A method and device for monitoring icing on a power transmission line

Abstract

The application discloses a kind of transmission line icing monitoring method and device, its method includes controlling unmanned aerial vehicle to fly to monitoring point along preset inspection route, obtains and calibrates the three-dimensional coordinates of unmanned aerial vehicle at monitoring point;At monitoring point, the spatial coordinates of wire, temperature distribution data, original ranging value data from unmanned aerial vehicle to wire surface and the icing thickness correlation data containing different axial and radial position are synchronously collected;Space coordinates fitting generates wire sag curve, calculates sag value, and the sag value is corrected in combination with the temperature distribution data;Based on original ranging value and the standard distance reference D0 without icing, the icing thickness is calculated;And based on icing thickness correlation data, the icing growth rate and the distribution information of icing are calculated;Establish the correlation model of icing thickness and sag variation.The present application can realize the synchronous accurate monitoring and linkage analysis of sag, icing thickness, growth rate and distribution uniformity.

Description

Technical Field

[0001] This invention relates to the field of power transmission line safety monitoring technology, and in particular to a method and device for monitoring icing on power transmission lines. Background Technology

[0002] As a core component of the power system, the safe operation of transmission lines directly affects the stability of power supply. In harsh weather conditions such as low temperatures and high humidity during winter, lines are prone to icing, leading to increased conductor sag and mechanical load, which can severely cause accidents such as line breaks and tower collapses. Simultaneously, factors such as temperature fluctuations and conductor aging can also cause abnormal changes in sag. Traditional monitoring methods have the following significant drawbacks:

[0003] Lag in sag monitoring: Existing manual inspections or fixed monitoring points cannot achieve real-time dynamic monitoring, making it difficult to capture subtle changes in sag in the early stages of icing and easily missing early warning opportunities.

[0004] Low accuracy in measuring ice thickness: Traditional laser ranging equipment is mostly fixed on the ground or towers, and is affected by terrain and viewing angle obstruction, resulting in large deviations in measurement data. Furthermore, it cannot simultaneously obtain the ice growth rate and distribution pattern.

[0005] Poor data linkage: Sag change and icing thickness monitoring are independent of each other, making it impossible to establish a correlation analysis model between the two. This leads to inaccurate assessment of the impact of icing on line safety and a lack of comprehensive data support for de-icing decisions. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies in transmission line icing monitoring, such as untimely dynamic monitoring of sag, low accuracy of ice thickness measurement, and poor data linkage, the purpose of this invention is to provide a method and device for monitoring transmission line icing, which can realize synchronous monitoring of sag and ice thickness, and data fusion analysis, providing accurate support for line de-icing decisions and safe operation.

[0007] The technical solution adopted by this invention to solve its technical problem is: a method for monitoring icing on transmission lines, comprising:

[0008] S1: Control the drone to fly along the preset inspection route to the monitoring point, and obtain and calibrate the three-dimensional coordinates of the drone at the monitoring point;

[0009] S2: At the monitoring point, multiple sensors simultaneously collect the spatial coordinates of the conductor, temperature distribution data, raw distance measurement data from the UAV to the surface of the conductor, and ice thickness correlation data including different axial and radial positions;

[0010] S3: Process and calculate the data collected in step S2, including:

[0011] Sag calculation: The spatial coordinates are fitted to generate a conductor sag curve, the sag value is calculated, and the sag value is corrected in combination with the temperature distribution data;

[0012] Icing analysis: Based on the original distance measurement value and the standard distance benchmark D0 without icing, the icing thickness is calculated; and based on the correlation data of icing thickness, the icing growth rate is calculated and the icing distribution information is identified;

[0013] Data fusion: Establishing a correlation model between icing thickness and sag variation;

[0014] S4: Early warning decision-making and output, specifically:

[0015] An early warning signal is triggered when the ice thickness exceeds a first preset threshold, or the ice growth rate exceeds a second preset threshold, or when severe local accumulation is detected.

[0016] The sag value, icing thickness, icing growth rate, distribution information, and early warning signals are transmitted to the ground monitoring center.

[0017] As a further improvement of the present invention: the correction of the sag value in the sag calculation step, based on the temperature distribution data, specifically includes:

[0018] The real-time temperature of the conductor is extracted from the infrared images of the conductor acquired by a multispectral camera.

[0019] Calculate the temperature change ΔT between the real-time temperature of the conductor and the reference temperature;

[0020] Based on the thermal expansion and contraction model ΔL=αL0ΔT, the change in conductor length ΔL is calculated, where L0 is the initial length of the conductor and α is the linear expansion coefficient based on the conductor material.

[0021] Based on the correlation model of sag-conductor length-horizontal tension, the change in sag Δf1 is initially estimated, and the fitted initial sag value is corrected to obtain the temperature-corrected sag value.

[0022] As a further improvement of the present invention, the calculation of ice thickness in the icing analysis step specifically includes:

[0023] Call the standard distance reference D0 corresponding to the current monitoring point in the non-icing state;

[0024] Obtain the original ranging value D1 after UAV position calibration, wherein the position calibration is achieved by calculating the spatial deviation ΔS between the current coordinates of the UAV and the standard coordinates;

[0025] If ΔS ≤ preset value, calculate the component of spatial deviation ΔS, ΔS⊥, based on the UAV offset direction, according to formula D. 测=D1±ΔS⊥ The original distance measurement value D1 is corrected to obtain the effective measurement distance D. 测 In this context, "+" indicates that the drone is moving closer to the guide wire, and "-" indicates that it is moving away from the guide wire.

[0026] According to the formula, ice thickness H=D 测 -D0, calculates the icing thickness.

[0027] As a further improvement of the present invention: the identification of ice distribution information in the icing analysis step specifically includes: obtaining ice thickness information at different axial positions and radial angles of the conductor based on ice thickness correlation data; and calculating the average ice thickness H_avg of the conductor in the current monitoring section.

[0028] Calculate the deviation ΔH_axis of the thickness of each axial sampling segment from H_avg. If ΔH_axis ≥ Max(H_avg×b%, first thickness threshold), where b is a set value, and the deviation ΔH_axis continuously covers no less than 2 adjacent axial sampling segments, then it is determined that there is axial local accumulation.

[0029] For each axial sampling segment, calculate the deviation ΔH_radius between the thickness at different radial angles and the average thickness of the axial segment. If ΔH_radius ≥ Max(average thickness of the segment × c%, second thickness threshold), where c is a set value, then it is determined that there is radial local accumulation.

[0030] As a further improvement of the present invention: the specific steps for establishing the correlation model between icing thickness and sag variation in the data fusion step are as follows:

[0031] The correlation between the increase in ice thickness and the change in sag in historical and real-time data was analyzed, and the amount of sag change caused by each unit increase in ice thickness was quantified as the influence coefficient K.

[0032] Based on the current rate of icing growth and the aforementioned influence coefficient K, the trend of sag over a future set time period is predicted.

[0033] As a further improvement of the present invention: after obtaining the temperature-corrected sag value, a precise correction step is also included:

[0034] The temperature-corrected sag value is compared with the sag reference value derived from the spatial coordinates of the lowest point of the conductor collected by the lidar sensor. When the difference between the two exceeds the tolerance, a second correction is performed through an iterative algorithm to output the final sag value.

[0035] As a further improvement to the present invention, it also includes:

[0036] The fitted conductor sag curve is compared with the preset temperature-sag standard curve to determine whether the sag is within the normal range; specifically:

[0037] If the sag value does not exceed the standard sag range corresponding to the current conductor temperature, the sag is determined to be abnormal and a warning signal is issued; otherwise, if the sag value does not exceed the standard range, the sag is determined to be normal.

[0038] A power transmission line icing monitoring device, comprising:

[0039] The drone body is equipped with a sag dynamic monitoring component, an icing thickness monitoring component, a data processing module, a communication module, and a power supply module.

[0040] The sag dynamic monitoring component includes a multispectral camera and a lidar sensor. The multispectral camera is used to acquire conductor temperature distribution data and conductor morphology images, and the lidar sensor is used to scan the three-dimensional contour of the conductor and acquire the spatial coordinates of different span segments of the conductor.

[0041] The icing thickness monitoring component includes a laser rangefinder and a radar sensor. The laser rangefinder is used to collect distance data from the UAV body to the guide wire. The radar sensor is used to acquire icing thickness correlation data at different axial / radial positions of the guide wire at different time points, and to calculate the icing growth rate based on multiple sets of continuously collected icing thickness correlation data.

[0042] The data processing module includes a built-in sag analysis unit, an icing analysis unit, and a data fusion unit. The sag analysis unit performs sag calculation in step S3 of the above-mentioned method for monitoring icing of transmission lines; the icing analysis unit performs icing analysis in step S3 of the above-mentioned method for monitoring icing of transmission lines; and the data fusion unit performs data fusion in step S3 of the above-mentioned method for monitoring icing of transmission lines.

[0043] As a further improvement of the present invention: the radar sensor is specifically configured as follows:

[0044] By transmitting high-frequency millimeter waves and receiving reflected signals from the icy surface, data on the correlation between ice thickness at different axial and radial positions of the conductor are collected.

[0045] Based on the continuously collected data on the correlation of multiple sets of ice thickness, preliminary data processing is performed to calculate the ice growth rate per unit time.

[0046] By comparing the correlation data of ice thickness at different axial and radial positions, the uniformity of ice distribution is identified, and the correlation data, growth rate and distribution identification results are transmitted to the data processing module.

[0047] As a further improvement of the present invention, the data module is also used to perform the early warning decision and output of step S4 in the above-mentioned method for monitoring icing of transmission lines.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] This invention utilizes a drone to integrate multiple sensors for synchronous data acquisition and fusion analysis, enabling real-time, accurate, and comprehensive monitoring of the sag and icing status of transmission lines. It offers high monitoring comprehensiveness by synchronously acquiring and correlating dynamic changes in sag with multiple key safety parameters such as icing thickness, growth rate, and spatial distribution, thus solving the problem of fragmented data in traditional monitoring. It boasts high measurement accuracy and reliability by correcting sag calculations with temperature data and cross-validating icing status using multi-source data (laser ranging and millimeter-wave radar), effectively improving the accuracy and anti-interference capabilities of monitoring results. Furthermore, it provides excellent early warning and foresight, focusing not only on the static thickness of icing but also on monitoring growth rate and identifying distribution, enabling early detection of high-risk situations such as localized accumulation and rapid icing. This provides crucial data support for proactive disaster prevention and precise de-icing decisions, significantly improving the safe operation and maintenance level of transmission lines. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0052] In order to solve the technical problems in the prior art, the present invention will now be further described in conjunction with the accompanying drawings and embodiments:

[0053] like Figure 1 As shown, this invention discloses a method for monitoring icing on transmission lines, comprising:

[0054] S1: Control the drone to fly along the preset inspection route to the monitoring point, and obtain and calibrate the three-dimensional coordinates of the drone at the monitoring point;

[0055] In some implementations, step S1 includes:

[0056] The specific steps for obtaining and calibrating the three-dimensional coordinates of the UAV at the monitoring point are as follows:

[0057] Obtain the current three-dimensional coordinates (X1, Y1, Z1) of the UAV at the current monitoring point.

[0058] Calculate the spatial deviation ΔS between the current 3D coordinates and the standard coordinates (X0, Y0, Z0), ΔS=√[(X1-X0)²+(Y1-Y0)²+(Z1-Z0)²];

[0059] If ΔS ≤ preset value, then the position deviation of the drone is determined to be within the allowable range;

[0060] If ΔS > preset value, adjust the drone's attitude to return it to the vicinity of the standard position until ΔS ≤ preset value.

[0061] S2: At the monitoring point, multiple sensors simultaneously collect the spatial coordinates of the conductor, temperature distribution data, raw distance measurement data from the UAV to the surface of the conductor, and ice thickness correlation data including different axial and radial positions;

[0062] S3: Process and calculate the data collected in step S2, including:

[0063] Sag calculation: The spatial coordinates are fitted to generate a conductor sag curve, the sag value is calculated, and the sag value is corrected in combination with the temperature distribution data;

[0064] Icing analysis: Based on the original distance measurement value and the standard distance benchmark D0 without icing, the icing thickness is calculated; and based on the correlation data of icing thickness, the icing growth rate is calculated and the icing distribution information is identified;

[0065] Data fusion: Establishing a correlation model between icing thickness and sag variation;

[0066] In some implementations, the step of calculating the sag, specifically correcting the sag value based on the temperature distribution data, includes: extracting the real-time temperature of the conductor from the infrared image of the conductor acquired by a multispectral camera; calculating the temperature change ΔT between the real-time temperature of the conductor and the reference temperature; calculating the change in conductor length ΔL according to the thermal expansion and contraction model ΔL=αL0ΔT, where L0 is the initial length of the conductor and α is the linear expansion coefficient based on the conductor material; and preliminarily estimating the sag change Δf1 based on the correlation model of sag-conductor length-horizontal tension, correcting the fitted initial sag value, and obtaining the temperature-corrected sag value.

[0067] Furthermore, after obtaining the temperature-corrected sag value, a precise correction step is also included: the temperature-corrected sag value is compared with the sag reference value obtained by inverting the spatial coordinates of the lowest point of the conductor based on the LiDAR sensor. When the difference between the two exceeds the tolerance, a second correction is performed through an iterative algorithm to output the final sag value.

[0068] In some implementations, the calculation of icing thickness in the icing analysis step specifically includes: calling the standard distance reference D0 corresponding to the current monitoring point in the ic-free state; obtaining the original ranging value D1 after UAV position calibration, wherein the position calibration is performed by calculating the spatial deviation ΔS between the current coordinates of the UAV and the standard coordinates; if ΔS ≤ a preset value, calculating the component ΔS⊥ of the spatial deviation ΔS, and according to the UAV offset direction, calculating the thickness using formula D.测 =D1±ΔS⊥ The original distance measurement value D1 is corrected to obtain the effective measurement distance D. 测 In this context, "+" indicates that the drone is moving closer to the guide wire, and "-" indicates that it is moving further away. According to the formula H=D, the icing thickness is... 测 -D0, calculates the icing thickness.

[0069] In some implementations, identifying ice distribution information in the icing analysis step specifically includes:

[0070] Based on the ice thickness correlation data, ice thickness information at different axial positions and radial angles of the conductor is obtained; the average ice thickness H_avg of the conductor in the current monitoring section is calculated.

[0071] Calculate the deviation ΔH_axis of the thickness of each axial sampling segment from H_avg. If ΔH_axis ≥ Max(H_avg×b%, first thickness threshold), where b is a set value, and the deviation ΔH_axis continuously covers no less than 2 adjacent axial sampling segments, then it is determined that there is axial local accumulation.

[0072] For each axial sampling segment, calculate the deviation ΔH_radius between the thickness at different radial angles and the average thickness of the axial segment. If ΔH_radius ≥ Max(average thickness of the segment × c%, second thickness threshold), where c is a set value, then it is determined that there is radial local accumulation.

[0073] In some implementations, the data fusion step of establishing a correlation model between icing thickness and sag change specifically involves: analyzing the correspondence between icing thickness growth and sag change in historical and real-time data, quantifying the sag change caused by each unit increase in icing thickness, and using this as an influence coefficient K; and predicting the sag change trend within a set future time period based on the current icing growth rate and the influence coefficient K.

[0074] S4: Early warning decision and output, specifically: when the ice thickness exceeds the first preset threshold, or the ice growth rate exceeds the second preset threshold, or when severe local accumulation is identified, an early warning signal is triggered; and the sag value, ice thickness, ice growth rate, distribution information and early warning signal are transmitted to the ground monitoring center.

[0075] In some implementations, the method further includes: comparing the fitted conductor sag curve with a preset temperature-sag standard curve to determine whether the sag is within the normal range; specifically: if the sag value does not exceed the standard sag range corresponding to the current conductor temperature, the sag is determined to be abnormal and a warning signal is issued; otherwise, if the sag value does not exceed the standard sag range, the sag is determined to be normal.

[0076] In practical applications, it mainly includes:

[0077] (1) Route planning: Based on the distribution of power transmission lines, the ground monitoring center pre-sets the drone inspection route, marks key monitoring sections (such as long-span sections that cross valleys and rivers), and sets the spacing between monitoring points (such as 50m / point).

[0078] (2) Takeoff and positioning: The UAV takes off according to the preset route, obtains its real-time coordinates through GPS / BeiDou dual-mode positioning, adjusts its flight attitude, and ensures that the monitoring module is aligned with the guide wire;

[0079] (3) Synchronous monitoring: After the UAV arrives at the monitoring point, the sag dynamic monitoring component and the icing thickness monitoring component are started synchronously to collect data on conductor images, spatial coordinates, temperature, icing thickness and growth rate.

[0080] (4) Data processing: The data processing module analyzes the collected data in real time to determine whether the sag is normal and whether the ice thickness exceeds the standard; if an abnormality occurs (such as the sag exceeding the normal range or the ice thickness exceeding the standard), an early warning signal is generated.

[0081] (5) Data transmission and decision support: The communication module transmits monitoring data and early warning signals to the ground monitoring center. The ground center combines historical data for further analysis and generates de-icing recommendations (e.g., when the ice thickness is ≥20mm, it is recommended to start de-icing operations within 24 hours).

[0082] (6) Return and data review: After completing the inspection mission, the UAV will automatically return to base. The data processing module will upload the raw data stored locally to the ground center for subsequent algorithm optimization and monitoring accuracy improvement.

[0083] The monitoring method of the present invention has the following advantages:

[0084] Real-time dynamic monitoring of sag and icing thickness can be achieved: the data acquisition interval can be shortened to 1 minute / time, and the early warning response speed is improved by ≥100 times compared with traditional manual inspection (the cycle is usually 1-3 days).

[0085] Improved measurement accuracy: the measurement accuracy of ice thickness can reach ±0.1mm, and the measurement accuracy of sag can reach ±0.2m, meeting the high-precision requirements of line safety monitoring.

[0086] More precise decision support: Through data fusion analysis, the impact of icing on the line can be accurately determined, avoiding blind de-icing (e.g., if the icing thickness does not exceed the standard but the growth rate is fast, preparations for de-icing can be made in advance), and reducing operation and maintenance costs.

[0087] Based on the same inventive concept, the present invention also provides a transmission line icing monitoring device, comprising:

[0088] The drone itself is equipped with a sag dynamic monitoring component, an icing thickness monitoring component, a data processing module, a communication module, and a power supply module. The communication module adopts a 4G / 5G + Beidou short message dual-mode communication method to transmit the output results of the data processing module to the ground monitoring center. The power supply module includes a lithium battery pack and a solar charging panel installed on the surface of the drone's wings to provide power for the entire device.

[0089] The sag dynamic monitoring component includes a multispectral camera and a lidar sensor. The multispectral camera is used to acquire conductor temperature distribution data and conductor morphology images, and the lidar sensor is used to scan the three-dimensional contour of the conductor and acquire the spatial coordinates of different span segments of the conductor.

[0090] The icing thickness monitoring component includes a laser rangefinder and a radar sensor. The laser rangefinder is used to collect distance data from the UAV body to the guide wire. The radar sensor is used to acquire icing thickness correlation data at different axial / radial positions of the guide wire at different time points, and to calculate the icing growth rate based on multiple sets of continuously collected icing thickness correlation data.

[0091] The data processing module includes a built-in sag analysis unit, an icing analysis unit, and a data fusion unit. The sag analysis unit performs sag calculation in step S3 of the above-mentioned method for monitoring icing of transmission lines; the icing analysis unit performs icing analysis in step S3 of the above-mentioned method for monitoring icing of transmission lines; and the data fusion unit performs data fusion in step S3 of the above-mentioned method for monitoring icing of transmission lines.

[0092] In some embodiments, the radar sensor is specifically configured as follows:

[0093] By transmitting high-frequency millimeter waves and receiving reflected signals from the icy surface, data on the correlation between ice thickness at different axial and radial positions of the conductor are collected.

[0094] Based on the continuously collected data on the correlation of multiple sets of ice thickness, preliminary data processing is performed to calculate the ice growth rate per unit time.

[0095] By comparing the correlation data of ice thickness at different axial and radial positions, the uniformity of ice distribution is identified, and the correlation data, growth rate and distribution identification results are transmitted to the data processing module.

[0096] In some embodiments, the data module is also used to perform the early warning decision and output in step S4 of the above-described method for monitoring icing on transmission lines.

[0097] This invention provides a multi-sensor fusion synchronous monitoring technology: integrating lidar, multispectral camera, millimeter-wave radar and laser rangefinder into a UAV platform to achieve synchronous monitoring of sag dynamic changes and icing thickness, growth rate and distribution uniformity, solving the data fragmentation problem in traditional monitoring.

[0098] Provides a temperature-corrected sag calculation model: By combining conductor temperature data collected by a multispectral camera, the model corrects the impact of temperature changes on sag calculation, improving the sag monitoring accuracy to ±0.5% (compared to approximately ±2% for traditional methods).

[0099] Construct an icing-sag correlation analysis mechanism: Establish a quantitative correlation model between icing thickness and sag change, which can directly assess the impact of icing on the mechanical load of the line and provide more comprehensive data support for de-icing decisions (such as determining "at what icing thickness will cause the sag to exceed the safety threshold").

[0100] Dual-mode communication and optimized battery life design: "4G / 5G + Beidou short message" dual-mode communication ensures data transmission reliability in remote areas; solar power supply design extends the drone's battery life and meets the needs of large-scale line inspection.

[0101] Implementation Case 1:

[0102] This invention discloses a method for monitoring icing on power transmission lines, comprising:

[0103] Acquire monitoring data for the conductor, including conductor spatial coordinates, temperature distribution data, raw distance measurement data from the UAV to the conductor surface, and icing thickness correlation data; calculate the conductor sag value after temperature correction based on the conductor spatial coordinates and temperature distribution data; calculate the icing thickness based on the raw distance measurement data and the standard distance benchmark without icing; calculate the icing growth rate and identify the uniformity of icing distribution based on the icing thickness correlation data; determine whether to trigger an early warning based on the icing thickness and / or the icing growth rate; establish a correlation model between sag and icing thickness, and output conductor sag value, icing thickness, icing growth rate, icing distribution information, and early warning signal.

[0104] Specifically, the ground monitoring center pre-sets drone inspection routes and marks key monitoring sections and the distance between monitoring points; the drone takes off and adjusts its attitude using GPS / BeiDou positioning to ensure that the monitoring module is aligned with the guide wire; after the drone arrives at the monitoring point, the sag dynamic monitoring component and the icing thickness monitoring component simultaneously collect data; the data processing module analyzes the collected data to determine whether the sag is normal and whether the icing thickness exceeds the standard, and if abnormal, an early warning is triggered; the communication module transmits the monitoring data and early warning signals to the ground monitoring center to generate de-icing decision suggestions; after the drone returns, it uploads the locally stored data for review and algorithm optimization.

[0105] On-site monitoring was implemented using a 220kV overhead transmission line (500m span, conductor type LGJ-400 / 35) as an example:

[0106] (1) The ground monitoring center plans the UAV inspection route, starting from pole A and ending at pole B, with 10 monitoring points (50m apart).

[0107] (2) After the UAV takes off, it flies along the route and when it reaches the first monitoring point, the monitoring module on the gimbal is activated: the multispectral camera collects visible light and infrared images of the conductor, the lidar scans the three-dimensional coordinates of the conductor, the laser rangefinder measures the ice thickness, and the millimeter-wave radar records the ice growth rate.

[0108] (3) Real-time analysis by the data processing module: The current sag value is calculated to be 8.2m. Compared with the standard curve of "temperature-sag" (current conductor temperature -5℃, standard sag range 7.5-8.5m), the sag is judged to be normal; the ice thickness measurement value is 12mm, and the growth rate is 0.3mm / h, which does not exceed the warning threshold.

[0109] (4) After the UAV completes the inspection of all monitoring points, it transmits the data to the ground center. The ground center generates the "Line Icing Monitoring Report", which displays the sag and icing data of each monitoring point and has no abnormal warnings.

[0110] (5) If the ice thickness at a certain monitoring point reaches 18mm, the data processing module will trigger a level 3 warning, and the communication module will transmit the warning signal to the ground center in real time. The ground center will combine the load data of the line segment and recommend that mechanical de-icing operations be started within 4 hours.

[0111] The method in this embodiment is based on a power transmission line icing monitoring device, which includes a drone body, a monitoring module, a data processing module, a communication module, and a power supply module. The modules work together to complete the icing-related monitoring functions.

[0112] The monitoring module, as the core execution unit, integrates a sag dynamic monitoring component and an icing thickness monitoring component, with the following specific structure:

[0113] The sag dynamic monitoring component includes a multispectral camera (visible light + infrared) and a lidar sensor. The multispectral camera is used to acquire real-time images of the conductor's shape, while the infrared mode can penetrate interference environments such as fog to obtain conductor temperature distribution data. The lidar sensor is used to scan the three-dimensional contour of the conductor, obtain the spatial coordinates of different span segments, and calculate the sag variation.

[0114] The icing thickness monitoring component includes a high-precision laser rangefinder and a millimeter-wave radar sensor. The laser rangefinder uses a pulse measurement method, vertically aligned with the conductor surface, to measure the distance between the UAV and the conductor surface. The millimeter-wave radar sensor is used to monitor the rate of icing growth. By continuously collecting icing thickness data at different time points, it calculates the rate of thickness change per unit time and can also identify the uniformity of icing distribution on the conductor surface (such as whether there is local accumulation).

[0115] The data processing module has a built-in embedded processor (such as the ARM Cortex-A9 architecture) and integrates the following functions:

[0116] Sag Analysis Unit: Fits the spatial coordinate data of the conductor collected by the lidar sensor to generate the conductor sag curve, compares it with the preset "temperature-sag" standard curve to determine whether the current sag is within the normal range; at the same time, it combines the conductor temperature data obtained by the multispectral camera to correct the sag calculation deviation (temperature changes will cause the conductor to expand and contract, affecting the sag value).

[0117] Icing analysis unit: Filters the icing thickness data acquired by the laser rangefinder (removing measurement errors caused by airflow turbulence), and combines it with the icing growth rate data from the millimeter-wave radar sensor to generate a "time-icing thickness" change curve; when the icing thickness exceeds the first preset threshold (e.g., 15mm) or the growth rate exceeds the second preset threshold (e.g., 0.5mm / h), an early warning signal is triggered.

[0118] Data fusion unit: Establishes a correlation model between sag and icing thickness, analyzes the influence coefficient of icing thickness increase on sag change (e.g., the specific value of sag increase for every 5mm increase in icing thickness), and provides data basis for line load assessment.

[0119] In this embodiment, the laser rangefinder is the "direct measurement source" of ice thickness. Through "pulse measurement + GPS / BeiDou positioning assistance", the vertical distance between the UAV and the surface of the ice-covered guide is directly obtained. Then, combined with the "standard distance without ice", the real-time and accurate absolute value of ice thickness (such as the ice thickness of 12mm at a certain moment) is calculated. This is the "basic data" for all subsequent ice-related analyses.

[0120] Laser rangefinders provide "static and accurate thickness data," solving the problem of "how thick is the current ice layer," and are the direct basis for judging early warning thresholds (such as triggering an early warning when the thickness exceeds 15mm); millimeter-wave radar, based on the static data from laser rangefinders, provides "dynamic growth trend data," solving the problem of "how fast the ice layer is thickening," and is the key basis for predicting risks in advance and making de-icing decisions (such as preparing for de-icing in advance when the thickness is not exceeded but is increasing rapidly).

[0121] In this embodiment, a "standard distance benchmark" is established between the UAV and the guide wire in an icing-free state before icing monitoring. This standard distance benchmark is calibrated in conjunction with positioning data. Specifically:

[0122] Select a time period when the line is free of ice, and control the drone to fly to the preset monitoring point of the target conductor (such as the midpoint of the span or a key section near the tower). Use GPS / BeiDou dual-mode positioning to obtain the three-dimensional coordinates (X0, Y0, Z0) of the drone at this time, where X / Y are the horizontal coordinates and Z is the elevation. Control the laser rangefinder to be vertically aligned with the surface of the conductor (by calibrating the drone's gimbal attitude to ensure that the ranging direction is consistent with the radial direction of the conductor), measure the initial distance D0 between the drone and the surface of the conductor, and record the "drone positioning coordinates (X0, Y0, Z0) + initial distance D0" corresponding to the monitoring point as the "ice-free standard data" of the monitoring point, that is, the benchmark parameter corresponding to the "standard distance when the conductor is free of ice" in the formula. Subsequent icing measurements need to be compared with this benchmark.

[0123] During the real-time measurement phase, the icing monitoring drone performs positioning calibration. That is, when entering the icing monitoring mode, when the drone flies to the same monitoring point, it first uses positioning data to ensure that its own position is consistent with the position of the "non-icing standard data" (or calculates the position deviation).

[0124] Specifically, after the UAV reaches the target monitoring point, it activates GPS / BeiDou dual-mode positioning to obtain the current three-dimensional coordinates (X1, Y1, Z1) of the UAV in real time. It then calculates the spatial deviation ΔS between the current coordinates and the standard coordinates when there is no icing, using the formula: ΔS=√[(X1-X0)²+(Y1-Y0)²+(Z1-Z0)²]. If ΔS≤0.1m, the position deviation is considered to be within the allowable range and no additional correction is required. If ΔS>0.1m, the UAV attitude needs to be adjusted to bring the coordinates back to near the standard position to ensure the consistency of the measurement benchmark.

[0125] After the UAV's positioning and calibration are completed, the laser rangefinder initiates measurement, using positioning data to help confirm the "perpendicularity of the ranging direction" and avoid ranging deviations caused by UAV tilt. Specifically, using the attitude sensor (such as a gyroscope) on the UAV gimbal, combined with GPS / BeiDou elevation data (Z1), the laser rangefinder's emission direction is adjusted to ensure it is strictly perpendicular to the guide wire surface (i.e., the ranging direction coincides radially with the guide wire, not obliquely). The laser rangefinder uses pulse measurement, emitting laser pulses and receiving reflected signals to record the initial ranging value D1 between the UAV and the iced guide wire surface; at this point, D1 is the "straight-line distance from the UAV to the iced guide wire surface."

[0126] Preferably, the accuracy of the ranging direction is further verified by combining the positioning data. If the positioning data shows that the deviation of the UAV elevation Z1 from Z0 when there is no ice is ΔZ = (Z1-Z0) ≤ 0.05m, it means that the verticality of the ranging direction meets the requirements and D1 is valid. If ΔZ > 0.05m, the gimbal angle needs to be readjusted until the verticality meets the standard.

[0127] Furthermore, the original ranging value D1 is corrected based on the positioning data. If there is a slight deviation between the current coordinates of the UAV and the standard coordinates (ΔS≤0.1m but not 0), the "impact of deviation on ranging value" is calculated using the positioning data, and D1 is corrected to obtain the true "measured distance D". 测 Specifically:

[0128] Analyze the direction of the deviation ΔS: If the deviation is along the "laser ranging direction" (i.e., perpendicular to the conductor surface), then D1 needs to be corrected; if the deviation is along the horizontal direction (parallel to the conductor surface), it has no effect on the vertical ranging value D1 and no correction is needed. Assuming that the deviation ΔS includes ΔS⊥ (the deviation component perpendicular to the conductor surface), then the corrected "measured distance D..." 测 "For: D" 测 =D1±ΔS⊥; If the drone deviates away from the guide line, ΔS⊥ is negative and "-" is used; if it deviates closer to the guide line, ΔS⊥ is positive and "+" is used, ensuring D 测 It reflects the true vertical distance.

[0129] Finally, the ice thickness is calculated by substituting the values ​​into the formula and combining the "standard distance without ice" from the preprocessing stage with the "measured distance" after real-time correction. Specifically:

[0130] Extract the "standard distance benchmark" for the monitoring point when there is no icing, that is, the vertical distance D0 from the UAV to the surface of the guide wire when there is no icing, and then use the corrected "measured distance D0" to measure the distance. 测 Substituting into the formula: Ice thickness H = D 测 -D0.

[0131] Preferably, to avoid calculation deviations caused by single-point positioning errors, cross-validation using positioning data from multiple monitoring points is required. Specifically:

[0132] Within the same conductor span, select 3-5 evenly distributed monitoring points (e.g., spaced 50m apart), and repeat the above steps regarding the initial distance D0 and the measurement distance D. 测 After correction and other steps, calculate the ice thickness H1, H2, H3... at each point;

[0133] Compare the UAV positioning coordinates (X1, Y1, Z1), (X2, Y2, Z2)... at each monitoring point to ensure the continuity of coordinates along the traverse (e.g., the X coordinate increases sequentially, conforming to the traverse span direction).

[0134] If the calculated ice thickness at a certain monitoring point deviates from that at adjacent points by more than 0.3 mm, the positioning data of that point needs to be rechecked (e.g., whether there is coordinate drift caused by GPS / BeiDou signal obstruction), and the measurement and calculation should be recalculated to ensure data consistency.

[0135] In the data processing module, a "conductor parameter database" is established, pre-storing standard distance data under icing-free conditions by conductor type (e.g., LGJ-400 / 35). This data needs to be determined in conjunction with conductor design parameters (e.g., conductor outer diameter, cross-sectional area) and on-site calibration results: During the non-icing season each year (e.g., summer), a drone is used to conduct a full-section inspection of the transmission line, collecting the measured distance of the conductors at each monitoring point under icing conditions (repeated measurements are taken 3 times and the average value is taken), which is used as the initial standard distance and entered into the database.

[0136] The standard distance is dynamically updated annually (e.g., quarterly): During the non-icing season, data is collected again during inspections. If the new data deviates from the original data in the database by more than 2%, the new data replaces the original standard distance. During the icing season, if the standard distance changes due to conductor aging, wear, etc., the change in conductor diameter can be monitored by using the infrared mode of a dual-spectrum camera (the conductor's thermal radiation profile in the infrared image reflects the diameter). Combined with the measurement data from the laser rangefinder in the ice melting gap, the standard distance is corrected.

[0137] "Benchmark monitoring points" are set up at the towers of the transmission lines, with one point on each side within 10 meters of each tower. The conductors of the benchmark monitoring points are in a relatively stable environment (minimally affected by external interference) and have been calibrated to the standard distance during the non-icing season.

[0138] Each time a drone performs an icing monitoring mission, it must first measure the reference monitoring points along the route and compare the currently measured "ice-free standard distance" with the pre-stored reference point standard distances in the database. If the deviation is ≤1%, the standard distance reference for this measurement is considered valid; if the deviation is >1%, the standard distance calibration process is automatically triggered, using the current measured data of the reference monitoring point as the benchmark, and linearly correcting the standard distances of all monitoring points within the same route (e.g., if the reference point deviation for a certain route is 2%, then the standard distances of all monitoring points in that route are multiplied by 1.02).

[0139] The millimeter-wave radar sensor in the icing thickness monitoring component identifies localized ice accumulation (uniformity) on the conductor. The core mechanism involves "multi-dimensional scanning + data comparison analysis" to capture differences in ice thickness across different areas of the conductor. Specific steps include:

[0140] Step 1: Determine the scanning range and sampling point division (preprocessing stage)

[0141] Before initiating icing distribution monitoring, it is necessary to determine the scanning coverage area of ​​the millimeter-wave radar by combining the conductor parameters and the UAV inspection plan to ensure that there are no monitoring blind spots.

[0142] Based on the target traverse model (e.g., LGJ-400 / 35, span, e.g., 500m) and the example field monitoring implementation, the ground monitoring center pre-sets the scanning segment division rules for the millimeter-wave radar. Centered on the monitoring point of the UAV inspection route, the traverse is divided into several continuous sampling segments along the "axial" (length direction) (e.g., each segment is 1m long, a single monitoring point covers a 10m traverse range, for a total of 10 sampling segments). At the same time, it is divided into 4-8 uniform sampling angles along the "radial" (circumferential) direction of the traverse (e.g., 0°, 90°, 180°, 270°, covering the entire circumference of the traverse).

[0143] After the drone flies to the monitoring point, it calibrates its position using GPS / BeiDou dual-mode positioning to ensure that the scanning range of the millimeter-wave radar accurately covers the preset wire sampling section, avoiding missed areas due to positional deviation.

[0144] Step 2: Millimeter-wave radar multi-dimensional data acquisition (real-time scanning stage)

[0145] Millimeter-wave radar acquires correlation data on ice thickness at different sampling points on the conductor through continuous "axial + radial" two-dimensional scanning:

[0146] Axial scanning: The millimeter-wave radar sequentially emits electromagnetic waves along the length of the conductor (axial direction) to 10 preset sampling segments, receives the reflected signals from the icy surface of each sampling segment, and calculates the "equivalent icing thickness value" (denoted as H1, H2...H10) for each axial sampling segment by using the signal strength and propagation time. This value is correlated and calibrated with the precise thickness value of the laser rangefinder to ensure the accuracy of relative differences.

[0147] Radial scanning: For each axial sampling segment, the millimeter-wave radar scans along the circumference (radial) of the conductor at four sampling angles to obtain the "radial icing thickness value" for each angle (denoted as H1-1, H1-2, H1-3, H1-4, corresponding to the thickness of the four radial angles of the first axial sampling segment).

[0148] Record the three-dimensional data set of "axial position-radial angle-ice thickness" for all sampling points to form the original data matrix of icing on the conductor at the monitoring point.

[0149] Step 3: Data Preprocessing and Baseline Setting (Data Calibration Stage)

[0150] To eliminate scanning errors caused by environmental interference (such as fog, airflow turbulence), the raw data needs to be filtered, and a "uniform icing baseline value" needs to be set:

[0151] The filtering algorithm (such as moving average filtering) of the data processing module is used to smooth the multiple scan data of the same sampling point (such as the thickness value of three consecutive scans) to remove abnormal fluctuation values ​​and obtain the "calibrated ice thickness value" of each sampling point.

[0152] Calculate the "average ice thickness value H_avg" of the current monitoring point conductor - take the arithmetic mean of the thickness values ​​of all calibrated axial and radial sampling points as the benchmark for judging whether the "ice coverage is uniform" (if the ice coverage is uniform, the thickness of each sampling point should be close to H_avg).

[0153] Step 4: Local Accumulation Judgment Rules and Difference Analysis (Core Identification Stage)

[0154] By comparing the deviation of the thickness at each sampling point from the baseline value, and combining this with a preset threshold, it is determined whether there is local accumulation. The specific rules are set based on the functional requirement of "uniform distribution".

[0155] Axial local accumulation judgment: Calculate the deviation ΔH_axis (ΔH_axis = |H_axis sampling point - H_avg|) between the calibrated thickness value and H_avg for each axial sampling segment. If the ΔH_axis of a certain axial sampling segment is greater than or equal to a preset threshold (such as H_avg × 20%, or a fixed first thickness threshold such as 2mm, and the larger of the two is taken), and this deviation continuously covers ≥ 2 adjacent axial sampling segments (such as 2 consecutive 1m segments, with a total range of 2m), then it is determined that there is "axial local accumulation" in this area (such as the ice covering a certain 2m segment of the conductor is significantly thicker than other areas).

[0156] Radial local accumulation judgment: For each axial sampling segment, calculate the deviation ΔH_diameter between the calibrated thickness value of its four radial angles and the average thickness of the axial segment (H_axis segment average) (ΔH_diameter = |H_diameter sampling point - H_axis segment average|). If ΔH_diameter of a certain radial angle is greater than or equal to a preset threshold (such as H_axis segment average × 30%, or a fixed second thickness threshold such as 3mm, taking the larger of the two), then it is determined that there is "radial local accumulation" in the axial segment (such as the ice thickness in a certain circumferential direction of the conductor being thicker than in other directions, such as accumulation at the bottom due to gravity).

[0157] If both axial and radial deviation threshold conditions are met simultaneously (e.g., the bottom radial angle thickness deviation of a 2m axial section exceeds the threshold), it is judged as "severe local accumulation" and needs to be marked.

[0158] Step 5: Results Output and Visualization (Application Stage)

[0159] The results of localized accumulation identification and monitoring data are simultaneously transmitted to the ground monitoring center to provide a basis for de-icing decisions.

[0160] The data processing module generates structured data from the "location information" (such as the axial distance from the tower and the radial angle), "stacking thickness" (the actual thickness of the sampling point with the largest deviation), and "stacking range" (axial coverage length and radial coverage angle) of the local accumulation.

[0161] By combining visible light images of the conductor captured by the UAV's multispectral camera, local accumulation areas are marked on the images (e.g., marked with red boxes) to form a "image + data" visualization report, which is then transmitted to the ground monitoring center.

[0162] If the localized ice accumulation thickness exceeds the warning threshold (e.g., 15mm), a related warning will be triggered, prompting the ground control center to prioritize the development of a de-icing plan for the accumulated area (e.g., targeted mechanical de-icing).

[0163] Regarding the correction of sag calculation deviation, the data processing module relies on the conductor temperature data acquired by the multispectral camera and performs the following steps:

[0164] 1. Real-time Temperature Data Acquisition: The multispectral camera, with its unique dual-channel imaging technology (visible and infrared), continuously monitors the conductor from all angles. The infrared channel sensitively captures the infrared radiation emitted by the conductor, converting it into accurate temperature data through algorithms. Every 10 seconds (the acquisition frequency can be adjusted according to actual needs), the latest conductor temperature value is transmitted to the data processing module in real time. For example, in a hot weather condition, the multispectral camera reports a conductor temperature of 40℃ in real time; this data becomes a crucial input for subsequent sag correction.

[0165] 2. Thermal Expansion and Contraction Model Construction: The data processing module incorporates a professional calculation model based on the physical principles of thermal expansion and contraction. For conductors of different materials (such as common steel-cored aluminum stranded wire), corresponding linear expansion coefficients are pre-set. According to the theory of thermal expansion and contraction, the change in conductor length ΔL is closely related to the initial length L0, the linear expansion coefficient α, and the temperature change ΔT. The calculation formula is ΔL = αL0ΔT. For example, if the initial length of a conductor is 100 meters, and the linear expansion coefficient of the steel-cored aluminum stranded wire used is 2.3 × 10⁻⁻⁻⁶. 5 / ℃, when the temperature rises from 25℃ to 40℃, the temperature change ΔT is 15℃. Substituting this into the formula, the change in conductor length ΔL can be calculated as 2.3 × 10⁻ 5 / ℃×100m×15℃=0.0345m, meaning the conductor elongated by 0.0345m due to the increase in temperature.

[0166] 3. Preliminary estimation of sag deviation: Changes in conductor length directly cause changes in sag. Based on the mechanical and geometric relationships of the conductor, the data processing module further constructs a correlation model between sag and changes in conductor length. Assuming the suspension points at both ends of the conductor are fixed, with a span of L, the conductor sag f has the following relationship with the conductor length L and the horizontal tension T (taking a parabolic approximation model as an example). (where w is the weight per unit length of the conductor). When the conductor elongates by ΔL due to temperature changes, the change in sag Δf1 can be preliminarily estimated through complex geometric and mechanical derivations (considering factors such as changes in conductor shape and tension adjustments). Continuing with the above case, model calculations show that after the conductor length elongates by 0.0345 meters, the estimated increase in sag is 0.01 meters.

[0167] 4. Precise Correction Based on LiDAR Data: The LiDAR sensor continuously collects spatial coordinate data of the conductor, which accurately reflects the actual shape of the conductor. The data processing module compares the initial sag correction result driven by the multispectral camera with the real-time spatial coordinates of the conductor acquired by the LiDAR. If a discrepancy is found (e.g., the coordinates of the lowest point of the conductor displayed by the LiDAR do not match the initially corrected sag calculation value), a complex iterative algorithm is used to comprehensively consider multiple factors such as temperature changes, conductor mechanical properties, and suspension point positions to perform a second precise correction of the sag deviation, obtaining the final accurate sag correction amount Δf. For example, through comparison and iterative calculation, the final determined actual increase in sag is 0.012 meters, which is more accurate than the initial estimate.

[0168] 5. Real-time Feedback and Dynamic Adjustment: The data processing module feeds back the corrected sag data to the system monitoring interface in real time, once per second, allowing maintenance personnel to promptly grasp the conductor sag status. Simultaneously, if continuous changes in conductor temperature are detected, the data processing module automatically repeats the above correction process, achieving dynamic tracking and adjustment of the sag. This ensures accurate monitoring of conductor sag in complex and ever-changing environments, guaranteeing the safe and stable operation of transmission lines.

[0169] Implementation Case 2:

[0170] This invention discloses a power transmission line icing monitoring device, which includes a drone body, a monitoring module, a data processing module, a communication module and a power supply module. The modules work together to complete the icing-related monitoring functions.

[0171] The UAV itself serves as the platform for the monitoring, data processing, communication, and power supply modules, providing stable flight capabilities and precise attitude control. Specifically, the UAV utilizes a GPS / BeiDou dual-mode positioning and flight control system to cruise along a preset route, adjusting its altitude and angle to ensure that the monitoring modules (such as laser rangefinders and millimeter-wave radars) are accurately aligned with the guide wire monitoring points. It also features a gimbal structure to compensate for the impact of airflow turbulence on the sensors, ensuring stable data acquisition.

[0172] The monitoring module mainly includes a sag dynamic monitoring component and an ice thickness monitoring component.

[0173] The sag dynamic monitoring component includes a multispectral camera and a lidar sensor. Specifically, the multispectral camera has visible light and infrared modes. The visible light mode acquires images of the conductor's morphology to help determine if deformation exists. The infrared mode captures temperature distribution data of the conductor, providing raw data for temperature correction in sag calculations. The lidar sensor scans the three-dimensional contour of the conductor, obtaining the spatial coordinates (X, Y, Z) of the conductor at different spans, providing core coordinate data for sag curve fitting.

[0174] The icing thickness monitoring component includes a laser rangefinder and a radar sensor, preferably a high-precision laser rangefinder and a millimeter-wave radar sensor.

[0175] The laser rangefinder uses pulse measurement, is vertically aligned with the surface of the conductor, and collects the vertical distance data from the UAV to the surface of the conductor, which serves as the basic distance parameter for calculating the ice thickness.

[0176] The radar sensor has data acquisition and preliminary data processing functions. Specifically, it scans the icy surface of the conductor with electromagnetic waves to collect reflected signal data from the icy area, directly obtaining "correlation data of icing thickness at different axial / radial positions of the conductor at different time points" (not relying on laser rangefinder data), which is used to identify the uniformity of icing distribution (such as local accumulation); based on multiple sets of icing thickness correlation data continuously collected by itself, it calculates the rate of change of icing thickness per unit time (i.e., growth rate), and outputs the raw trend data of "time-thickness change" to the data processing module.

[0177] The principle of radar sensor data acquisition is as follows: Millimeter-wave radar, based on the electromagnetic wave reflection characteristics, emits high-frequency millimeter waves (such as the 24GHz / 77GHz band) towards the ice-covered surface of the conductor. The electromagnetic waves are reflected after encountering the ice-covered surface. The radar receives the reflected signal and records the signal propagation time and intensity. The distance between the radar and the ice-covered surface (i.e., the ice thickness correlation data) is calculated by the signal propagation time. The uniformity of the ice distribution is determined by the difference in signal intensity at different locations (the greater the difference in intensity, the more uneven the distribution).

[0178] The core function of millimeter-wave radar sensors is "active detection" (detecting ice distribution and preliminary detection of thickness-related data), and they only call laser rangefinder data in the "growth rate accuracy calibration" stage.

[0179] The data processing module is mainly used for data reception, analysis, correlation modeling, and early warning triggering. It has built-in sag analysis unit, icing analysis unit, and data fusion unit. Specifically, it receives raw / preliminary data transmitted from the monitoring module, such as coordinate data from the lidar sensor; the sag analysis unit fits the sag curve, the icing analysis unit filters the thickness data, and the data fusion unit establishes a correlation model between sag and icing thickness to achieve accurate data analysis. When the icing thickness / growth rate exceeds the standard, an early warning signal is triggered and output to the communication module.

[0180] The millimeter-wave radar's "preliminary data processing" is only "calculating the growth rate based on its own collected data" (single-dimensional time-series calculation), which does not involve complex algorithms; the "icing analysis unit" of the data processing module needs to filter the growth rate data (remove airflow interference) and combine it with the precise thickness data of the laser rangefinder for calibration, and finally generate a precise "time-icing thickness" curve. The two are in a progressive relationship of "preliminary calculation → precise optimization", rather than a "substitute processing" relationship.

[0181] The communication module is used for data transmission and local backup. Preferably, it adopts dual-mode communication of "4G / 5G + Beidou short message" to transmit the monitoring results (sag value, icing thickness, and early warning signal) output by the data processing module to the ground monitoring center in real time. In areas without public network access, Beidou short message is used to ensure uninterrupted transmission, while the original data is stored on an SD card to prevent data loss.

[0182] The power supply module provides continuous and stable power to each module. A lithium battery pack (≥20000mAh) is preferred as the main power source to meet basic power requirements; the solar charging panels on the drone's wing surface replenish the lithium battery during flight, extending the single flight time to ≥2 hours and ensuring long-term inspection.

[0183] In summary, after reading this invention document, those skilled in the art can make various other corresponding modifications to the technical solutions and concepts based on this invention without creative mental effort, and all of these modifications fall within the scope of protection of this invention.

Claims

1. A method for monitoring icing on transmission lines, characterized in that, include: S1: Control the drone to fly along the preset inspection route to the monitoring point, and obtain and calibrate the three-dimensional coordinates of the drone at the monitoring point; S2: At the monitoring point, multiple sensors simultaneously collect the spatial coordinates of the conductor, temperature distribution data, raw distance measurement data from the UAV to the surface of the conductor, and ice thickness correlation data including different axial and radial positions; S3: Process and calculate the data collected in step S2, including: Sag calculation: The spatial coordinates are fitted to generate a conductor sag curve, the sag value is calculated, and the sag value is corrected in combination with the temperature distribution data; Icing analysis: Based on the original distance measurement value and the standard distance benchmark D0 without icing, the icing thickness is calculated; and based on the correlation data of icing thickness, the icing growth rate is calculated and the icing distribution information is identified; Data fusion: Establishing a correlation model between icing thickness and sag variation; S4: Early warning decision-making and output, specifically: An early warning signal is triggered when the ice thickness exceeds a first preset threshold, or the ice growth rate exceeds a second preset threshold, or when local accumulation is detected. The sag value, icing thickness, icing growth rate, distribution information, and early warning signals are transmitted to the ground monitoring center.

2. The method for monitoring icing on transmission lines according to claim 1, characterized in that, In the sag calculation step, the correction of the sag value based on the temperature distribution data specifically includes: The real-time temperature of the conductor is extracted from the infrared images of the conductor acquired by a multispectral camera. Calculate the temperature change ΔT between the real-time temperature of the conductor and the reference temperature; Based on the thermal expansion and contraction model ΔL=αL0ΔT, the change in conductor length ΔL is calculated, where L0 is the initial length of the conductor and α is the linear expansion coefficient based on the conductor material. Based on the correlation model of sag-conductor length-horizontal tension, the change in sag Δf1 is initially estimated, and the fitted initial sag value is corrected to obtain the temperature-corrected sag value.

3. The method for monitoring icing on transmission lines according to claim 1, characterized in that, The calculation of icing thickness in the icing analysis process specifically includes: Call the standard distance reference D0 corresponding to the current monitoring point in the non-icing state; Obtain the original ranging value D1 after UAV position calibration, wherein the position calibration is achieved by calculating the spatial deviation ΔS between the current coordinates of the UAV and the standard coordinates; If ΔS ≤ preset value, calculate the component of spatial deviation ΔS, ΔS⊥, based on the UAV offset direction, according to formula D. 测 =D1±ΔS⊥ The original distance measurement value D1 is corrected to obtain the effective measurement distance D. 测 In this context, "+" indicates that the drone is moving closer to the guide wire, and "-" indicates that it is moving away from the guide wire. According to the formula, ice thickness H=D 测 -D0, calculates the icing thickness.

4. The method for monitoring icing on transmission lines according to claim 1, characterized in that, The specific steps in the icing analysis process to identify icing distribution information include: Based on the ice thickness correlation data, ice thickness information at different axial positions and radial angles of the conductor is obtained; Calculate the average ice thickness H_avg of the current monitoring section of the conductor; Calculate the deviation ΔH_axis of the thickness of each axial sampling segment from H_avg. If ΔH_axis ≥ Max(H_avg×b%, first thickness threshold), where b is a set value, and the deviation ΔH_axis continuously covers no less than 2 adjacent axial sampling segments, then it is determined that there is axial local accumulation. For each axial sampling segment, calculate the deviation ΔH_radius between the thickness at different radial angles and the average thickness of the axial segment. If ΔH_radius ≥ Max(average thickness of the segment × c%, second thickness threshold), where c is a set value, then it is determined that there is radial local accumulation.

5. The method for monitoring icing on transmission lines according to claim 1, characterized in that, The specific steps in the data fusion process to establish a correlation model between icing thickness and sag variation are as follows: The correlation between the increase in ice thickness and the change in sag in historical and real-time data was analyzed, and the amount of sag change caused by each unit increase in ice thickness was quantified as the influence coefficient K. Based on the current rate of icing growth and the aforementioned influence coefficient K, the trend of sag over a future set time period is predicted.

6. The method for monitoring icing on transmission lines according to claim 2, characterized in that, After obtaining the temperature-corrected sag value, a precise correction step is also included: The temperature-corrected sag value is compared with the sag reference value derived from the spatial coordinates of the lowest point of the conductor collected by the lidar sensor. When the difference between the two exceeds the tolerance, a second correction is performed through an iterative algorithm to output the final sag value.

7. The method for monitoring icing on transmission lines according to claim 1, characterized in that, Also includes: Compare the fitted conductor sag curve with the preset temperature-sag standard curve to determine whether the sag is within the normal range. Specifically: If the sag value does not exceed the standard sag range corresponding to the current conductor temperature, the sag is determined to be abnormal and a warning signal is issued; otherwise, if the sag value does not exceed the standard range, the sag is determined to be normal.

8. A transmission line icing monitoring device, characterized in that, include: The drone body is equipped with a sag dynamic monitoring component, an icing thickness monitoring component, a data processing module, a communication module, and a power supply module. The sag dynamic monitoring component includes a multispectral camera and a lidar sensor. The multispectral camera is used to acquire conductor temperature distribution data and conductor morphology images, and the lidar sensor is used to scan the three-dimensional contour of the conductor and acquire the spatial coordinates of different span segments of the conductor. The icing thickness monitoring component includes a laser rangefinder and a radar sensor. The laser rangefinder is used to collect distance data from the UAV body to the guide wire. The radar sensor is used to acquire icing thickness correlation data at different axial / radial positions of the guide wire at different time points, and to calculate the icing growth rate based on multiple sets of continuously collected icing thickness correlation data. The data processing module includes a built-in sag analysis unit, an icing analysis unit, and a data fusion unit. The sag analysis unit performs the sag calculation in step S3 of the transmission line icing monitoring method as described in any one of claims 1-7; the icing analysis unit performs the icing analysis in step S3 of the transmission line icing monitoring method as described in any one of claims 1-7; and the data fusion unit performs the data fusion in step S3 of the transmission line icing monitoring method as described in any one of claims 1-7.

9. A transmission line icing monitoring device according to claim 8, characterized in that, The radar sensor is specifically configured as follows: By transmitting high-frequency millimeter waves and receiving reflected signals from the icy surface, data on the correlation between ice thickness at different axial and radial positions of the conductor are collected. Based on the continuously collected data on the correlation of multiple sets of ice thickness, preliminary data processing is performed to calculate the ice growth rate per unit time. By comparing the correlation data of ice thickness at different axial and radial positions, the uniformity of ice distribution is identified, and the correlation data, growth rate and distribution identification results are transmitted to the data processing module.

10. A transmission line icing monitoring device according to claim 8, characterized in that, The data module is also used to perform the early warning decision and output of step S4 in the transmission line icing monitoring method as described in any one of claims 1-7.

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