteristics and the initial disaster resistance capacity model; generate a structural damage index based on seismic motion parameters, landslide displacement field, and the time-varying disaster resistance capacity model; adjust the time-varying disaster resistance capacity model based on the structural damage indication and historical earthquake damage records; output a dynamic risk level map based on the adjusted model and risk assessment model; when a risk assessment request for the target transmission tower is received, conduct an earthquake and landslide risk assessment based on the dynamic risk level map.

[0145] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0146] In this embodiment, after acquiring the structural health monitoring data of the target transmission tower, the structural aging characteristics corresponding to the structural health monitoring data are constructed. Then, based on the tower structure physics model and preset disaster resistance indicators, an initial disaster resistance capacity model is constructed. Next, based on the aging factor in the structural aging characteristics and the initial disaster resistance capacity model, a time-varying disaster resistance capacity model is constructed. Based on the seismic motion parameters, landslide displacement field, and time-varying disaster resistance capacity model, a structural damage index is generated. Subsequently, based on the structural damage indication and historical earthquake damage records, the time-varying disaster resistance capacity model is adjusted. Finally, based on the adjusted model and the risk assessment model, a dynamic risk level map is output. When a risk assessment request for the target transmission tower is received, an earthquake and landslide risk assessment is performed based on the dynamic risk level map. In the earthquake landslide risk assessment scheme for transmission towers provided in this application, an initial disaster resistance model is built by combining the tower's physical model with disaster resistance indicators. An aging factor is incorporated to construct a time-varying disaster resistance model, which replaces the fixed disaster resistance factor and accurately reflects the degradation of tower material strength and foundation stability over time. Subsequently, the time-varying model is used to generate a damage index and adjust the model. Combined with the risk assessment model, the output spectrum is used to respond to assessment requests. Considering the coupled risk response under the time-varying characteristics of the structure, the accuracy of the assessment is greatly improved, closely matches the actual risk level of the tower, and solves the problem of poor accuracy in traditional schemes.

[0147] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0148] Therefore, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the earthquake landslide risk assessment methods for transmission towers provided in embodiments of this application. For example, the instructions can execute the following steps: Acquire structural health monitoring data of the target transmission tower; construct structural aging characteristics corresponding to the structural health monitoring data; construct an initial disaster resistance capacity model based on the tower structure physics model and preset disaster resistance indicators; construct a time-varying disaster resistance capacity model based on the aging factors in the structural aging characteristics and the initial disaster resistance capacity model; generate a structural damage index based on seismic motion parameters, landslide displacement field, and the time-varying disaster resistance capacity model; adjust the time-varying disaster resistance capacity model based on the structural damage indication and historical earthquake damage records; output a dynamic risk level map based on the adjusted model and risk assessment model; when a risk assessment request for the target transmission tower is received, conduct an earthquake and landslide risk assessment based on the dynamic risk level map.

[0149] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0150] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0151] Since the instructions stored in the storage medium can execute the steps in any of the earthquake landslide risk assessment methods for transmission towers provided in the embodiments of this application, the beneficial effects that any of the earthquake landslide risk assessment methods for transmission towers provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0152] The above provides a detailed description of a method and related equipment for assessing the seismic landslide risk of transmission towers provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of assessing the risk of seismic landslide for a power transmission tower, characterized by, The method comprises the following steps: obtaining structural health monitoring data of a target power transmission tower; constructing a structure aging feature corresponding to the structural health monitoring data; constructing an initial disaster resistance capability model according to a tower structure physics model and a preset disaster resistance index; constructing a time-varying disaster resistance capability model based on an aging factor in the structure aging feature and the initial disaster resistance capability model; generating a structure damage index according to seismic motion parameters, a landslide displacement field, and the time-varying disaster resistance capability model; adjusting the time-varying disaster resistance capability model according to the structure damage index and historical earthquake damage records; outputting a dynamic risk level atlas based on the adjusted model and a risk assessment model; when receiving a risk assessment request of a target power transmission tower, performing seismic landslide risk assessment based on the dynamic risk level atlas.

2. The method of claim 1, wherein, The method comprises the following steps: determining a preset disaster resistance index; establishing a physics model corresponding to each disaster resistance index; constructing a mapping relationship between each disaster resistance index and disaster resistance capability; constructing an initial disaster resistance capability model based on the physics model corresponding to each disaster resistance index and the mapping relationship between each disaster resistance index and disaster resistance capability.

3. The method of claim 1, wherein, The method comprises the following steps: extracting an aging factor in the structure aging feature; determining a physical influence mechanism of the aging factor on the initial disaster resistance capability model index; constructing a dynamic mapping function of the aging factor and the disaster resistance index based on the physical influence mechanism; constructing a time-varying disaster resistance capability model according to the dynamic mapping function and the initial disaster resistance capability model.

4. The method of claim 1, wherein, The method comprises the following steps: constructing a multi-dimensional dynamic sequence according to seismic motion parameters and a landslide displacement field; generating a structure damage index based on the multi-dimensional dynamic sequence and the time-varying disaster resistance capability model.

5. The method for evaluating a seismic landslide risk of a power transmission tower according to claim 1, wherein The method comprises the following steps: standardizing the structure damage index and historical earthquake damage records to generate observation reference data; adjusting the time-varying disaster resistance capability model based on the observation reference data.

6. The method of seismic landslide risk assessment for a power transmission tower according to claim 1, wherein, The method comprises the following steps: normalizing the adjusted model to obtain a normalized model; obtaining original data of seismic landslide susceptibility of the target area and performing grid processing on the original data to obtain a strength matrix; outputting a dynamic risk level atlas according to the normalized model, the strength matrix, and a risk assessment model.

7. The method for evaluating a seismic landslide risk of a power transmission tower according to claim 1, wherein The method comprises the following steps: when receiving a risk assessment request of a target power transmission tower, determining to-be-evaluated tower information from the risk assessment request; obtaining basic risk data of the to-be-evaluated tower based on the to-be-evaluated tower information; performing seismic landslide risk assessment on the to-be-evaluated tower according to the basic risk data and the dynamic risk level atlas.

8. An apparatus for evaluating a seismic landslide risk of a power transmission tower, characterized by, The method comprises the following steps: An acquisition module is configured to acquire structural health monitoring data of a target power transmission tower; A first construction module is configured to construct a structural aging feature corresponding to the structural health monitoring data; A second construction module is configured to construct an initial disaster resistance capability model according to a tower structure physics model and a preset disaster resistance index; A third construction module is configured to construct a time-varying disaster resistance capability model based on an aging factor in the structural aging feature and the initial disaster resistance capability model; A generation module is configured to generate a structural damage index according to a ground motion parameter, a landslide displacement field, and the time-varying disaster resistance capability model; An adjustment module is configured to adjust the time-varying disaster resistance capability model according to the structural damage index and a historical seismic disaster record; An output module is configured to output a dynamic risk level atlas based on the adjusted model and a risk assessment model; An evaluation module is configured to perform a seismic landslide risk assessment based on the dynamic risk level atlas when receiving a risk assessment request of the target power transmission tower.

9. An electronic device, comprising: The method comprises the following steps: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the seismic landslide risk assessment method of the power transmission tower according to any one of claims 1-7.

10. A storage medium, characterized by The computer program stored in the memory can be loaded and executed by the processor to implement the seismic landslide risk assessment method of the power transmission tower according to any one of claims 1-7.

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