Power transmission line fitting failure diagnosis method and system considering near wind field airflow field

Abstract

The invention provides a power transmission line fitting failure diagnosis method and system considering a near-wind-field airflow field, and relates to the technical field of power transmission line fitting failure assessment, and the method comprises the steps: obtaining a to-be-diagnosed fitting structure finite element model, building a to-be-diagnosed fitting simulation scheme based on the environment characteristics of the near-wind-field airflow field of a power transmission line, determining a stress aggregation region, and obtaining a to-be-diagnosed fitting failure assessment model; and determining a first constraint index for failure diagnosis of the to-be-diagnosed hardware fitting based on the obtained failure diagnosis constraint signal of the stress aggregation region and the stress aggregation region microscopic fatigue aggregation index, performing deviation extraction on the environmental characteristics of the near wind field and airflow field of the power transmission line, and judging the simulation failure diagnosis evaluation level of the to-be-diagnosed hardware fitting. According to the method, the problems that the traditional inspection tour is difficult to meet the accurate prediction requirement of the hardware failure in a complex environment and cannot meet the requirement of the dynamic failure diagnosis, evaluation and response of the power transmission line hardware are solved, the accuracy of fault prediction is greatly improved, and the safety risk caused by sudden fracture is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of failure assessment of power transmission line hardware, and in particular to a method and system for diagnosing failure of power transmission line hardware taking into account a near-wind airflow field. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Transmission line hardware is exposed outdoors all year round, and is subject to complex environmental stresses such as wind loads, ice and snow, and temperature fluctuations. These stresses are often uncertain, especially the changes in airflow in the near wind field and the influence of severe climatic conditions, which make the hardware subject to alternating loads for a long time, resulting in fatigue accumulation and damage. The failure of hardware is often fatigue failure, that is, under the periodic action of smaller stresses, the material gradually accumulates damage until cracks or fractures appear. Since most transmission lines are located in remote areas, hardware failure is usually difficult to detect in time, which may cause safety accidents such as large-scale power outages and bring serious losses. With the development of computer technology, failure diagnosis and evaluation technology based on finite element analysis (FEA) and multi-physics field simulation methods has become possible. Simulation calculations can simulate the stress distribution, temperature changes and fatigue accumulation process of hardware under different environmental and load conditions, thereby predicting possible failure modes and failure times, which can effectively reduce the dependence on manual inspections and improve the accuracy and efficiency of diagnosis.

[0004] At present, there are still some deficiencies in the research on simulation calculation of failure diagnosis and evaluation of transmission line hardware, including: the traditional intelligent inspection scheme uses drones equipped with high-definition cameras, thermal imagers, ultraviolet imagers and other equipment to conduct close-range inspections of transmission line hardware, which has insufficient dynamic response capabilities, unstable recognition accuracy, and low detection accuracy. It is difficult to meet the needs of accurate prediction of hardware failure in complex environments, and it is also unable to meet the requirements of dynamic failure diagnosis and evaluation response of transmission line hardware. It is difficult to adapt to environmental changes, resulting in low detection accuracy and difficulty in achieving the purpose of accurate prediction of failure. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a failure diagnosis method and system for power transmission line hardware considering the near-wind airflow field, constructs a finite element model and simulates the environmental characteristics of the near-wind airflow field of the transmission line, analyzes the stress characteristics of the hardware in a real environment, and clearly identifies the impact of dynamic environmental changes on the diagnosis and evaluation of hardware through the deviation extraction of the near-wind airflow field environment of the transmission line, identifies stress aggregation areas and microscopic fatigue characteristic data, clearly captures early failure signals such as hardware material fatigue and crack extension, and greatly improves the accuracy of fault prediction.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] The failure diagnosis method of transmission line hardware considering the airflow field near the wind field includes:

[0008] Obtain the finite element model of the transmission line hardware structure to be diagnosed;

[0009] Obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculation on the hardware of the transmission line to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the hardware of the transmission line to be diagnosed;

[0010] Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions:

[0012] The power transmission line hardware failure diagnosis system considering the air flow field near the wind field includes:

[0013] An initialization module is used to obtain a finite element model of the hardware structure of the power transmission line to be diagnosed;

[0014] The failure simulation module is used to obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculations on the transmission line hardware to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the transmission line hardware to be diagnosed;

[0015] Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method for diagnosing failure of power transmission line hardware taking into account the airflow field near the wind field is implemented.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the power transmission line hardware failure diagnosis method considering the near-wind airflow field is implemented.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the transmission line hardware failure diagnosis method considering the near-wind airflow field.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The disclosed method for diagnosing failure of hardware of power transmission lines considering the near-wind airflow field constructs a finite element model and simulates the environmental characteristics of the near-wind airflow field of the power transmission lines, and can accurately analyze the stress characteristics of the hardware in a real environment, so as to better identify potential failure risks, identify stress aggregation areas and microscopic fatigue characteristic data, and clearly capture early failure signals such as fatigue of hardware materials and crack propagation. The accuracy of fault prediction is greatly improved, and the safety risks caused by sudden fractures are reduced. By extracting the deviation of the near-wind airflow field environment of the transmission line, the impact of dynamic environmental changes on the diagnosis and evaluation of hardware can be clearly identified, and the diagnosis and evaluation level can be adjusted according to the actual wind field environment. The diagnosis level can be adjusted automatically, which reduces the workload of inspection personnel and improves the intelligence level of the overall management process.

[0024] The present invention discloses a method for diagnosing failure of power transmission line fittings that takes into account the near-wind airflow field. By acquiring magnetic induction characteristic data of the stress aggregation area, a failure diagnosis constraint signal of the stress aggregation area is obtained. The magnetic induction characteristics of the stress aggregation area reflect the development of local stress concentration and internal defects of the material, and can capture the impact of environmental loads (such as changes in wind speed and wind direction) on the fittings in real time. The generation of failure diagnosis constraint signals enables the monitoring system to respond quickly to environmental changes and provide timely warnings under dynamic stress, which is helpful to make preventive decisions before failure occurs. Changes in magnetic induction characteristic data usually show obvious signal characteristics before fatigue crack propagation and failure. The generated failure diagnosis constraint signal can capture early signs of fatigue failure before the stress accumulates to the critical point of failure, thereby achieving early warning, providing sufficient response time for preventive maintenance, and reducing the risk of sudden failures.

[0025] The disclosed method for diagnosing failure of hardware fittings of power transmission lines taking into account the airflow field near the wind field, extracts deviations from the environmental characteristics of the airflow field near the wind field of the transmission line, and judges the simulation failure diagnosis evaluation level of the hardware fittings to be diagnosed in combination with the first constraint index of the failure diagnosis of the hardware fittings to be diagnosed. Through the deviation extraction, the system can timely capture the real-time changes of wind speed, wind direction and turbulence intensity around the transmission line. This dynamic adjustment takes into account the impact of the environment on the stress and fatigue damage of the hardware fittings. If the impact on the hardware fittings in a strong wind environment and a weak wind environment is the same, it means that the higher the failure diagnosis evaluation level of the hardware fittings in a strong wind environment, the better the stability, which significantly improves the accuracy of the failure diagnosis evaluation. This automated method relies on environmental characteristic deviations and diagnostic constraint indicators to dynamically evaluate the failure level, reducing the need for manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0027] Figure 1 A schematic diagram of a method flow of an embodiment of the present disclosure;

[0028] Figure 2 This is a flowchart of the method steps for judging the evaluation level of hardware simulation failure in combination with the first constraint index of failure diagnosis according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0031] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0032] Example 1

[0033] In one embodiment of the present disclosure, a method for diagnosing failure of power transmission line hardware considering a near-wind airflow field is provided, comprising:

[0034] Step 1: Obtain the finite element model of the transmission line hardware structure to be diagnosed;

[0035] Step 2: Obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculation on the hardware of the transmission line to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the hardware of the transmission line to be diagnosed;

[0036] Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

[0037] As an embodiment, the present invention discloses a method for diagnosing failure of hardware of a power transmission line considering a near-wind airflow field, by providing a method for diagnosing failure of hardware of a power transmission line considering a near-wind airflow field, simulating the environmental characteristics of the near-wind airflow field of the power transmission line, accurately analyzing the stress characteristics of the hardware in a real environment, identifying the stress aggregation area and microscopic fatigue characteristic data, and extracting the deviation of the near-wind airflow field environment of the power transmission line, so as to clearly identify the influence of dynamic environmental changes on the diagnostic evaluation of the hardware, and adjust the diagnostic evaluation level according to the actual wind field environment. The specific implementation process of the method disclosed in the present invention includes:

[0038] Step 1: Obtain the finite element model of the transmission line hardware structure to be diagnosed and build a simulation environment solution.

[0039] Specifically, based on the category of the transmission line fittings to be diagnosed, the finite element model of the transmission line fitting structure to be diagnosed is extracted from the database, the airflow field environment near the wind field of the transmission line is obtained, and the airflow field environment near the wind field of the transmission line is used as a simulation scheme for the transmission line fittings to be diagnosed.

[0040] Step 2: Under the simulation scheme of the transmission line fittings to be diagnosed, the failure simulation calculation of the transmission line fittings to be diagnosed is performed based on the environmental characteristics of the airflow field near the wind field of the transmission line to obtain the failure assessment level of the transmission line fittings to be diagnosed;

[0041] Specifically, step 21: analyzing the stress characteristics of each analysis element based on the obtained finite element model of the hardware structure to be diagnosed, and determining the stress aggregation area. The specific process includes:

[0042] Step 210: Obtain a force characteristic data set of each analysis element, wherein the force characteristic data set of each analysis element specifically includes a deformation ratio of the surface area of ​​the analysis element, an average stress of the analysis element, and a displacement distance of the analysis element perpendicular to the wind direction plane;

[0043] Among them, 1) the surface area deformation ratio of the analysis element refers to the area change ratio of the surface of the hardware or structural unit under the action of external force, which is used to describe the deformation degree of the analysis element under stress and reflects the deformation of the material such as stretching, compression or bending under specific load conditions. The deformation of the hardware surface is analyzed by high-precision laser scanning, and the change in area before and after scanning is compared to calculate the surface area deformation ratio;

[0044] 2) The average stress of the analysis element refers to the average stress in the specified direction inside the analysis element under the action of load. It reflects the stress condition of the area under the action of external force. It is an important parameter for evaluating the uniformity of local stress and fatigue accumulation. It is obtained based on the fiber Bragg grating sensor;

[0045] Furthermore, the average stress reflects the stress conditions inside the analysis element under the action of external forces. When the average stress on the analysis element increases, the stress inside the material will cause the deformation to increase, thereby increasing the deformation ratio of the surface area. Under the action of wind load, the analysis element may produce a displacement perpendicular to the wind direction. This displacement will cause the overall structure to shift. During the displacement process, the stress on the surface of the analysis element is redistributed, which may cause local stress concentration or distortion, thereby affecting the local deformation ratio. Under the action of wind load, the average stress and vertical displacement are often positively correlated. The wind load will cause the displacement of the analysis element, and this displacement in turn changes the force direction and distribution of the material, further affecting the average stress.

[0046] 3) The displacement distance of the analysis element perpendicular to the wind direction plane refers to the vertical displacement of the analysis element relative to the original position under the action of wind load. It describes the degree of displacement of the structure in the direction of wind load and is an important indicator for judging the stability and wind resistance of the structure in an airflow environment. It is obtained based on a laser displacement sensor.

[0047] Step 211: Based on the acquired force characteristic data sets of each analysis element, a comprehensive analysis is performed to obtain a force characteristic determination factor of each analysis element, and the force characteristic determination factor is used as an analysis basis for determining the stress aggregation area.

[0048] Specifically, the force characteristic determination factor integrates the deformation, stress and displacement information of each analysis element, which can comprehensively and meticulously reflect the actual stress state of each area under the action of wind load, and can more accurately identify high stress concentration areas, improve the accuracy of judgment on failure risk, and reduce the possibility of misjudgment or missed judgment. By analyzing the surface area deformation ratio, average stress and displacement distance, the force characteristic determination factor reveals the stress condition of each analysis element from multiple dimensions, avoiding the limitations of single indicator evaluation, and providing a more reliable basis for the determination of stress concentration areas, ensuring the scientificity and comprehensiveness of the diagnosis results. Since the force characteristic data set of each analysis element dynamically reflects the influence of environmental stress, it can adapt to changes in different wind speeds, wind directions and other conditions, helping the system to automatically adjust the diagnosis standards in different environments. The comprehensive analysis method of the determination factor enables the diagnosis model to adapt to the mechanical performance of transmission line hardware in a changeable natural environment, enhancing the adaptability of the system.

[0049] Among them, the specific analysis process of the force characteristic determination factor is as follows:

[0050]

[0051] Among them, γ is the force characteristic determination factor, bl is the deformation ratio of the analysis element surface area, pj is the average stress of the analysis element, wy is the displacement distance of the analysis element perpendicular to the wind direction plane, μ1 is the compensation factor of the set bl, μ2 is the compensation factor of the set pj, μ3 is the compensation factor of the set wy, and e is a natural constant.

[0052] Furthermore, the stress characteristic determination factor is calculated by analyzing the deformation ratio of the surface area of ​​the element, the average stress of the element, and the displacement distance of the element perpendicular to the wind direction plane. bl, pj, and wy are normalized, and the data of the three dimensions of deformation ratio, average stress, and displacement distance are combined to enable the stress characteristic determination factor to more comprehensively reflect the stress state of the element under load, and to more accurately capture details such as stress concentration and local deformation, thereby avoiding the deviation that may be caused by a single indicator. By calculating the stress characteristic determination factor, high-risk areas (stress aggregation areas) with abnormal stress can be effectively identified. High stress or high deformation areas are often high-incidence areas of crack initiation and fatigue damage. Early identification of these areas is helpful to take preventive maintenance measures before failure occurs, thereby extending the service life of the structure. By integrating multiple indicators into a stress characteristic determination factor, the diagnosis and evaluation process can be simplified. Using determination factors instead of complicated single analysis not only reduces the complexity of data processing, but also improves the evaluation efficiency. It is particularly suitable for application in multi-region stress analysis of large-scale structures.

[0053] Among them, the set compensation factors of bl, pj, and wy are obtained from the database. According to the historical data, a mapping set of the historically measured analysis element surface area deformation ratio, the analysis element average stress, the displacement distance of the analysis element perpendicular to the wind direction plane and the compensation factors of bl, pj, and wy is established to obtain the compensation factors of bl, pj, and wy corresponding to the current bl, pj, and wy.

[0054] Step 212: Determine stress concentration areas

[0055] Specifically, the force characteristic determination factor of each analysis element is compared with the force characteristic determination threshold stored in the database; if the force characteristic determination factor is not lower than the force characteristic determination threshold, the analysis element corresponding to the force characteristic determination factor is a stress aggregation area; if the force characteristic determination factor is lower than the force characteristic determination threshold, the analysis element corresponding to the force characteristic determination factor is not a stress aggregation area.

[0056] By comparing the stress characteristic determination factor with the preset threshold one by one, the stress aggregation area above the threshold can be accurately identified. The threshold-based quantitative determination method is more scientific than empirical judgment, reduces misjudgment and missed judgment, and improves the accuracy of diagnosis. The comparison process between the stress characteristic determination factor and the threshold is simple and fast, and can quickly screen out high-risk areas that meet the conditions. For complex structures or large-scale analysis models, this method greatly improves the recognition efficiency, enabling the system to efficiently judge stress concentration in a short time. The stress characteristic determination factor adopts a clear numerical threshold standard, which can reduce the interference of factors such as environmental fluctuations and provide a consistent judgment basis. Compared with subjective judgment, the numerical threshold judgment is more objective, ensuring stable diagnostic results under different analysis conditions. The threshold can be dynamically adjusted according to different working conditions, material properties and service life to meet different diagnostic needs. Such flexibility improves the adaptability of the diagnostic system, enabling it to accurately identify stress aggregation areas in a variety of environments and usage scenarios.

[0057] Step 22: Acquire magnetic induction characteristic data of the stress aggregation area, and obtain a failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data.

[0058] Specifically, step 220: obtaining magnetic induction characteristic data of the stress aggregation area, wherein the magnetic induction characteristic data of the stress aggregation area specifically includes the maximum magnetic flux density deviation rate of the stress aggregation area, the maximum hysteresis loss of the stress aggregation area, and the maximum remanence of the stress aggregation area;

[0059] Among them, 1) the magnetic flux density deviation rate refers to the relative change rate of the magnetic flux density of the material under stress, and the maximum magnetic flux density deviation rate indicates the percentage change of the maximum magnetic flux density in the stress aggregation area relative to the reference magnetic flux density. The change of magnetic flux density is directly measured by the Hall effect sensor to calculate the deviation rate;

[0060] 2) Hysteresis loss refers to the energy loss caused by the magnetization and demagnetization process of magnetic materials in an alternating magnetic field. The highest hysteresis loss in the stress aggregation area reflects the increase in the width of the hysteresis loop due to internal stress in this area, that is, in the stress aggregation area, the magnetic properties decrease while the loss increases. The higher the hysteresis loss, the greater the internal friction of the material due to stress, which may be accompanied by degradation of magnetic properties. It is obtained based on a vector magnetic field analyzer;

[0061] 3) Remanence refers to the magnetic intensity remaining inside the material after the external magnetic field is removed. The highest remanence indicates the maximum magnetization intensity remaining after the external magnetic field is removed in the stress aggregation area. Stress and fatigue damage can cause changes in the microstructure inside the material, resulting in an increase or decrease in remanence. The higher the remanence, the more likely it is that stress concentration has caused the rearrangement of magnetic domains inside the material or the enhancement of the hysteresis effect. It is obtained based on the remanence meter.

[0062] Furthermore, stress concentration causes significant changes in the magnetic characteristics inside the material, including magnetic domain rearrangement and magnetic wall movement. Due to the microstructural changes caused by stress inside the material, these changes will lead to an increase in the magnetic flux density deviation rate, an increase in hysteresis loss, and may affect the residual magnetism strength. When the stress increase causes a significant deviation in the magnetic flux density, the hysteresis loss will usually increase as well. Since the deviation in magnetic flux density means that the movement of magnetic domains is hindered, more energy is required to overcome internal friction, which makes the hysteresis loop wider, that is, the hysteresis loss is higher. When the stress increase causes the magnetic flux density deviation rate to increase, the magnetic domain structure often adjusts, especially after loading and unloading, some magnetic domains will remain in the new orientation, thereby increasing the residual magnetism value. High hysteresis loss means that the magnetic material has greater internal friction and energy dissipation in the magnetization and demagnetization cycle in the alternating magnetic field. This process changes the magnetic domain structure and causes an increase in residual magnetism.

[0063] Magnetic induction characteristic data such as flux density deviation, hysteresis loss and residual magnetism can sensitively reflect the changes in the internal microstructure of the hardware material, such as stress concentration, fatigue accumulation and crack extension. By monitoring these microscopic changes, the potential failure risk of the hardware can be accurately captured and the accuracy of failure diagnosis can be improved. Magnetic induction characteristic data can reflect the changes in the mechanical and magnetic states of the stress aggregation area in real time. During the process of stress accumulation or fatigue crack extension, indicators such as flux density and hysteresis loss will change significantly. The failure diagnosis constraint signal generated based on these data can be used as an early warning signal to ensure that preventive measures are taken before failure occurs. Improve the safety of the system. Analyzing multiple magnetic characteristics data (such as flux density deviation, hysteresis loss and residual magnetism) can more comprehensively reveal the various performance characteristics of materials under stress concentration. Compared with a single indicator, comprehensive analysis can improve the sensitivity of diagnosis, ensure that different types of failure signs can be captured, and avoid missing important failure signs. Failure diagnosis constraint signals can be used to provide graded warnings for the status of different hardware, providing a priority basis for maintenance work. For high-risk areas with obviously abnormal magnetic characteristics, the system can prioritize maintenance, reduce unnecessary comprehensive inspections, and improve maintenance resource utilization and work efficiency.

[0064] Step 221: Based on the acquired magnetic induction characteristic data of the stress aggregation area, a comprehensive analysis is performed to obtain a failure diagnosis constraint signal of the stress aggregation area, and the failure diagnosis constraint signal of the stress aggregation area is used as an analysis basis for determining the first constraint index of the failure diagnosis of the hardware to be diagnosed.

[0065] Specifically, the specific analysis process of the failure diagnosis constraint signal of the stress aggregation area is as follows:

[0066]

[0067] Among them, α is the stress aggregation area failure diagnosis constraint signal, mp is the maximum magnetic flux density deviation rate of the stress aggregation area, cz is the maximum hysteresis loss of the stress aggregation area, cs is the maximum remanence of the stress aggregation area, σ1 is the compensation factor of the set mp, σ2 is the compensation factor of the set cz, σ3 is the compensation factor of the set cs, and e is a natural constant. σ1, σ2, and σ3 are all obtained through the mapping set of historical data and compensation factors established in the database, which is the same as in the above step 211, and is also the corresponding compensation factor obtained.

[0068] Furthermore, the failure diagnosis constraint signal of the stress aggregation area is calculated by the maximum magnetic flux density deviation rate, the maximum hysteresis loss and the maximum residual magnetism in the stress aggregation area. The failure diagnosis constraint signal can comprehensively reflect the magnetic and structural properties of the material under stress, and reveal the microscopic changes inside the material from different angles. Through multi-dimensional analysis, the accuracy of failure diagnosis is greatly improved, and the misjudgment caused by single data is reduced. The changes of these three magnetic properties in the stress aggregation area will increase significantly, which usually indicates that early failure signs such as stress concentration and micro cracks appear inside the material. The failure diagnosis constraint signal can keenly capture these changes and identify high-risk areas of failure in advance, which is helpful to take preventive measures before failure occurs and improve the safety of the system. By calculating the failure diagnosis constraint signal, multiple magnetic indicators are simplified into a comprehensive indicator. Compared with analyzing each parameter one by one, the comprehensive signal is convenient for quickly judging the failure risk, especially in large-scale monitoring, which can significantly improve the judgment efficiency.

[0069] Step 23: Acquire micro fatigue characteristic data of the stress aggregation area, determine the micro fatigue aggregation index of the stress aggregation area, and determine the first constraint index of the failure diagnosis of the power transmission line hardware to be diagnosed in combination with the failure diagnosis constraint signal of the stress aggregation area;

[0070] Specifically, step 230: obtaining microscopic fatigue characteristic data of the stress aggregation area, the microscopic fatigue characteristic data of the stress aggregation area specifically includes the maximum length of microscopic cracks in the stress aggregation area, the average crack propagation rate in the stress aggregation area, the slip band density in the stress aggregation area, and the microscopic fatigue particle peeling rate in the stress aggregation area.

[0071] Among them, 1) the maximum length of micro cracks refers to the maximum length of cracks formed in the stress aggregation area. The crack length directly reflects the degree of damage inside the material under stress. Longer micro cracks usually indicate that the material has undergone significant fatigue damage and is close to the failure criticality. It is obtained based on laser confocal microscopy;

[0072] 2) The average crack growth rate refers to the average speed at which microscopic cracks grow per unit time or per unit load cycle under fatigue load. It reflects the dynamic behavior of crack growth under fatigue load and is one of the key indicators for evaluating fatigue life. It is obtained based on scanning electron microscopy.

[0073] 3) Slip bands are microscopic band structures formed by crystal slip during the plastic deformation of materials. Slip band density indicates the number of slip bands per unit area. A higher slip band density usually indicates a larger accumulation of plastic deformation inside the material, which is an early feature of fatigue damage. It is obtained based on scanning electron microscopy.

[0074] 4) The micro fatigue particle spalling rate refers to the rate at which tiny particles on the material surface in the stress aggregation area peel off due to fatigue damage. It is usually expressed as the number of particles peeled off per unit area or per unit time. The higher the spalling rate, the more serious the fatigue damage of the material, which may lead to further crack extension and failure. It is obtained based on scanning electron microscopy.

[0075] Furthermore, the slip band density reflects the accumulation of internal grain slip during the plastic deformation of the material. In the stress concentration area, a higher slip band density often means an increase in plastic slip within the material grains. When the plastic deformation reaches a certain level, micro cracks may occur at the grain boundaries. In the stress concentration area, the initially initiated micro cracks will gradually expand under the alternating load. The crack growth rate is closely related to the maximum length of the crack, because the longer the crack length, the stronger the stress concentration effect at the crack tip, which may accelerate the crack growth rate. Micro fatigue particle spalling is a concomitant phenomenon of crack growth and slip band accumulation. In the fatigue cycle, crack growth causes tiny particles to gradually peel off the surface of the material. As the crack length increases and the crack growth rate accelerates, the rate of surface micro particle spalling will also increase. The increase in slip band density indicates that plastic deformation is gradually accumulating. The increase in the number of slip bands will weaken the surface strength of the material. Especially under fatigue loads, micro particle spalling is more likely to occur in the slip band area.

[0076] Step 231: determining a microscopic fatigue aggregation index of the stress aggregation region based on the acquired microscopic fatigue characteristic data of the stress aggregation region;

[0077] Specifically, based on the acquired microscopic fatigue characteristic data of the stress aggregation area, the microscopic fatigue aggregation index of the stress aggregation area is obtained through comprehensive analysis;

[0078] Specifically, the microscopic fatigue aggregation index of the stress aggregation area, the specific analysis process is:

[0079]

[0080] Among them, β is the micro fatigue aggregation index of the stress aggregation area, cd is the maximum length of the micro crack in the stress aggregation area, kz is the average crack growth rate in the stress aggregation area, md is the slip band density in the stress aggregation area, bl is the micro fatigue particle peeling rate in the stress aggregation area, τ1 is the compensation factor of the set cd, τ2 is the compensation factor of the set kz, τ3 is the compensation factor of the set md, and τ4 is the compensation factor of the set bl. τ1, τ2, τ3, and τ4 are also obtained through the mapping set of historical data and compensation factors established in the database, and are also the corresponding compensation factors obtained.

[0081] Furthermore, the above-mentioned stress aggregation area micro fatigue aggregation index is calculated by the maximum length of the micro crack in the stress aggregation area, the average crack growth rate in the stress aggregation area, the slip band density in the stress aggregation area, and the micro fatigue particle peeling rate in the stress aggregation area. cd, kz, md, and bl are normalized. Combined with the micro crack length, crack growth rate, slip band density, and micro fatigue particle peeling rate, the micro fatigue aggregation index can more comprehensively reflect the cumulative damage of the material under fatigue stress, and can capture the fatigue damage characteristics that are difficult to identify with a single parameter, avoiding the limitation of a single data. The micro fatigue aggregation index can reflect the cumulative progress of material fatigue. With the increase of crack growth rate and spalling rate, the aggregation index will gradually increase, forming a trend curve of fatigue damage. The dynamic change helps to predict the fatigue life of the material, so that the system can take measures in advance when failure is approaching, and avoid sudden failure.

[0082] Step 232: Based on the micro fatigue aggregation index of the stress aggregation area and in combination with the failure diagnosis constraint signal of the stress aggregation area, determine the first constraint index for failure diagnosis of the power transmission line fitting to be diagnosed;

[0083] Specifically, the microscopic fatigue aggregation index of the stress aggregation area is used as the analysis basis for determining the first constraint index of the failure diagnosis of the hardware to be diagnosed; the failure diagnosis constraint signal of the stress aggregation area and the microscopic fatigue aggregation index of the stress aggregation area are stored as a designated label, and the designated label is compared with the first constraint index of the failure diagnosis of the hardware to be diagnosed of the power transmission line corresponding to each designated label stored in the database to obtain the first constraint index of the failure diagnosis of the hardware to be diagnosed corresponding to the designated label.

[0084] By storing and comparing designated tags, the system has established a unified failure diagnosis standard, which can ensure the consistency and comparability of diagnosis under different equipment and conditions, reduce the judgment errors caused by external environment and individual differences, and ensure the reliability of diagnosis results. The micro-fatigue aggregation index reflects the accumulation of micro-fatigue damage of the material, while the failure diagnosis constraint signal reflects the abnormality of the magnetic characteristic data. By comparing these two important data at the same time, potential failure risks can be identified more accurately, more comprehensive diagnostic analysis can be achieved, and the accuracy of diagnosis can be improved. Storing micro-fatigue aggregation indicators and failure diagnosis constraint signals as tags helps to achieve automatic grading of different diagnostic levels. The system can determine the failure diagnosis level of different hardware based on the comparison results, and give priority to maintenance work for high-risk equipment, optimize resource utilization, improve maintenance efficiency and effectiveness, dynamically adjust diagnostic standards, enhance adaptability, and improve the level of diagnostic automation.

[0085] Step 24: extract deviations from the environmental characteristics of the airflow field near the wind field of the transmission line, and determine the assessment level of the hardware simulation failure in combination with the first constraint index of failure diagnosis;

[0086] Specifically, step 240: obtaining a data set of environmental characteristics of the airflow field in the wind field near the transmission line, wherein the data set of environmental characteristics of the airflow field in the wind field near the transmission line specifically includes wind speed of the airflow field in the wind field, turbulence intensity of the airflow field in the wind field, and wind pressure of the airflow field in the wind field;

[0087] Among them, 1) wind speed refers to the speed of the airflow in the near-wind airflow field, usually in meters per second (m / s). Ultrasonic wave is used to measure wind speed, and the wind speed is calculated by detecting the difference in the propagation time of sound waves in the air. It is suitable for high-precision and fast wind speed measurement;

[0088] 2) Turbulence intensity is a parameter that describes the degree of turbulence fluctuation in the airflow. It is defined as the ratio of the standard deviation of wind speed to the average wind speed. The greater the turbulence intensity, the stronger the fluctuation of the airflow, and the greater the impact on the stability of the transmission line hardware. High turbulence intensity often leads to irregular vibration and fatigue accumulation. It is obtained based on a three-dimensional acoustic Doppler anemometer;

[0089] 3) Wind pressure refers to the pressure generated when wind acts on the surface of an object, usually measured in Pascals (Pa). Wind pressure is proportional to the square of wind speed. Increased wind pressure will exert greater external force on transmission line fittings and conductors. Wind pressure is an important parameter for evaluating the wind resistance and stress distribution of fittings, and is obtained based on a porous plate pressure sensor.

[0090] Furthermore, wind pressure is proportional to the square of wind speed. The greater the wind speed, the higher the wind pressure. Turbulence intensity is the ratio of wind speed fluctuation to average wind speed. Although wind pressure is mainly proportional to the square of wind speed, high turbulence intensity means that wind speed will fluctuate greatly in a short period of time, resulting in fluctuations in wind pressure. This fluctuation will impose alternating dynamic loads on the structure. Wind speed, turbulence intensity and wind pressure jointly affect the fatigue accumulation of transmission line hardware. When the wind speed is high, the wind pressure increases, increasing the force on the hardware. If the turbulence intensity is high at the same time, it means that the hardware needs to bear alternating dynamic loads under greater loads, resulting in faster fatigue accumulation.

[0091] Step 241: Based on the acquired data set of characteristics of the airflow field environment in the near wind field of the transmission line, a comprehensive analysis is performed to obtain an airflow field environment deviation factor in the near wind field of the transmission line. The airflow field environment deviation factor in the near wind field of the transmission line is used as an analysis basis for determining the simulation failure diagnosis evaluation level of the hardware to be diagnosed.

[0092] Specifically, the environmental deviation factor combines the effects of wind speed, turbulence intensity and wind pressure, and can comprehensively reflect the actual stress of the near-wind environment on the hardware. It can more accurately simulate the force characteristics under real environmental conditions, reduce the errors caused by ignoring environmental changes, and improve the accuracy of diagnosis. The deviation factor can reflect the impact of the dynamic airflow environment on the hardware, such as sudden strong winds or turbulent fluctuations. The dynamic characteristics can help the system capture environmental changes more sensitively, ensure that the diagnostic evaluation level reflects the failure risk of the hardware in the real environment in real time, and adjust the diagnostic strategy in time. The deviation factor can be directly integrated with the automated diagnostic system to judge the failure risk through real-time data and set deviation thresholds. Automated analysis reduces manual intervention and improves the intelligence level of diagnosis. When a high-risk environment is detected, an early warning can be automatically issued to ensure the timeliness of maintenance work. If the impact on the hardware in a strong wind environment and a weak wind environment is the same, it means that the higher the hardware failure diagnosis evaluation level in a strong wind environment, the better the stability.

[0093] Among them, the specific analysis process of the environmental deviation factor of the airflow field near the wind field of the transmission line is as follows:

[0094]

[0095] Among them, ω is the environmental deviation factor of the airflow field near the wind field of the transmission line, fs is the wind speed of the airflow field near the wind field, qd is the turbulence intensity of the airflow field near the wind field, fy is the wind pressure of the airflow field near the wind field, ε1 is the compensation factor of the set fs, ε2 is the compensation factor of the set qd, and ε3 is the compensation factor of the set fy. ε1, ε2, and ε3 are also obtained through the mapping set of historical data and compensation factors established in the database, and are also the corresponding compensation factors obtained.

[0096] Furthermore, the environmental deviation factor of the airflow field near the wind field of the transmission line is calculated by the wind speed of the airflow field near the wind field, the turbulence intensity of the airflow field near the wind field, and the wind pressure of the airflow field near the wind field. fs, qd, and fy are normalized, and the three factors of wind speed, turbulence intensity and wind pressure are integrated. The environmental deviation factor can accurately reflect the overall dynamic characteristics of the airflow field. The multi-dimensional environmental assessment method can more comprehensively reveal the impact of wind field conditions on transmission line hardware, reduce the error caused by a single parameter, and improve the accuracy of diagnosis. Since wind speed, turbulence intensity and wind pressure will change over time, the deviation factor can dynamically track environmental fluctuations and help identify the risks caused by instantaneous changes. By real-time monitoring of the deviation factor, the system can issue an early warning when the environmental conditions exceed the preset safety range, thereby improving the sensitivity of environmental monitoring and safety warnings. The deviation factor can keenly capture the impact of extreme weather on transmission line hardware. By calculating the environmental deviation factor of the airflow field, designers can more accurately evaluate the actual stress of the airflow on the transmission line structure, thereby improving the accuracy and efficiency of the evaluation.

[0097] Step 242: Based on the airflow field environment deviation factor of the near-wind field of the transmission line and in combination with the first constraint index of failure diagnosis, the assessment level of the hardware simulation failure is determined.

[0098] Specifically, Figure 2 As shown, the first constraint index of the failure diagnosis of the hardware to be diagnosed and the environmental deviation factor of the airflow field in the near wind field of the transmission line are stored as a specified label, and the specified label is compared with the failure diagnosis constraint characteristics of the hardware to be diagnosed corresponding to each specified label stored in the database to obtain the failure diagnosis constraint characteristics of the hardware to be diagnosed corresponding to the specified label; the failure diagnosis constraint characteristics of the hardware to be diagnosed are compared with the reference range of the failure diagnosis constraints of the hardware to be diagnosed stored in the database; if the failure diagnosis constraint characteristics of the hardware to be diagnosed fall within the reference range of the failure diagnosis constraints of the hardware to be diagnosed, then the simulation failure diagnosis evaluation level of the hardware to be diagnosed corresponding to the failure diagnosis constraint characteristics of the hardware to be diagnosed is level two; if If the failure diagnosis constraint feature of the hardware to be diagnosed does not fall within the reference range of the failure diagnosis constraint of the hardware to be diagnosed, and the failure diagnosis constraint feature of the hardware to be diagnosed is less than the minimum value in the reference range of the failure diagnosis constraint of the hardware to be diagnosed, then the simulation failure diagnosis assessment level of the hardware to be diagnosed corresponding to the failure diagnosis constraint feature of the hardware to be diagnosed is level one, and a failure warning is issued; if the failure diagnosis constraint feature of the hardware to be diagnosed does not fall within the reference range of the failure diagnosis constraint of the hardware to be diagnosed, and the failure diagnosis constraint feature of the hardware to be diagnosed is greater than the maximum value in the reference range of the failure diagnosis constraint of the hardware to be diagnosed, then the simulation failure diagnosis assessment level of the hardware to be diagnosed corresponding to the failure diagnosis constraint feature of the hardware to be diagnosed is level three.

[0099] Through multi-level evaluation standards, the diagnosis is divided into three levels. Different levels correspond to different risk levels, which is conducive to the realization of refined failure diagnosis. The first level failure level indicates high risk and requires emergency treatment, the second level represents the normal risk range, and the third level indicates low risk of the hardware to be diagnosed. This classification method significantly improves the accuracy of diagnosis and avoids misjudgment of partial generalization. If the failure diagnosis constraint feature is lower than the minimum value of the reference range (i.e., the first level), the system will issue an early warning in time. The early warning function allows the maintenance team to handle it before failure, reduce the risk of sudden failures, effectively prevent equipment damage, and ensure the continuous operation of the transmission system. For different levels of failure risks, the maintenance team can In order to formulate differentiated resource allocation strategies, during the diagnosis process, automatic comparison of labels can directly trigger failure level judgment without human intervention. The automated judgment mechanism reduces the time and workload of manual inspections and improves diagnosis efficiency, especially in large-scale transmission line systems. By quickly identifying and processing high-risk equipment (level 1) and giving priority to the status of level 1 equipment, the overall safety and reliability of the system can be improved, the impact of hardware failure on the power grid can be reduced, and the stable operation of the transmission system under high load and complex environments can be ensured. It should be noted that the reference range of the hardware failure diagnosis constraints to be diagnosed stored in the above database is a left-closed and right-closed interval.

[0100] Example 2

[0101] In one embodiment of the present disclosure, a specific implementation of a method for diagnosing failure of power transmission line fittings taking into account a near-wind airflow field is provided, as follows:

[0102] Step 1: Based on the finite element model of the transmission line fittings to be diagnosed, the transmission line fittings to be diagnosed are divided into five analysis elements. The airflow field environment near the wind field of the transmission line is used as the simulation scheme for the fittings to be diagnosed. The airflow speed in the near wind field is 12m / s, the turbulence intensity is 0.3, the wind pressure is 150Pa, the compensation factor of the surface area deformation ratio is 0.8, the compensation factor of the average stress is 1.2, and the compensation factor of the vertical displacement is 0.5. Table 1 shows the analysis results of the force characteristic determination factors of each analysis element.

[0103] Table 1: Analysis results of the force characteristics determination factors of each analysis element

[0104]

[0105] The stress characteristic determination threshold stored in the database is 70, and the set of analysis element 2 and analysis element 5 is a stress aggregation area.

[0106] Step 2: Obtain the magnetic induction characteristic data of the stress aggregation area. The maximum magnetic flux density deviation rate in the stress aggregation area is 0.15, the maximum hysteresis loss in the stress aggregation area is 0.10 (joule per cubic meter), the maximum residual magnetism in the stress aggregation area is 0.08 (Tesla), the magnetic flux density deviation rate compensation factor is 0.9, the hysteresis loss compensation factor is 1.1, and the residual magnetism compensation factor is 0.7. The failure diagnosis constraint signal of the stress aggregation area is calculated to be 1.844.

[0107] Step 3: Obtain the micro fatigue characteristic data of the stress aggregation area. The maximum length of the micro crack is 0.02 mm, the average crack extension rate is 0.005 (mm per cycle), the slip band density is 0.4, the micro fatigue particle spalling rate is 0.001, the crack length compensation factor is 1.2, the crack extension rate compensation factor is 0.8, the slip band density compensation factor is 1.0, and the spalling rate compensation factor is 0.6. The micro fatigue aggregation index of the stress aggregation area is calculated to be 1.864, and the first constraint index of the failure diagnosis of the hardware to be diagnosed is determined to be 37.08.

[0108] Step 4: The wind speed of the near-wind field is 12m / s, the turbulence intensity is 0.3, the wind pressure is 150Pa, the wind speed compensation factor of the near-wind field airflow is 1.1, the turbulence intensity compensation factor of the near-wind field airflow is 0.9, and the wind pressure compensation factor of the near-wind field airflow is 1.3. The environmental deviation factor of the near-wind field airflow of the transmission line is calculated to be 9.870, and the failure diagnosis constraint feature of the hardware to be diagnosed is determined to be 46.95. The reference range of the failure diagnosis constraint of the hardware to be diagnosed stored in the database is [40, 50]. The failure diagnosis constraint feature of the transmission line hardware to be diagnosed falls within the reference range of the failure diagnosis constraint of the hardware to be diagnosed. The failure diagnosis assessment level of the hardware to be diagnosed corresponding to the failure diagnosis constraint feature of the hardware to be diagnosed is level two.

[0109] Example 3

[0110] In one embodiment of the present disclosure, a power transmission line hardware failure diagnosis system considering a near-wind airflow field is provided, comprising:

[0111] An initialization module is used to obtain a finite element model of the hardware structure of the power transmission line to be diagnosed;

[0112] The failure simulation module is used to obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculations on the transmission line hardware to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the transmission line hardware to be diagnosed;

[0113] Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

[0114] Example 4

[0115] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for diagnosing failure of power transmission line hardware considering the near-wind airflow field is implemented.

[0116] Example 5

[0117] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method for diagnosing failure of power transmission line hardware considering the near-wind airflow field is implemented.

[0118] Example 6

[0119] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the transmission line hardware failure diagnosis method considering the near-wind airflow field.

[0120] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for diagnosing failure of transmission line hardware considering the airflow field near the wind field, characterized in that: include: Obtain the finite element model of the transmission line hardware structure to be diagnosed; Obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculation on the hardware of the transmission line to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the hardware of the transmission line to be diagnosed; Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

2. The power transmission line fitting failure diagnosis method considering the near-wind airflow field according to claim 1, characterized in that: Based on the categories of the transmission line fittings to be diagnosed, the finite element model of the transmission line fitting structure to be diagnosed is extracted from the database, the airflow field environment near the wind field of the transmission line is obtained, and the airflow field environment near the wind field of the transmission line is used as the simulation environment of the fittings to be diagnosed.

3. The power transmission line fitting failure diagnosis method considering the near-wind airflow field according to claim 1, characterized in that: Analyzing the force characteristics of each analysis element based on the finite element model includes: obtaining a force characteristic data set of each analysis element, wherein the force characteristic data set of each analysis element specifically includes a deformation ratio of the surface area of ​​the analysis element, an average stress of the analysis element, and a displacement distance of the analysis element perpendicular to the wind direction plane; Based on the acquired force characteristic data sets of each analysis element, the force characteristic determination factors of each analysis element are obtained through comprehensive analysis.

4. The power transmission line fitting failure diagnosis method considering the near-wind airflow field according to claim 1, characterized in that: Determining the stress aggregation area includes: using a stress characteristic determination factor as an analysis basis for determining the stress aggregation area, and comparing the stress characteristic determination factor of each analysis element with a stress characteristic determination threshold stored in a database; If the stress characteristic determination factor is not lower than the stress characteristic determination threshold, the analysis element corresponding to the stress characteristic determination factor is the stress aggregation area; or, If the stress characteristic determination factor is lower than the stress characteristic determination threshold, the analysis element corresponding to the stress characteristic determination factor is not a stress aggregation area.

5. The power transmission line fitting failure diagnosis method considering the near-wind airflow field according to claim 1, characterized in that: Based on the magnetic induction characteristic data, the failure diagnosis constraint signal of the stress aggregation area is obtained, including: The magnetic induction characteristic data of the stress aggregation area is obtained, and based on the obtained magnetic induction characteristic data of the stress aggregation area, a comprehensive analysis is performed to obtain a failure diagnosis constraint signal of the stress aggregation area. The failure diagnosis constraint signal of the stress aggregation area is used as an analysis basis for determining the first constraint indicator of the failure diagnosis of the hardware to be diagnosed.

6. The power transmission line fitting failure diagnosis method considering the near-wind airflow field according to claim 1, characterized in that: The deviation extraction of the airflow field environment characteristics near the wind field of the transmission line is carried out, and the specific analysis process is as follows: Acquire a data set of environmental characteristics of the airflow field in the wind field near the transmission line, wherein the data set of environmental characteristics of the airflow field in the wind field near the transmission line specifically includes wind speed of the airflow field in the wind field, turbulence intensity of the airflow field in the wind field, and wind pressure of the airflow field in the wind field; Based on the acquired data set of environmental characteristics of the airflow field near the wind field of the transmission line, a comprehensive analysis is performed to obtain the environmental deviation factor of the airflow field near the wind field of the transmission line. The environmental deviation factor of the airflow field near the wind field of the transmission line is used as the analysis basis for judging the simulation failure diagnosis evaluation level of the hardware to be diagnosed.

7. A power transmission line hardware failure diagnosis system considering the near-wind airflow field, characterized in that: include: An initialization module is used to obtain a finite element model of the hardware structure of the power transmission line to be diagnosed; The failure simulation module is used to obtain the environmental characteristics of the airflow field near the wind field of the transmission line, perform failure simulation calculations on the transmission line hardware to be diagnosed based on the environmental characteristics of the airflow field near the wind field of the transmission line, and obtain the failure assessment level of the transmission line hardware to be diagnosed; Among them, the failure simulation calculation process includes: in the airflow field environment near the wind field of the transmission line, analyzing the force characteristics of each analysis element based on the finite element model, determining the stress aggregation area and obtaining the magnetic induction characteristic data of the stress aggregation area, and obtaining the failure diagnosis constraint signal of the stress aggregation area based on the magnetic induction characteristic data; determining the first constraint index of the failure diagnosis of the transmission line hardware to be diagnosed based on the failure diagnosis constraint signal of the stress aggregation area; extracting the deviation of the environmental characteristics of the airflow field near the wind field of the transmission line, and judging the assessment level of the hardware simulation failure in combination with the first constraint index of the failure diagnosis.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for diagnosing failure of power transmission line fittings taking into account the near-wind airflow field as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the method for diagnosing failure of power transmission line hardware considering the near-wind airflow field as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the method for diagnosing failure of power transmission line fittings taking into account the near-wind airflow field as described in any one of claims 1-6.

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