A power tower risk level assessment method based on running condition analysis

Abstract

The application relates to the technical field of power system risk assessment, and discloses a power tower risk grade evaluation method based on operation condition analysis. The method first investigates the tower distribution and geological environment through a geographic information system, collects meteorological, soil and physical parameters, secondly studies the frost heaving mechanism and establishes a mechanical and response model to deduce the long-term deformation law of the structure, then constructs a deformation early warning system by using a multidimensional sensor network and an intelligent algorithm to realize data cleaning, filtering and automatic alarm, and finally defines a multistage safety risk standard to generate a differentiated operation and maintenance decision scheme based on the monitoring results. The application realizes quantitative analysis of the influence of frost heaving by fusing climate characteristics and soil dynamics mechanism, effectively makes up for the deficiency of artificial inspection in timeliness by combining high-precision real-time monitoring, improves the evaluation accuracy and reliability of power facilities in extremely cold regions, and guarantees the safe and stable operation of the power system in complex geological environments.

Description

Technical Field

[0001] This invention relates to the field of power system risk assessment technology, specifically a method for assessing the risk level of power poles based on operational status analysis. Background Technology

[0002] With the rapid development of power systems, the operational stability of their infrastructure under extreme climate and complex geological conditions has become crucial to ensuring regional power supply security. As the core supporting structure of the transmission network, the mechanical state of power pole foundations is significantly affected by environmental temperature and humidity, soil stability, and geological changes, directly determining the disaster resistance and service life of the power grid. Especially in seasonally frozen soil areas and seismically active zones, continuous monitoring and scientific risk quantification of power pole foundations are essential requirements for achieving refined operation and maintenance and intelligent transformation of the power system.

[0003] Among them, the risk level assessment method for power poles based on operational status analysis aims to achieve dynamic tracking of pole structural safety through real-time perception and analysis of multi-dimensional parameters. This technology integrates environmental excitation and structural response data to construct an assessment system covering permafrost evolution, geological hazards, and foundation deformation. This efficient risk assessment technology can identify minor structural anomalies early, providing accurate early warning information and decision support for power operation and maintenance departments.

[0004] However, existing monitoring technologies are inadequate in addressing the unique frost heave phenomenon in frigid regions. The primary method of manual inspection is not only inefficient and has limited coverage, but also fails to capture the nonlinear deformation caused by the frequent expansion and contraction of the permafrost layer. Furthermore, traditional assessment methods lack the ability to deeply integrate multi-source heterogeneous environmental data with structural state data, making it impossible to accurately correlate foundation stability with catastrophic factors in complex geological environments. In addition, existing assessment models are mostly based on fixed threshold settings, making it difficult to cope with the chain reactions triggered by sudden disasters such as landslides and ground subsidence. This results in risk level assessments lacking real-time and predictive capabilities, failing to meet the requirements for ensuring the safe operation of power facilities in extreme geographical environments.

[0005] Therefore, a method for assessing the risk level of power poles based on operational status analysis is desired. Summary of the Invention

[0006] The purpose of this invention is to provide a method for assessing the risk level of power poles based on operational status analysis, which can effectively solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a method for assessing the risk level of power poles based on operational status analysis, comprising the following specific steps: Step 1: Conduct a current status survey and data statistical analysis of the operation of power pole foundations. Use geographic information system technology to sort out the distribution patterns of power poles in different regions and their geological environment characteristics, identify areas affected by frost heave, foundation settlement and landslide geological disasters, and collect corresponding climate change data, soil type data and physical parameters of existing pole structures. Step 2: Conduct research on the deformation mechanism of power pole tower foundations in frigid regions. Based on the principle of soil frost heave, analyze the interaction between the moisture field and temperature field of the soil during the freezing process. Establish a mechanical model describing the relationship between the tangential frost heave force and the frost heave rate of seasonally frozen soil. Combine numerical simulation technology to construct power pole tower foundation response models under different geological disaster conditions. Derive the long-term deformation law of frost heave phenomenon and geological disasters on the stability of pole tower structure. Step 3: Develop a power pole foundation deformation status monitoring system, select and deploy a multi-dimensional sensor network to obtain environmental factors and structural response data of the pole, design a hardware architecture with redundant power supply and self-healing mechanism, and develop a data processing software platform that integrates data cleaning, filtering and correction functions. Construct a pole deformation early warning model through big data technology and artificial intelligence algorithms. Step 4: Develop a risk level assessment method and operation and maintenance strategy for power pole foundations. Based on the structural characteristics of the poles, the severity of environmental changes, and the frequency of disasters, define multi-level safety risk assessment standards, and generate differentiated operation and maintenance management decision-making plans for daily maintenance, local reinforcement, or emergency repairs based on real-time monitoring data and risk assessment results.

[0008] Preferably, in step 1, geographic information system technology is used to spatially grid the area covered by the transmission line, and by overlaying topographic features, geological structures and meteorological monitoring layers, target towers located in high-intensity earthquake zones, areas prone to soil liquefaction and seasonal deep permafrost zones are identified.

[0009] Preferably, in step 1, the collected climate change data includes daily temperature fluctuations, precipitation records, and surface snow depth within a preset period; the soil type data covers soil moisture content, clay particle ratio, porosity, and rock stratum burial depth; and the physical parameters of the existing tower structure include foundation type, burial depth, material strength grade, and cumulative settlement recorded in historical maintenance records.

[0010] Preferably, in step 2, the analysis process of the soil frost heave principle fully considers the volume expansion effect of in-situ water and migrating water in the soil during the phase change process, describes the behavior of water migrating to the frozen surface due to temperature gradient, and then determines the non-uniform expansion displacement of the soil at different depths.

[0011] Preferably, in step 2, the established mechanical model decomposes the frost heave force into the normal frost heave force acting vertically on the bottom surface of the foundation, the tangential frost heave force acting on the side surface of the foundation, and the horizontal frost heave force acting on the horizontal structure; the nonlinear relationship between the tangential frost heave force of seasonal frozen soil and the frost heave rate of the soil is expressed by a logarithmic fitting function, in order to predict the upward frost pull-out of the foundation under extremely cold conditions.

[0012] Preferably, in step 2, the numerical simulation technology uses the finite element analysis method to simulate the interaction process between the tower foundation and the surrounding soil under the action of landslide thrust, seismic intensity load and uneven settlement load of the foundation, calculate the stress distribution at the foundation bolts, main materials and supports, and determine the critical deformation threshold at which the structure yields or becomes unstable.

[0013] Preferably, in step 3, the deployed multi-dimensional sensor network includes, but is not limited to, displacement sensors installed on the top of the foundation, strain gauges attached to the main material of the tower foot, inclinometers set at key nodes of the tower body, and temperature and humidity sensors buried around the foundation.

[0014] Preferably, in step 3, the displacement sensor uses a sensing unit with high-precision positioning capability, which can acquire the small displacement components of the foundation in the vertical and horizontal directions in real time, and its static horizontal positioning accuracy and static elevation positioning accuracy both reach the preset millimeter level standard.

[0015] Preferably, in step 3, the hardware architecture of the monitoring system adopts a modular design, and the monitoring data is remotely transmitted to the cloud platform through a low-power wide area network communication protocol; the hardware unit has protective characteristics such as resistance to low temperature, corrosion and strong wind interference, and is equipped with a solar power supply system and a large-capacity energy storage battery pack to ensure continuous operation under extreme weather conditions.

[0016] Preferably, in step 3, the data processing software platform performs analog-to-digital conversion on the received raw electrical signal, applies mean-mode filtering and Kalman filtering algorithms to remove sensor noise, and uses adaptive reference point correction technology to eliminate the influence of ambient temperature fluctuations on the measured value.

[0017] Preferably, in step 3, the tower deformation early warning model uses a deep learning network to train historical monitoring sequences and extract feature vectors reflecting the structural health status; the model compares the current monitoring value with the preset risk warning threshold in real time, and automatically triggers a multi-level alarm mechanism when the rate of change of displacement, tilt angle or strain rate exceeds a specific range.

[0018] Preferably, in step 4, the multi-level safety risk assessment standard defines the safety status of power poles into five levels: normal status, minor risk status, medium risk status, severe risk status, and critical risk status.

[0019] Preferably, the normal state refers to all monitoring indicators being within the preset stable range, with no obvious structural deformation, and it is recommended to perform routine periodic inspection tasks; the slightly risky state refers to some indicators showing slight deviations but not reaching the warning value, reflecting that the permafrost layer is beginning to become active or there is slight soil erosion, and it is recommended to increase the inspection frequency and continue to observe.

[0020] Preferably, the medium-risk state refers to the monitoring data approaching or reaching the first-level warning threshold, with noticeable settlement or tilting of the tower foundation, and it is recommended to conduct a special on-site inspection and formulate a preventive maintenance plan; the severe-risk state refers to the monitoring indicators exceeding the second-level warning threshold, with the structural stability being significantly threatened, and it is recommended to take measures such as foundation balancing, soil improvement, or adding anti-freezing and pull-out measures for local reinforcement.

[0021] Preferably, the critical risk state refers to the situation where the monitoring indicators reach the critical value of structural instability, the tower faces the risk of collapse, the system must immediately issue a red warning, and the operation and maintenance personnel must initiate emergency repair procedures and take measures such as structural support, load adjustment or emergency power outage replacement to prevent the accident from escalating.

[0022] Preferably, the operation and maintenance management decision-making scheme combines meteorological forecast trends and geological dynamic assessment results, enabling it to adjust the risk level in advance based on future precipitation and temperature drop forecasts, thus shifting from a passive emergency repair to a proactive prevention-oriented operation and maintenance model. Compared with the prior art, the beneficial effects achieved by the present invention are: First, by deeply integrating the climate characteristics and soil dynamics mechanisms of frigid regions, this invention establishes an accurate analysis model for frost heave phenomena, which can quantitatively describe the effect of tangential frost heave force on the foundation of power poles and towers. This overcomes the shortcomings of traditional assessment methods that cannot accurately quantify the impact of seasonal permafrost on structures, and significantly improves the accuracy of stability assessment of power facilities in extremely cold regions.

[0023] Secondly, this invention constructs a real-time monitoring system based on a multi-dimensional sensor network and intelligent algorithms, realizing continuous sensing of tower displacement, tilt, and strain. Its high-precision positioning capability and fast data processing efficiency can capture the subtle evolution signals of basic deformation, effectively filling the gaps in timeliness and coverage of manual inspection.

[0024] Third, the hardware architecture designed in this invention has strong environmental adaptability and redundancy assurance capabilities. Through self-repair mechanism and energy-saving power supply mode, it ensures the long-term reliable operation of the monitoring system in harsh geographical environments such as extreme low temperature and strong wind and snow, reducing the system's own maintenance costs and failure risks.

[0025] Fourth, the multi-level risk assessment standards and classified operation and maintenance decision-making schemes formulated in this invention realize the standardization and scientification of power facility safety management, provide power companies with intuitive risk distribution maps and auxiliary decision-making suggestions, optimize the allocation of operation and maintenance resources, improve the pertinence of emergency response, and thus ensure the safe and stable operation of the entire power system in high-risk geological areas.

[0026] Fifth, the implementation of this invention can effectively reduce the incidence of power pole accidents caused by freezing, subsidence and geological disasters, reduce the direct economic losses and social impact caused by power outages, and provide a complete set of theoretical support and technical framework for infrastructure safety in similar cold regions, with significant social benefits and technological innovation value. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the overall technical solution architecture of the evaluation method of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the power pole tower foundation deformation mechanism analysis based on the soil frost heave principle and mechanical model in this invention; Figure 3 This is a schematic diagram of the main stages of the tower deformation monitoring and early warning system integrating multi-dimensional sensor networks and deep learning algorithms in this invention. Figure 4 This is a schematic diagram of the logical flow of multi-level risk assessment and operation and maintenance management decision-making for power poles based on real-time monitoring data in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0030] In the specific implementation of the risk level assessment method for power poles based on operational status analysis, this invention integrates geographic information processing, permafrost mechanics modeling, multi-dimensional sensing networks, and artificial intelligence decision-making technologies to construct a full life-cycle risk management system. The following provides a detailed engineering description of each step of this method.

[0031] Step 1 involves conducting a current status survey and data statistical analysis of the power pole foundation's operational condition. In this process, the system first establishes a multi-source heterogeneous data fusion platform, configured to access data from the power geographic information system. Geographic information system technology is used to perform high-precision spatial gridding of the transmission line coverage area. Specifically, the system divides the target area into regular spatial grids with a unit side length of 500 meters in a three-dimensional coordinate system. Each grid is assigned a unique global spatial code. The system achieves digital modeling of the environment where the poles are located by overlaying digital terrain and geomorphological models, deep geological structure layers, and historical meteorological monitoring layers.

[0032] Based on grid-based segmentation, the system uses an overlay analysis algorithm to identify target towers located in high-intensity earthquake zones, areas prone to soil liquefaction, and seasonal deep permafrost zones. The identification of high-intensity earthquake zones is based on the National Seismic Intensity Standard Database; the system automatically compares the peak ground acceleration (PGA) values ​​of each grid. The identification of areas prone to soil liquefaction combines groundwater level monitoring data with a sand distribution layer. When the groundwater level within a grid is less than 5 meters deep and the soil is saturated loose silt, the grid is marked as a liquefaction-sensitive area. The identification of seasonal deep permafrost zones relies on historical multi-year average geothermal field data. The system extracts temperature change curves at a depth of 2 meters below the surface to identify geographical areas where winter temperatures consistently remain below 0 degrees Celsius.

[0033] In step 1, the climate change data collected by the system has an extremely high sampling frequency and multidimensional characteristics. This data includes daily temperature fluctuations within a preset period, specifically detailed as daily maximum, minimum, and average temperatures, as well as the diurnal variation of surface temperature. Precipitation records are accurate to the hourly rainfall intensity and daily cumulative precipitation. Surface snow depth is cross-calibrated using optical remote sensing data and meteorological station measured data. Soil type data is obtained through gridded geological borehole data, covering initial soil moisture content, clay particle ratio (i.e., the percentage of particles smaller than 0.005 mm), porosity, plasticity index, and stratum depth. The physical parameters of the existing tower structure are obtained by retrieving digital archives from the power facility asset management system, including the tower foundation type (e.g., stepped foundation, slab foundation, or pile foundation), foundation depth, yield strength grade of tower materials, corrosion resistance grade of protective coatings, and cumulative settlement and tilt angle deviation values ​​obtained through precise leveling measurements in historical operation and maintenance records.

[0034] Step 2 involves studying the deformation mechanism of power pole foundations in frigid regions. The core engineering aspect of this step lies in revealing the stress evolution logic of the foundation under multi-physics coupling. The analysis of soil frost heave fully considers the volumetric expansion effect of in-situ and migrating water during phase transition. When the ambient temperature drops below freezing, pore water in the soil begins to undergo phase transition, forming ice crystals. The system describes the migration behavior of unfrozen water towards the frozen surface driven by temperature gradients, a migration jointly driven by matrix potential energy and osmotic potential energy. As the freezing front continues to move downwards, migrating water accumulates at the freezing edge and forms ice lenses, leading to significant volumetric expansion of the soil. The system calculates water flow and phase transition volume changes to determine the non-uniform expansion displacement of the soil at different depths.

[0035] In step 2, the established mechanical model decomposes the complex frost heave interaction into three orthogonal force components. First, there is the normal frost heave force acting vertically on the foundation bottom surface, generated by the frost heave of the soil below the foundation bottom surface; its magnitude is positively correlated with the contact pressure on the bottom surface and the freezing depth. Second, there is the tangential frost heave force acting on the foundation side surfaces, originating from the freezing strength of the foundation sidewalls and the surrounding frozen soil. This invention uses a logarithmic fitting function to describe the nonlinear relationship between the tangential frost heave force and the soil frost heave rate in seasonally frozen soil; that is, the intensity of the tangential frost heave force is linearly proportional to the natural logarithm of the soil frost heave rate. Finally, there is the horizontal frost heave force acting on the horizontal structure; this force, under asymmetric freezing conditions, will cause lateral thrust in the foundation. Through this mechanical model, the system can accurately predict the upward frost pull-out of the foundation under extremely cold conditions, that is, the vertical upward displacement of the top of the foundation relative to the initial installation plane.

[0036] Based on mechanical analysis, numerical simulation technology employs the finite element method for high-fidelity simulation. The system constructs a three-dimensional finite element model comprising the tower steel structure, foundation concrete components, and soil elements within a 50-meter radius. This model simulates the dynamic interaction between the tower foundation and the surrounding soil under the influence of landslide thrust (simulating the sliding thrust load after slope instability), seismic intensity load (simulating inertial force and foundation vibration acceleration), and uneven foundation settlement load (simulating asymmetric displacement constraints at different side supports). The system calculates the equivalent stress distribution at the foundation connecting bolts, main material connecting plates, and supports. Through iterative calculations, the critical deformation thresholds for material yielding, weld cracking, or overall structural instability are determined. For example, the failure displacement threshold is defined as the displacement value corresponding to when the main material stress reaches 90% of the yield strength.

[0037] Step 3 involves developing a power pole tower foundation deformation monitoring system. The core of this system is a multi-dimensional sensor network. Displacement sensors are installed on embedded parts at the top of the foundation, acquiring displacement data via BeiDou high-precision satellite positioning technology or a pull-wire encoder. These sensors utilize sensing units with high-precision positioning capabilities and carrier phase differential processing, enabling real-time acquisition of minute vertical and horizontal displacement components. Their static horizontal positioning accuracy and static elevation positioning accuracy both meet the preset 1-3 mm standard. Strain gauges are attached to the inner centerlines of the four main members at the tower base to capture changes in structural internal forces caused by foundation settlement. Inclinometers are installed at a critical node approximately 5 meters above the ground to monitor the tower's verticality deviation. Temperature and humidity sensors are buried at different depths in the soil around the foundation (e.g., 0.5 meters, 1.2 meters, and 2 meters below the surface) to acquire real-time hydrothermal data of the frozen soil layer.

[0038] The monitoring system's hardware architecture adopts a modular design. The central processing unit uses an industrial-grade low-power microcontroller with hardware watchdog functionality and an automatic restart sequence for abnormalities. The communication section uses low-power wide-area network (LPWAN) communication protocols, such as LoRa or NB-IoT, to achieve remote encrypted transmission of monitoring data to the cloud platform. The hardware units are protected against extremely low temperatures, salt spray corrosion, and strong wind impacts, achieving an IP68 protection rating. The power system is equipped with a large-capacity low-temperature lithium iron phosphate battery pack, along with solar panels with a photoelectric conversion efficiency of no less than 22%, ensuring normal operation even under conditions of 15 consecutive days of overcast and snowy weather without sunlight in extremely cold regions.

[0039] The data processing software platform preprocesses the received raw analog or digital electrical signals. The system first performs analog-to-digital conversion and initial outlier screening. A mean-mode filtering algorithm is applied to remove noise points generated by pulsed electromagnetic interference, and a Kalman filter algorithm is applied to perform state estimation and smoothing of the sensor sequence, effectively suppressing random fluctuations caused by wind vibration. Furthermore, the system utilizes adaptive reference point correction technology, employing compensated sensors installed on stable bedrock to offset the thermal drift effects of ambient temperature fluctuations on the measuring elements in real time.

[0040] In step 3, the tower deformation early warning model is trained on historical monitoring sequences using a deep learning network (specifically a bidirectional long short-term memory network). The model extracts feature vectors reflecting the structural health status using a sliding window technique, including displacement change rate, strain slope, and frequency distribution characteristics. The model compares the current monitoring values ​​with the risk warning threshold determined in step 2 in real time. When the rate of change of displacement, tilt angle, or strain rate exceeds a specific range (e.g., the displacement change rate exceeds 2 mm per day for three consecutive days), the system automatically triggers a multi-level alarm mechanism, including pushing early warning messages to the mobile APP and issuing audible and visual alarms to the monitoring center.

[0041] Step 4 involves developing a risk level assessment method and operation and maintenance strategy for power pole foundations. This step achieves a closed-loop transformation from "sensored data" to "operation and maintenance instructions." The multi-level safety risk assessment standard refines the safety status of power poles into five levels.

[0042] Level 1 is the normal state. The judgment criteria are: all monitoring indicators are within the preset stable range, such as vertical displacement less than 5 mm and tilt angle change less than 0.2 degrees. Under this state, the system considers that the structure has no obvious risk of deformation and recommends to perform routine periodic inspection tasks, usually once a quarter with manual assistance from the machine.

[0043] Level 2 is a slightly risky state. The criteria for judgment are: some indicators show slight deviations but do not reach the warning value, such as vertical displacement between 5 mm and 15 mm. This reflects that the permafrost layer is beginning to become active, with slight frost heave or mild soil erosion due to rainfall. The recommended strategy is to increase the monitoring frequency to once per hour, and have the system continuously monitor the displacement rate.

[0044] Level 3 is a medium-risk condition. The criteria for determination are: monitoring data approaching or reaching the Level 1 warning threshold, displacement exceeding 20 mm, or significant jumps in strain gauge readings. At this point, noticeable settlement or tilting of the tower foundation is observed. It is recommended to conduct a special on-site inspection, with maintenance personnel carrying precision measuring instruments for re-measurement, and to develop a preventative maintenance plan.

[0045] Level 4 is a severe risk condition. The criteria for determination are: monitoring indicators exceeding the Level 2 warning threshold, strain values ​​approaching the allowable stress of the material, and displacement exceeding 35 mm. At this point, structural stability is significantly threatened. Recommended strategies include immediate reinforcement of the foundation (adding weight blocks around the foundation), soil improvement (grouting reinforcement), or adding frost-resistant measures (such as laying an insulation layer around the foundation) for localized reinforcement.

[0046] Level 5 is a critical risk state. The criteria for judgment are: monitored indicators reach or exceed the critical value for structural instability, such as a tilt angle exceeding 1.5 degrees and a continuously increasing displacement rate. At this point, the tower faces the risk of collapse. The system must immediately issue the highest-level red alert. Maintenance personnel must initiate emergency repair procedures, implementing measures including temporary mechanical structural support, adjusting the transmission line load to reduce tension, or, if necessary, taking emergency power outage replacement measures to prevent the accident from escalating and causing a large-scale power grid blackout.

[0047] The proposed operation and maintenance management decision-making scheme embodies the essence of predictive maintenance. The system combines meteorological forecasts for the next 72 hours with dynamic geological assessments. When the system receives a forecast of a significant temperature drop or blizzard in the next 24 hours, it automatically adjusts the risk level weighting coefficient of towers in high-risk areas. This means that even if the current displacement has not yet reached the threshold, but is highly likely to exceed the limit within 24 hours based on the predicted trend, the system will issue an early warning, achieving an intelligent shift from reactive emergency repairs to proactive prevention.

[0048] Example 2 Based on Example 1, this example provides a deeper microscopic description and logical expansion of the interaction between the moisture field and the temperature field in the study of the deformation mechanism of power pole foundations.

[0049] In step 2, the soil frost heave process is considered a complex coupled process of thermal, mass, and mechanical fields. The temperature field changes follow the heat conduction equation, and the system considers the different thermal conductivity coefficients of mineral particles, liquid water, and solid ice within the soil. As the cold front penetrates downwards from the surface, a significant temperature gradient forms within the soil. The moisture field changes are constrained by Darcy's law and the theory of water migration in unsaturated soils. Under the influence of the temperature gradient, an adsorption potential gradient is formed on the particle surface by the unfrozen water film, driving deep unfrozen water to migrate towards the frozen area. This embodiment specifically introduces a dynamic equilibrium calculation logic for ice pressure and pore water pressure terms.

[0050] When the ice pressure within the pores exceeds the sum of the confining pressure and effective cohesion of the surrounding soil, the ice crystals begin to expand and compress soil particles, resulting in macroscopic displacement. The water migration behavior described in this embodiment includes in-situ freezing of water and migration of water continuously replenished from the groundwater level. The system quantifies the rate of water accumulation in the frozen zone by calculating the water flow density, i.e., the mass of water passing through a unit cross-sectional area per unit time. This rate is positively proportional to the absolute value of the temperature gradient and is limited by the soil permeability coefficient, which itself decreases exponentially with decreasing unfrozen water content.

[0051] In the mechanical model construction, the distribution law of tangential frost heave force is further refined into a depth function. The system describes that the tangential frost heave force is not uniformly distributed along the foundation depth, but reaches its maximum value near the freezing front. As the strength of the frozen soil increases, the tangential frost heave force gradually approaches a limiting shear strength value. The logarithmic fitting function is specifically expressed as follows: the tangential frost heave force intensity equals a material characteristic constant multiplied by the natural logarithm of the soil frost heave rate plus a correction coefficient. This expression can extremely accurately fit the actual mechanical response of seasonally frozen soil at different freezing depths.

[0052] This embodiment further describes the application of contact nonlinear algorithms in finite element analysis using numerical simulation techniques. At the interface between the foundation and the soil, the system employs specialized sliding contact elements. These elements possess specific tangential and normal stiffness. When the calculated contact shear stress exceeds the interface freezing strength, the elements slip, simulating the physical process of the foundation being pulled out by frost. Simultaneously, when simulating seismic loads, the system uses time-history analysis, inputting a real seismic wave acceleration sequence to calculate the maximum dynamic stress response of the tower during vibration, and then vector-superimposing it with the static frost heave stress to assess safety under combined conditions.

[0053] Example 3 This embodiment focuses on detailing the data cleaning and algorithm correction of the monitoring system to demonstrate the technical depth of this invention in data quality assurance.

[0054] In step 3, the Kalman filter algorithm executed by the data processing software platform comprises two alternating stages: prediction update and measurement update. The prediction stage uses the displacement and estimated deformation rate from the previous moment to predict the prior state value at the current moment based on the physical motion model. The measurement stage receives actual observation data from the displacement sensor and calculates the Kalman gain to weightedly fuse the predicted and observed values. The magnitude of the Kalman gain depends on the ratio of the variance of the sensor noise to the variance of the model prediction error. When the displacement sensor experiences abnormal jumps due to instantaneous wind load interference, the system can automatically reduce the weight of the observed values ​​by adjusting the gain weights, thereby obtaining a smooth posterior state estimate that closely reflects the actual deformation trend.

[0055] Adaptive benchmark calibration technology is crucial for eliminating systematic errors. Because the physical casing of the sensor and the fiber optic materials themselves expand and contract with temperature changes, these minute material deformations can be misread as ground displacement by the sensor's sensitive elements. In this embodiment, the system sets a reference benchmark point 10 meters away from the tower foundation in bedrock or deep stable soil layers, a point unaffected by permafrost fluctuations. The system simultaneously collects readings from the benchmark point sensor and the foundation monitoring sensor, and combines this with the temperature change component from the ambient temperature sensor. The calibration algorithm calculates the regression coefficient between temperature and sensor zero-point drift, subtracting the thermal drift component caused by temperature from the original signal, ensuring that the final output displacement data is purely physical displacement caused by foundation geological deformation.

[0056] In deep learning-based early warning models, feature vector extraction is not limited to numerical values ​​but also includes frequency domain features. The system analyzes the spectral distribution of tower vibration signals using Fast Fourier Transform (FFT). When the foundation becomes loose or suspended, the overall stiffness of the structure decreases, causing its natural frequency to shift towards lower frequencies. The early warning model can capture the evolution of this frequency characteristic, identifying potential deep foundation weakening even before the displacement exceeds the limit through the trend of frequency decline.

[0057] Example 4 This embodiment further describes the operation and maintenance decision-making process after risk level assessment, demonstrating its deep integration with the standard process of the power industry.

[0058] In step 4, the differentiated operation and maintenance management decision-making solution is not just a written suggestion, but an automatically generated digital work order instruction. When the risk level is determined to be "medium risk" or above, the decision-making system will automatically extract the latitude and longitude, tower type drawings, and historical spare parts consumption records of the affected towers from the geographic information database, and generate a detailed on-site verification checklist.

[0059] For "Severe Risk" level towers, the decision-making process automatically matches a reinforcement scheme library based on the tower's structural parameters. For example, for pile foundation towers, the system recommends circulating cooling grouting, using cryogenic nitrogen or cooling circulation pumps to lower the temperature of the soil surrounding the foundation, allowing it to thaw quickly, or filling voids formed by frost heave through chemical grouting. For slab foundation towers, the system calculates the required counterweight weight, which must be sufficient to offset the calculated maximum total upward frost pull-out force.

[0060] The decision-making system also possesses resource scheduling optimization logic. It compares the current geographical location, skill level, and loading resources of repair vehicles for each maintenance team. When multiple towers trigger warnings simultaneously, the system calculates priorities using a weighted algorithm, prioritizing the key towers on the core backbone network. This intelligent scheduling achieves optimal allocation of operation and maintenance resources, ensuring that limited human and material resources can be deployed to the areas with the highest risk and widest impact during extreme weather conditions.

[0061] The shift in the operation and maintenance model is achieved through a web-based visual risk cloud map. Managers can visually view the risk color markers (green, yellow, orange, red, dark red) for each grid on the map. The system not only displays the current risk but also predicts the risk evolution over the next 24 and 48 hours using a time slider function. By clicking on a specific tower, the system displays real-time trend charts of displacement, strain, and temperature. This highly integrated information display method provides precise decision support for power companies to formulate special inspection plans for the spring freeze-thaw period.

[0062] Example 5 This embodiment fully illustrates the implementation process of this method through a specific application scenario in a high-altitude, frigid mountainous region.

[0063] In a section of a power transmission line at an altitude of 3,500 meters, the lowest winter temperature reaches minus 40 degrees Celsius, and it is located in a landslide-sensitive area.

[0064] The first step involves the system conducting a grid-based survey, as described in Step 1. The Geographic Information System (GIS) identified towers 5 through 12 in the area as being located in a 3-meter-thick layer of seasonally frozen soil, with the soil being high-moisture clay. Historical meteorological data shows that the area has an average of over 40 days of heavy snow per year. The system marked these towers as highly sensitive monitoring targets and recorded the burial depth of their stepped foundations as 3.5 meters.

[0065] The second step involves applying the deformation mechanism model from step 2. Simulation results show that under extreme low-temperature conditions, the frost heave rate of this clay layer can reach 15%. Calculations indicate that the maximum tangential frost heave force on the foundation's side surface will reach 120 kN per square meter. The mechanical model predicts that without intervention, the residual displacement of the foundation after freeze-thaw cycles may increase by 10 mm per year. Finite element simulations show that when landslide thrust is superimposed with frost heave force, the stress on the tower anchor bolts will approach the critical fracture value.

[0066] The third step involved deploying the monitoring system from step 3. Millimeter-level BeiDou displacement sensors, high-precision MEMS inclinometers, and thermistor arrays distributed at different depths were installed on tower No. 8. During winter operation, the monitoring system recorded that the ground temperature dropped to -5 degrees Celsius at a depth of 2 meters. At this point, the displacement data after Kalman filtering showed that the foundation had risen by 12 millimeters, and strain gauges detected a 15% increase in compressive stress in the main tower foot material.

[0067] Fourth, the system performs a risk assessment based on step 4. The current status is assessed as "slight risk." However, weather forecasts indicate that a cold wave will arrive in the next 48 hours, with temperatures expected to drop further by 15 degrees Celsius. The warning model, through deep learning, predicts that the displacement will exceed the "moderate risk" threshold of 25 millimeters.

[0068] The fifth step involves early intervention by the decision-making system. Twelve hours before the cold wave arrives, the system raises the risk level warning to "medium risk" and issues a preventative maintenance work order to the local power supply station. Following the system's recommended plan, maintenance personnel cover the foundation perimeter with polystyrene foam insulation boards and apply antifreeze grease to the tower foot connections to reduce the transmission of tangential frost heave forces.

[0069] The sixth step was that after the cold wave, monitoring data showed that the displacement rate slowed down significantly and eventually stabilized at 18 mm, without entering a "severe risk" state, effectively avoiding structural damage caused by excessive freezing and pulling.

[0070] Example 6 In the data processing software platform of step 3, this invention employs a multi-dimensional feature fusion correlation analysis method to distinguish between external environmental interference and actual structural damage. This logic is described as follows: the system establishes a multi-dimensional matrix containing wind speed, wind direction, solar radiation intensity, and tower response data.

[0071] Uneven solar radiation from different sides can cause thermal bending deformation of the tower's main structure. The system calculates the theoretical thermally induced tilt of the tower at each moment by establishing a geometrical correlation model between the solar altitude angle and the tower's temperature difference. This theoretical correction is then subtracted from the actual values ​​measured by the inclinometer. This meticulous data cleaning process ensures that even under strong sunlight, the system will not issue false tilt alarms due to temperature differences.

[0072] At the data storage level, the platform adopts a time-series database-based compressed storage strategy. Each sensor record contains a timestamp, sensor ID, raw value, quality marker (used to indicate whether the data has been filtered), and spatial coordinates. For data under normal conditions, the system performs 10:1 downsampling storage to save space; however, once any level of risk warning is triggered, the system immediately switches to full-frequency lossless recording mode to provide high-fidelity raw samples for subsequent accident analysis or model optimization.

[0073] Example 7 In the operation and maintenance management decision-making scheme of step 4, the present invention also integrates a life prediction and evaluation module. This module is based on the cumulative damage theory and can be described in words as follows: for each freeze-thaw cycle of "medium risk" or above experienced by the tower, the system will calculate the fatigue damage increment generated based on the displacement and strain amplitude.

[0074] The system calculates the remaining safe service life of a tower by linearly or nonlinearly weighting the cumulative damage amount for each year. When the predicted remaining life of a tower is less than 10% of its design life, the system automatically marks the tower as an "old and critical asset" and suggests prioritizing the replacement of the entire tower or comprehensive reinforcement of the foundation in the next year's technical renovation plan, thus realizing the transition from short-term emergency repairs to long-term asset management.

[0075] This full lifecycle management strategy, combined with risk assessment based on real-time monitoring, constitutes a comprehensive power facility safety assurance system, encompassing everything from macro-geographical planning to micro-structural damage detection. Through deep integration of multi-dimensional data and intelligent decision-making, this invention can effectively address the complex geographical and climatic challenges of frigid regions, ensuring the long-term stable operation of power lines.

[0076] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the risk level of power poles based on operational status analysis, characterized in that, Includes the following steps: Conduct a current status survey and data statistical analysis of the operation of power pole foundations, use geographic information system technology to sort out the distribution patterns of power poles in different regions and their geological environment characteristics, identify areas affected by geological disasters, and collect corresponding climate change data, soil type data and physical parameters of existing pole structures; Research on the deformation mechanism of power pole tower foundations in frigid regions was conducted. Based on the principle of soil frost heave, the interaction between the moisture field and temperature field of the soil during the freezing process was analyzed. A mechanical model describing the relationship between the tangential frost heave force and the frost heave rate of seasonally frozen soil was established. In addition, numerical simulation technology was used to construct response models of power pole tower foundations under different geological disaster conditions, and the long-term deformation law of structural stability was derived. Develop a power pole foundation deformation status monitoring system, select and deploy a multi-dimensional sensor network to obtain environmental factors and structural response data of the pole, establish a data processing software platform that integrates data cleaning, filtering and correction functions, and construct a pole deformation early warning model through artificial intelligence algorithms; Develop a risk level assessment method and operation and maintenance strategy for power pole foundations. Based on the structural characteristics of the poles, the severity of environmental changes, and the frequency of disasters, define multi-level safety risk assessment standards and generate operation and maintenance management decision-making schemes based on real-time monitoring data and risk assessment results.

2. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: In the current status survey and data statistical analysis of the operation of power pole foundations, geographic information system technology is used to divide the transmission line coverage area into regular spatial grids with unique global spatial codes in a three-dimensional coordinate system. By overlaying digital topographic models, deep geological structure layers, and historical meteorological monitoring layers, overlay analysis logic is executed to identify target poles located in high-intensity earthquake zones, areas prone to soil liquefaction, and seasonal deep permafrost zones. Among them, the identification of areas prone to soil liquefaction is achieved by extracting groundwater level monitoring data and sand distribution layers within the regular spatial grids. When the groundwater level is within a preset depth range and the soil quality meets the preset saturated loose characteristics, the corresponding grid is marked as a liquefaction sensitive area.

3. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: The collection of corresponding climate change data includes obtaining the daily maximum temperature, daily minimum temperature, daily average temperature, diurnal temperature range, hourly rainfall intensity, daily cumulative precipitation, and surface snow depth corrected by optical remote sensing data within a preset period; the collection of soil type data includes obtaining geological borehole data within the regular spatial grid, and extracting the initial soil moisture content, the proportion of clay particles below a specific size, the porosity, the plasticity index, and the burial depth of the rock strata; the collection of physical parameters of existing tower structures includes retrieving the tower foundation type, foundation burial depth, yield strength grade of tower materials, corrosion resistance grade of protective coatings, and cumulative settlement from historical operation and maintenance records.

4. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: The analysis of the interaction between the moisture field and temperature field in soil during freezing, based on the principle of soil frost heave, constructs a heat conduction model including mineral particles, liquid water, and solid ice to describe the evolution of the temperature gradient from the surface to the ground as the ambient temperature decreases. By combining Darcy's law and the theory of unsaturated soil water migration, the behavior of unfrozen water migrating to the frozen surface driven by both matrix potential energy and osmotic potential energy is described. The water flow density and the volume of the ice lens formed by water accumulation at the frozen edge are calculated, thereby quantifying the non-uniform expansion displacement of the soil at different depths.

5. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: In the mechanical model describing the relationship between tangential frost heave force and soil frost heave rate in seasonally frozen soil, the frost heave interaction force is decomposed into normal frost heave force acting vertically on the bottom surface of the foundation, tangential frost heave force acting on the side surface of the foundation, and horizontal frost heave force acting on the horizontal structure under asymmetric freezing conditions. The relationship between tangential frost heave force and soil frost heave rate is expressed by a logarithmic fitting function, that is, the intensity of the tangential frost heave force is equal to the sum of the product of the material characteristic constant and the natural logarithm of the soil frost heave rate and the correction coefficient. Based on the mechanical model, the total vertical displacement of the foundation under extremely cold climate conditions is predicted.

6. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: In the numerical simulation technology used to construct the response model of power pole foundation under different geological disaster conditions, a three-dimensional model including the steel structure of the pole, the concrete components of the foundation and the surrounding soil elements is constructed using the finite element analysis method. Sliding contact elements with specific tangential stiffness and normal stiffness are set at the interface between the foundation and the soil. The dynamic interaction process between the tower foundation and the surrounding soil is simulated under the constraints of landslide thrust load, inertial force load generated by seismic intensity, and asymmetric displacement caused by uneven settlement of the foundation. Calculate the equivalent stress distribution at the foundation connection bolts, main material connection plates, and supports, and determine the critical deformation threshold for structural instability based on the material yield strength.

7. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: In the multi-dimensional sensor network, displacement sensors with carrier phase differential processing capabilities are deployed at the top of the foundation to acquire vertical and horizontal displacement components; strain gauges are attached to the inner centerline of the main tower base to capture changes in internal forces caused by foundation displacement; inclinometers are deployed at a predetermined height on the tower body to monitor the verticality deviation; and thermistor arrays are embedded in soil layers at different depths around the foundation to acquire real-time hydrothermal state data of the frozen soil layer. The hardware architecture of the monitoring system adopts a modular design, remotely transmitting monitoring data to a cloud platform via a low-power wide-area network communication protocol, and is equipped with a microcontroller with a self-healing mechanism and a solar power supply system.

8. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: The data cleaning, filtering, and correction functions performed by the data processing software platform include: applying a mean-mode filtering algorithm to remove noise points generated by pulsed electromagnetic interference; applying a Kalman filtering algorithm to estimate the state of the sensor sequence and suppressing random fluctuations through the alternation of prediction updates and measurement updates; and performing adaptive reference point correction technology, which uses a reference reference point sensor deployed in a stable stratum, combined with the ambient temperature change component, to calculate the regression coefficient between temperature and sensor zero-point drift, and subtract the thermal drift component caused by thermal expansion and contraction of the sensor material from the original signal.

9. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: In the tower deformation early warning model constructed using artificial intelligence algorithms, a bidirectional long short-term memory network is used to train historical monitoring sequences, and a feature vector including displacement change rate, strain slope, and frequency distribution characteristics is extracted using sliding window technology. The early warning model compares the current monitoring value with the preset risk warning threshold in real time. When the rate of change of displacement, tilt angle, or strain rate exceeds the preset change rate limit, a multi-level alarm mechanism is automatically triggered. The frequency distribution characteristics are obtained by performing a fast Fourier transform on the tower vibration signal, and are used to identify potential foundation stiffness weakening hazards by the offset of the structure's natural frequency towards lower frequencies.

10. The method for assessing the risk level of power poles based on operational status analysis according to claim 1, characterized in that: The operation and maintenance management decision-making scheme includes: calculating the fatigue damage increment of the tower after each freeze-thaw cycle of a specific level or above based on the cumulative damage theory, and predicting the remaining safe service life of the tower by performing a weighted summation of the annual cumulative damage; combining the meteorological forecast trend and geological dynamic assessment results within the future preset forecast period, and increasing the risk level weight coefficient of towers in high-risk areas in advance when a significant drop in temperature or precipitation is predicted in the future; automatically matching technical measures in the reinforcement scheme library according to the determined risk level, and intelligently scheduling the operation and maintenance team in combination with geographical location, skill level, and emergency repair resource status to achieve optimized allocation of operation and maintenance resources among different risk grids.

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