Power transmission line operation and maintenance management method and system based on tower inclination visual angle

Abstract

The invention provides a power transmission line operation and maintenance management method and system based on a tower inclination visual angle, and belongs to the technical field of operation and maintenance management. The comprehensive risk values of any type of natural disasters such as debris flow, flood, typhoon and earthquake in different places of the area are obtained, and the comprehensive risk values of any type of natural disasters such as debris flow, flood, typhoon and earthquake in the positions where the towers are located are respectively utilized to obtain the comprehensive risk values of any type of natural disasters such as debris flow, flood, typhoon and earthquake; the inclination risk coefficient of the tower is determined by using the comprehensive risk value of the natural disaster type with the maximum comprehensive risk value of the position of the tower, and the inspection processing strategy is generated in a targeted manner, so that the inspection processing efficiency and reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation and maintenance management, and particularly relates to a method and system for operation and maintenance management of transmission lines based on the perspective of tower inclination. Background Art

[0002] With the continuous expansion of the power grid scale and the increasing complexity of transmission lines, the safety and stability of transmission channels have become one of the core issues for ensuring the efficient operation of the power system. The extra-high voltage transmission line system is a key infrastructure of the national power grid, which plays an important role in supporting emergency rescue, reducing losses, and maintaining social stability, and is crucial for national security. However, due to its complexity and the variability of the external environment, it is vulnerable to various natural and social disasters, which may lead to serious crisis events.

[0003] In order to realize the operation and maintenance management of ultra-high voltage transmission channels, in the invention patent application CN202310379768.3 "Method for Making Decision on Operation and Maintenance of UHV Transmission Lines Based on Big Data", the UHV transmission lines are corrected based on the operation years and the expected service life of equipment, the health status of UHV transmission lines is accurately evaluated, and the differential grading of the transmission line status is realized, which can further optimize the operation and maintenance cost of UHV transmission lines. However, there are the following technical problems:

[0004] When carrying out operation and maintenance management, since the length of the ultra-high voltage transmission channel line is relatively long, there are deviations in the weather data of the line areas of different transmission lines. Therefore, due to the differences in wind speed, wind direction, and icing conditions under different weather conditions of the transmission channels associated with the towers, the probability of tower inclination varies greatly. Therefore, if the operation and maintenance inspection strategy cannot be determined according to the differences in the weather data of the line data, the reliability of the inspection and treatment cannot be guaranteed.

[0005] In view of the above technical problems, specifically, the present application provides a method and system for operation and maintenance management of transmission lines based on the perspective of tower inclination. Summary of the Invention

[0006] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, there is provided a method for operation and maintenance management of transmission lines based on the perspective of tower inclination.

[0008] A method for operation and maintenance management of transmission lines based on the perspective of tower inclination specifically includes:

[0009] S1 Divide the ultra - extra - high - voltage power transmission channel line into multiple regions, determine the distribution data of power monitoring devices in different regions, and combine the historical deviation conditions of the monitoring data of different types of power monitoring devices. When it is determined that the monitoring reliability of the region does not meet the requirements, proceed to the next step;

[0010] S2 Obtain the weather data of different regions within a preset time period in the future. When it is determined that the inclination risk coefficient of the poles and towers of the power transmission channel line in the region meets the requirements by using the analysis result of the weather data, transfer to the next step;

[0011] S3 Determine the reference poles and towers of different poles and towers based on the positions of different poles and towers in the region. Determine the similarity with the weather data within a preset time period in the future based on the analysis result of the historical inclination data of the reference poles and towers, and use the similarity to determine the risk - inclined poles and towers in the region;

[0012] S4 Based on the weather data within a preset time period in the future, determine the force prediction data of different risk - inclined poles and towers, and combine the distribution data of the risk - inclined poles and towers in the region and the historical inclination data of different risk - inclined poles and towers to determine the inspection and treatment strategy for the region.

[0013] The beneficial effects of the present invention are as follows:

[0014] By using the analysis result of the weather data, determine whether the inclination risk coefficient of the poles and towers of the power transmission channel line in the region meets the requirements, thus realizing the evaluation of the inclination risk coefficient of the poles and towers of the power transmission channel line in the region from the analysis of the weather data, and also laying a foundation for determining the differential inspection and treatment strategy according to the inclination risk coefficient of the poles and towers of the power transmission channel line. It not only ensures the reliability of the inspection and treatment of the power transmission channel line with a relatively large inclination risk, but also reduces the difficulty of the inspection and treatment of the power transmission channel line with a relatively small inclination risk.

[0015] Determine the inspection and treatment strategy for the region according to the distribution data of the risk - inclined poles and towers in the region and the historical inclination data of different risk - inclined poles and towers, taking into account both the number of risk - inclined poles and towers and the degree of dispersion of the distribution, and at the same time considering the historical inclination situation of the risk - inclined positions, realizing the determination of the inspection and treatment strategies for different regions from multiple perspectives and achieving the differential inspection and treatment of the power transmission channel line.

[0016] A further technical solution is that dividing the ultra - extra - high - voltage power transmission channel line into multiple regions specifically includes:

[0017] Divide the ultra - extra - high - voltage power transmission channel line into multiple regions by using a preset interval distance.

[0018] A further technical solution lies in that the distribution data of the power monitoring devices includes the distribution positions in the area.

[0019] A further technical solution lies in that the historical deviation conditions of the monitoring data of the power monitoring devices include the historical deviation times of the monitoring data of the power monitoring devices and the monitoring data deviation amounts for different historical deviation times.

[0020] A further technical solution lies in that the historical deviation times are determined according to the deviation conditions between the monitoring data and the preset monitoring data range. Specifically, the monitoring moments of the monitoring data outside the preset monitoring data range are used as the historical deviation times.

[0021] A further technical solution lies in that the method for determining the inspection and processing strategy for the area is as follows:

[0022] Based on the distribution data of the risk - inclined poles and towers in the area, determine the number of risk - inclined poles and towers in the area;

[0023] Based on the historical inclination data of different risk - inclined poles and towers, determine the historical inclination angles of different risk - inclined poles and towers, and in combination with the force prediction data of different risk - inclined poles and towers, determine the inclination weight coefficients of different risk - inclined poles and towers;

[0024] Determine the area risk coefficient of the area according to the sum of the inclination weight coefficients of different risk - inclined poles and towers in the area, and use the area risk coefficient to determine the inspection and processing strategy for the area.

[0025] A further technical solution lies in that using the area risk coefficient to determine the inspection and processing strategy for the area specifically includes:

[0026] Based on the area risk coefficient, determine the preset matching strategy corresponding to the area risk coefficient, and use the preset matching strategy to determine the inspection and processing strategy for the area.

[0027] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above - mentioned method for power transmission line operation and maintenance management based on the perspective of pole and tower inclination.

[0028] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description and the drawings.

[0029] To make the above - mentioned objectives, features and advantages of the present invention more obvious and understandable, the following specific embodiments are given, and in conjunction with the attached drawings, the detailed description is as follows. Brief Description of the Drawings

[0030] The above and other features and advantages of the present invention will become more apparent by describing its exemplary embodiments in detail with reference to the accompanying drawings.

[0031] Figure 1 is a flowchart of a method for operation and maintenance management of a transmission line based on the tilt angle of a pole tower;

[0032] Figure 2 is a flowchart for determining that the monitoring reliability of a region does not meet the requirements;

[0033] Figure 3 is a flowchart of a method for determining the tilt risk coefficient of a pole tower of a transmission line corridor in a region;

[0034] Figure 4 is a flowchart of a method for determining risk-tilted pole towers in a region;

[0035] Figure 5 is a flowchart of a method for determining an inspection processing strategy for a region;

[0036] Figure 6 is a flowchart of a method for determining an inspection processing strategy for a pole tower of a transmission line corridor in a region. Detailed Description of the Embodiments

[0037] In order to enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0038] Specifically, as Figure 6 shown, the Bayesian method is used to determine the comprehensive risk value of a certain type of natural disaster such as debris flow, flood, typhoon, and earthquake in a region, and the tilt risk coefficient of the pole tower of the transmission line corridor in the region is determined by using the comprehensive risk values of different types of natural disasters.

[0039] Specifically, based on the Bayesian method, weather data such as temperature, wind speed, and light intensity on different dates are used to obtain the comprehensive risk value of any type of natural disaster such as debris flow, flood, typhoon, and earthquake occurring at different locations in the region.

[0040] When determining the comprehensive risk values of natural disasters such as debris flows, floods, typhoons, and earthquakes occurring at different locations in the area, respectively use the comprehensive risk values of any type of natural disaster such as debris flows, floods, typhoons, and earthquakes occurring at the location of the pole tower, and use the comprehensive risk value of the natural disaster type with the largest comprehensive risk value at the location of the pole tower to determine the tilt risk coefficient of the pole tower.

[0041] Specifically, it includes the following content:

[0042] The goal of the disaster risk probability distribution model is to quantify the likelihood of tilt risk occurring and provide a prior probability distribution of risk levels. Combining historical data analysis, mechanism analysis, and machine learning methods, the specific modeling steps are as follows:

[0043] ① Bayesian network modeling. Construct a causal relationship graph of disaster risk factors and use the Bayesian network to quantify the causal relationship. The joint probability distribution formula of the Bayesian network is:

[0044]

[0045] where X i is the i-th risk factor, and Pa(X i ) is the set of its parent nodes.

[0046] Calculation of conditional probability of disaster risk. According to historical data and expert knowledge, calculate the conditional probability P(X i |Pa(X i ))).

[0047] ② Risk level division. Taking meteorology as an example. Connect meteorological forecast data to the model and dynamically adjust the risk prediction results. Let the weather data be W = {w1, w2,..., w k}}, and through time series analysis of future meteorological conditions Combined with the Bayesian network model, calculate the posterior probability of occurrence, that is: Divide into five risk levels R(t):

[0048]

[0049] where L i represents the weight of the risk level.

[0050] The risk level is divided into five levels (Level I to Level V), and the classification is achieved by setting thresholds {T1, T2,..., T4}:

[0051]

[0052] ③ Dynamically adjust the prior probability to the posterior probability

[0053] By dynamically adjusting the prior probability and posterior probability, the risk changes under different disaster scenarios are reflected. The posterior probability is calculated based on Bayes' formula:

[0054]

[0055] P(H∣E): The posterior probability that event H occurs after observing evidence E.

[0056] P(H): The prior probability of event H, usually set according to historical data or experience.

[0057] P(E∣H): The probability that evidence E appears under the condition that event H occurs.

[0058] P(E): The total probability that evidence E appears.

[0059] Suppose in a certain area of a UHV transmission corridor in Shanxi, it is recently predicted that strong rainfall may induce landslides. The relevant parameters collected are as follows: (a) The prior probability P(H) of strong rainfall inducing landslides: According to historical data statistics, the probability of strong rainfall inducing landslides in this area is 30%; (b) The current rainfall intensity monitoring value (evidence E): The rainfall in 24 consecutive hours exceeds 100 mm; (c) The conditional probability P(E∣H) of the current rainfall under strong rainfall: In the landslide event, the probability that the continuous rainfall exceeds 100 mm is 80%; (d) The total probability P(E) of the rainfall exceeding 100 mm under the current meteorological conditions: According to meteorological statistics, the probability of the rainfall exceeding 100 mm under the current conditions is 50%.

[0060]

[0061] Therefore, after observing that the continuous rainfall exceeds 100 mm, the posterior probability of landslide is 48%.

[0062] The above application cases of the model are illustrated as follows:

[0063] Suppose the posterior probabilities of certain disasters in Shanxi area are as follows:

[0064] Risk event N1: Strong rainfall induces landslides (probability 40%);

[0065] Risk event N2: Ground subsidence in goaf areas (probability 30%);

[0066] Risk event N3: Thunderstorm strong winds (probability 20%);

[0067] Risk event N4: Equipment failure (probability 10%).

[0068] At this time, the system weights are set as: L1 = 0.4, L2 = 0.3, L3 = 0.2, L4 = 0.1; the risk level thresholds are T1 = 20%, T2 = 40%, T3 = 60%, T4 = 80%. Calculate the comprehensive risk value R(t):

[0069] R(t) = (40%·0.4) + (30%·0.3) + (20%·0.2) + (10%·0.1) = 30%

[0070] Then the current comprehensive risk value R(t) = 30%, which belongs to level II.

[0071]

[0072] Rationality analysis of the accuracy rate higher than 80%: For example, assume that at a certain moment, the prior probability is 60% and the posterior probability is updated to 75%. If the actual risk level is level IV and the predicted risk level is also level IV, then the error is 0 and the accuracy rate is 100%. Even if there is an error of 1 level between the predicted value and the actual value (for example, predicting level III and the actual is level IV), this error is still within an acceptable range, meaning that the system can predict disasters in advance and the prediction accuracy rate is greater than 80%.

[0073] Specifically, after obtaining the tilt risk coefficients of different poles and towers in the area, determine the tilt risk coefficient of the poles and towers in the area according to the proportion of the number of tilt risk coefficients of the poles and towers in the area that are greater than the preset coefficient threshold.

[0074] It should be noted that the tilt risk coefficient of the poles and towers of the transmission channel line is determined according to the temperature, wind speed, and light amount of the date according to a preset mapping function, where the input of the preset mapping function is the temperature, wind speed, and light amount of the date, and the output is the tilt risk coefficient of the poles and towers.

[0075] In another possible embodiment, the method for determining the tilt risk coefficient of the poles and towers of the transmission channel line is as follows:

[0076] Based on the distribution data of the poles and towers of the transmission channel line in the area, determine whether an earthquake has occurred in the area. If so, determine that the tilt risk coefficient of the poles and towers of the transmission channel line in the area does not meet the requirements. If not, proceed to the next step;

[0077] Based on the weather data in the preset time period in the future in the area, determine whether there is a typhoon. If so, determine that the tilt risk coefficient of the poles and towers of the transmission channel line in the area does not meet the requirements. If not, proceed to the next step;

[0078] Based on the weather data of the area within a preset time period in the future, determine the probabilities of debris flow and flood occurring at different tower positions, and determine the risk tower positions among the tower positions based on the probabilities of debris flow and flood occurring. Determine whether the tilt risk coefficient of the towers on the transmission line of the area meets the requirements according to the preset risk coefficient corresponding to the number of the risk tower positions.

[0079] Optionally, the preset mapping function is constructed using the PSO-LSTM model, specifically including:

[0080] Step 1: Create an initial sample training set. Select the initial population size N of the particle swarm and set the control acceleration coefficients C1 and C2, and generate the initial positions and velocities of the particles within a reasonable range. Then use the PSO algorithm to optimize the important parameters C and σ of the LSTM.

[0081] Step 2: Train the LSTM. Train the LSTM through the sample training set, calculate the fitness function values of each particle, and compare the best positions experienced by each particle with the fitness function values. If the best position is inferior to the fitness function value, this fitness function value can be used as the new fitness function value. To ensure the stability of the fitness function, the average relative error is used as the value of the fitness function, that is

[0082]

[0083] Step 3: Comparison of fitness function values. Compare the fitness function value of each particle with the fitness function values of all the particles in the group. If the fitness function value of the group particle is greater than the fitness function of each particle, then the global optimal position G best , will be replaced by the optimal position P best of the current particle, and at the same time, adjust the positions and velocities of the particles respectively.

[0084] Step 4: Determine whether to terminate the calculation. If the termination condition is met, end the optimization search. At the same time, output the optimal parameters of the LSTM. If the condition is not met, it is necessary to repeat Step 2.

[0085] Step 5: Substitute the optimal parameters into the model. Substitute the optimal C and σ obtained through PSO training into the LSTM model, and re-perform sample training and learning to obtain a relatively ideal LSTM prediction model.

[0086] Embodiment 1

[0087] To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, a transmission line operation and maintenance management method based on the perspective of tower tilt is provided, specifically including:

[0088] S1 Divide the ultra-high voltage power transmission channel line into multiple regions, determine the distribution data of power monitoring devices in different regions, and combine the historical deviation conditions of the monitoring data of different types of power monitoring devices. When it is determined that the monitoring reliability of the region does not meet the requirements, proceed to the next step;

[0089] Further, dividing the ultra-high voltage power transmission channel line into multiple regions specifically includes:

[0090] Divide the ultra-high voltage power transmission channel line into multiple regions using a preset interval distance.

[0091] Specifically, the distribution data of the power monitoring devices includes the distribution positions in the region.

[0092] It can be understood that the historical deviation conditions of the monitoring data of the power monitoring devices include the historical deviation times of the monitoring data of the power monitoring devices and the monitoring data deviation amounts of different historical deviation times.

[0093] Further, the historical deviation times are determined according to the deviation conditions between the monitoring data and the preset monitoring data range. Specifically, the monitoring times of the monitoring data that are not within the preset monitoring data range are used as the historical deviation times.

[0094] Specifically, as Figure 2 shown, determining that the monitoring reliability of the region does not meet the requirements specifically includes:

[0095] Based on the distribution data of the power monitoring devices in the region, determine the distribution of different types of power monitoring devices in the region, and use the distribution to determine the matching monitoring devices in the region;

[0096] According to the historical deviation conditions of the monitoring data of different matching monitoring devices, determine the historical deviation times of the monitoring data of different matching monitoring devices, and based on the historical deviation times, determine the reliable monitoring devices among the matching monitoring devices;

[0097] Based on the number of reliable monitoring devices, determine whether the monitoring reliability of the region meets the requirements.

[0098] Optionally, the matching monitoring devices include an inclination monitoring device, a settlement monitoring device, a line swing monitoring device, and a force monitoring device.

[0099] Optionally, the reliable monitoring devices among the matching monitoring devices are the matching monitoring devices whose historical deviation times do not meet the requirements.

[0100] Further, when the number of reliable monitoring devices in the region is greater than the preset number of monitoring devices, it is determined that the monitoring reliability of the region meets the requirements.

[0101] In another embodiment, determining that the monitoring reliability of the area does not meet the requirements specifically includes:

[0102] Based on the distribution data of the power monitoring devices in the area, determine the distribution of different types of power monitoring devices in the area, and use the distribution to determine the matching monitoring devices in the area;

[0103] According to the historical deviation situations of the monitoring data of different matching monitoring devices, determine the historical deviation times of the monitoring data of different matching monitoring devices, and based on the historical deviation times, determine the reliable monitoring devices among the matching monitoring devices;

[0104] Determine the reliable monitoring coefficient based on the proportion of the number of reliable monitoring devices in the matching monitoring devices, and use the reliable monitoring coefficient to determine whether the monitoring reliability of the area meets the requirements.

[0105] Further, when the reliable monitoring coefficient is less than the preset monitoring coefficient threshold, it is determined that the monitoring reliability of the area does not meet the requirements.

[0106] In another embodiment, determining that the monitoring reliability of the area does not meet the requirements specifically includes:

[0107] S11 Based on the distribution data of the power monitoring devices in the area, determine the distribution of different types of power monitoring devices in the area, and use the distribution to determine the matching monitoring devices in the area;

[0108] Optionally, the above step S11 includes the following content:

[0109] S111 Based on the distribution data of the power monitoring devices in the area, determine the distribution of different types of power monitoring devices in the area, and use the distribution to determine the matching monitoring devices in the area. When the number of matching monitoring devices in the area is less than the preset number of monitoring devices, it is determined that the monitoring reliability of the area does not meet the requirements. When the number of matching monitoring devices in the area is not less than the preset number of monitoring devices, proceed to step S112;

[0110] S112 Divide the ultra-high voltage transmission channel lines in the area into multiple line intervals using the unit distance. When there is no line interval where the number of matching monitoring devices is less than the preset matching number, proceed to step S12. When there is a line interval where the number of matching monitoring devices is less than the preset matching number, proceed to step S113;

[0111] S113 uses the line section where the number of matching monitoring devices is less than the preset matching number as the monitoring deviation section. When the number of such monitoring deviation sections does not meet the requirements, it is determined that the monitoring reliability of the area does not meet the requirements. When the number of such monitoring deviation sections meets the requirements, it proceeds to step S12.

[0112] S12 determines the historical deviation times of the monitoring data of different matching monitoring devices according to the historical deviation conditions of the monitoring data of different matching monitoring devices, and determines the monitoring deviation coefficients of different matching monitoring devices based on the proportion of the historical deviation times.

[0113] Optionally, the above step S12 includes the following content:

[0114] S121 determines the historical deviation times of the monitoring data of different matching monitoring devices according to the historical deviation conditions of the monitoring data of different matching monitoring devices. When there is no matching monitoring device with the historical deviation times of the monitoring data not meeting the requirements, it is determined that the monitoring reliability of the area meets the requirements. When there is a matching monitoring device with the historical deviation times of the monitoring data not meeting the requirements, it proceeds to step S122.

[0115] S122 uses the matching monitoring device with the historical deviation times of the monitoring data not meeting the requirements as the monitoring data deviation device. When the number of such monitoring data deviation devices does not meet the requirements, it is determined that the monitoring reliability of the area does not meet the requirements. When the number of such monitoring data deviation devices meets the requirements, it proceeds to step S123.

[0116] S123 determines the monitoring deviation coefficients of different matching monitoring devices based on the proportion of the historical deviation times. When the number of matching monitoring devices with the monitoring deviation coefficient greater than the preset deviation coefficient does not meet the requirements, it is determined that the monitoring reliability of the area does not meet the requirements. When the number of matching monitoring devices with the monitoring deviation coefficient greater than the preset deviation coefficient meets the requirements, it proceeds to step S13.

[0117] S13 determines the comprehensive deviation coefficient based on the average value of the monitoring deviation coefficients of different matching monitoring devices, and uses the comprehensive deviation coefficient to determine whether the monitoring reliability of the area meets the requirements.

[0118] Furthermore, when the comprehensive deviation coefficient is greater than the preset deviation coefficient threshold, it is determined that the monitoring reliability of the area does not meet the requirements.

[0119] S2 obtains the weather data of different areas in a preset future time period, and when the analysis result of the weather data determines that the inclination risk coefficient of the poles and towers of the transmission line in the area meets the requirements, it proceeds to the next step.

[0120] Further, the weather data of the area within a preset time period in the future includes the temperature, wind speed, and light intensity on different dates within the preset time period in the future.

[0121] It should be noted that, as Figure 3 shown, the method for determining the inclination risk coefficient of the poles and towers of the transmission line in the area is as follows:

[0122] Based on the analysis results of the weather data within a preset time period in the future, determine the temperature, wind speed, and light intensity on different dates within the preset time period in the future;

[0123] Based on the temperature, wind speed, rainfall, and light intensity on different dates, determine the probability of inclination on different dates, and use the probability of inclination on different dates to determine the inclination risk dates;

[0124] Determine the inclination risk coefficient of the poles and towers of the transmission line in the area by the proportion of the number of inclination risk dates within a preset time period in the future.

[0125] It should be noted that the probability of inclination is determined according to the proportion of the number of inclinations in similar dates under the temperature, wind speed, rainfall, and light intensity on the date.

[0126] It should be noted that the similar dates are historical dates whose deviations from the temperature, wind speed, rainfall, and light intensity on the date are all within a preset deviation range.

[0127] Optionally, the inclination risk date is the date when the probability of inclination is greater than a preset probability.

[0128] It should be noted that the value range of the inclination risk coefficient of the poles and towers of the transmission line in the area is between 0 and 1. When the inclination risk coefficient of the poles and towers of the transmission line in the area is greater than the preset risk coefficient, it is determined that the inclination risk coefficient of the poles and towers of the transmission line in the area does not meet the requirements.

[0129] Further, when the inclination risk coefficient of the poles and towers of the transmission line in the area does not meet the requirements, the inspection and treatment strategy for the area is determined based on a preset inspection and treatment strategy.

[0130] Optionally, the preset inspection and treatment strategy includes the inspection assignment processing of the inspection personnel in the area according to a preset mileage.

[0131] In another embodiment, the method for determining the inclination risk coefficient of the poles and towers of the transmission line in the area is as follows:

[0132] S21 determines the temperature, wind speed, and light intensity on different dates within a preset future time period based on the analysis result of weather data in the preset future time period. Based on the temperature, wind speed, and light intensity on different dates, it determines the probability of tilt occurring on different dates, and uses the probability of tilt occurring on different dates to determine the tilt risk dates within the preset future time period;

[0133] S22 determines the weight coefficients of different tilt risk dates based on the interval duration between different tilt risk dates and the current moment and the probability of tilt occurring on different future dates;

[0134] S23 determines the tilt risk coefficient of the poles and towers of the transmission line in the area based on the weight coefficients of different tilt risk dates and the probability of tilt occurring.

[0135] Optionally, the above step S21 includes the following content:

[0136] S211 determines the temperature, wind speed, and light intensity on different dates within a preset future time period based on the analysis result of weather data in the preset future time period. Based on the temperature, wind speed, and light intensity on different dates, it determines the probability of tilt occurring on different dates. If it is determined that there are no tilt risk dates within the preset future time period using the probability of tilt occurring on different dates, then it is determined that the tilt risk coefficient of the poles and towers of the transmission line in the area meets the requirements. When it is determined that there are tilt risk dates within the preset future time period using the probability of tilt occurring on different dates, it proceeds to step S212;

[0137] S212 obtains the number of tilt risk dates. When the number of tilt risk dates does not meet the requirements, it is determined that the tilt risk coefficient of the poles and towers of the transmission line in the area does not meet the requirements. When the number of tilt risk dates meets the requirements, it proceeds to step S213;

[0138] S213 determines the tilt anomaly coefficient based on the probability of tilt occurring on different tilt risk dates and the proportion of the number of tilt risk dates. When the tilt anomaly coefficient does not meet the requirements, it is determined that the tilt risk coefficient of the poles and towers of the transmission line in the area does not meet the requirements. When the tilt anomaly coefficient meets the requirements, it proceeds to step S22.

[0139] Optionally, the above step S22 includes the following content:

[0140] S221 determines the weight coefficients of different tilt risk dates based on the time intervals between different tilt risk dates and the current moment, as well as the probabilities of tilt occurring on different future dates. When the sum of the weight coefficients of different tilt risk dates does not meet the requirements, it is determined that the tilt risk coefficient of the poles and towers of the transmission channel line in the area does not meet the requirements. When the sum of the weight coefficients of different tilt risk dates meets the requirements, proceed to step S222;

[0141] S222 determines the date tilt risk coefficients of different tilt risk dates based on the weight coefficients of different tilt risk dates and the probabilities of tilt occurring. When there is a tilt risk date with a date tilt risk coefficient greater than the preset tilt coefficient threshold, it is determined that the tilt risk coefficient of the poles and towers of the transmission channel line in the area does not meet the requirements. When there is no tilt risk date with a date tilt risk coefficient greater than the preset tilt coefficient threshold, proceed to step S223;

[0142] S223 obtains the average value of the date tilt risk coefficients of different tilt risk dates. When the average value of the date tilt risk coefficients of different tilt risk dates is greater than the preset coefficient threshold, proceed to step S224. When the average value of the date tilt risk coefficients of different tilt risk dates is not greater than the preset coefficient threshold, proceed to step S23;

[0143] S224 When the number of tilt risk dates is within the preset date number range, it is determined that the tilt risk coefficient of the poles and towers of the transmission channel line in the area does not meet the requirements. When the number of tilt risk dates is not within the preset date number range, proceed to step S23.

[0144] S3 determines the reference poles and towers of different poles and towers based on the positions of different poles and towers in the area, determines the similarity with the weather data in the preset future time period based on the analysis results of the historical tilt data of the reference poles and towers, and uses the similarity to determine the risk-tilt poles and towers in the area;

[0145] Further, the method for determining the reference poles and towers is as follows:

[0146] Based on the position of the pole and tower, determine the terrain data corresponding to the position of the pole and tower, and use it as the matching terrain data;

[0147] According to the deviation situations of the terrain data of other poles and towers and the matching terrain data in different dimensions, determine the deviation coefficients of the terrain data in different dimensions;

[0148] Based on the average value of the deviation coefficients of the terrain data in different dimensions, determine the comprehensive deviation coefficient of the other poles and towers, and use the comprehensive deviation coefficient to determine whether the other poles and towers are reference poles and towers.

[0149] Specifically, the dimensions of the terrain data include altitude, inclination, slope, direction, terrain undulation, and underground support structure.

[0150] Further, the similarity with the weather data within a preset time period in the future is determined based on the average value of the similarity degrees of the weather data between different dates.

[0151] Optionally, the similarity degree of the weather data is determined based on the average value of the deviation amounts of the weather in different dimensions.

[0152] Specifically, as Figure 4 shown, the method for determining the risk-inclined pole tower in the area is as follows:

[0153] Based on the similarity between the weather data in different historical time periods and the preset time period in the future, determine the average value of the similarity degrees between different historical time periods and the preset time period in the future, and use it as the average similarity coefficient;

[0154] Use the average similarity coefficient to determine the similar time periods within the historical time period;

[0155] Based on the inclination data of different reference pole towers of the pole tower in different similar time periods, determine the similar time periods when inclination occurs, and use them as the inclination time periods. Use the number of the inclination time periods to determine whether the pole tower is a risk-inclined pole tower.

[0156] Further, when the number of the inclination time periods is greater than the number of the preset time periods, it is determined that the pole tower is a risk-inclined pole tower.

[0157] It can be understood that the similar time period is the historical time period when the average similarity coefficient is greater than the preset similarity coefficient.

[0158] Optionally, the method for determining the risk-inclined pole tower in the area is as follows:

[0159] Obtain the historical inclination data of the reference pole tower of the pole tower. When none of the reference pole towers of the pole tower has experienced inclination, it is determined that the pole tower does not belong to the risk-inclined pole tower;

[0160] When there is a reference pole tower of the pole tower that has experienced inclination:

[0161] Based on the similarity between the weather data in different historical time periods and the preset time period in the future, determine the average value of the similarity degrees between different historical time periods and the preset time period in the future, and use it as the average similarity coefficient. Use the average similarity coefficient to determine the similar time periods within the historical time period;

[0162] When different reference poles have not tilted during different similar time periods, it is determined that the pole does not belong to a risk-tilting pole;

[0163] When the reference pole has tilted during the similar time period:

[0164] When different reference poles have all tilted during the similar time period, it is determined that the pole belongs to a risk-tilting pole;

[0165] When there are reference poles that have not tilted during the similar time period, the reference lighthouses that have tilted during the similar time period are used as reference tilting poles. When the number of the reference tilting poles is greater than the preset number of tilting poles, it is determined that the pole belongs to a risk-tilting pole;

[0166] When the number of the reference tilting poles is not greater than the preset number of tilting poles:

[0167] Based on the tilting data of different reference poles of the pole during different similar time periods, the similar time period in which tilting occurs is determined and used as the tilting time period. When the number of the tilting time periods does not meet the requirements, it is determined that the pole is a risk-tilting pole;

[0168] When the number of the tilting time periods meets the requirements: Based on the tilting data of different reference poles during different similar time periods, the number of times the reference lighthouses tilt during different similar time periods is determined, and in combination with the tilting angles of different reference lighthouses that tilt, the tilting anomaly coefficient during different similar time periods is determined. When there is a similar time period in which the tilting anomaly coefficient does not meet the requirements, it is determined that the pole is a risk-tilting pole;

[0169] When there is no similar time period in which the tilting anomaly coefficient does not meet the requirements:

[0170] Obtain the proportion of the number of similar time periods in which the reference pole has tilted, and in combination with the average value of the tilting anomaly coefficients during the similar time periods in which the reference pole has tilted, determine the anomaly coefficient evaluation quantity, and use the anomaly coefficient evaluation quantity to determine whether the pole is a risk-tilting pole.

[0171] It should be noted that when the anomaly coefficient evaluation quantity is greater than the preset anomaly coefficient threshold, it is determined that the pole is a risk-tilting pole.

[0172] S4 Based on the weather data within a future preset time period, determine the force prediction data of different risk-tilting poles, and in combination with the distribution data of the risk-tilting poles in the area and the historical tilting data of different risk-tilting poles, determine the inspection and processing strategy for the area.

[0173] It should be noted that the method for determining the force prediction data of the risk-tilted tower is as follows:

[0174] Based on the similarity of weather data in different historical time periods and a future preset time period, determine the average value of the similarity degree between different historical time periods and the future preset time period, and use it as the average similarity coefficient;

[0175] Use the average similarity coefficient to determine the similar time period within the historical time period;

[0176] Based on the historical force data of the risk-tilted tower within the similar time period, determine the force prediction data of the risk-tilted tower.

[0177] It can be understood that the force prediction data is determined based on the maximum value of the historical force data of the risk-tilted tower within the similar time period.

[0178] It should be noted that the force prediction data is not only related to the weight of the overhead transmission line, but also related to weather data such as wind speed, wind direction, and ice accretion.

[0179] It should be noted that as Figure 5 shown, the method for determining the inspection and treatment strategy for the area is as follows:

[0180] Based on the distribution data of the risk-tilted towers in the area, determine the number of risk-tilted towers in the area;

[0181] Based on the historical tilt data of different risk-tilted towers, determine the historical tilt angles of different risk-tilted towers, and combine with the force prediction data of different risk-tilted towers to determine the tilt weight coefficients of different risk-tilted towers;

[0182] Based on the sum of the tilt weight coefficients of different risk-tilted towers in the area, determine the area risk coefficient of the area, and use the area risk coefficient to determine the inspection and treatment strategy for the area.

[0183] Optionally, the method for determining the tilt weight coefficient of the risk-tilted tower is as follows:

[0184] When the preset coefficient corresponding to the risk-tilted tower under the force prediction data is 0.05 and the preset coefficient corresponding to the historical tilt angle is 0.03, then the tilt weight coefficient is the preset coefficient, that is, 0.08. The product of 0.08 and 10 is 0.8, so the tilt weight coefficient is 0.8.

[0185] Furthermore, using the area risk coefficient to determine the inspection and treatment strategy for the area specifically includes:

[0186] Based on the regional risk coefficient, determine the preset matching strategy corresponding to the regional risk coefficient, and use the preset matching strategy to determine the inspection and processing strategy for the region.

[0187] In addition, it should be noted that the benchmark inspection and processing strategy includes using a drone to conduct inspection and processing of the region.

[0188] In addition, it should be noted that the inspection and processing strategy for the region also includes a second inspection and processing strategy and a third inspection and processing strategy. Neither the second inspection and processing strategy nor the third inspection and processing strategy uses a drone, and they are divided according to the difference in the distance thresholds for inspection assignment processing by different inspectors.

[0189] When performing operation and maintenance processing, the following factors need to be considered:

[0190] The inclination of the transmission tower may cause uneven distribution of mechanical stress in the transmission line, affecting the safety and stability of the line. Especially under complex geological and meteorological conditions (such as earthquakes, strong winds, soil settlement, etc.), when the inclination of the transmission tower exceeds 5°, the system will determine that it is in a fault state and immediate measures need to be taken.

[0191] The calculation formula for the inclination rate θ of the transmission tower is:

[0192]

[0193] where Δh is the horizontal offset between the top and bottom of the tower pole, and L is the height of the tower pole.

[0194] Use a Kalman filter to fuse geological data, weather data, and transmission tower inclination monitoring data to improve the accuracy of fault identification. When the inclination rate reaches the critical value (such as 5°), the system will trigger an early warning and initiate an emergency response.

[0195] The damage to the tower foundation will directly affect the stability of the transmission tower, especially under long-term soil settlement, erosion, or mechanical stress. This indicator is used to monitor the health status of the tower foundation and provide priority suggestions for repair in combination with the degree of damage.

[0196] Base damage index D base The calculation formula is:

[0197]

[0198] where A damaged is the damaged area of the base, A total is the total area of the base, and S severityis a coefficient for the severity of damage (evaluated based on the depth and type of damage). Using the Bayesian method, combining historical data and real-time monitoring data of GIS (Geographic Information System) technology, dynamically predict the possible damage process of the base and optimize the priority of the repair plan.

[0199] Embodiment 2

[0200] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned method for operation and maintenance management of a transmission line based on the tilt angle of a pole tower.

[0201] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0202] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0203] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A method for operation and maintenance management of transmission lines based on the tilt angle of the pole tower, characterized in that, Specifically, it includes: Dividing the ultra - extra - high - voltage power transmission channel line into multiple regions, determining the distribution data of power monitoring devices in different regions, and when it is determined that the monitoring reliability of the region does not meet the requirements by combining the historical deviation situations of the monitoring data of different types of power monitoring devices, proceed to the next step; Obtaining the weather data of different regions within a preset time period in the future, and when it is determined that the tilt risk coefficient of the poles and towers of the power transmission channel line in the region meets the requirements by using the analysis result of the weather data, transfer to the next step; Determining the reference poles and towers of different poles and towers in the region based on the positions of different poles and towers in the region, determining the similarity with the weather data within a preset time period in the future based on the analysis result of the historical tilt data of the reference poles and towers, and using the similarity to determine the risk - tilted poles and towers in the region; Based on the weather data within a preset time period in the future, determining the force prediction data of different risk - tilted poles and towers, and combining the distribution data of the risk - tilted poles and towers in the region and the historical tilt data of different risk - tilted poles and towers, determining the inspection and handling strategy for the region.

2. The power transmission line operation and maintenance management method based on the pole tower inclination perspective according to claim 1, wherein Dividing the ultra - extra - high - voltage power transmission channel line into multiple regions specifically includes: Dividing the ultra - extra - high - voltage power transmission channel line into multiple regions by using a preset interval distance.

3. The transmission line operation and maintenance management method based on the pole tower inclination perspective according to claim 1, characterized in that, The distribution data of the power monitoring devices includes the distribution positions in the region.

4. The transmission line operation and maintenance management method based on the tower inclination perspective according to claim 1, characterized in that The historical deviation situations of the monitoring data of the power monitoring devices include the historical deviation times of the monitoring data of the power monitoring devices and the monitoring data deviation amounts of different historical deviation times.

5. The transmission line operation and maintenance management method based on the pole tower tilt perspective according to claim 1, characterized in that Determining that the monitoring reliability of the region does not meet the requirements specifically includes: Based on the distribution data of the power monitoring devices in the region, determining the distribution situations of different types of power monitoring devices in the region, and using the distribution situations to determine the matching monitoring devices in the region; According to the historical deviation situations of the monitoring data of different matching monitoring devices, determining the historical deviation times of the monitoring data of different matching monitoring devices, and determining the reliable monitoring devices among the matching monitoring devices based on the historical deviation times; Determining whether the monitoring reliability of the region meets the requirements based on the number of the reliable monitoring devices.

6. The transmission line operation and maintenance management method based on the pole tower tilt perspective according to claim 5, wherein, The matching monitoring devices include tilt monitoring devices, settlement monitoring devices, line swing monitoring devices, and force monitoring devices.

7. The transmission line operation and maintenance management method based on the pole tower inclination perspective according to claim 5, characterized in that The reliable monitoring devices among the matching monitoring devices are the matching monitoring devices with historical deviation times not meeting the requirements.

8. The transmission line operation and maintenance management method based on the tower inclination perspective according to claim 1, characterized in that The weather data of the region within a preset time period in the future includes the temperature, wind speed, and light amount on different dates within a preset time period in the future.

9. The transmission line operation and maintenance management method based on the tower inclination perspective according to claim 1, wherein, The method for determining the inspection and handling strategy of the region is: Based on the distribution data of the risk - tilted poles and towers in the region, determining the number of the risk - tilted poles and towers in the region; Based on the historical tilt data of different risk - tilted poles and towers, determining the historical tilt angles of different risk - tilted poles and towers, and combining the force prediction data of different risk - tilted poles and towers, determining the tilt weight coefficients of different risk - tilted poles and towers. Determine the regional risk coefficient of the region according to the sum of the inclination weight coefficients of different risk-inclined poles and towers in the region, and use the regional risk coefficient to determine the inspection and processing strategy of the region.

10. A computer system, comprising: A memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes a transmission line operation and maintenance management method according to any one of claims 1-9 based on the inclination perspective of poles and towers.

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