GIS precise monitoring and anti-galloping treatment auxiliary system for icing galloping of power transmission line in area easy to be iced

Abstract

The invention relates to the technical field of power transmission line galloping monitoring, in particular to a GIS precise monitoring and galloping prevention treatment auxiliary system for icing galloping of a power transmission line in an easy icing area, and the system comprises a data collection layer, a core function module layer, a GIS visual overview layer and an AI dynamic threshold self-optimization module. The data acquisition layer acquires line operation data, meteorological data and on-site video data accessed by a GB / T28181 protocol; the core function module layer realizes data fusion and dynamic evaluation through a galloping online monitoring module, an icing ratio prediction module and a galloping risk evaluation module; the GIS visual overview layer is used for realizing spatial display and hierarchical multi-channel early warning pushing; the AI dynamic threshold value self-optimization module adjusts an early warning threshold value through a gradient descent algorithm, has the functions of multi-modal data linkage wave recording and anti-galloping treatment effect backtracking, can achieve accurate monitoring, dynamic early warning and treatment assistance of icing galloping, and remarkably improves the capability of resisting the icing galloping risk of a line.

Description

Technical Field

[0001] This invention relates to the technical field of transmission line galloping monitoring, and more particularly to a GIS-based precise monitoring and anti-galloping auxiliary system for transmission lines in areas prone to icing. Background Technology

[0002] Transmission lines are a crucial component of the power system. In areas prone to icing, transmission lines are highly susceptible to conductor icing under extreme weather conditions such as low temperatures, high humidity, and rain / snow. Icing can cause irregular conductor galloping. This galloping not only leads to conductor fatigue damage and tower tilting, but in severe cases, it can also cause major faults such as line tripping and line breaks, directly threatening the safe and stable operation of the power system and causing significant losses to social production and daily life.

[0003] To address the aforementioned issues, some technologies related to monitoring and preventing icing and galloping of transmission lines have emerged in the existing technology, but there are still significant shortcomings in technical support, specifically in the following aspects: Insufficient data acquisition and processing capabilities: Existing monitoring technologies mostly focus on single types of data, such as collecting only conductor operation data or meteorological data. They lack the ability to integrate and analyze multi-source data such as conductor operation data, meteorological data, and on-site video data. Furthermore, the raw data is not effectively preprocessed, resulting in large amounts of data redundancy, high transmission pressure, and affecting the efficiency of subsequent analysis. At the same time, video data access lacks standardized protocol support, resulting in poor compatibility.

[0004] Poor adaptability of warning thresholds: Existing warning systems mostly use fixed thresholds to judge the risk of icing galloping, without considering the differences in different conductor models, spans, voltage levels and tower models, and without dynamically adjusting the thresholds by combining historical icing galloping data with real-time data, resulting in low warning accuracy and easy false or missed warnings.

[0005] The effectiveness of anti-surge measures is not verified: Existing technologies can only assist in the formulation of anti-surge measures (such as installing anti-surge devices and adjusting conductor tension). However, after the measures are implemented, there is a lack of a systematic retrospective and evaluation mechanism for the effectiveness of the measures. It is impossible to judge the effectiveness of the measures and it is difficult to provide data support for the optimization of subsequent treatment plans for similar lines.

[0006] Insufficient adaptability of early warning push notifications to operations and maintenance: Existing early warning push notifications mostly adopt a single channel and homogeneous content push mode, without taking into account the risk level and the different needs of different user roles such as operations and maintenance personnel and dispatchers, resulting in untimely delivery of key early warning information and irrelevant information interfering with operations and maintenance work, thus reducing operations and maintenance efficiency.

[0007] In summary, existing technologies are insufficient for accurate monitoring, dynamic early warning, and efficient anti-galling management of icing-prone transmission lines in icing-prone areas. There is an urgent need for a comprehensive auxiliary system that integrates multi-source data fusion, dynamic threshold early warning, management effect verification, and hierarchical push notifications to enhance the ability of transmission lines to resist the risks of icing-prone galloping. Summary of the Invention

[0008] This invention discloses a GIS-based precise monitoring and anti-galloping auxiliary system for icing-prone transmission lines in icing-prone areas, aiming to address the insufficient technical support for monitoring and anti-galloping of transmission lines under extreme icing weather conditions. To achieve the above objective, the technical solution adopted by this invention is as follows: A GIS-based precision monitoring and anti-galling system for transmission lines in icing-prone areas is provided, including: Data acquisition layer, core functional module layer, GIS visualization overview layer, and AI dynamic threshold self-optimization module; The data acquisition layer collects multi-source monitoring data of the transmission lines, including line operation data, meteorological data, and on-site video data accessed via the GB / T28181 protocol. The core functional module layer includes a galloping online monitoring module, an icing ratio prediction module, and a galloping risk assessment module. The galloping online monitoring module integrates MQTT, file, and video services to achieve multi-source data fusion analysis. The icing ratio prediction module combines meteorological data and equipment ledger parameters to output short-term icing ratios and early warnings. The galloping risk assessment module performs dynamic risk assessment based on galloping data and icing prediction results. The GIS visualization overview layer is used to realize the spatial display of monitoring terminal distribution, icing prediction, and risk warnings, as well as video retrieval. The AI ​​dynamic threshold self-optimization module is linked with the galloping risk assessment module. Based on historical icing and galloping data and real-time monitoring data, it dynamically adjusts the galloping amplitude and dynamic tension warning thresholds corresponding to different conductor types and spans using a gradient descent algorithm.

[0009] Preferably, the online monitoring module for troop movement also has a multimodal data linkage recording function; when the conductor current exceeds the threshold, the troop movement amplitude reaches the warning value, or the video image identifies that the icing thickness is greater than or equal to 5mm, the multimodal linkage recording is automatically triggered, and the conductor operation data, meteorological data and video clips of the corresponding time period are collected simultaneously, and the recording data is marked with spatiotemporal alignment.

[0010] As a preferred option, it also includes a track-back module for anti-galling control effects; the track-back module for anti-galling control effects is linked with the GIS visualization overview layer and the online galling monitoring module. After the implementation of anti-galling control measures, it collects line galling data and icing data within a preset time period after the control, compares and analyzes them with the data from the same period before the control, generates a control effect evaluation report, and links the evaluation results to the corresponding line segment on the GIS map.

[0011] Preferably, the data acquisition layer also includes an edge computing acquisition terminal; the edge computing acquisition terminal has a built-in lightweight data processing algorithm to preprocess the acquired raw multi-source data: filtering redundant data, calculating local dancing feature values ​​in real time, and judging data anomaly thresholds; only the preprocessed valid data is uploaded to the dancing online monitoring module via the MQTT protocol.

[0012] More preferably, the anti-fighting measures include installing anti-fighting devices and adjusting the tension of the conductors; the evaluation report on the effectiveness of the measures includes an effectiveness level of excellent, good, medium, and poor.

[0013] Preferably, the line operation data includes galloping frequency, amplitude, acceleration, angular velocity, and conductor current; the meteorological data includes temperature, humidity, wind speed, and rainfall.

[0014] As a preferred option, it also includes an early warning decision module, which is used to automatically correct the icing thickness warning threshold and the galloping amplitude safety threshold based on the transmission line voltage level, tower type and historical fault data.

[0015] Preferably, the GIS visualization overview layer adopts a multi-channel push mechanism based on the risk level and user role: extremely high risk is pushed to the dispatch center and the corresponding line maintenance personnel; high risk is pushed to the dispatch center and maintenance personnel; medium and low risk are pushed only to maintenance personnel; extremely high risk includes on-site video links and emergency handling procedures, high risk includes risk duration prediction, and medium and low risk include routine inspection suggestions.

[0016] Compared with the prior art, the beneficial effects of the present invention are: Improving data acquisition and processing efficiency and reducing transmission pressure: This system uses edge computing acquisition terminals in the data acquisition layer, with built-in lightweight data processing algorithms to perform redundancy filtering, local feature value calculation, and anomaly detection on raw multi-source data, uploading only valid data, which can reduce the amount of data by more than 40%, significantly reducing the data transmission pressure and storage costs in the cloud; at the same time, the data acquisition layer supports the GB / T28181 protocol to access on-site video data, and combines line operation data and meteorological data to achieve standardized fusion of multi-source data, laying a data foundation for subsequent accurate analysis.

[0017] To improve the accuracy of icing and galloping warnings and reduce false and missed warnings: The system is equipped with an AI dynamic threshold self-optimization module, which is linked to the galloping risk assessment module. Based on historical icing and galloping data and real-time monitoring data, the system dynamically adjusts the galloping amplitude and dynamic tension warning thresholds corresponding to different conductor types and spans through a gradient descent algorithm. In addition, the warning decision module can combine line voltage level, tower type and historical fault data to correct the icing thickness and galloping amplitude thresholds, solving the problem of poor adaptability of existing fixed thresholds. This improves the warning accuracy by more than 30% and effectively reduces false and missed warnings.

[0018] To enable traceability of event causes and aid in fault analysis: The online monitoring module for icing can be equipped with multi-modal data linkage recording function. When the current of the conductor exceeds the threshold, the icing amplitude reaches the warning value, or the icing thickness is ≥5mm, it can simultaneously collect conductor operation data, meteorological data and corresponding video clips and perform spatiotemporal alignment marking. This solves the problem that existing single data recording cannot trace the cause of the event, and provides complete data support for the root cause analysis of icing and icing-related faults.

[0019] To improve the closed-loop management of anti-galling and optimize subsequent solutions: The newly added anti-galling effect tracking module can collect line galloping and icing data 30-90 days after the implementation of measures such as installing anti-galling devices and adjusting conductor tension. The data can be compared with the data of the same period before the management and an evaluation report with excellent, good, medium and poor effect levels can be generated. The system can also be linked to the corresponding line segment on the GIS map, filling the gap of existing technology in verifying the management effect. This provides data basis for optimizing the anti-galling management solution for similar lines in the future and improves the targeting of management.

[0020] Optimize early warning push efficiency and adapt to operation and maintenance needs: The GIS visualization overview layer implements hierarchical multi-channel push based on risk level and user role. Extremely high risk is notified to the dispatch center and operation and maintenance personnel through platform pop-ups + SMS + telephone. High risk is pushed through APP + SMS. Medium / low risk is pushed only through APP. The push content is differentiated (including video links, emergency procedures or inspection suggestions) to avoid information homogenization, ensure that key early warnings are delivered in a timely manner, and prevent irrelevant information from interfering, thereby improving operation and maintenance response efficiency.

[0021] In summary, through multi-dimensional technological innovation, this system has achieved full-process control of icing galloping on transmission lines in icing-prone areas, including "precise monitoring, dynamic early warning, assisted management, and effect verification," significantly improving the ability of lines to resist the risk of icing galloping and ensuring the safe and stable operation of the power system. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of a GIS-based precise monitoring and anti-galling auxiliary system for icing-prone transmission lines in areas prone to icing, as described in a specific embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This system adopts a layered architecture, including a data acquisition layer, a core functional module layer, a GIS visualization overview layer, and an AI dynamic threshold self-optimization module. The layers interact and link functions through standardized interfaces. The data acquisition layer serves as the data input source, the core functional module layer undertakes the core tasks of data processing and analysis, the GIS visualization overview layer is responsible for information display and early warning push, and the AI ​​dynamic threshold self-optimization module provides adaptive early warning threshold support. These four layers work together to achieve full-process control of the ice-covered dancing system.

[0025] Please refer to Figure 1 As shown, this application proposes a GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas, comprising: Data acquisition layer, core functional module layer, GIS visualization overview layer, and AI dynamic threshold self-optimization module; The data acquisition layer collects multi-source monitoring data of the transmission lines, including line operation data, meteorological data, and on-site video data accessed via the GB / T28181 protocol. The core functional module layer includes a galloping online monitoring module, an icing ratio prediction module, and a galloping risk assessment module. The galloping online monitoring module integrates MQTT, file, and video services to achieve multi-source data fusion analysis. The icing ratio prediction module combines meteorological data and equipment ledger parameters to output short-term icing ratios and early warnings. The galloping risk assessment module performs dynamic risk assessment based on galloping data and icing prediction results. The GIS visualization overview layer is used to realize the spatial display of monitoring terminal distribution, icing prediction, and risk warnings, as well as video retrieval. The AI ​​dynamic threshold self-optimization module is linked with the galloping risk assessment module. Based on historical icing and galloping data and real-time monitoring data, it dynamically adjusts the galloping amplitude and dynamic tension warning thresholds corresponding to different conductor types and spans using a gradient descent algorithm.

[0026] The data acquisition layer mainly consists of edge computing acquisition terminals, various sensors, and video surveillance equipment, and is used to collect multi-source monitoring data of power transmission lines.

[0027] The data acquisition layer utilizes low-power industrial-grade edge gateways as edge computing acquisition terminals, deploying one unit per 10 kilometers of transmission line, with priority given to monitoring platforms at the top of towers or in the middle of the line. This terminal incorporates lightweight data processing algorithms to preprocess the raw, multi-source data, including: filtering redundant data (such as removing abnormal current data with a signal-to-noise ratio below 10dB and invalid wind speed data with fluctuations less than 0.1m / s), real-time calculation of local galloping characteristic values ​​(such as the average galloping amplitude and maximum galloping angular velocity within 5 minutes), and determining data anomaly thresholds (such as comparing the deviation between conductor current and rated current to see if it exceeds a preset threshold). After preprocessing, only valid data is uploaded to the galloping online monitoring module in the core functional module layer via the MQTT protocol. Actual testing shows that this preprocessing reduces the amount of uploaded data, significantly lowering the transmission pressure on the cloud.

[0028] Line operation data acquisition: Line operation data, including galloping frequency, galloping amplitude, conductor acceleration, conductor angular velocity, and conductor current, are collected by using accelerometers, angular velocity sensors, and current sensors installed on the conductors. The sampling frequency of the accelerometers is set to 10Hz, and the sampling frequency of the current sensors is set to 5Hz to ensure that the data acquisition accuracy meets the analysis requirements.

[0029] Meteorological data acquisition: Meteorological data, including temperature, humidity, wind speed, and rainfall, are collected through an integrated meteorological station deployed at the base of the tower; the sampling frequency of the meteorological station is set to 1Hz, which can capture changes in meteorological conditions in real time.

[0030] On-site video data acquisition: High-definition network cameras conforming to the GB / T 28181 protocol are used, with one camera deployed every 5 kilometers of line. The cameras are pointed towards sections of the conductor prone to icing, with a resolution of 1080P and a frame rate of 25fps to ensure clear capture of conductor icing and galloping. The video data is accessed to the edge computing acquisition terminal via the GB / T 28181 protocol and then synchronously uploaded to the core functional module layer.

[0031] The core functional module layer includes a goose motion online monitoring module, an icing ratio prediction module, and a goose motion risk assessment module. Each module implements its specific functions through software programming, as detailed below: Development and implementation of the online monitoring module for dancing: The system integrates MQTT, file, and video services using the Java language. The MQTT service utilizes the Eclipse Mosquitto open-source component, supporting the reception, parsing, and forwarding of MQTT messages. The message transmission QoS level is set to 2 to ensure reliable data transmission. The file service supports uploading and downloading monitoring data files, such as historical vibration data Excel files and waveform recording data CSV files, with a maximum file size of 100MB. The video service is developed based on the GB / T 28181 protocol, enabling video stream decoding and real-time playback with a playback latency controlled within 1 second.

[0032] Multimodal data linkage waveform recording function: When any of the following conditions are detected, multimodal linkage waveform recording will be automatically started: conductor current exceeds the threshold, such as exceeding the rated current by 15%; the galloping amplitude reaches the warning value (exceeding the initial warning amplitude corresponding to the current conductor model); the ice thickness in the video image is determined to be greater than or equal to 5mm through video image recognition algorithm; during waveform recording, conductor operation data, meteorological data and video clips of the corresponding time period are collected simultaneously, and the three types of data are marked with time and space alignment by timestamp; the waveform recording data is compressed and stored using H.264 encoding, with a compression rate of over 80%, and supports breakpoint resume to avoid data loss.

[0033] Data Query and Display: Develop a web-based data query interface that supports custom queries by time range, line number, and data type (such as galloping amplitude or conductor current). Query results are displayed in tables or line graphs for easy viewing by users.

[0034] Development and implementation of the icing ratio prediction module: Input parameter acquisition: This module obtains meteorological data, including temperature, humidity, wind speed, and rainfall, from the data acquisition layer through an interface, and obtains equipment ledger parameters, including conductor type, conductor cross-section, span length, suspension point height, and tower type, from the system's built-in equipment ledger database.

[0035] Prediction model construction: A multiple linear regression algorithm was used to construct an icing ratio prediction model, with meteorological data and equipment ledger parameters as input variables and the icing ratio as the output variable. The model was trained using historical icing data from the past 3 years to keep the prediction accuracy within ±5%.

[0036] Output results: The model outputs the short-term icing ratio and warning signal for the next 72 hours. The warning signal is divided into four levels: blue, yellow, orange and red, corresponding to icing ratios of 0.2-0.4, 0.4-0.6, 0.6-0.8 and above 0.8, respectively. The warning results are transmitted to the GIS visualization overview layer in real time.

[0037] Development and implementation of the dance risk assessment module: Evaluation parameter input: This module receives line galloping characteristic data output by the galloping online monitoring module, such as galloping amplitude, galloping frequency, and icing ratio prediction results output by the icing ratio prediction module.

[0038] Risk assessment model construction: A fuzzy comprehensive evaluation method was used to construct a gossamer risk assessment model, with gossamer amplitude, gossamer frequency, and icing ratio as evaluation indicators. Weights were assigned according to the degree of influence of each indicator on gossamer risk, with gossamer amplitude having a weight of 0.4, gossamer frequency having a weight of 0.3, and icing ratio having a weight of 0.3. The comprehensive risk score was calculated, and the corresponding risk levels were divided into four levels: low, medium, high, and very high, with score ranges of 0-20, 20-40, 40-60, and 60-100, respectively.

[0039] Fault tracing function: The module integrates a conductor fault tracing sub-module. When a conductor fault current event occurs, it combines galloping data at the time of the fault, such as peak galloping amplitude, peak angular velocity, icing ratio prediction results, GIS positioning information, and equipment ledgers, such as conductor material and span, to analyze the fault cause through a decision tree algorithm. For example, if the galloping amplitude exceeds the warning threshold and the icing ratio is greater than 0.6, it is determined to be a "conductor fatigue fault caused by icing galloping"; if only the fault current exceeds the threshold and the meteorological data is normal, it is determined to be a "non-galloping fault". According to actual tests, the accuracy of this tracing is over 85%. The tracing results generate a fault tracing report and push it to the dispatch center.

[0040] The GIS visualization overview layer is used to spatially display the distribution of monitoring terminals, icing prediction, and risk warnings, as well as to retrieve video footage. Based on a geographic information system, this layer enables the spatial display of monitoring data. Users can view the distribution of monitoring terminals, icing prediction results, and risk warning information through a map interface, and directly retrieve on-site videos for visual verification. Furthermore, this layer employs a multi-channel push mechanism, automatically pushing warning information based on the icing risk level and user role: for example, extremely high-risk information is pushed to the dispatch center and corresponding line maintenance personnel, including on-site video links and emergency response procedures; high-risk information is pushed to the dispatch center and maintenance personnel, including a predicted duration of the risk; medium- and low-risk information is only pushed to maintenance personnel, including routine inspection recommendations.

[0041] The AI ​​dynamic threshold self-optimization module continuously learns from historical icing galloping data and real-time monitoring data using a gradient descent algorithm, dynamically optimizing the galloping amplitude and dynamic tension warning thresholds. This module makes personalized adjustments for different conductor types and spans, ensuring that the warning thresholds match actual operating conditions and improving monitoring accuracy.

[0042] The system also includes a track-back module for anti-galling measures, which is linked to the GIS visualization overview layer and the online galloping monitoring module. After anti-galling measures are implemented, such as installing anti-galling devices or adjusting conductor tension, this module automatically collects line galloping and icing data within a preset time period after the measures are implemented, and compares and analyzes this data with the data from the same period before the measures were implemented. The analysis results generate a performance evaluation report, which includes performance levels such as excellent, good, average, and poor. The evaluation results are then linked to the corresponding line segment on the GIS map, facilitating maintenance personnel to assess the effectiveness of the measures.

[0043] The system also integrates an early warning decision module, which automatically corrects the icing thickness warning threshold and the galloping amplitude safety threshold based on the transmission line voltage level, tower type, and historical fault data, ensuring that the early warning information conforms to the actual operating environment and improving the system's adaptability.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A GIS-based precision monitoring and anti-galling auxiliary system for transmission lines in icing-prone areas, characterized in that, include: Data acquisition layer, core functional module layer, GIS visualization overview layer, and AI dynamic threshold self-optimization module; The data acquisition layer collects multi-source monitoring data of transmission lines, including line operation data, meteorological data, and on-site video data accessed via the GB / T28181 protocol. The core functional module layer includes a galloping online monitoring module, an icing ratio prediction module, and a galloping risk assessment module. The galloping online monitoring module integrates MQTT, file, and video services to achieve multi-source data fusion analysis. The icing ratio prediction module combines meteorological data and equipment ledger parameters to output short-term icing ratios and early warnings. The galloping risk assessment module performs dynamic risk assessment based on galloping data and icing prediction results. The GIS visualization overview layer is used to realize the spatial display of monitoring terminal distribution, icing prediction, and risk warnings, as well as video retrieval. The AI ​​dynamic threshold self-optimization module is linked with the galloping risk assessment module. Based on historical icing and galloping data and real-time monitoring data, it dynamically adjusts the galloping amplitude and dynamic tension warning thresholds corresponding to different conductor types and spans using a gradient descent algorithm.

2. The GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, The online monitoring module for the dancing conductor also has a multimodal data linkage recording function. When the current of the conductor exceeds the threshold, the dancing amplitude reaches the warning value, or the video image identifies that the icing thickness is greater than or equal to 5mm, the multimodal linkage recording is automatically triggered. The conductor operation data, meteorological data and video clips of the corresponding time period are collected simultaneously during the recording period, and the recording data is marked with spatiotemporal alignment.

3. The GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, It also includes a track-back module for anti-galling control effects; the track-back module for anti-galling control effects is linked with the GIS visualization overview layer and the galling online monitoring module. After the implementation of anti-galling control measures, it collects line galling data and icing data within a preset time period after the control, compares and analyzes them with the data of the same period before the control, generates a control effect evaluation report, and links the evaluation results to the corresponding line segment on the GIS map.

4. The GIS-based precise monitoring and anti-galling control auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, The data acquisition layer also includes an edge computing acquisition terminal; the edge computing acquisition terminal has a built-in lightweight data processing algorithm to preprocess the acquired raw multi-source data: filtering redundant data, calculating local dancing feature values ​​in real time, and judging data anomaly thresholds; Only the pre-processed valid data is uploaded to the Dance Online Monitoring Module via the MQTT protocol.

5. The GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 3, is characterized in that... The anti-fighting measures include installing anti-fighting devices and adjusting the tension of the conductors; the evaluation report on the effectiveness of the measures includes an effectiveness level of excellent, good, average, and poor.

6. The GIS-based precise monitoring and anti-galling control auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, The line operation data includes galloping frequency, amplitude, acceleration, angular velocity, and conductor current; the meteorological data includes temperature, humidity, wind speed, and rainfall.

7. The GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, It also includes an early warning decision module, which is used to automatically correct the icing thickness warning threshold and the galloping amplitude safety threshold based on the transmission line voltage level, tower type and historical fault data.

8. The GIS-based precise monitoring and anti-galling auxiliary system for icing and galloping of transmission lines in icing-prone areas as described in claim 1, characterized in that, The GIS visualization overview layer uses a multi-channel push mechanism based on the risk level and user role: extremely high risk is pushed to the dispatch center and the corresponding line maintenance personnel; high risk is pushed to the dispatch center and maintenance personnel; medium and low risk are pushed only to maintenance personnel. Extremely high risk includes on-site video links and emergency response procedures; high risk includes risk duration prediction; medium and low risk include routine inspection recommendations.

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