Power transmission line conductor icing remote monitoring device and monitoring method based on Internet of Things, and icing dynamic early warning method

Abstract

The invention discloses a power transmission line lead icing remote monitoring device based on the Internet of Things, and relates to the technical field of monitoring devices.The power transmission line lead icing remote monitoring device comprises a monitoring module, a data processing module, a communication module and a self-powered module.When the power transmission line lead icing remote monitoring device is used, a camera and an image sensor collect image information of a cable in real time, and the icing thickness is calculated and judged through an AI module; the wireless transmission module feeds back the information to the background terminal, so that the operation and maintenance personnel can check and judge conveniently, the monitoring is more flexible on the basis of the Internet of Things, the function of remotely monitoring the line icing condition is realized, and the problems of low manual ice observation efficiency and high risk of the device are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring devices, and particularly to a remote monitoring device for icing on transmission line conductors based on the Internet of Things. Background Art

[0002] In daily power transmission operation and maintenance work, for the icing-prone sections of transmission lines, ice observation mainly still relies on maintenance personnel to go to the site for ice observation. Manual ice observation has many drawbacks. First, the work intensity is high, and personnel need to frequently go to the site. Especially in bad weather, it increases the work difficulty and danger. Second, the observation results are easily affected by subjective factors, and the accuracy is difficult to guarantee. Third, the coverage of manual observation is limited, and it is impossible to comprehensively and timely grasp the icing condition of the line. Fourth, the efficiency is low, the time consumption is long, real-time monitoring cannot be carried out, and it is difficult to respond to sudden icing situations in a timely manner, posing a certain risk to the safe operation of the power system.

[0003] The work content of manual ice observation includes directly observing and measuring the icing situation, recording data, taking photos, and analyzing the potential risks of icing to power facilities. Mountainous areas or rugged terrains will make manual ice observation more difficult and time-consuming. Compared with plain areas, the moving speed of maintenance personnel in complex terrains will decrease significantly. If a large number of maintenance personnel are dispatched at the same time, ice observation can be carried out simultaneously in different areas, which can significantly reduce the required time. However, when the number of personnel is insufficient, it may be necessary to carry out ice observation in batches, thus increasing the overall time consumption. Severe weather conditions such as strong winds and heavy snow will seriously affect the efficiency of ice observation work. It not only slows down the moving speed of personnel but also may bring safety risks, making the work must be carried out more carefully. The time consumption and efficiency of manual ice observation are affected by various factors. The time consumption usually ranges from several hours to one day, affected by factors such as terrain, the number of technical personnel, and weather conditions.

[0004] Now, a new remote monitoring device for icing on transmission line conductors based on the Internet of Things is proposed to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a remote monitoring device for icing on transmission line conductors based on the Internet of Things to solve the problems of low efficiency and high risk of manual ice observation mentioned in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A remote monitoring device for icing on transmission line conductors based on the Internet of Things, comprising:

[0007] A monitoring module, arranged inside the housing assembly, for collecting environmental parameters and image data of conductor icing; the monitoring module includes a temperature sensor, a humidity sensor, a camera, and an image sensor;

[0008] A data processing module, connected to the monitoring module, includes an AI module and a data acquisition module, and is used to analyze image data and calculate the ice coating thickness;

[0009] A communication module, connected to the data processing module, includes a wireless transmission module and a local storage module, and is used to send ice coating data to a remote terminal;

[0010] A self-power supply module, includes an open current transformer and a power management unit. The open current transformer is used to inductively obtain energy from the transmission wire, and the power management unit is used to provide stable electric energy for the device;

[0011] The AI module is specifically used for: denoising, segmenting, and edge detecting the ice coating image collected by the camera, extracting the wire contour features, calculating the ice coating thickness based on the geometric imaging principle, and dynamically correcting it in combination with the data of the temperature sensor and the humidity sensor.

[0012] As a preferred technical solution, the housing assembly includes a lower housing and an upper housing. An open current transformer is movably connected between the lower housing and the upper housing. The bottom end of the open current transformer is fixedly connected with a third circuit board, and the third circuit board integrates a rectification module, a filtering module, a voltage stabilizing module, and a lithium battery.

[0013] As a preferred technical solution, the temperature sensor and the humidity sensor of the monitoring module are symmetrically distributed at the front bottom of the lower housing. The camera and the image sensor are arranged on the first circuit board, and the front end of the camera lens is flush with the front end of the upper housing.

[0014] As a preferred technical solution, the AI module of the data processing module identifies the ice coating features in the image through a machine learning algorithm, and dynamically corrects the ice coating thickness in combination with the data of the temperature sensor and the humidity sensor.

[0015] As a preferred technical solution, the power management unit of the self-power supply module includes an instantaneous overvoltage suppression module, a DC-DC module, and an overcharge protection module, and is used for rectifying, filtering, stabilizing the voltage, and managing the battery charge and discharge of the inductive electric energy.

[0016] As a preferred technical solution, an anti-icing assembly is further provided inside the upper housing, including a second temperature sensor and a lens cover. A polymer organic film is attached inside the lens cover. When the second temperature sensor detects low temperature, it triggers the polymer organic film to generate heat by being powered on to eliminate ice or fog on the lens surface.

[0017] As a preferred technical solution, an anti-slip rubber strip is provided on the inner wall of the open current transformer. The anti-slip rubber strip has elasticity and is used to enhance the friction with the transmission wire.

[0018] An Internet of Things-based remote monitoring method for ice coating on transmission line conductors includes the following steps:

[0019] Step a: Induce energy from the transmission wire through an open current transformer to supply power to the device;

[0020] Step b: Use a camera and an image sensor to collect wire image data in real time;

[0021] Step c: Extract icing features and calculate the thickness from the image data through the AI module, including:

[0022] Denoise, grayscale, and enhance the image;

[0023] Segment the icing area and detect the wire edge;

[0024] Calculate the icing thickness based on the geometric imaging model;

[0025] Dynamically correct the calculation result by combining temperature and humidity data;

[0026] Step d: Send the icing data to the background terminal through the wireless transmission module and locally store it in the storage module.

[0027] As a preferred technical solution, in step c, the AI module uses a convolutional neural network model to segment the icing image and estimate the thickness, and optimizes the real-time performance of the calculation result through environmental parameters.

[0028] An icing dynamic warning method based on multi-sensor data fusion, including:

[0029] Step 1: Collect wire icing image, temperature, and humidity data in real time;

[0030] Step 2: Calculate the icing thickness through the AI module and correct the icing model based on temperature and humidity data;

[0031] Step 3: Generate a warning signal when the icing thickness exceeds a preset threshold or the environmental parameters trigger a risk condition;

[0032] Step 4: Push the warning signal and the corrected icing data to the background terminal through the wireless transmission module.

[0033] Compared with the prior art, the beneficial effects of the present invention are: The remote monitoring device for icing on transmission line conductors based on the Internet of Things not only realizes the function of remotely monitoring the icing condition of the line, but also realizes the functions of lens heating for anti-fogging and anti-icing, and also realizes the function of stable energy supply;

[0034] (1) Improve the monitoring efficiency and safety

[0035] Remote automated monitoring: Through Internet of Things technology and wireless transmission modules (such as 4G / 5G), real-time remote monitoring of the icing condition of transmission lines is achieved, replacing traditional on-site manual ice observation, significantly shortening the ice observation time (from several hours to within 10 minutes), and significantly reducing the operation risks of maintenance personnel in bad weather. AI intelligent analysis: The built-in AI module uses convolutional neural network (CNN) to automatically segment, detect edges and calculate the thickness of icing images, and dynamically corrects the results by combining temperature and humidity sensor data, reducing human errors and improving the monitoring accuracy and efficiency (experiments show that the calculation error of icing thickness < 5%).

[0036] (2)Enhance system reliability and adaptability

[0037] Self-powered design: Energy is induced from the transmission wire through an open current transformer, combined with a power management unit (rectification, filtering, voltage stabilization module and lithium battery), to ensure stable power supply for the device in extreme environments from -25°C to 80°C (the output voltage fluctuation is controlled within ±5V), solving the problem of unstable solar power supply in rainy weather or at night. Anti-icing function: The polymer organic film inside the lens housing generates heat when powered on at low temperatures, and cooperates with the second temperature sensor for real-time monitoring, effectively preventing the camera lens from icing or fogging (the icing probability in the test < 1%), ensuring the clarity of image acquisition. Anti-slip design: The elastic anti-slip rubber strip on the inner wall of the open current transformer enhances the friction with the wire, preventing the device from slipping and adapting to complex terrains such as mountainous areas and high-cold regions.

[0038] (3)Multi-dimensional data fusion and intelligent early warning

[0039] Multi-sensor collaboration: Temperature and humidity sensors cooperate with high-resolution CCD cameras to integrate environmental parameters and image data, constructing a dynamic icing model to improve the accuracy of icing prediction. Active early warning mechanism: When the icing thickness exceeds the preset threshold or environmental parameters are abnormal, warning signals are automatically pushed to the background terminal (such as mobile phones, Web platforms) through the wireless transmission module, helping maintenance personnel to respond quickly and preventing accidents such as wire breakage and tower collapse (experiments show that the early warning response time < 1 minute).

[0040] (4)Economy and popularization

[0041] Low power consumption and long life: Adopting low-power CMOS sensors and edge computing technology, combined with supercapacitor energy storage, reduces energy consumption; The design of polymer organic film and silicon steel energy-taking coil extends the service life of the equipment (expected life > 10 years), reducing maintenance costs. Modularization and standardization: The device structure adopts modular design (monitoring, communication, and power supply modules are independent), facilitating mass production and customized upgrades. Description of the drawings

[0042] Figure 1Front view structural diagram of the present invention;

[0043] Figure 2 Side view structural diagram of the present invention;

[0044] Figure 3 Front view structural diagram of the first temperature sensor of the present invention;

[0045] Figure 4 Front view structural diagram of the first circuit board of the present invention;

[0046] Figure 5 Front view structural diagram of the second circuit board of the present invention;

[0047] Figure 6 Front view structural diagram of the third circuit board of the present invention;

[0048] Figure 7 Method flow chart of the ice thickness image of the transmission line based on image recognition of the present invention;

[0049] Figure 8 Schematic diagram of the estimation method of the ice thickness of the present invention;

[0050] Figure 9 Verification experiment result table of the present invention.

[0051] In the figure: 1. Lower housing; 2. First temperature sensor; 3. Humidity sensor; 4. First circuit board; 5. Image sensor; 6. Data acquisition module; 7. AI module; 8. Wireless transmission module; 9. Local storage module; 10. Upper housing; 11. Camera; 12. Second circuit board; 13. Second temperature sensor; 14. Lens cover; 15. Polymer organic film; 16. Split-core current transformer; 17. Anti-slip rubber strip; 18. Third circuit board; 19. Transient overvoltage suppression module; 20. Rectification module; 21. Filter module; 22. Voltage regulation module; 23. DC-DC module; 24. Overcharge protection module; 25. Lithium battery. Specific implementation manners

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment: Please refer to Figures 1-6, An Internet of Things-based remote monitoring device for icing on transmission line conductors, comprising a lower housing 1. The top end of the lower housing 1 is fixedly connected to an upper housing 10. The rear end inside the upper housing 10 is fixedly connected to a first circuit board 4. The front end inside the upper housing 10 is fixedly connected to a second circuit board 12. An open current transformer 16 is movably connected between the lower housing 1 and the upper housing 10. The bottom end of the open current transformer 16 is fixedly connected to a third circuit board 18. A monitoring component for facilitating icing monitoring is arranged inside the lower housing 1;

[0054] Please refer to Figures 1-6 , An Internet of Things-based remote monitoring device for icing on transmission line conductors further comprises a monitoring component. The monitoring component includes a first temperature sensor 2. The first temperature sensor 2 is fixedly connected to the bottom of the front end inside the lower housing 1. Humidity sensors 3 are respectively installed on both sides of the first temperature sensor 2. The top end of the first circuit board 4 is fixedly connected to a camera 11. An image sensor 5 is arranged at the top of the left front side of the first circuit board 4. A data acquisition module 6 is arranged at the bottom of the left front side of the first circuit board 4. An AI module 7 is fixedly connected to the middle position of the front end of the first circuit board 4. A wireless transmission module 8 is arranged at the bottom of the right front side of the first circuit board 4. A local storage module 9 is arranged at the top of the right front side of the first circuit board 4;

[0055] The first temperature sensor 2, the humidity sensors 3, and the first circuit board 4 are electrically connected. The front ends of the lower housing 1 and the first temperature sensor 2 are flush. The front end of the camera 11 and the front end of the upper housing 10 are flush. The first circuit board 4 and the camera 11 are electrically connected. The first circuit board 4, the second circuit board 12, and the third circuit board 18 are electrically connected. The humidity sensors 3 are symmetrically distributed about the vertical center line of the lower housing 1. Remote monitoring can be realized based on the Internet of Things;

[0056] Specifically, as Figure 1 , Figure 2 , Figure 3 and Figure 4 shown, the camera 11 and the image sensor 5 collect the image information of the cable in real time, and the AI module 7 calculates and judges the icing thickness. The wireless transmission module 8 feeds back the information to the background terminal, facilitating the operation and maintenance personnel to view and judge. The first temperature sensor 2 and the humidity sensors 3 can comprehensively judge the weather conditions, making the monitoring more flexible.

[0057] A second temperature sensor 13 is installed at the front end of the second circuit board 12. A lens cover 14 is fixedly connected to the top end of the second circuit board 12. A polymer organic film 15 is arranged inside the lens cover 14. The rear end of the polymer organic film 15 is in contact with the front end of the camera 11. The second circuit board 12, the second temperature sensor 13, and the polymer organic film 15 are electrically connected, which can prevent the lens from frosting and fogging;

[0058] Specifically, as Figure 1 and Figure 5 shown, the second temperature sensor 13 senses the external temperature in real time. When the temperature is lower than the preset threshold, the polymer organic film 15 inside the lens cover 14 is electrified to generate heat, removing the frost and fog on the surface of the lens of the camera 11.

[0059] Three groups of anti-slip rubber strips 17 are adhesively bonded to the upper and lower ends of the inner wall of the split-core current transformer 16. An instantaneous overvoltage suppression module 19 is provided at the top left front of the third circuit board 18, a rectification module 20 is provided at the bottom left front of the third circuit board 18, a filtering module 21 is provided at the top of the middle position of the front of the third circuit board 18, a voltage stabilizing module 22 is provided at the bottom of the middle position of the front of the third circuit board 18, an overcharge protection module 24 is provided at the top right front of the third circuit board 18, a DC-DC module 23 is provided at the bottom right front of the third circuit board 18, and a lithium battery 25 is fixedly connected to the right side of the third circuit board 18. The anti-slip rubber strips 17 have elasticity, and the split-core current transformer 16, the third circuit board 18, and the lithium battery 25 are electrically connected, ensuring stable energy supply;

[0060] Specifically, as Figure 1 and Figure 6 shown, the split-core current transformer 16 senses the electric energy on the power transmission wire through the energy-taking coil. The front end of the circuit is connected to the instantaneous overvoltage suppression module 19 to form instantaneous overvoltage protection. The induced alternating current is converted into direct current through a rectifier bridge, and then after filtering by the filtering module 21, a relatively smooth direct current is obtained. Then it is sent to the voltage stabilizing module 22 for voltage stabilizing processing. When in the no-load state, the lithium battery 25 is charged through the overcharge protection module 24; when in the load state, on the one hand, the lithium battery 25 can be charged through the overcharge protection module 24, and on the other hand, after being processed by the DC-DC module 23, the voltage required by the load is obtained to supply power to the load.

[0061] Working principle: When the present invention is in use, first, the lower housing 1 and the upper housing 10 are closed, clamping the cable in the middle, and the device main body is powered through the split-core current transformer 16. The camera 11 and the image sensor 5 collect the image information of the cable in real time, and the AI module 7 calculates and judges the ice coating thickness. The wireless transmission module 8 feeds back the information to the background terminal, facilitating the operation and maintenance personnel to view and judge. The first temperature sensor 2 and the humidity sensor 3 can comprehensively judge the weather conditions, making the monitoring more flexible. In winter with low temperature, the second temperature sensor 13 on the second circuit board 12 senses the external temperature in real time. When the temperature is lower than the preset threshold, the polymer organic film 15 in the lens hood 14 is electrified to generate heat, removing the frost and fog on the surface of the camera 11 lens. The split-core type split-core current transformer 16 clamps the cable, and the anti-slip rubber strip 17 increases the friction force to prevent sliding. The energy-taking coil senses the electric energy on the transmission wire, and the front end of the circuit is connected to the transient overvoltage suppression module 19 to form transient overvoltage protection. The induced alternating current is converted into direct current through the rectifier bridge, and then after being filtered by the filtering module 21, a relatively smooth direct current is obtained, and then sent to the voltage stabilizing module 22 for voltage stabilizing processing. In the no-load state, the lithium battery 25 is charged through the overcharge protection module 24; in the load state, on the one hand, the lithium battery 25 can be charged through the overcharge protection module 24, and on the other hand, after being processed by the DC-DC module 23, the voltage required by the load is obtained to supply power to the load.

[0062] Among them, the ice coating image shooting part specifically includes:

[0063] 1. Camera selection

[0064] Select the Sony ICX814 CCD camera, with a resolution of 4096×3072 pixels, supporting high-definition imaging under different lighting conditions (sunlight, cloudy, night), low noise and large dynamic range, meeting the requirements for capturing ice coating details.

[0065] 2. Built-in AI module

[0066] Built-in AI module, with on-site edge computing ability. It can calculate the ice coating thickness through the ice coating image information, facilitating users to comprehensively and efficiently master the ice coating state of the line.

[0067] The image extraction process of the ice coating thickness of the transmission line based on image recognition can be mainly divided into the processes of image acquisition, image preprocessing, image segmentation, wire edge detection, and calculation of the ice coating thickness. The specific implementation process is as Figure 7 shown. The process is as follows:

[0068] (1) Image denoising and preprocessing

[0069] Image denoising is a crucial step in ice-covered image recognition. Since transmission line images are often affected by various factors such as environmental noise and electronic circuit noise, the details of the images are blurred, which in turn affects the subsequent analysis process. To reduce the impact of noise on image quality, algorithms such as adaptive filtering or morphological filtering can be used. These methods can effectively retain the key features of the image and avoid losing important information during the denoising process. In practice, the most suitable denoising algorithm should be selected according to the type and intensity of the noise. By comparing the experimental results and adjusting the filter parameters, ensure that the denoised image still has sufficient details to support the subsequent analysis steps.

[0070] Median filtering denoising: For the pixel point ((i, j)), take the 3×3 neighborhood gray values and sort them, and output the median value: g(i, j) = median{f(i - k, j - l)|k, l ∈ {-1, 0, 1}},

[0071] Histogram equalization enhancement:

[0072] Gray mapping function: r is the original gray level, \(n_i\) is the number of pixels at the i-th level, and N is the total number of pixels.

[0073] (2) Image segmentation and edge detection

[0074] After image denoising, the next steps are image segmentation and edge detection. These two steps are directly related to the accuracy of ice thickness calculation. First of all, image segmentation can effectively distinguish the transmission line from the background objects, and the common segmentation methods include threshold segmentation, edge detection-based segmentation methods, and mathematical morphology segmentation, etc. During the segmentation process, the appropriate algorithm should be selected according to the characteristics of the image. For example, for the straight line feature of the transmission line, an edge detection-based segmentation method can be used and combined with differential operator edge detection to strengthen the extraction of linear features.

[0075] Edge detection is a further refinement after segmentation. By detecting the discontinuous regions in the image, such as the parts with obvious gray level changes, the edges of the transmission line are identified. The Radon transform and the Hough transform are two commonly used methods in edge detection. They can efficiently extract the straight line features in the image and thus provide basic data for the calculation of ice thickness. Combining the phase grouping method, the straight lines in low-contrast images can be accurately detected. Although the computational complexity is relatively high, it can provide more accurate results when dealing with transmission line images.

[0076] Otsu threshold method:

[0077] Maximize the between-class variance σ 2 = ω0(μ0 - μ T ) 2 + ω1(μ1 - μT 2 ,

[0078] ω0 / ω1: background / foreground pixel ratio, μ0 / μ1: background / foreground mean, μ T : global mean

[0079] Optimal threshold T * = arg max(σ 2 ), to distinguish the conductor (low gray level) from the ice coating (high gray level).

[0080] (3) Ice coating thickness estimation

[0081] As Figure 8 shown, after denoising, segmentation and edge detection of the image are completed, the ice coating thickness can be estimated. Based on the imaging principle of the camera, by comparing the images before and after ice coating and using the geometric relationship of similar triangles, the calculation formula for the ice coating thickness can be derived.

[0082] The three-dimensional point \((X, Y, Z)\) is projected onto the pixel coordinates \((u, v)\)

[0083]

[0084] \(((f_x, f_y))\) is the focal length (pixels), \(((c_x, c_y))\) is the optical center coordinates.

[0085] Original radius pixel value of the conductor (r0 is the actual radius, Z is the distance from the camera to the conductor)

[0086] Radius pixel value after ice coating

[0087] The thickness is solved as follows:

[0088] 3. Countermeasure objective inspection

[0089] To verify whether the selected CCD camera has achieved the expected resolution target, we conducted multiple experimental tests under different lighting conditions (including sunlight, cloudy days and nights). First, fix the camera on a stable bracket and align it with the set ice-coated line area. Then, take multiple transmission line images under each lighting condition and save the collected images for subsequent analysis. Next, use image analysis software to measure the image resolution to ensure it exceeds 4000×3000 pixels. At the same time, evaluate the clarity, noise level and detail performance of the high-contrast areas of the image to verify the performance of the camera under various environmental conditions.

[0090] Based on the Mackay ice growth model, the correction coefficient is:

[0091] d 修正 = d·(1 + k·(RH - 85%)·(0 - T));

[0092] RH is the humidity (%), T is the temperature (°C, T < 0),

[0093] k = 0.01% -1 ·°C -1 (empirical coefficient, fitted by historical data).

[0094] The experimental results ( Figure 9 ) show that the selected CCD camera has achieved the target resolution (>4000×3000px) under various environmental conditions, and its performance in terms of image clarity, noise level, and high-contrast regions meets the expected requirements. Therefore, this camera can meet the high requirements of transmission line icing monitoring, ensuring the accuracy and reliability of data collection.

[0095] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claimed rights.

Claims

1. A remote monitoring device for icing on transmission line conductors based on the Internet of Things, characterized in that, Comprising: A monitoring module, disposed inside the housing assembly, for collecting environmental parameters and image data of conductor icing; the monitoring module includes a temperature sensor (2), a humidity sensor (3), a camera (11) and an image sensor (5); A data processing module, connected to the monitoring module, includes an AI module (7) and a data acquisition module (6), for analyzing the image data and calculating the icing thickness; A communication module, connected to the data processing module, includes a wireless transmission module (8) and a local storage module (9), for sending the icing data to a remote terminal; A self-power supply module, includes an open current transformer (16) and a power management unit, the open current transformer (16) is used to inductively extract energy from the transmission conductor, and the power management unit is used to provide stable electrical energy for the device; The AI module (7) is specifically used for: denoising, segmenting and edge detecting the icing image collected by the camera, extracting the conductor contour features, calculating the icing thickness based on the geometric imaging principle, and dynamically correcting it in combination with the data of the temperature sensor (2) and the humidity sensor (3).

2. The remote monitoring device for conductor icing of transmission lines based on the Internet of Things according to claim 1, characterized in that: The housing assembly includes a lower housing (1) and an upper housing (10), an open current transformer (16) is movably connected between the lower housing (1) and the upper housing (10), the bottom end of the open current transformer (16) is fixedly connected with a third circuit board (18), and the third circuit board (18) integrates a rectification module (20), a filtering module (21), a voltage stabilizing module (22) and a lithium battery (25).

3. The remote monitoring device for icing of transmission line conductors based on the Internet of Things according to claim 1, characterized in that: The temperature sensor (2) and the humidity sensor (3) of the monitoring module are symmetrically distributed at the front bottom of the lower housing (1), the camera (11) and the image sensor (5) are arranged on the first circuit board (4), and the front end of the lens of the camera (11) is flush with the front end of the upper housing (10).

4. The remote monitoring device for icing on transmission line conductors based on the Internet of Things according to claim 1, characterized in that: The AI module (7) of the data processing module identifies the icing features in the image through machine learning algorithms, and dynamically corrects the icing thickness in combination with the data of the temperature sensor (2) and the humidity sensor (3).

5. The remote monitoring device for icing of transmission line conductors based on the Internet of Things according to claim 1, wherein: The power management unit of the self-power supply module includes an instantaneous overvoltage suppression module (19), a DC-DC module (23) and an overcharge protection module (24), for rectifying, filtering, stabilizing the induced electrical energy and battery charge and discharge management.

6. The remote monitoring device for icing on transmission line conductors based on the Internet of Things according to claim 5, characterized in that: An anti-icing component is further provided inside the upper housing (10), including a second temperature sensor (13) and a lens cover (14), a polymer organic film (15) is attached inside the lens cover (14), when the second temperature sensor (13) detects low temperature, it triggers the polymer organic film (15) to be energized and heated to eliminate icing or fog on the lens surface.

7. The remote monitoring device for icing on transmission line conductors based on the Internet of Things according to claim 1, characterized in that: The inner wall of the open current transformer (16) is provided with an anti-slip rubber strip (17), and the anti-slip rubber strip (17) has elasticity, for enhancing the friction with the transmission conductor.

8. A remote monitoring method for icing on transmission line conductors based on the Internet of Things, characterized in that, Including the following steps: Step a, inductively extract energy from the transmission conductor through the open current transformer (16) to supply power to the device; Step b, use the camera (11) and the image sensor (5) to collect the conductor image data in real time; Step c: Extract icing features and calculate the thickness from the image data by the AI module (7), including: Perform denoising, grayscale conversion, and enhancement on the image; Segment the icing area and detect the conductor edge; Calculate the icing thickness based on the geometric imaging model; Dynamically correct the calculation results by combining temperature and humidity data; Step d: Send the icing data to the background terminal through the wireless transmission module (8) and locally store it in the storage module (9).

9. A remote monitoring method for icing on transmission line conductors based on the Internet of Things according to claim 8, characterized in that: In step c, the AI module (7) uses a convolutional neural network model to segment the icing image and estimate the thickness, and optimizes the real-time performance of the calculation results through environmental parameters.

10. A dynamic icing warning method based on multi-sensor data fusion, characterized in that, Including: Step 1: Collect the conductor icing image, temperature, and humidity data in real time; Step 2: Calculate the icing thickness by the AI module (7) and correct the icing model based on temperature and humidity data; Step 3: Generate a warning signal when the icing thickness exceeds the preset threshold or the environmental parameters trigger a risk condition; Step 4: Push the warning signal and the corrected icing data to the background terminal through the wireless transmission module (8).

Images