A power transmission line intelligent anti-icing monitoring and de-icing control system and control method

Abstract

This invention discloses an intelligent anti-icing monitoring and de-icing control system and method for transmission lines. The system includes a distributed sensing monitoring module, an edge computing processing module, a cloud decision-making platform, an adaptive de-icing execution module, a fault self-diagnosis module, and an operation and maintenance management terminal. The distributed sensing monitoring module collects multi-dimensional data and performs calibration calculations. The edge computing processing module preprocesses the data using algorithms such as filtering and standardization, calculates icing trends using a prediction model, and triggers tiered early warnings. The cloud decision-making platform calculates the optimal de-icing scheme based on a multi-objective optimization algorithm. The adaptive de-icing execution module accurately executes de-icing operations through calculations of power and regulation. The fault self-diagnosis module determines faults through quantitative calculations. The operation and maintenance management terminal achieves intelligent operation and maintenance based on statistical calculation indicators. This application is applicable to transmission lines of different voltage levels and complex terrains, effectively reducing the risk of faults caused by icing and ensuring the safe and stable operation of the power grid.

Description

Technical Field

[0001] This invention belongs to the field of power transmission line operation and maintenance technology, and in particular relates to an intelligent anti-icing monitoring and de-icing control system for power transmission lines. Background Technology

[0002] Transmission lines are the core infrastructure of the power system. Many of my country's lines traverse complex terrain, and the low temperatures and high humidity of winter make them prone to icing, leading to serious faults such as tower collapse, line breaks, and ice flashover. Existing anti-icing technologies suffer from several drawbacks: passive protection cannot completely remove ice, and traditional active de-icing methods are inefficient, energy-intensive, and lack adaptability. Monitoring technologies are mostly single-point, with insufficient predictive accuracy and a lack of clear computational logic, resulting in a lack of closed-loop coordination among various components and hindering system reliability. Therefore, a smart anti-icing monitoring and de-icing control system for transmission lines is needed to address these issues. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent anti-icing monitoring and de-icing control system for power transmission lines, which achieves accurate icing monitoring, early warning, adaptive and efficient de-icing, and intelligent operation and maintenance by clarifying the calculation process.

[0004] To achieve the above technical solution, the technical solution adopted by the present invention is as follows: A smart anti-icing monitoring and de-icing control system for power transmission lines includes: The distributed sensing and monitoring module is used to collect multi-dimensional data of the transmission line and its surrounding environment. The multi-dimensional data includes at least line temperature, ice thickness, conductor deformation angle, ambient wind speed and humidity. The edge computing processing module communicates with the distributed sensing and monitoring module to receive and preprocess multi-dimensional data, output icing trend prediction results through the built-in icing prediction model, and determine whether to trigger an early warning based on the three-level de-icing threshold. The cloud-based decision-making platform communicates with the edge computing processing module to receive early warning information, integrate line load rate and weather forecast data, and use the NSGA-Ⅲ multi-objective optimization algorithm to formulate the optimal de-icing solution. The adaptive de-icing execution module communicates with the cloud decision platform and is used to adaptively select the de-icing mode of current heating and / or ultrasonic vibration according to the instructions issued by the cloud decision platform to perform the de-icing operation. The fault self-diagnosis module establishes bidirectional communication connections with the distributed sensing and monitoring module, the edge computing processing module, the cloud decision-making platform, and the adaptive de-icing execution module, respectively, to monitor the operating status of each module in real time, automatically process the detected faults, and issue alarms. The operation and maintenance management terminal communicates with the cloud-based decision-making platform to enable real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

[0005] Preferably, a control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines includes the following steps: S1 collects multi-dimensional data on the transmission line and its surroundings through a distributed sensing and monitoring module, including line temperature. Ice thickness , conductor deformation angle Ambient wind speed and humidity ; S2 uses the edge computing processing module to preprocess the collected data, outputs the icing trend prediction result through the icing prediction model, and determines whether to trigger the warning by combining the three-level de-icing threshold. S3, after receiving the early warning information, the cloud-based decision-making platform integrates the line load rate. And meteorological forecast data, using III. Multi-objective optimization algorithms are used to determine the optimal de-icing plan; S4, the adaptive de-icing execution module, selects either single or combined de-icing modes such as current heating and ultrasonic vibration according to cloud instructions, and accurately executes the de-icing operation. S5, the fault self-diagnosis module monitors the operating status of each module in real time, and automatically processes and alarms the detected faults through the fault judgment formula; The S6 operation and maintenance management terminal enables real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

[0006] Preferably, the specific method of step S1 is as follows: The distributed sensing and monitoring module consists of a fiber Bragg grating temperature sensor, a capacitive icing thickness sensor, a fiber Bragg grating tilt sensor, a wind speed sensor, and a humidity sensor. It is deployed in a distributed manner at key sections of conductors and ground wires, both ends of insulator strings, tower connections, and the top of towers. The capacitive icing thickness sensor has a measurement range of... precision Response time The formula for calculating icing thickness is: ; In the formula, For sensor calibration coefficients; This represents the change in capacitance before and after icing. Fiber Bragg grating temperature sensor measurement range precision sampling frequency It can be remotely adjusted; the temperature conversion formula is: ; In the formula, This is the temperature sensitivity coefficient; This represents the grating wavelength offset. Reference temperature; Converting wind speed sensor readings to standard wind speed: ; In the formula, These are the original measured values; The remaining sensor data are corrected according to the corresponding calibration formula, and the collected data is transmitted to the edge computing processing module through shielded twisted pair cables.

[0007] Preferably, the specific method of step S2 is as follows: The edge computing processing module uses median filtering for noise reduction: ; In the formula, , which is the filtering window; perform Z-Score normalization: ; In the formula, The mean; Standard deviation; combined Outlier detection and removal criteria The data is then processed; the icing prediction model uses... The architecture integrates neural networks and attention mechanisms, with the input feature vector as follows: ; In the formula, This data represents the past 72 hours of historical data. Execute prediction output: ; In the formula, The input feature vector; Prediction error: ; The threshold for Level 3 de-icing is: Level 1: ; Level 2: ; Level 3: ; Warning priority: ; In the formula, These are the weighting coefficients. This represents the rate of ice accumulation growth.

[0008] Preferably, the specific method of step S3 is as follows: The cloud-based decision-making platform is built on a cloud computing architecture and has a built-in multi-scenario de-icing strategy library; III. The multi-objective optimization algorithm aims to maximize de-icing efficiency, minimize energy consumption, and minimize line damage risk. De-icing efficiency: ; De-icing energy consumption: ; Line damage risk: ; In the formula, For the duration of overheating, The vibration amplitude exceeds the limit; the constraint condition is: , In the formula Maximum permissible energy consumption; Optimize the objective function: ; The optimal de-icing scheme is selected by non-dominated sorting and crowding calculation. The platform stores various types of data from the past 5 years and supports sorting by... Multi-dimensional statistical queries.

[0009] Preferably, the specific method of step S4 is as follows: The adaptive de-icing execution module includes a line current heating unit, an ultrasonic vibration unit, and a drive control submodule; the heating power of the line current heating unit is: ; In the formula This is the heating current; For the temperature-dependent resistance of the conductor; heating temperature control objective: ; The adjustment formula is: ; In the formula, The vibration energy of the ultrasonic vibration unit is: ; In the formula, The vibration power is given by: ; In the formula, The vibration frequency; Energy conversion coefficient; drive control submodule according to Perform single or combined de-icing operations.

[0010] Preferably, the specific method of step S5 is as follows: the fault self-diagnosis module includes hardware, communication, and data anomaly detection units; Hardware fault diagnosis: Power supply voltage In the formula, Rated voltage; operating current Temperature of key components The communication failure is determined as follows: three consecutive failures to receive a heartbeat packet, i.e.: ; Automatically switch to Backup link (communication distance) Transmission rate ); Data anomaly determination: 5 consecutive sampling periods ; In the formula , The historical data include the mean and standard deviation; the percentage of outliers. Alarms are triggered at certain times; the fault level is calculated as follows: ; In the formula, It assigns weights to hardware, communication, and data anomalies and sends alarm messages containing the fault location, type, level, and handling suggestions.

[0011] Preferably, the specific method of step S6 is as follows: The operation and maintenance management terminal includes a mobile APP and a PC, which are connected to the cloud decision-making platform via encrypted communication; it supports real-time viewing of monitoring data, early warning information, de-icing status, and equipment operating status; and the sampling frequency can be remotely adjusted after authorization. De-icing threshold Parameters for remote start / stop de-icing operations; features functions for generating, dispatching, processing, and managing maintenance work orders in a closed loop; supports historical data query and statistical analysis; generates report metrics. .

[0012] Preferably, a computer device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the intelligent anti-icing monitoring and de-icing control method for power transmission lines.

[0013] Preferably, a computer-readable storage medium stores computer instructions that cause a computer to execute the aforementioned intelligent anti-icing monitoring and de-icing control method for power transmission lines.

[0014] The beneficial effects of this invention are as follows: This invention collects multi-dimensional data through a distributed sensing and monitoring module, performs calibration calculations, and an edge computing processing module preprocesses the data using algorithms such as filtering and standardization. Combined with a predictive model, it calculates icing trends and triggers tiered early warnings. A cloud-based decision-making platform then... III. A multi-objective optimization algorithm calculates the optimal de-icing scheme, and the adaptive de-icing execution module uses power, The invention features precise calculations for de-icing operations, a fault self-diagnosis module that uses quantitative calculations to determine faults, and an operation and maintenance management terminal that uses statistical calculation indicators to achieve intelligent operation and maintenance. This invention includes a complete calculation process, providing accurate monitoring, forward-looking prediction, efficient and energy-saving de-icing, high reliability, and convenient operation and maintenance. It is applicable to transmission lines of different voltage levels and complex terrains, effectively reducing the risk of faults caused by icing and ensuring the safe and stable operation of the power grid. Attached Figure Description

[0015] Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the deployment of the distributed sensing and monitoring module in an embodiment of the present invention; Figure 3 This is a system workflow diagram in an embodiment of the present invention; Figure 4 This is a schematic diagram of the adaptive de-icing execution module in an embodiment of the present invention. Detailed Implementation

[0016] Example 1: like Figure 1 As shown, an intelligent anti-icing monitoring and de-icing control system for power transmission lines includes: The distributed sensing and monitoring module is used to collect multi-dimensional data of the transmission line and its surrounding environment. The multi-dimensional data includes at least line temperature, ice thickness, conductor deformation angle, ambient wind speed and humidity. The edge computing processing module communicates with the distributed sensing and monitoring module to receive and preprocess multi-dimensional data, output icing trend prediction results through the built-in icing prediction model, and determine whether to trigger an early warning based on the three-level de-icing threshold. The cloud-based decision-making platform communicates with the edge computing processing module to receive early warning information, integrate line load rate and weather forecast data, and use the NSGA-Ⅲ multi-objective optimization algorithm to formulate the optimal de-icing solution. The adaptive de-icing execution module communicates with the cloud decision platform and is used to adaptively select the de-icing mode of current heating and / or ultrasonic vibration according to the instructions issued by the cloud decision platform to perform the de-icing operation. The fault self-diagnosis module establishes bidirectional communication connections with the distributed sensing and monitoring module, the edge computing processing module, the cloud decision-making platform, and the adaptive de-icing execution module, respectively, to monitor the operating status of each module in real time, automatically process the detected faults, and issue alarms. The operation and maintenance management terminal communicates with the cloud-based decision-making platform to enable real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

[0017] like Figure 3 As shown, preferably, a control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines includes the following steps: S1 collects multi-dimensional data on the transmission line and its surroundings through a distributed sensing and monitoring module, including line temperature. Ice thickness , conductor deformation angle Ambient wind speed and humidity ; S2 uses the edge computing processing module to preprocess the collected data, outputs the icing trend prediction result through the icing prediction model, and determines whether to trigger the warning by combining the three-level de-icing threshold. S3, after receiving the early warning information, the cloud-based decision-making platform integrates the line load rate. And meteorological forecast data, using III. Multi-objective optimization algorithms are used to determine the optimal de-icing plan; S4, the adaptive de-icing execution module, selects either single or combined de-icing modes such as current heating and ultrasonic vibration according to cloud instructions, and accurately executes the de-icing operation. S5, the fault self-diagnosis module monitors the operating status of each module in real time, and automatically processes and alarms the detected faults through the fault judgment formula; The S6 operation and maintenance management terminal enables real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

[0018] like Figure 2 As shown, preferably, the specific method of step S1 is as follows: The distributed sensing and monitoring module consists of a fiber optic temperature sensor, a capacitive icing thickness sensor, a fiber optic tilt sensor, a wind speed sensor, and a humidity sensor. It is distributed and deployed in key sections of conductors and ground wires, at both ends of insulator strings, at tower connections, and at the top of towers. The specific deployment is shown in Table 1 below.

[0019] Table 1: Distributed sensor deployment;

[0020] Capacitive ice thickness sensor measurement range precision Response time The formula for calculating icing thickness is: ; In the formula, For sensor calibration coefficients; This represents the change in capacitance before and after icing. Fiber Bragg grating temperature sensor measurement range Precision sampling frequency It can be remotely adjusted; the temperature conversion formula is: ; In the formula, This is the temperature sensitivity coefficient; This represents the grating wavelength offset. Reference temperature; Converting wind speed sensor readings to standard wind speed: ; In the formula, These are the original measured values; The remaining sensor data are corrected according to the corresponding calibration formula, and the collected data is transmitted to the edge computing processing module through shielded twisted pair cables.

[0021] Preferably, the specific method of step S2 is as follows: The edge computing processing module uses median filtering for noise reduction: ; In the formula, , which is the filtering window; perform Z-Score normalization: ; In the formula, The mean; Standard deviation; combined Outlier detection and removal criteria The data is then processed; the icing prediction model uses... The architecture integrates neural networks and attention mechanisms, with the input feature vector as follows: ; In the formula, This data represents the past 72 hours of historical data. Execute prediction output: ; In the formula, The input feature vector; Prediction error: ; The threshold for Level 3 de-icing is: Level 1: ; Level 2: ; Level 3: ; Warning priority: ; In the formula, These are the weighting coefficients. This represents the rate of ice accumulation growth.

[0022] Preferably, the specific method of step S3 is as follows: The cloud-based decision-making platform is built on a cloud computing architecture and has a built-in multi-scenario de-icing strategy library; III. The multi-objective optimization algorithm aims to maximize de-icing efficiency, minimize energy consumption, and minimize line damage risk. De-icing efficiency: ; De-icing energy consumption: ; Line damage risk: ; In the formula, For the duration of overheating, The vibration amplitude exceeds the limit; the constraint condition is: , In the formula Maximum permissible energy consumption; Optimize the objective function: ; The optimal de-icing scheme is selected by non-dominated sorting and crowding calculation. The platform stores various types of data from the past 5 years and supports sorting by... Multi-dimensional statistical queries.

[0023] like Figure 4 As shown, preferably, the specific method of step S4 is as follows: The adaptive de-icing execution module includes a line current heating unit, an ultrasonic vibration unit, and a drive control submodule; the heating power of the line current heating unit is: ; In the formula This is the heating current; For the temperature-dependent resistance of the conductor; heating temperature control objective: ; The adjustment formula is: ; In the formula, The vibration energy of the ultrasonic vibration unit is: ; In the formula, The vibration power is given by: ; In the formula, The vibration frequency; Energy conversion coefficient; drive control submodule according to Perform single or combined de-icing operations.

[0024] Preferably, the specific method of step S5 is as follows: the fault self-diagnosis module includes hardware, communication, and data anomaly detection units; Hardware fault diagnosis: Power supply voltage In the formula, Rated voltage; operating current Temperature of key components The communication failure is determined as follows: three consecutive failures to receive a heartbeat packet, i.e.: ; Automatically switch to Backup link (communication distance) Transmission rate ); Data anomaly determination: 5 consecutive sampling periods ; In the formula , The historical data include the mean and standard deviation; the percentage of outliers. Alarms are triggered at certain times; the fault level is calculated as follows: ; In the formula, It assigns weights to hardware, communication, and data anomalies and sends alarm messages containing the fault location, type, level, and handling suggestions.

[0025] Preferably, the specific method of step S6 is as follows: The operation and maintenance management terminal includes a mobile APP and a PC, which are connected to the cloud decision-making platform via encrypted communication; it supports real-time viewing of monitoring data, early warning information, de-icing status, and equipment operating status; and the sampling frequency can be remotely adjusted after authorization. De-icing threshold Parameters for remote start / stop de-icing operations; features functions for generating, dispatching, processing, and managing maintenance work orders in a closed loop; supports historical data query and statistical analysis; generates report metrics. .

[0026] Preferably, a computer device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the intelligent anti-icing monitoring and de-icing control method for power transmission lines.

[0027] Preferably, a computer-readable storage medium stores computer instructions that cause a computer to execute the aforementioned intelligent anti-icing monitoring and de-icing control method for power transmission lines.

[0028] Example 2: This embodiment provides a specific process for a light icing scenario application. A 110kV transmission line traverses a hilly area in southern China. In winter, a distributed sensing monitoring module collects data at a frequency of 5Hz, including the original icing thickness and capacitance change. ,according to Calculated Temperature original wavelength offset ,according to Calculated Original wind speed measurement , revised to ,humidity The data was filtered by median ( After Z-score standardization, the result is calculated by inputting it into the prediction model. Early warning priority This triggered a Level 1 warning. The cloud-based decision-making platform combined this with the line load rate. And weather forecasting, using the NSGA-Ⅲ algorithm to calculate the objective function. Select a low-power current heating scheme and set the heating temperature. ,according to Calculate heating current The process lasted 40 minutes. The adaptive de-icing module maintained a stable current through PID control, and the ice thickness was calculated after 30 minutes. De-icing will stop once the safety threshold is reached. Energy consumption for this de-icing operation... Compared with traditional technology, it reduces De-icing efficiency ,promote .

[0029] Example 3: This embodiment provides a specific process for applying a heavy icing scenario. A 220kV transmission line encounters heavy icing, and data is collected and calculated. , , Predictive model calculation Early warning priority This triggered a Level 3 warning. The cloud-based decision-making platform calculated and selected a combination of high-intensity current heating and high-amplitude ultrasonic vibration, and set the heating temperature. Calculate heating power ultrasonic frequency ,power Calculate the vibration amplitude The de-icing time was set to 2 hours. During the de-icing process, a certain wind speed sensor failed to send a heartbeat packet three times consecutively. A communication failure is detected, and the system automatically switches to the LoRa backup link. The icing thickness is calculated after 1 hour and 50 minutes. De-icing efficiency Compared with traditional mechanical de-icing, it improves No line damage was found.

[0030] Example 4: This embodiment provides a specific process for application in severe extreme low temperature scenarios. Ultra-high voltage transmission lines The ice thickness was calculated under extreme low temperatures. The cloud-based decision-making platform employs a preheating + combined de-icing solution, first preheating for 12 minutes and then calculating the preheating temperature. Corresponding heating current Then start the medium power heating ( ) Medium amplitude ultrasonic vibration ( The de-icing time is set to 1.5 hours. During the de-icing process, the conductor temperature and vibration parameters are calculated in real time to maintain stable operation and prevent secondary freezing. Maintenance personnel can view statistical indicators via mobile devices. No on-site supervision is required.

Claims

1. A smart anti-icing monitoring and de-icing control system for power transmission lines, characterized in that, include: The distributed sensing and monitoring module is used to collect multi-dimensional data of the transmission line and its surrounding environment. The multi-dimensional data includes at least line temperature, ice thickness, conductor deformation angle, ambient wind speed and humidity. The edge computing processing module communicates with the distributed sensing and monitoring module to receive and preprocess multi-dimensional data, output icing trend prediction results through the built-in icing prediction model, and determine whether to trigger an early warning based on the three-level de-icing threshold. The cloud-based decision-making platform communicates with the edge computing processing module to receive early warning information, integrate line load rate and weather forecast data, and use the NSGA-Ⅲ multi-objective optimization algorithm to formulate the optimal de-icing solution. The adaptive de-icing execution module communicates with the cloud decision platform and is used to adaptively select the de-icing mode of current heating and / or ultrasonic vibration according to the instructions issued by the cloud decision platform to perform the de-icing operation. The fault self-diagnosis module establishes bidirectional communication connections with the distributed sensing and monitoring module, the edge computing processing module, the cloud decision-making platform, and the adaptive de-icing execution module, respectively, to monitor the operating status of each module in real time, automatically process the detected faults, and issue alarms. The operation and maintenance management terminal communicates with the cloud-based decision-making platform to enable real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

2. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, Includes the following steps: S1 collects multi-dimensional data on the transmission line and its surroundings through a distributed sensing and monitoring module, including line temperature. Ice thickness , conductor deformation angle Ambient wind speed and humidity ; S2 uses the edge computing processing module to preprocess the collected data, outputs the icing trend prediction result through the icing prediction model, and determines whether to trigger the warning by combining the three-level de-icing threshold. S3, after receiving the early warning information, the cloud-based decision-making platform integrates the line load rate. And meteorological forecast data, using III. Multi-objective optimization algorithms are used to determine the optimal de-icing plan; S4, the adaptive de-icing execution module, selects either single or combined de-icing modes such as current heating and ultrasonic vibration according to cloud instructions, and accurately executes the de-icing operation. S5, the fault self-diagnosis module monitors the operating status of each module in real time, and automatically processes and alarms the detected faults through the fault judgment formula; The S6 operation and maintenance management terminal enables real-time monitoring of system operation status, remote control, and closed-loop management of operation and maintenance work orders.

3. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method for step S1 is as follows: The distributed sensing and monitoring module consists of a fiber Bragg grating temperature sensor, a capacitive icing thickness sensor, a fiber Bragg grating tilt sensor, a wind speed sensor, and a humidity sensor. It is distributed across key sections of the conductor and ground wire, both ends of the insulator string, tower connections, and the top of the tower. The icing thickness is calculated using the following formula: ； In the formula, For sensor calibration coefficients; This represents the change in capacitance before and after icing; the temperature conversion formula is: ； In the formula, This is the temperature sensitivity coefficient; This represents the grating wavelength offset. Reference temperature; Converting wind speed sensor readings to standard wind speed: ； In the formula, These are the original measured values; The remaining sensor data are corrected according to the corresponding calibration formula, and the collected data is transmitted to the edge computing processing module through shielded twisted pair cables.

4. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method for step S2 is as follows: The edge computing processing module uses median filtering for noise reduction: ； In the formula, For the filter window; perform Z-Score normalization: ； In the formula, The mean; Standard deviation; combined Outlier detection and removal criteria The data is then processed; the icing prediction model uses... The architecture integrates neural networks and attention mechanisms, with the input feature vector as follows: ； In the formula, This data represents the past 72 hours of historical data. Execute prediction output: ； In the formula, The input feature vector; Prediction error: ； The threshold for Level 3 de-icing is: Level 1: ; Level 2: ; Level 3: ; Warning priority: ； In the formula, These are the weighting coefficients. This represents the rate of ice accumulation growth.

5. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method for step S3 is as follows: The cloud-based decision-making platform is built on a cloud computing architecture and has a built-in multi-scenario de-icing strategy library; III. The multi-objective optimization algorithm aims to maximize de-icing efficiency, minimize energy consumption, and minimize line damage risk. De-icing efficiency: ; De-icing energy consumption: ; Line damage risk: ; In the formula, For the duration of overheating, The vibration amplitude exceeds the limit; the constraint condition is: , In the formula Maximum permissible energy consumption; Optimize the objective function: ； The optimal de-icing scheme is selected by non-dominated sorting and crowding calculation. The platform stores various types of data and supports sorting by... Multi-dimensional statistical queries.

6. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method for step S4 is as follows: The adaptive de-icing execution module includes a line current heating unit, an ultrasonic vibration unit, and a drive control submodule; the heating power of the line current heating unit is: ； In the formula This is the heating current; The temperature-dependent resistance of the conductor; Heating temperature control target: ; The adjustment formula is: ； In the formula, The vibration energy of the ultrasonic vibration unit is: ； In the formula, The vibration power is given by: ； In the formula, The vibration frequency; Energy conversion coefficient; drive control submodule according to Perform single or combined de-icing operations.

7. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method of step S5 is as follows: the fault self-diagnosis module includes hardware, communication, and data anomaly detection units; Hardware fault diagnosis: Power supply voltage In the formula, Rated voltage; operating current Temperature of key components The communication failure is determined as follows: three consecutive failures to receive a heartbeat packet, i.e.: ； Automatically switch to Backup link; data anomaly determination: 5 consecutive sampling periods ; In the formula , The historical data include the mean and standard deviation; the percentage of outliers. Alarms are triggered at certain times; the fault level is calculated as follows: ； In the formula, It assigns weights to hardware, communication, and data anomalies and sends alarm messages containing the fault location, type, level, and handling suggestions.

8. The control method for an intelligent anti-icing monitoring and de-icing control system for transmission lines according to claim 1, characterized in that, The specific method for step S6 is as follows: The operation and maintenance management terminal includes a mobile APP and a PC, which are connected to the cloud decision-making platform via encrypted communication; it supports real-time viewing of monitoring data, early warning information, de-icing status, and equipment operating status; and the sampling frequency can be remotely adjusted after authorization. De-icing threshold Parameters for remote start / stop de-icing operations; features functions for generating, dispatching, processing, and managing maintenance work orders in a closed loop; supports historical data query and statistical analysis; generates report metrics. .

9. A computer device, characterized in that, It includes a memory and a processor, which are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the intelligent anti-icing monitoring and de-icing control method for transmission lines as described in any one of claims 2 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the intelligent anti-icing monitoring and de-icing control method for transmission lines as described in any one of claims 2 to 8.

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