Method and system for evaluating stability of high-voltage transmission tower in complex environment

Abstract

The invention discloses a method and system for evaluating the stability of a high-voltage transmission tower in a complex environment, and the method comprises the steps: constructing an icing space arrangement vector, calculating the stress of the transmission tower, and determining an important bearing member; combining environmental factors and component amplitude and period, obtaining component energy loss and slope change rate vectors through a model test, and further obtaining a rigidity degradation function and an overall stability index; and finally, constructing a BP neural network prediction model, calculating indexes and matching a stability stage, and completing tower stability evaluation. According to the method, multiple key influence factors are fully considered, two core working conditions of no icing and icing are comprehensively covered, meanwhile, an energy loss vector and a slope change rate vector are introduced to serve as damage evolution core indexes, a rigidity degradation function of an important bearing component is combined, quantitative correlation of energy dissipation and rigidity degradation is established, and the damage evolution performance of the bearing component is improved. And an overall stability index is constructed, so that multi-dimensional comprehensive evaluation of the stability of the high-voltage transmission tower is realized.

Description

Technical Field

[0001] This invention relates to evaluation methods, belonging to the field of safety monitoring, and particularly to an evaluation method and system for the stability of high-voltage transmission towers under complex environments. Background Technology

[0002] High-voltage transmission towers are an important foundation and node for the operation of the entire power grid. Their stability and reliability during operation have a significant impact on the power grid. Among these factors, line icing and wind loads have a particularly prominent impact on the safe operation of high-voltage transmission towers. In addition, my country's terrain is mostly mountainous, and the climate in mountainous areas is drastic, making high-voltage transmission towers located in mountainous areas more susceptible to the effects of line icing and wind loads.

[0003] Power grid lines are mostly built in mountainous areas. When a high-voltage transmission tower experiences an accident, the disruption of transportation and weather conditions inevitably hinder emergency repairs. Therefore, assessing and accurately predicting the stability of high-voltage transmission towers is crucial for preventing safety accidents. In recent years, with the development and application of machine learning algorithms, predicting the icing thickness of transmission cables and the wind load on high-voltage towers using different algorithms to calculate the stress on the towers and assess their stability has become a common practice. However, in reality, transmission cables vibrate under wind, causing vibrations in the high-voltage transmission towers. The tower itself also vibrates under wind loads. When transmission cables are covered in ice, the vibrations become more intense and complex, making the internal structural components more susceptible to damage and impacting the tower's stability. Therefore, assessing the stability of high-voltage transmission towers solely based on their stress conditions is somewhat one-sided. Therefore, there is an urgent need for an evaluation method that comprehensively considers the impact of icing on power transmission cables on the stability of high-voltage transmission towers, in order to solve the aforementioned problems in the existing technology. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned defects and problems in the prior art and to provide a method and system for evaluating the stability of high-voltage transmission towers under complex environments that comprehensively consider the effects of icing.

[0005] To achieve the above objectives, the technical solution of this invention is: a method for evaluating the stability of high-voltage transmission towers under complex environments, comprising:

[0006] Divide the power transmission cables into equal-spacing sections and construct a spatial arrangement vector for the icing of the power transmission cables;

[0007] Based on the spatial arrangement vector of icing on transmission cables, the stress on the tower body of the transmission tower under icing conditions is calculated, and the important load-bearing components of the transmission tower are determined.

[0008] The environmental factors of the transmission tower and the maximum amplitude and average vibration period of the important load-bearing components under the influence of environmental factors were obtained. A full-scale model was established and an experiment was conducted based on the stress condition of the transmission tower body and the maximum amplitude and average vibration period of the important load-bearing components. The energy loss vector and the rate of change of the slope of the stress-strain curve origin of the important load-bearing components were obtained.

[0009] Based on the energy loss vector of important load-bearing components and the rate of change of the slope of the stress-strain curve origin, the stiffness degradation function of important load-bearing components is obtained, and the overall stability index of important load-bearing components is determined.

[0010] Based on the overall stability index of important load-bearing components, the stability stages of important load-bearing components are divided;

[0011] Based on environmental factors of transmission towers, spatial arrangement vector of icing on transmission cables, and maximum amplitude and average vibration period of important load-bearing components, a first BP neural network prediction model under non-icing conditions and a second BP neural network prediction model under icing conditions are constructed respectively; and a third BP neural network prediction model is constructed based on the first and second BP neural network prediction models.

[0012] The stability index of important load-bearing components is calculated based on the third BP neural network model, and the corresponding stability stage of important load-bearing components is matched to complete the stability assessment of transmission towers.

[0013] The process of dividing power transmission cables into equidistant sections and constructing a spatial arrangement vector for cable icing specifically includes:

[0014] The power transmission cable is divided into several segments at equal intervals, and the icing situation of each segment is monitored. A spatial arrangement vector of icing on the power transmission cable is constructed, and its expression is as follows:

[0015] ;

[0016] in: This represents the spatial arrangement vector for icing of power transmission cables. For the first Vector of icing conditions on a section of power transmission cable. For the first The vector of icing thickness on the power transmission cable section. For the first The eccentricity angle vector of a power transmission cable segment; This is for averaging operations.

[0017] The acquisition of the energy loss vector and the rate of change vector of the slope at the origin of the stress-strain curve of the important load-bearing components specifically includes:

[0018] Environmental factors of the transmission towers, including maximum wind speed and corresponding wind direction angle, were obtained. Based on these factors, the static load, maximum amplitude, and average vibration period of the important load-bearing components of the transmission cable in both un-iced and iced states were acquired. Instability failure tests were then conducted on a full-scale model sequentially, alternating between the un-iced and iced states of the transmission cable. The energy loss vector and the rate of change of the slope of the stress-strain curve origin of the important load-bearing components were obtained, and their expressions are as follows:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] in: This is the energy loss vector; For the first One energy loss component; The initial sinusoidal signal wave energy value, The energy value of the sinusoidal signal wave after the experiment; The vector representing the rate of change of the slope of the stress-strain curve origin; For the first The rate of change of the slope at the origin of the stress-strain curve; The slope of the initial stress-strain curve origin; The slope of the stress-strain curve originating from the test result is denoted as .

[0024] The method involves obtaining the stiffness degradation function of important load-bearing components based on the energy loss vector of the important load-bearing components and the rate of change of the slope of the stress-strain curve origin, and determining the overall stability index of the important load-bearing components, specifically including:

[0025] Based on the energy loss vector of important load-bearing components and the rate of change of the slope of the stress-strain curve origin, the spatial influence function and the local energy dissipation density are determined, and their expressions are as follows:

[0026] ;

[0027] ;

[0028] in: It is a spatial influence function; This represents the local energy dissipation density. The radius of influence of the feature; It is an exponential function; The spatial distance between the target point and other neighboring points; For the first The first sinusoidal signal wave and the second The spatial distance between sinusoidal signal waves; For the first One energy loss value;

[0029] Based on the spatial influence function and local energy dissipation density, a stiffness degradation function for important load-bearing components is constructed, and its expression is as follows:

[0030] ;

[0031] in: For the first The rate of change of the slope at the origin of each stress-strain curve; This is the stiffness degradation coefficient; It is a non-linear exponent.

[0032] The determination of the overall stability index of important load-bearing components specifically includes:

[0033] To obtain the maximum local energy dissipation density, average local energy dissipation density, standard deviation of local energy dissipation density, and maximum and average values ​​of local energy dissipation density gradients, we establish an overall stability feature vector and construct an overall stability feature matrix, the expression of which is as follows:

[0034] ;

[0035] ; ;

[0036] ; ;

[0037] in: This is the overall stability characteristic matrix; This represents the overall stability feature vector; This represents the maximum local energy dissipation density. This represents the average local energy dissipation density. The standard deviation of local energy dissipation density; This represents the local energy dissipation density gradient. , These are the maximum and average values ​​of the local energy dissipation density gradient, respectively.

[0038] Based on the overall stability characteristic matrix, the overall stability index of important load-bearing components is determined, and its expression is as follows:

[0039] ;

[0040] in: As an indicator of the overall stability of important load-bearing components; This is the normalized overall stability feature matrix; These are the weighting coefficients; For the first This is the second test.

[0041] The method of dividing the stability stages of important load-bearing components based on the overall stability index of important load-bearing components specifically includes:

[0042] The evolution curves of the overall stability index of important load-bearing components are analyzed. Based on the characteristic inflection points appearing in the evolution curves, stages are divided into four phases: the safe stage, the local damage initiation stage, the rapid damage development stage, and the instability precursor stage. The expressions are as follows:

[0043] ;

[0044] in: As the starting point; This marks the inflection point at which the patient transitions from the safe phase to the localized initiation phase of damage. This is the inflection point that accelerates the transition from the initial stage of local injury to the rapid development stage of the injury. This marks the critical inflection point at which the rapid progression of damage transitions into the pre-instability stage. This is the point of instability.

[0045] The construction of the first BP neural network prediction model under the non-icing state and the second BP neural network prediction model under the icing state specifically includes:

[0046] Based on the correlation between the maximum wind speed, corresponding wind direction angle, constraint force on the ends of important load-bearing components, and the spatial arrangement vector of icing on transmission cables and the maximum amplitude and average period of important load-bearing components among the environmental factors of transmission towers, a dataset vector of the main influencing factors under non-icing and icing conditions is constructed, and their expressions are as follows:

[0047] ; ; ; ;

[0048] in: , Let be the dataset vectors of the main influencing factors formed under the conditions of no icing and icing, respectively; , The first two conditions are the power transmission cable under non-icing and icing conditions, respectively. Dataset of key influencing factors; , , These are the maximum wind speed, wind direction angle, and end constraint force of important load-bearing components of power transmission cables under icy conditions. , , These are the maximum wind speed, wind direction angle, and end constraint force of important load-bearing components of power transmission cables under icing conditions. Vector for the spatial arrangement of icing on power transmission cables; The maximum information coefficient;

[0049] Based on the datasets of major influencing factors under both icing and non-icing conditions, a first BP neural network prediction model for the non-icing condition and a second BP neural network prediction model for the icing condition are constructed, with the following expressions:

[0050] ;

[0051] ;

[0052] in: This is the first BP neural network prediction model; This is the second BP neural network prediction model; , The first under the condition of no icing The maximum amplitude and average period; , The first under the icing state The maximum amplitude and average period.

[0053] The construction of the third BP neural network prediction model based on the first and second BP neural network prediction models specifically includes:

[0054] ;

[0055] in: For the first A vector of energy loss; For the first A stiffness degradation coefficient; For the first A non-linear exponent; For the first Each feature affects the radius; This is the third BP neural network prediction model; , These are the ratios of the minimum and maximum constraint forces exerted on the ends of power transmission cables in both icy and non-iced states, respectively. , These are the sum of the number of cycles of the vibration curve experienced by the power transmission cable under both icing and non-icing conditions.

[0056] A system for evaluating the stability of high-voltage transmission towers under complex environments, the system being applied to the aforementioned method, the system comprising:

[0057] The layout vector acquisition module is used to divide the power transmission cables into equal intervals and construct the spatial layout vector of the power transmission cables covered by ice.

[0058] The module for determining important load-bearing components is used to calculate the stress on the tower body of the transmission tower under the condition of ice accumulation of the transmission cable based on the spatial arrangement vector of the transmission cable ice accumulation, and to determine the important load-bearing components of the transmission tower.

[0059] The module for obtaining energy loss and slope change vectors is used to obtain the environmental factors of transmission towers and the maximum amplitude and average vibration period of important load-bearing components under the influence of environmental factors. A full-scale model is established and experiments are conducted based on the stress conditions of the transmission tower body and the maximum amplitude and average vibration period of important load-bearing components to obtain the energy loss vector and the slope change rate vector of the stress-strain curve origin of important load-bearing components.

[0060] The stability index acquisition module is used to obtain the stiffness degradation function of important load-bearing components based on the energy loss vector of important load-bearing components and the rate of change of the slope of the stress-strain curve origin, and to determine the overall stability index of important load-bearing components.

[0061] The stability stage division module is used to divide the stability stages of important load-bearing components based on the overall stability index of the important load-bearing components.

[0062] The neural network construction module is used to construct a first BP neural network prediction model under the non-icing state and a second BP neural network prediction model under the icing state, based on the environmental factors of the transmission tower, the spatial arrangement vector of the icing of the transmission cable, and the maximum amplitude and average vibration period of the important load-bearing components; and to construct a third BP neural network prediction model based on the first and second BP neural network prediction models.

[0063] The stability assessment module is used to calculate the overall stability index of important load-bearing components based on the third BP neural network model and match the corresponding stability stage of important load-bearing components to complete the stability assessment of transmission towers.

[0064] An evaluation device for the stability of high-voltage transmission towers under complex environments, the device comprising a processor and a memory;

[0065] The memory is used to store computer program code and to transmit the computer program code to the processor;

[0066] The processor is used to execute the above-described method for evaluating the stability of high-voltage transmission towers under complex environments, according to instructions in the computer program code.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] This invention discloses a method and system for evaluating the stability of high-voltage transmission towers under complex environments. The method first constructs an icing spatial arrangement vector, using this vector to calculate the forces on the transmission tower and identify key load-bearing components. Then, combining environmental factors and the maximum amplitude and average vibration period of the components, a full-scale model test is conducted to obtain the vector of the rate of change of the slope of the energy loss and stress-strain curves at the origin of the components. This yields the stiffness degradation function and overall stability index, and the stability stages are divided. Finally, three types of BP neural network prediction models are constructed: no icing, icing, and fused. The models calculate the index and match the stability stages to complete the tower stability assessment. In application, this design closely reflects the actual operating state of transmission towers, fully considering key influencing factors such as icing spatial arrangement and environmental factors, and comprehensively covering both no-icing and icing conditions. It also introduces the energy loss vector and the rate of change of slope vector as core indicators of damage evolution. Combined with the stiffness degradation function of key load-bearing components, a quantitative correlation is established from energy dissipation to stiffness deterioration, and an overall stability index for key load-bearing components is constructed, achieving a multi-dimensional and comprehensive assessment of the stability of high-voltage transmission towers. Attached Figure Description

[0069] Figure 1 This is a flowchart of the method of the present invention.

[0070] Figure 2 This is a diagram of the neural network structure in Embodiment 1 of the present invention.

[0071] Figure 3 This is a system structure diagram of the present invention.

[0072] Figure 4 This is a structural diagram of the device of the present invention.

[0073] In the diagram: 1. Arrangement vector acquisition module; 2. Important load-bearing component determination module; 3. Energy loss and slope change vector acquisition module; 4. Stability index acquisition module; 5. Stability stage division module; 6. Neural network construction module; 7. Stability assessment module; 8. Processor; 9. Memory; 9. Computer program code. Detailed Implementation

[0074] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0075] Example 1:

[0076] See Figure 1 A method for evaluating the stability of high-voltage transmission towers under complex environments, comprising:

[0077] Divide the power transmission cables into equal-spacing sections and construct a spatial arrangement vector for the icing of the power transmission cables;

[0078] In this embodiment, based on the length and tension distribution of the power transmission cable, the cable is divided into several segments at equal intervals. When the cable is iced, the icing condition of each segment is monitored over a certain period to determine whether each segment is uniformly or eccentrically iced. If it is uniformly iced, its icing thickness is determined; if it is eccentrically iced, its maximum eccentric icing thickness and eccentricity angle are determined. Simultaneously, a spatial distribution vector for cable icing is introduced to reflect the spatial distribution of icing in each segment, as detailed below:

[0079] When high-voltage transmission tower cables are iced, the icing status of each cable segment is monitored in a cycle of P days to determine the arrangement of uniformly iced and eccentrically iced segments. There are D transmission cables erected on the high-voltage tower. Uniformly iced segments are assigned a value of 0, and eccentrically iced segments are assigned a value of 1. Their arrangement is represented by a vector, resulting in a vector U with a value of 0 or 1 and a length equal to the product of the number of transmission cables D and the number of segments N. When monitoring uniformly iced segments, the icing thickness of each segment is determined. When monitoring eccentric icing sections, determine the maximum eccentric icing thickness for each section. and eccentric angle ; Eccentric angle The angle between the centroid of the cable cross-section in the eccentrically icy section and the centroid of the cable cross-section in the uniced section is defined as [0, 360].

[0080] uniform ice thickness Maximum eccentric icing thickness and corresponding eccentric angle This forms a matrix; the matrix has 3 rows and the number of columns is the same as the number of columns in the arrangement vector, with each column representing the icing status of a certain section of the transmission cable. The first row of the matrix represents the icing state. The second row corresponds to the icing thickness. or The third row corresponds to the eccentric angle. When a column is uniformly iced, the value at that position is 0. Finally, the average value of each column of the matrix is ​​calculated to form the spatial distribution vector of icing on the power transmission cables. ,vector The number of rows is 1, and the number of columns represents the icing situation. The number of columns ultimately forms the spatial arrangement vector of the transmission cable icing, and its expression is as follows:

[0081] ;

[0082] in: This represents the spatial arrangement vector for icing of power transmission cables. For the first Vector of icing conditions on a section of power transmission cable. For the first The vector of icing thickness on the power transmission cable section. For the first The eccentricity angle vector of a power transmission cable segment; To perform an averaging operation; Each column represents the icing condition of a certain section, with a value of 0 or 1, where 0 represents uniform icing and 1 represents eccentric icing; the vector... Let be the icing thickness vector, and let be the vector. Each column corresponds one-to-one, when When a column in the vector is 0, the corresponding position has a uniform ice thickness; when it is 1, the corresponding position has a maximum eccentric ice thickness. Let eccentricity angle vector be the vector, and vector be the vector. Each column corresponds one-to-one, when When a certain column is 0, the corresponding position is uniformly covered with ice and the angle value is 0. When it is 1, the corresponding position is eccentrically covered with ice.

[0083] Based on the spatial arrangement vector of icing on transmission cables, the stress on the tower body of the transmission tower under icing conditions is calculated, and the important load-bearing components of the transmission tower are determined.

[0084] In this embodiment, based on the spatial arrangement vector of the icing of the transmission cable, the stress on the high-voltage transmission tower under the icing state of the cable is calculated by finite element software. Combined with relevant cases of high-voltage tower instability and failure, the important load-bearing components of the high-voltage transmission tower can be identified. When this structural part is directly subjected to wind load and the vibration transmitted by the transmission cable, its internal micro-cracks are more likely to develop and cause instability and failure, which in turn causes the high-voltage transmission tower instability problem.

[0085] The environmental factors of the transmission tower and the maximum amplitude and average vibration period of the important load-bearing components under the influence of environmental factors were obtained. A full-scale model was established and an experiment was conducted based on the stress condition of the transmission tower body and the maximum amplitude and average vibration period of the important load-bearing components. The energy loss vector and the rate of change of the slope of the stress-strain curve origin of the important load-bearing components were obtained.

[0086] In this embodiment, under normal operating conditions, i.e., when the cable is free of ice, the maximum wind speed V1 (m / s) and the corresponding wind direction angle Q1 around the high-voltage transmission tower are monitored in each cycle, with P days as the cycle. At the same time, the vibration amplitude A (mm) and period T (s) of important load-bearing components are monitored by fiber optic grating vibration sensing technology, and the maximum amplitude A1 (mm) and average period T1 (s) under this operating condition are determined.

[0087] When high-voltage transmission towers and cables are covered with ice, the maximum wind speed V2 (m / s) and corresponding wind direction angle Q2 around the high-voltage transmission tower are monitored in each cycle of P days. Using fiber optic grating vibration sensing technology, the amplitude A (mm) and period T (s) of the vibration of important load-bearing components are monitored, and the maximum amplitude A2 (mm) and average period T2 (s) under this operating condition are determined. Simultaneously, the icing condition of each cable segment is monitored, and relevant icing parameters are recorded.

[0088] Then, based on the identified key load-bearing components of the high-voltage transmission tower, a full-scale model of each component was established. Strain gauges and piezoelectric ceramic plates were installed at the nodes and mid-sections of the model and connected to a data acquisition instrument. Vibration curves were generated based on the maximum amplitude A1 (mm) and average period T1 (s) of the monitored key load-bearing components within one cycle.

[0089] Based on the calculated stress state of the high-voltage transmission tower body, the average static load of important load-bearing components during each monitoring period under both icing and non-icing conditions is determined, i.e., the constraint force at its ends. and and standard end restraints under windless load and no icing. Static loads are applied to the model at a certain loading rate. When the load is increased Unloading was performed at that time, and the slope of the origin of the initial stress-strain curve obtained by each strain gauge during model loading was determined. ; constraint , and All are in vector form, representing the constraint forces at each end of important load-bearing components.

[0090] Subsequently, a sinusoidal longitudinal wave is input to one end of the model via the servo system. The piezoelectric ceramic sheet generates an electrical signal due to the action of the sinusoidal longitudinal wave. The data acquisition instrument processes the electrical signal to form a vibration signal curve. This curve reflects the amount of energy carried by the sinusoidal longitudinal wave when it passes through the location of the piezoelectric ceramic sheet. The initial sinusoidal longitudinal wave energy collected by each piezoelectric ceramic sheet is calculated using the following formula:

[0091] ;

[0092] in: The sinusoidal longitudinal wave energy collected by each piezoelectric ceramic sheet. The voltage time-domain signal received by the data acquisition instrument. It is a differential time element.

[0093] Next, the full-scale model was set up on an electric vibration table, and the constraint forces were applied through the electric vibration table. The corresponding vibration curves are applied to the model to simulate the vibration of important load-bearing components during normal operation; subsequently, static loads are applied to the model. And determine the slope of the origin of the stress-strain curve obtained by each strain gauge when the model is loaded. Finally, a sinusoidal longitudinal wave signal is input to one end of the model via the servo system, and the energy of the sinusoidal longitudinal wave received by each piezoelectric ceramic sheet and the energy loss of the sinusoidal longitudinal wave received by each piezoelectric ceramic sheet after the model loading test are calculated according to the following formulas. Energy loss vector Change in slope of the stress-strain curve origin With slope rate of change vector Finally, the vibration parameters collected during each cycle under icy conditions were compared with the corresponding constraint forces. Following the above experimental method and applying it sequentially according to the cycle, the energy loss values ​​of the sinusoidal longitudinal wave for each cycle were collected. Change in slope of the stress-strain curve origin The parameters are as follows:

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] in: Let the energy loss vector be the vector. The elements in the vector, from left to right, correspond to the energy loss values ​​calculated by each piezoelectric ceramic sheet in the order of receiving the sinusoidal signal wave. This vector characterizes the irreversible energy dissipation of the structure during vibration, which originates from damage mechanisms such as microcrack propagation and internal friction. This vector is sensitive to early structural damage and can effectively capture precursors of local plastic hinges such as those caused by eccentric loading with ice accretion. It is an important basis for predicting the life of a structure. For the first One energy loss component, The corresponding number of piezoelectric ceramic plates deployed; This represents the initial sinusoidal signal energy value acquired by the piezoelectric ceramic sheet. The sinusoidal signal wave energy value collected by the piezoelectric ceramic sheet after the model has undergone vibration and loading tests is used to quantify the degradation rate of the stiffness of important load-bearing components, thereby revealing the intrinsic relationship between the structural load-bearing capacity and stability, and thus directly characterizing the damage process of the material's mechanical properties. Let the vector be the rate of change of the slope of the stress-strain curve origin. Order and Vectors The order in the text remains consistent; For the first The rate of change of the slope of the stress-strain curve origin is a component. The corresponding number of strain gauges deployed; The slope of the initial stress-strain curve origin; The slope of the stress-strain curve originating from the test result is denoted as .

[0099] Based on the above method, the full-scale model was then placed on an electric vibration table, and the cable was kept in an icy state. The corresponding vibration parameters are applied to the model to reflect the impact of cable icing on important load-bearing components. Then, a loading device applies a static load to the model at a certain loading rate. When the load is increased Unloading is performed periodically. Finally, following the above method and in cyclical order, the vibration parameters collected in each cycle are sequentially matched with the corresponding constraint forces. Applying this to a full-scale model, and calculating the energy loss vector for each period using the above formula. The vector of the rate of change of the slope at the origin of the stress-strain curve .

[0100] Repeat the above experimental method, and continue to apply vibration and load tests to the model in both un-iced and iced cable conditions during each monitoring cycle, and calculate the energy loss vector. Vector of the slope of the stress-strain curve origin This continues until the model becomes unstable and breaks down.

[0101] Based on the energy loss vector of important load-bearing components and the rate of change of the slope of the stress-strain curve origin, the stiffness degradation function of important load-bearing components is obtained, and the overall stability index of important load-bearing components is determined.

[0102] Furthermore, a spatial influence function is established to reflect the impact of energy dissipation at other points on the stiffness degradation of the target point. Its expression is as follows:

[0103] ;

[0104] in: It is a spatial influence function; The radius of influence is a characteristic feature, reflecting the material properties of the component; It is an exponential function; The spatial distance between the target point and other neighboring points;

[0105] Based on energy loss vector Spatial influence function A local energy dissipation density is established to reflect the relationship between local damage and overall damage state of important weighing components. Its expression is as follows:

[0106] ;

[0107] in: For the first The local energy dissipation density corresponding to each piezoelectric ceramic sheet characterizes the energy concentration effect during the material failure process, enabling precise location and severity of damage, and more effectively predicting the direction of damage evolution. For the first The piezoelectric ceramic sheet that received the sinusoidal signal wave and the first The spatial distance between piezoelectric ceramic sheets that receive a sinusoidal signal wave; For the first The energy loss value corresponding to each piezoelectric ceramic sheet; This represents the number of piezoelectric ceramic sheets.

[0108] Based on the spatial influence function and local energy dissipation density, a stiffness degradation function for important load-bearing components is constructed, and its expression is as follows:

[0109] ;

[0110] in: For the first The rate of change of the slope of the stress-strain curve at the origin corresponding to the strain gauge; This is the stiffness degradation coefficient; It is a nonlinear exponent used to reflect the sensitivity of stiffness degradation to energy accumulation. A value greater than 1 indicates accelerated stiffness degradation; when... When it equals 1, it is linearly degenerate; when... A value less than 1 indicates a decrease in stiffness due to deceleration.

[0111] To obtain the maximum local energy dissipation density, average local energy dissipation density, standard deviation of local energy dissipation density, and maximum and average values ​​of local energy dissipation density gradient, an overall stability feature vector reflecting the structural damage state is established, expressed as follows:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] in: The maximum local energy dissipation density reflects the state of the location with the most severe loss; The average local energy dissipation density reflects the overall damage level; The standard deviation of local energy dissipation density reflects the non-uniformity of damage distribution; This represents the local energy dissipation density gradient, reflecting the degree of localization of structural damage. , These are the maximum and average values ​​of the local energy dissipation density gradient, respectively.

[0117] Based on the vector of the slope of the origin of the force-strain curve With energy loss vector Establish the overall stability characteristic vector after each vibration and loading test. This forms the overall stability feature matrix. Its expression is as follows:

[0118] ;

[0119] in: The overall stability characteristic matrix is ​​5×m; This represents the overall stability feature vector; The first time before the model of an important load-bearing component fails due to instability The number of times vibration and loading are applied.

[0120] For the overall stability characteristic matrix Each indicator in the matrix is ​​normalized to form a normalized matrix. Its expression is as follows:

[0121] ;

[0122] in: for The normalized value of a certain element in a certain column. This is the original data value of the element. and These are the minimum and maximum values ​​of the data in this column, respectively.

[0123] Then calculate the weight matrix of each indicator. With information entropy value Their respective expressions are as follows:

[0124] ;

[0125] in: These are the elements corresponding to the first row and first column of the matrix;

[0126] ;

[0127] in: A 1×5 vector, representing from left to right... , , , , The information entropy value.

[0128] Finally, based on information entropy value The weighting coefficients for each indicator are calculated, and their expressions are as follows:

[0129] ;

[0130] Where: Indicator weight coefficient This is a 1×5 vector, representing the overall stability characteristic matrix of important load-bearing components. The relative importance of each indicator, and the meaning of each indicator in each column. Maintain consistency.

[0131] Based on the overall stability characteristic matrix, the overall stability index of important load-bearing components is determined, and its expression is as follows:

[0132] ;

[0133] in: As an indicator of the overall stability of important load-bearing components; This is the normalized overall stability feature matrix; These are the weighting coefficients; For the first Secondary vibration and loading tests.

[0134] Based on the overall stability index of important load-bearing components, the stability stages of important load-bearing components are divided;

[0135] Furthermore, based on the overall stability index of important load-bearing components, an evolution curve of its stability with the increase of the number of load tests was plotted; through the key change points of this curve, the stability stage was divided into four stages: when When the curve reaches its first stable upward inflection point, it indicates that the important load-bearing component has transitioned from the safe phase to the local damage initiation phase; when the curve's growth rate accelerates significantly, it enters the rapid damage development phase; when the curve exhibits a sharply rising nonlinear trend, it enters the instability precursor phase, as expressed below:

[0136] ;

[0137] in: As the starting point; This marks the inflection point at which the patient transitions from the safe phase to the localized initiation phase of damage. This is the inflection point that accelerates the transition from the initial stage of local injury to the rapid development stage of the injury. This marks the critical inflection point at which the rapid progression of damage transitions into the pre-instability stage. This is the point of instability.

[0138] Based on environmental factors of transmission towers, spatial arrangement vector of icing on transmission cables, and maximum amplitude and average vibration period of important load-bearing components, a first BP neural network prediction model under non-icing conditions and a second BP neural network prediction model under icing conditions are constructed respectively; and a third BP neural network prediction model is constructed based on the first and second BP neural network prediction models.

[0139] In this embodiment, based on the operating environment parameters of the transmission tower, namely the maximum wind speed, the corresponding wind direction angle, and the end constraint force of important load-bearing components, and combined with the spatial arrangement vector of icing on the transmission cable, the maximum information coefficient method is used to construct the main influencing factors set for the local vibration characteristics of the important load-bearing components where each strain gauge is located under both icing and non-icing conditions. Specifically, firstly, based on the overall maximum amplitude and average period of the important load-bearing components, the maximum amplitude and average period of the corresponding local parts of each strain gauge are calculated through the dynamic response transfer relationship in structural dynamics. On this basis, for the local vibration response parameters of each strain gauge location, the correlation between them and various environmental factors is analyzed, thereby constructing an independent set of main influencing factors for each measuring point, forming a multi-level influencing factor identification system from the overall to the local.

[0140] Furthermore, based on the environmental factors of the transmission tower, including the maximum wind speed V1 (m / s), the corresponding wind direction angle Q1, the maximum amplitude A1 (mm), the average period T1 (s), and the constraint force on the ends of important load-bearing components... By utilizing the dynamic response transfer relationship in structural dynamics, the maximum amplitude and average period of the local parts corresponding to each strain gauge are calculated. Based on the maximum information coefficient (MIC) method, a set of main influencing factors that are highly correlated with the maximum amplitude and average period of each strain gauge part are constructed, and a main factor dataset vector is further formed. The expressions are as follows:

[0141] ; ;

[0142] in: This is a vector consisting of the main influencing factors data generated by each strain gauge under icing-free conditions; This represents the set of main influencing factors corresponding to the maximum amplitude and average period of the first strain gauge under icing-free conditions. Then it is the first One strain gauge; , , These are the maximum wind speed, wind direction angle, and end constraint force of important load-bearing components of power transmission cables under icy conditions.

[0143] Based on the maximum wind speed V2 (m / s), corresponding wind direction angle Q2, and spatial arrangement vector of icing on transmission cables in the environmental factors of transmission towers. and the constraint forces on the ends of important load-bearing components. Using the above method, a set of major influencing factors that are highly correlated with the maximum amplitude and average period of each strain gauge location is constructed, and further, a vector of major factor datasets is formed, the expression of which is as follows:

[0144] ; ;

[0145] in: This is a vector consisting of the dataset of the main influencing factors formed by each strain gauge under icing conditions; This represents the set of main influencing factors corresponding to the maximum amplitude and average period of the first strain gauge under icing conditions. Then it is the first One strain gauge; , , These are the maximum wind speed, wind direction angle, and end constraint force of important load-bearing components of power transmission cables under icing conditions. Vector for the spatial arrangement of icing on power transmission cables;

[0146] See Figure 2 Based on the data sets of the main influencing factors under both icing and non-icing conditions, normalization processing was performed to eliminate the influence of wind speed, wind direction angle, and constraint force dimensions on the data analysis. Furthermore, vibration parameter prediction models for important load-bearing components of transmission cables under both icing and non-icing conditions were established, namely, a first BP neural network prediction model under non-icing conditions and a second BP neural network prediction model under icing conditions, with the expressions as follows:

[0147] ;

[0148] ;

[0149] in: The first BP neural network prediction model represents the vector of the maximum amplitude, average period, and main influencing factors of the important load-bearing components of the transmission cable under icing-free conditions. Functional mapping relationship between them; The second BP neural network prediction model represents a vector of the maximum amplitude, average period, and main influencing factors of the important load-bearing components of the transmission cable under icing conditions. Functional mapping relationship between them; , The first under the condition of no icing The maximum amplitude and average period corresponding to the location of each strain gauge; , The first under the icing state The maximum amplitude and average period corresponding to the location of each strain gauge.

[0150] Based on the vector of the slope change rate at the origin of the stress-strain curve With energy loss vector In addition, the stiffness degradation function of important load-bearing components was determined by parametric inversion method to determine the stiffness degradation coefficient after each vibration and loading test. Nonlinear exponent and the radius of influence of features .

[0151] And based on the energy loss vector Stiffness degradation coefficient Nonlinear exponent With characteristic influence radius The sum of the number of cycles of vibration curves experienced by important load-bearing components of the cable under icing and non-icing conditions was established using a BP neural network. and The maximum amplitude and average period of each strain gauge location in both iced and uniced cable conditions. and The ratio of minimum constraint force to maximum constraint force and With energy loss vector Stiffness degradation coefficient Nonlinear exponent and the radius of influence of features The functional mapping relationship between them, i.e., the third BP neural network prediction model, is expressed as follows:

[0152] ;

[0153] in: For the first A vector of energy loss; For the first A stiffness degradation coefficient; For the first A non-linear exponent; For the first Each feature affects the radius; This is the third BP neural network prediction model; , These are the ratios of the minimum and maximum constraint forces exerted on the ends of power transmission cables in both icy and non-iced states, respectively. , These are the sum of the number of cycles of the vibration curve experienced by the power transmission cable under both icing and non-icing conditions.

[0154] The stability index of important load-bearing components is calculated based on the third BP neural network model, and the corresponding stability stage of important load-bearing components is matched to complete the stability assessment of transmission towers.

[0155] Furthermore, the environmental factors and vibration parameters of the high-voltage transmission towers collected on-site are input into the third BP neural network prediction model to obtain the corresponding energy loss vector. Stiffness degradation coefficient Nonlinear exponent With characteristic influence radius Based on the above parameters, the local energy dissipation density of the component is calculated, and the overall stability index of the important load-bearing component is obtained. The corresponding stability stage interval is matched to realize the stability state assessment of the high-voltage transmission tower in a complex environment. Finally, combined with the stiffness degradation function of the important load-bearing component, the damage area of ​​the component is identified and located.

[0156] This approach breaks through the limitations of traditional stability assessments that only focus on apparent parameters such as macroscopic amplitude and overall strain in selecting core influencing factors. Instead, it starts from the energy dissipation nature of structural damage and the mechanism of material performance degradation, specifically selecting energy loss vector, the rate of change of the slope of the stress-strain curve origin, and local energy dissipation density as in-depth evaluation indicators. These indicators correspond to different dimensions of early damage initiation, stiffness degradation, and damage location, forming a complete process from damage detection, performance diagnosis, and location assessment. This method closely aligns with the actual response mechanism of transmission towers under complex conditions such as icing and wind vibration. By establishing an independent set of influencing factors for each measuring point, it achieves refined analysis from the overall to the local, covering both non-icing and icing conditions. Through the quantitative correlation between energy dissipation and stiffness degradation, it achieves a multi-dimensional and accurate assessment of structural stability, thus possessing innovative advantages in comprehensiveness, specificity, and engineering applicability.

[0157] Example 2:

[0158] See Figure 3 A system for evaluating the stability of high-voltage transmission towers under complex environments, the system being applied to the method described in Example 1, the system comprising:

[0159] The arrangement vector acquisition module 1 is used to divide the power transmission cables into equal intervals and construct the spatial arrangement vector of the power transmission cables covered by ice.

[0160] The important load-bearing component determination module 2 is used to calculate the stress on the tower body of the transmission tower under the icing state of the transmission cable based on the spatial arrangement vector of the transmission cable icing, and to determine the important load-bearing components of the transmission tower.

[0161] The energy loss and slope change vector acquisition module 3 is used to acquire the environmental factors of the transmission tower and the maximum amplitude and average vibration period of the important load-bearing components under the influence of environmental factors. A full-scale model is established and an experiment is conducted based on the stress condition of the transmission tower body and the maximum amplitude and average vibration period of the important load-bearing components to acquire the energy loss vector and the slope change rate vector of the origin of the stress-strain curve of the important load-bearing components.

[0162] The stability index acquisition module 4 is used to obtain the stiffness degradation function of important load-bearing components based on the energy loss vector of important load-bearing components and the slope change rate vector of the stress-strain curve origin, and to determine the overall stability index of important load-bearing components.

[0163] Stability stage division module 5 is used to divide the stability stages of important load-bearing components based on the overall stability index of important load-bearing components;

[0164] The neural network construction module 6 is used to construct a first BP neural network prediction model under the non-icing state and a second BP neural network prediction model under the icing state, based on the environmental factors of the transmission tower, the spatial arrangement vector of the icing of the transmission cable, and the maximum amplitude and average vibration period of the important load-bearing components; and to construct a third BP neural network prediction model based on the first and second BP neural network prediction models.

[0165] Stability assessment module 7 is used to calculate the overall stability index of important load-bearing components based on the third BP neural network model and match the corresponding stability stage of important load-bearing components to complete the stability assessment of transmission towers.

[0166] Furthermore, the steps for implementing the specific functions of the above modules are described in Example 1, and will not be repeated here.

[0167] Example 3:

[0168] See Figure 4 An evaluation device for the stability of high-voltage transmission towers under complex environments, the device comprising a processor 8 and a memory 9;

[0169] The memory 9 is used to store computer program code 91 and to transmit the computer program code 91 to the processor 8;

[0170] The processor 8 is used to execute the evaluation method for the stability of high-voltage transmission towers under complex environments as described in Embodiment 1, according to the instructions in the computer program code 91.

[0171] This embodiment also includes a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed on a computer, the method for evaluating the stability of high-voltage transmission towers under complex environments described in Embodiment 1 is implemented.

[0172] Generally, the computer instructions for implementing the method of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for the signal itself, which is temporarily propagating.

[0173] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EKROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0174] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smarttalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or to an external computer (e.g., via the Internet using an Internet service provider) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0175] For details regarding the aforementioned equipment and non-transitory computer-readable storage media, please refer to the specific description of the evaluation method and beneficial effects of high-voltage transmission tower stability under complex environments, which will not be repeated here.

[0176] Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the stability of a high-voltage transmission tower in a complex environment, characterized in that, The method comprises the following steps: The transmission cable is equally divided and the transmission cable icing spatial arrangement vector is constructed; Based on the transmission cable icing spatial arrangement vector, the stress of the transmission tower body under the transmission cable icing state is calculated, and the important load-bearing component of the transmission tower is determined; The environmental factors of the transmission tower and the maximum amplitude and average vibration period of the important load-bearing component under the influence of the environmental factors are obtained, a full-size model is established, and based on the stress of the transmission tower body and the maximum amplitude and average vibration period of the important load-bearing component, the test is carried out to obtain the energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector; Based on the energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector, the stiffness degradation function of the important load-bearing component is obtained, and the overall stability index of the important load-bearing component is determined; Based on the overall stability index of the important load-bearing component, the stability stage of the important load-bearing component is divided; Based on the environmental factors of the transmission tower, the transmission cable icing spatial arrangement vector, and the maximum amplitude and average vibration period of the important load-bearing component, the first BP neural network prediction model under the non-icing state and the second BP neural network prediction model under the icing state are constructed respectively; and based on the first and second BP neural network prediction models, the third BP neural network prediction model is constructed; Based on the third BP neural network model, the overall stability index of the important load-bearing component is calculated and matched with the corresponding important load-bearing component stability stage, and the stability evaluation of the transmission tower is completed.

2. The method for evaluating the stability of the high-voltage transmission tower in a complex environment according to claim 1, wherein: The transmission cable is equally divided and the transmission cable icing spatial arrangement vector is constructed, specifically comprising: The transmission cable is equally divided into several sections, and the icing condition of each section of the transmission cable in the icing state is monitored to construct the transmission cable icing spatial arrangement vector, and the expression is as follows: ； wherein: is an icing spatial arrangement vector for the power cable, is an icing condition vector for the first section of the power cable, is an icing thickness vector for the first section of the power cable, is an eccentricity angle vector for the first section of the power cable; is an averaging operation.

3. The method for evaluating the stability of the high-voltage transmission tower in a complex environment according to claim 1, wherein: The energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector are obtained, specifically comprising: The environmental factors of the transmission tower are obtained, including the maximum wind speed and the corresponding wind direction angle, and based on the environmental factors of the transmission tower, the static load, the maximum amplitude, and the average vibration period of the important load-bearing component of the transmission cable under the non-icing state and the icing state are obtained, and the full-size model is sequentially subjected to instability damage test in the order of the non-icing state and the icing state of the transmission cable to obtain the energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector, and the expression is as follows: ； ； ； ； wherein: is the energy loss vector; is the energy loss component number is the energy loss component number is the initial sinusoidal signal wave energy value, is the post-test sinusoidal signal wave energy value; is the stress-strain curve origin slope rate of change vector; is the stress-strain curve origin slope rate of change component number is the stress-strain curve origin slope rate of change component number is the initial stress-strain curve origin slope; is the post-test stress-strain curve origin slope.

4. The method for evaluating the stability of the high-voltage transmission tower in a complex environment according to claim 1, wherein: Based on the energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector, the stiffness degradation function of the important load-bearing component is obtained, and the overall stability index of the important load-bearing component is determined, specifically comprising: Based on the energy loss vector of the important load-bearing component and the stress-strain curve origin slope rate change vector, the spatial influence function and the local energy dissipation density are determined, and the expressions are as follows: ； ； wherein: is a spatial influence function; is a local energy dissipation density; is a characteristic influence radius; is an exponential function; is a spatial distance between a target point and other neighboring points; is a spatial distance value between the th sinusoidal signal wave and the th sinusoidal signal wave; is the th energy loss value; Based on the spatial influence function and the local energy dissipation density, a stiffness degradation function of the important load-bearing component is constructed, and the expression is as follows: ； wherein: is the rate of change of the slope of the stress-strain curve at the origin; is the stiffness degradation coefficient; is the nonlinearity exponent.

5. The method for evaluating the stability of a high-voltage transmission tower in a complex environment according to claim 4, characterized in that: The determination of the overall stability index of the important load-bearing component specifically comprises: The maximum local energy dissipation density, the average local energy dissipation density, the standard deviation of the local energy dissipation density, the maximum value and the average value of the local energy dissipation density gradient in the local energy dissipation density are obtained, an overall stability feature vector is established, and an overall stability feature matrix is constructed, and the expression is as follows: ； ； ； ； ； wherein: is the overall stability characteristic matrix; is the overall stability characteristic vector; is the maximum local energy dissipation density; is the average local energy dissipation density; is the standard deviation of the local energy dissipation density; is the local energy dissipation density gradient; , are the maximum and average values of the local energy dissipation density gradient, respectively; is the th trial; Based on the overall stability feature matrix, the overall stability index of the important load-bearing component is determined, and the expression is as follows: ； wherein: is an overall stability index of the important load-bearing component; is a normalized overall stability feature matrix; is a weight coefficient; is the th test.

6. The method for evaluating the stability of a high-voltage transmission tower in a complex environment according to claim 1, characterized in that: Based on the overall stability index of the important load-bearing component, the stability stage of the important load-bearing component is divided, and the specific steps include: The evolution curve of the overall stability index of the important load-bearing component is analyzed, the stage is divided based on the characteristic turning points appearing in the evolution curve, and the stages are sequentially divided into the safe stage, the damage local initiation stage, the damage rapid development stage, and the instability precursor stage, and the expression is as follows: ； wherein: is the starting point; is the starting inflection point from the safe phase to the local initiation phase of damage; is the accelerating inflection point from the local initiation of damage to the rapid development phase of damage; is the critical inflection point from the rapid development of damage to the pre-instability phase; is the instability point.

7. The method for evaluating the stability of a high-voltage transmission tower in a complex environment according to claim 1, characterized in that: The first BP neural network prediction model in the non-icing state and the second BP neural network prediction model in the icing state are constructed, and the specific steps include: Based on the correlation degree between the maximum wind speed, the corresponding wind direction angle, the constraint force on the end of the important load-bearing component, the maximum amplitude and the average period of the spatial arrangement vector of the transmission cable icing and the important load-bearing component in the environmental factors of the transmission tower, a main influencing factor data set vector in the non-icing state and a main influencing factor data set vector in the icing state are constructed, and the expressions are as follows: ； ； ； ； wherein: , are the main influencing factor dataset vectors formed under the ice-free state and the icing state, respectively; , are the first main influencing factor datasets of the power transmission cable under the ice-free state and the icing state, respectively; , , are the maximum wind speed, the wind direction angle and the constraint force at the end of the important load-bearing member of the power transmission cable under the ice-free state, respectively; , , are the maximum wind speed, the wind direction angle and the constraint force at the end of the important load-bearing member of the power transmission cable under the icing state, respectively; is the spatial arrangement vector of the power transmission cable under the icing state; is the maximum information coefficient; Based on the main influencing factor data set in the non-icing state and the main influencing factor data set in the icing state, the first BP neural network prediction model in the non-icing state and the second BP neural network prediction model in the icing state are constructed, and the expressions are as follows: ； ； in: This is the first BP neural network prediction model; This is the second BP neural network prediction model; , The first under the condition of no icing The maximum amplitude and average period; , The first under the icing state The maximum amplitude and average period.

8. The method for evaluating the stability of a high-voltage transmission tower in a complex environment according to claim 1, characterized in that: Based on the first BP neural network prediction model and the second BP neural network prediction model, a third BP neural network prediction model is constructed, and the specific steps include: ； wherein: is the energy loss vector for the first is the stiffness degradation coefficient for the first is the non-linear exponent for the first is the characteristic influence radius for the first is the third BP neural network prediction model; , are the minimum and maximum constraint force ratio of the end of the power cable under the non-icing state and the icing state, respectively; , are the sum of the number of periods of the vibration curve experienced by the power cable under the non-icing state and the icing state, respectively.​​​​ 9. An evaluation system for stability of a high-voltage transmission tower in a complex environment, characterized by, The system is applied to the method of any one of claims 1-8, and the system comprises: The arrangement vector acquisition module (1) is used for equally dividing the transmission cable and constructing the spatial arrangement vector of the transmission cable icing; The important load-bearing component determination module (2) is used for calculating the stress of the tower body of the transmission tower in the transmission cable icing state based on the spatial arrangement vector of the transmission cable icing, and determining the important load-bearing component of the transmission tower. An energy loss and slope change vector acquisition module (3) is configured to acquire environmental factors of a power transmission tower and maximum amplitudes and average vibration periods of important load-bearing components under the influence of the environmental factors, establish a full-scale model, and perform a test based on stress conditions of a tower body of the power transmission tower and the maximum amplitudes and average vibration periods of the important load-bearing components, to acquire an energy loss vector of the important load-bearing components and a slope change rate vector of an origin of a stress-strain curve; A stability index acquisition module (4) is configured to acquire a stiffness degradation function of the important load-bearing components based on the energy loss vector of the important load-bearing components and the slope change rate vector of the origin of the stress-strain curve, and determine an overall stability index of the important load-bearing components; A stability stage division module (5) is configured to divide a stability stage of the important load-bearing components based on the overall stability index of the important load-bearing components; A neural network construction module (6) is configured to construct a first BP neural network prediction model in a non-icing state and a second BP neural network prediction model in an icing state based on environmental factors of the power transmission tower, a spatial arrangement vector of icing of a power transmission cable, and the maximum amplitudes and average vibration periods of the important load-bearing components, and construct a third BP neural network prediction model based on the first and second BP neural network prediction models; A stability evaluation module (7) is configured to calculate the overall stability index of the important load-bearing components based on the third BP neural network model, match a corresponding stability stage of the important load-bearing components, and complete stability evaluation of the power transmission tower.

10. An evaluation device for stability of a high-voltage power transmission tower in a complex environment, characterized in that: the device comprises a processor (8) and a memory (9); the memory (9) is configured to store computer program code (91) and transmit the computer program code (91) to the processor (8); the processor (8) is configured to execute the method for evaluating stability of a high-voltage power transmission tower in a complex environment according to instructions in the computer program code (91).

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