Device and method for limiting wind deviation and fluttering of high-altitude power transmission lines

Abstract

The present invention provides a device and a method for using the device for limiting the wind swing of a high-altitude power transmission line, and relates to the technical field of high-altitude power transmission lines. The device architecture includes a line suppression module, a data monitoring and processing module, a data collaborative processing module and a line repair module, a pulse jet device and a plasma flow generator, which can disrupt the airflow around the line and destroy the aerodynamic conditions of the wind swing. The magnetic field regulation of the superconducting coil and the intelligent distribution of the magnetic material can generate a dynamically changing magnetic field to offset the wind swing force. At the same time, a micro-crawling robot and a flying micro-robot cluster can respond quickly when a sensor detects damage to the line or the wind swing protection device. The micro-crawling robot can crawl on the surface of the power transmission line to check and repair local damage, and the flying micro-robot cluster can cope with large-area damage or damage to multiple parts, thereby reducing the possibility and amplitude of the wind swing from the root, improving the reliability of power transmission, and effectively suppressing the wind swing.

Description

Technical Field

[0001] The invention relates to the technical field of high-altitude power transmission lines, and in particular to a device for limiting wind deviation and fluttering of high-altitude power transmission lines and a method for using the device. Background Art

[0002] The bare conductors of overhead transmission lines are generally not static. They will dance under the influence of various natural conditions such as wind and ice. If the dancing amplitude is too large, it will cause phase flashover, short circuit, and even tripping of the line, resulting in serious accidents such as large-scale power outages, causing significant economic losses to society.

[0003] According to a device for suppressing the dancing of an overhead power transmission line disclosed in China Publication No. "CN111478261B", the device includes a conductor, a support rod, a pulley assembly, a counterweight, a synthetic insulator, a main insulating rope and a load-bearing insulating rope. The support rod is buried obliquely on the ground, the fixed pulley is arranged above the movable pulley, the fixed pulley is connected to the support rod, the main insulating rope is wound around the conductor, two synthetic insulators are connected to the two ends of the main insulating rope, one end of the load-bearing insulating rope is connected to the upper end of the movable pulley, and the other end of the load-bearing insulating rope is connected to the lower end of one of the synthetic insulators after passing through the fixed pulley and the movable pulley in turn, and the counterweight is connected to the lower end of the movable pulley. The present invention is flexible and reliable to use, has a reasonable design, and uses a pulling method to suppress the dancing of the line to ensure the safe operation of the power transmission line. In addition, there is no need to arrange a pulling device at both ends of the line, and the line can be pulled at any position of the transmission line.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: Traditional wind deflection prevention technology for transmission lines can only be applied to a specific wind speed range or simple wind direction. It is difficult to effectively suppress the wind deflection of the line under complex and changeable wind conditions. Previous methods were mostly passive defense, such as increasing the strength of the tower and increasing the line spacing, but these measures cannot fundamentally solve the problem of wind deflection.

[0006] Problem 2: The existing transmission line monitoring system only monitors a few parameters and cannot fully reflect the actual status of the line. The sensor accuracy is limited, resulting in inaccurate judgment of wind yaw. Traditional control decisions often rely on manual experience or simple models, with slow data processing speed and unable to adjust control strategies in time according to changes in wind yaw.

[0007] Problem three: Traditional transmission line repair relies on manual inspections to find problems before arranging maintenance, which results in time lags. In addition, in some complex environments, such as mountains and canyons, it is difficult for maintenance personnel to quickly reach the damaged location. Manual inspections and repairs of high-altitude transmission lines pose great safety risks, especially in severe weather or high altitude areas, where the maintenance risk is high and is not conducive to actual use. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a device for limiting the wind yaw movement of high-altitude power transmission lines, which solves the problem that the wind yaw movement of lines is difficult to control under complex wind conditions and lacks active suppression means.

[0009] Another object of the present invention is to provide a method for using a device for limiting wind yaw and fluttering of an overhead power transmission line.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A device for limiting wind deviation and dancing of high-altitude power transmission lines includes a line suppression module, a data monitoring and processing module, a data collaborative processing module and a line repair module, wherein:

[0012] The line suppression module includes an active airflow interference module and a dynamic magnetic field suppression module. The active airflow interference module is used to disturb the airflow around the line and destroy the aerodynamic conditions of wind deviation dancing to reduce line dancing. The dynamic magnetic field suppression module is used to generate a dynamically changing magnetic field to offset the force generated by wind deviation to suppress line dancing.

[0013] The data monitoring and processing module includes an Internet of Things monitoring network construction module and a big data analysis and early warning model establishment module. The Internet of Things monitoring network construction module is used to connect sensors distributed on the transmission line into a network to realize real-time data collection and transmission. The big data analysis and early warning model establishment module is used to mine and analyze monitoring data through a big data analysis platform, establish a wind yaw prediction model using a machine learning algorithm, combine meteorological data and transmission line operation data to predict the probability, amplitude and time of wind yaw, and issue early warning information when wind yaw occurs.

[0014] The data collaborative processing module includes an edge computing node setting module and a cloud computing platform collaborative module. The edge computing node setting module is used to set a computing node at the edge end of the wind deviation protection device to perform preliminary processing on the real-time collected wind deviation data, including feature extraction and risk assessment. The cloud computing platform collaborative module is used to upload the key data processed by the edge computing node to the cloud computing platform, perform model calculation and data mining in the cloud, and issue control instructions to the edge computing node according to the evaluation results.

[0015] The line repair module is used to repair the line or wind deflection protection device when the sensor detects damage. The line repair module includes a micro crawling robot and a flying micro robot cluster. The micro crawling robot crawls on the surface of the transmission line to inspect and repair the damage. The flying micro robot cluster obtains energy through electromagnetic induction or wireless charging technology, repairs the damaged position when repair is needed, and cooperates with the communication module.

[0016] Preferably, the active airflow interference module includes a pulse jet device and a plasma flow generator. The pulse jet device is connected to a wind direction sensor and a wind speed sensor to spray high-speed gas into the airflow according to the jet angle and frequency under different wind speed and wind direction conditions. The plasma flow generator excites the gas to generate plasma through a high-frequency power supply, and adjusts the power and direction of the plasma according to environmental conditions and line conditions to change the flow direction and pressure distribution of the wind around the transmission line.

[0017] Preferably, the dynamic magnetic field suppression module includes a superconducting coil magnetic field control module and a magnetic material intelligent distribution module. The superconducting coil magnetic field control module controls the current in the superconducting coil to generate a dynamically changing magnetic field by installing a superconducting coil near the line, and adjusts the magnetic field strength and direction according to the direction and amplitude of the dancing. The magnetic material intelligent distribution module coats the intelligent magnetic material on the surface of the transmission line or embeds it into the equipment casing, adjusts the magnetism of the magnetic material according to the external magnetic field and the line stress conditions, and changes the magnetic field environment around the line to adjust the stress state of the line in the wind.

[0018] Preferably, the superconducting coil is mounted on an insulating support around the transmission line, connected to a superconducting current controller, and equipped with a cryogenic cooling system.

[0019] Preferably, the smart magnetic material is made into a film or particles, and the control system of the smart magnetic material is connected to a magnetic field sensor and a stress sensor installed on the transmission line.

[0020] Preferably, the sensors of the Internet of Things monitoring network building module include a wind direction sensor, a wind speed sensor, a line vibration sensor, a superconducting coil current sensor, a jet device state sensor, and a magnetic material state sensor.

[0021] Preferably, in the big data analysis and early warning model establishment module, the cloud data center adopts the distributed storage system HDFS to store data, classifies and stores data according to time series, sensor type, and line location dimensions, establishes a data index and metadata management mechanism, and performs data cleaning and feature extraction to remove noise, outliers, and duplicate data, and extracts key features such as wind speed change rate, wind direction change frequency, line vibration spectrum, and magnetic field intensity change trend as input for the machine learning model.

[0022] Preferably, the edge computing node of the data collaborative processing module adopts an embedded computing device to perform data analysis tasks such as data screening, local anomaly detection, and equipment status evaluation locally, monitor and preliminarily adjust the pressure of the jet device and the temperature of the superconducting coil, and upload the data and local decision results to the cloud computing platform; after receiving the data from the edge computing node, the cloud computing platform combines the meteorological data and the power grid operation data for analysis, uses the machine learning model to update and optimize the wind deviation and dance prediction model and control strategy, and sends collaborative control instructions for the equipment control parameters to the edge computing node.

[0023] The method for using the device for limiting wind deviation and swaying of high-altitude power transmission lines comprises the following steps:

[0024] Sp1: Measure transmission line parameters and assess wind yaw risk in combination with geographic and meteorological data to determine the number, type and location of installations;

[0025] Sp2: Install the pulse jet device, debug and check the jet pressure, frequency and angle adjustment functions; install the plasma flow generator, and test its plasma generation and power regulation functions;

[0026] Sp3: Install superconducting coils and test magnetic field changes; connect sensors and control systems with intelligent magnetic materials, calibrate sensors and test control system functions;

[0027] Sp4: Install various sensors on the transmission line and connect them to local data acquisition nodes, and configure the data acquisition protocol, sampling frequency and communication parameters of the acquisition nodes;

[0028] Sp5: Install edge computing nodes in towers or substations, deploy cloud computing platforms in the cloud, and establish data transmission channels;

[0029] Sp6: Place the micro-crawling robot at the start of the transmission line or at a designated drop point; assemble and debug the flying micro-robot cluster, and set up take-off and landing platforms around the transmission line;

[0030] Sp7: After the system is running, the Internet of Things continuously collects data and transmits it to the edge computing nodes and cloud computing platform. The edge computing nodes perform preliminary analysis in real time, and the cloud computing platform globally analyzes and updates the model, dynamically adjusting the airflow and magnetic field system parameters accordingly, and regularly checking and repairing the system status.

[0031] The present invention has the following beneficial effects:

[0032] The present invention uses a pulse jet device and a plasma flow generator in an active interference airflow module to disrupt the airflow around the line and destroy the aerodynamic conditions of wind swing. At the same time, the superconducting coil magnetic field control and the intelligent distribution of magnetic materials in the dynamic magnetic field suppression module can generate a dynamically changing magnetic field to offset the wind swing force. This combination of multiple means reduces the possibility and amplitude of wind swing from the root, ensures the stable operation of transmission lines under severe weather conditions such as strong winds, reduces the occurrence rate of line damage, tripping and other faults caused by wind swing, improves the reliability of power transmission, and effectively suppresses wind swing. The various modules in the device cooperate with each other and can adapt to the complex changes of various wind speeds and wind directions, so that the transmission lines can remain stable under complex and changeable wind conditions, reduce the risk of power transmission interruption due to wind swing, and ensure the safe operation of the power grid.

[0033] The present invention adopts a monitoring network constructed by a data monitoring and processing module using the Internet of Things technology, integrates a variety of sensors, and can collect various types of data on the transmission line in all directions. Through the big data analysis platform and machine learning algorithm, the massive data is deeply mined, and the probability, amplitude and time of wind yaw fluctuation can be accurately analyzed, which provides a scientific basis for timely prevention and control measures, helps to give early warning and formulate reasonable response strategies to avoid damage to the transmission line caused by wind yaw fluctuation. The edge computing nodes and the cloud computing platform work together to effectively reduce the amount of data transmission and reduce latency. The edge computing nodes can quickly perform preliminary processing on the data collected in real time, and the cloud computing platform uses a wider range of data for complex analysis and model updates, and issues precise control instructions to the edge computing nodes, realizing efficient control of the entire system, enabling each module to quickly adjust the working status according to the actual situation, improving the response speed and processing efficiency of the system, and ensuring the stable operation of the transmission line.

[0034] 3. The present invention adopts the micro-crawling robot and the flying micro-robot cluster in the line repair module to respond quickly when the sensor detects damage to the line or the wind deflection protection device. The micro-crawling robot can crawl on the surface of the transmission line to inspect and repair local damage, and the flying micro-robot cluster can deal with large-area damage or damage to multiple parts. They carry different repair materials and tools, and can repair damaged parts in time through collaborative work, reduce the expansion of faults caused by accumulated damage, extend the service life of transmission lines and related devices, reduce maintenance costs, and greatly reduce the need for manual intervention, especially in severe weather or complex terrain conditions, to avoid people from being exposed to danger. At the same time, the robot can inspect and maintain the line regularly or on demand, which improves the frequency and quality of maintenance and ensures the long-term stable operation of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a diagram of the device architecture of the present invention;

[0036] Figure 2 A diagram showing a method of using the device of the present invention;

[0037] Figure 3 It is a data transmission trend diagram of the present invention;

[0038] Figure 4 It is the installation schematic diagram of the present invention;

[0039] Figure 5 For the present invention Figure 4 Partial structural diagram of

[0040] Figure 6 This is a schematic diagram of the front installation part of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Specific embodiment 1: Figure 1-6 As shown, a device for limiting wind deviation and dancing of high-altitude power transmission lines, the device architecture includes a line suppression module, a data monitoring and processing module, a data collaborative processing module and a line repair module, wherein:

[0043] The line suppression module includes an active airflow interference module and a dynamic magnetic field suppression module. The active airflow interference module is used to disrupt the airflow around the line and destroy the aerodynamic conditions of wind deviation dancing to reduce the possibility of line dancing. The active airflow interference module includes a pulse jet device and a plasma flow generator. The pulse jet device is connected to a wind direction sensor and a wind speed sensor, and sprays high-speed gas into the airflow according to the optimal jet angle and frequency under different wind speed and wind direction conditions determined by computer simulation. The plasma flow generator excites the gas to generate plasma through a high-frequency power supply, and adjusts the power and direction of the plasma according to environmental conditions and line conditions to change the flow direction and pressure distribution of the wind around the transmission line. The dynamic magnetic field suppression module is used to generate a dynamically changing magnetic field to offset the force generated by the wind deviation and thus suppress the line dancing. The dynamic magnetic field suppression module includes superconducting coil magnetic field control and intelligent distribution of magnetic materials. The superconducting coil magnetic field control installs superconducting coils near the line, controls the current in the superconducting coils to generate a dynamically changing magnetic field, and accurately adjusts the magnetic field strength and direction according to the direction and amplitude of the dancing. Direction, intelligent distribution of magnetic materials: by coating intelligent magnetic materials on the surface of transmission lines or installing them in specific locations, the magnetism of magnetic materials is intelligently adjusted according to the external magnetic field and the stress conditions of the lines, and the magnetic field environment around the lines is changed to adjust the stress state of the lines in the wind. The pulse jet device and plasma flow generator in the active interference airflow module can disrupt the airflow around the line and destroy the aerodynamic conditions of wind deviation. At the same time, the magnetic field regulation of the superconducting coil in the dynamic magnetic field suppression module and the intelligent distribution of magnetic materials can produce a dynamically changing magnetic field to offset the wind deviation force. This combination of multiple means reduces the possibility and amplitude of wind deviation from the root, ensures the stable operation of transmission lines under severe weather conditions such as strong winds, reduces the occurrence rate of line damage, tripping and other faults caused by wind deviation, improves the reliability of power transmission, and effectively suppresses wind deviation. The various modules in the device cooperate with each other and can adapt to the complex changes of various wind speeds and wind directions, so that the transmission lines remain stable under complex and changeable wind conditions, reduce the risk of power transmission interruption caused by wind deviation, and ensure the safe operation of the power grid.

[0044] The data monitoring and processing module includes the construction of an Internet of Things monitoring network and the establishment of a big data analysis and early warning model. The Internet of Things monitoring network is used to connect multiple wind deviation and dancing monitoring devices such as wind direction sensors, wind speed sensors, line vibration sensors, superconducting coil current sensors, jet device status sensors, and magnetic material status sensors distributed on the transmission line into a network with the help of Internet of Things technology to realize real-time data collection and transmission. The big data analysis and early warning model is used to deeply mine and analyze massive monitoring data through a big data analysis platform, and use machine learning algorithms to establish a more accurate wind deviation and dancing prediction model. Combined with meteorological data and transmission line operation data, the probability, amplitude and time of wind deviation and dancing are predicted in advance, and early warning information is issued when it may occur. The data monitoring and processing module uses the monitoring network built by the Internet of Things technology, which integrates a variety of sensors and can collect all kinds of data of the transmission line in an all-round way. Through the big data analysis platform and machine learning algorithms, the massive data is deeply mined, and the probability, amplitude and time of wind deviation and dancing can be accurately analyzed, which provides a scientific basis for timely prevention and control measures, helps to warn in advance and formulate reasonable response strategies to avoid wind deviation and dancing. Damage to the transmission line;

[0045] The data collaborative processing module includes edge computing node setting and cloud computing platform collaboration. The edge computing node setting is used to set up computing nodes at the edge end close to the wind deflection protection device to perform preliminary processing on the wind deflection data collected in real time, including simple feature extraction and preliminary risk assessment. The cloud computing platform collaboration is used to upload the key data processed by the edge computing node to the cloud computing platform, perform more complex model calculations and data mining in the cloud, and issue control instructions to the edge computing node based on the evaluation results, thereby reducing the amount of data transmitted, reducing latency, and improving the system response speed and processing efficiency. The edge computing node and the cloud computing platform work together to effectively reduce the amount of data transmitted and reduce latency. The edge computing node can perform rapid preliminary processing on the real-time collected data. The cloud computing platform uses a wider range of data for complex analysis and model updates, and issues precise control instructions to the edge computing node, thereby achieving efficient control of the entire system, enabling each module to quickly adjust its working status according to actual conditions, improving the system response speed and processing efficiency, and ensuring the stable operation of the transmission line.

[0046] The line repair module is used to repair the line or wind deflection protection device when the sensor detects damage. The line repair module includes a micro crawling robot and a flying micro robot cluster. The micro crawling robot crawls on the surface of the transmission line and uses the micro camera, welding equipment, sealing materials and other tools it carries to inspect and repair the damage. The flying micro robot cluster obtains energy through electromagnetic induction or wireless charging technology. When a large area is damaged or multiple parts need to be repaired, it carries different repair materials and tools to work together to fly to the damaged location for repair, and collaborates through the communication module to ensure the efficiency and accuracy of the repair work. The micro crawling robot and flying micro robot cluster in the line repair module can be used in the sensor The device can respond quickly when it detects damage to the line or wind deflection protection device. The micro crawling robot can crawl on the surface of the transmission line to inspect and repair local damage. The flying micro robot cluster can deal with large-area damage or damage to multiple parts. They carry different repair materials and tools, and can repair damaged parts in time through collaborative work, reduce the expansion of faults caused by accumulated damage, extend the service life of transmission lines and related devices, reduce maintenance costs, and greatly reduce the need for human intervention, especially in severe weather or complex terrain conditions, to avoid danger to personnel. At the same time, robots can inspect and maintain lines regularly or on demand, which improves the frequency and quality of maintenance and ensures the long-term and stable operation of transmission lines.

[0047] Specific embodiment 2: Figure 1-6 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0048] The device architecture is used as follows:

[0049] Sp1: Preliminary preparation: Detailed measurement and recording of relevant parameters of the transmission line, including line length, tower location and height, conductor model and specifications, etc. At the same time, based on the geographical environment of the line, including topography, landform, distribution of surrounding buildings, etc. and meteorological data, including annual wind speed, wind direction distribution, and extreme weather conditions, assess the risk level of wind yaw and flutter, and determine the number, type and distribution location of the devices to be installed;

[0050] Sp2: Install the active interference airflow module: For the pulse jet device, install the jet device bracket on the pole tower at the predetermined spacing, firmly install the jet device on the bracket, and connect the gas supply pipeline and control cable to ensure that the angle between the jet nozzle and the line direction meets the design requirements, and then debug the jet device, send different instructions to the jet device through the control system, and check whether the adjustment functions of the jet pressure, frequency and angle are normal. When installing the plasma flow generator, fix the generator on the pole tower or a specially designed installation platform, connect the power supply and gas supply system, adjust the direction of the generator so that it can adjust the plasma airflow direction according to the wind direction, and test each generator separately to check the plasma generation and power regulation function;

[0051] Sp3: Install the dynamic magnetic field suppression module: When installing the superconducting coil, first install the insulating bracket at the predetermined position, then carefully place the superconducting coil on the bracket, connect the superconducting current controller and the cryogenic cooling system, ensure that the distance between the superconducting coil and the transmission line meets the safety and electromagnetic requirements, calibrate the superconducting current controller, test the intensity and direction changes of the magnetic field under different current settings, and for the intelligent distribution part of the magnetic material, during the production or on-site maintenance of the transmission line, evenly coat the prepared intelligent magnetic material on the line surface or install it in a specified position in a specific protective shell, install the magnetic field sensor and stress sensor, and connect them to the magnetic material control system, and perform sensor calibration and control system functional testing;

[0052] Sp4: Build an IoT monitoring network: Install various sensors at key locations of the transmission line, including wind direction sensors, wind speed sensors, temperature sensors, humidity sensors, line displacement sensors, superconducting coil temperature sensors, jet device pressure sensors, plasma generator power sensors, magnetic field sensors, and stress sensors. Each sensor must be installed firmly and accurately, and connected to the corresponding local data acquisition node. Configure the data acquisition node, set the data acquisition protocol, sampling frequency, and communication parameters to ensure that the data can be transmitted accurately.

[0053] Sp5: Deploy edge computing nodes and cloud computing platforms: Install edge computing nodes in towers or substations close to transmission lines, connect power supplies and network communication equipment, initialize and configure edge computing nodes, install local data processing and analysis software, establish communication links with local sensors and cloud computing platforms, and deploy cloud computing platforms in the cloud, configure distributed storage systems, computing resource management systems, and machine learning model training environments, and establish data transmission channels between edge computing nodes and cloud computing platforms;

[0054] Sp6: Installing the repair module based on micro-robots: For the micro-crawling robot, place it at the starting position of the transmission line or the designated delivery point. Before delivery, conduct a comprehensive inspection of the robot, including the testing of the functions of the adsorption device, detection tools, repair tools and autonomous navigation system, to ensure that the robot can start normally and establish a communication connection with the control center. The flying micro-robot cluster is assembled and debugged on the ground, and the flight performance, energy acquisition device, communication module and repair tools are checked. The take-off and landing platforms of the flying micro-robots are set at appropriate locations around the transmission line to ensure that the robot can take off smoothly to perform the repair task when needed.

[0055] Sp7: System debugging and initialization: Start the entire system and conduct joint debugging. By simulating different wind speed and wind direction conditions, check the working conditions of the active interference airflow system and the dynamic magnetic field suppression system, observe whether the sensor data collection is accurate, whether the data transmission and processing between the edge computing node and the cloud computing platform are normal, and whether the micro-robot repair system can respond correctly to the simulated damage. Adjust and repair the problems found during the debugging process in a timely manner to ensure that all functions of the system are in the best condition before normal operation;

[0056] Sp8: Formal operation and monitoring: After the system is put into formal operation, various types of data are continuously collected through the Internet of Things monitoring network, and the data are transmitted to the edge computing nodes and cloud computing platforms. The edge computing nodes process and preliminarily analyze the data in real time, and the cloud computing platform performs global data analysis and predictive model updates. According to the prediction results and real-time monitoring conditions, the parameters of the active interference airflow system and the dynamic magnetic field suppression system are dynamically adjusted to effectively suppress wind deviation and flutter. At the same time, the monitoring system regularly checks the status of the micro-robot-based repair system to ensure that it can respond to possible line damage at any time.

[0057] This device integrates a variety of cutting-edge technologies such as the Internet of Things, big data, edge computing, cloud computing, micro-robotics, superconducting technology, and plasma technology, and innovatively proposes a comprehensive solution to limit wind deviation and repair. The comprehensive application of these technologies not only improves the performance of the system, but also provides new ideas and methods for the power industry to deal with similar problems, and promotes the development of transmission line maintenance technology. The architecture design of the device has certain flexibility and scalability. With the development of technology, each module can be easily upgraded or new functions can be added. For example, in the data monitoring and processing module, new types of sensors can be added to obtain more data, in the data collaborative processing module, the algorithm and computing architecture can be further optimized, and in the repair module, more advanced robotics technology can be developed. This scalability enables the system to adapt to the changing needs of the future and maintain its advancement and practicality.

[0058] Specific embodiment 3: Figure 1-6 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0059] In the above device architecture, the specific contents of each device are as follows:

[0060] The wind direction sensor of the pulse jet device adopts three-dimensional ultrasonic wind direction measurement technology, and the wind speed sensor uses the laser Doppler speed measurement principle with a measurement accuracy of ±0.1m / s. The pulse jet device is equipped with a pressure regulating valve and a pulse controller. The pressure regulating valve adjusts the jet pressure according to the command, and the pulse controller is used to accurately control the frequency and duration of the jet. The jet device is set up in a group every 5-10 meters along the transmission line. The specific value can be adjusted according to factors such as the line voltage level and the wind conditions in the area. For example, for high-voltage and windy areas, it can be set to a group of 5 meters, and for areas with slightly lower voltage levels and relatively mild wind conditions, it can be set to a group of 10 meters. Each group contains 3-5 jet nozzles, which are determined according to factors such as the line diameter. The number of jet nozzles can be appropriately increased for thick lines. The direction of the jet nozzle is at an angle of 30°-60° to the direction of the line. This angle is obtained through wind tunnel experiments and simulation calculations. Within this angle range, it is better to destroy the aerodynamic conditions of wind deviation and dancing.

[0061] The power module of the plasma flow generator adopts a high-frequency high-voltage power supply, which can output electrical energy with a frequency of 10kHz-50kHz and a voltage of 5kV-20kV. The electrode system adopts special electrode materials such as tungsten-copper alloy, which has a high melting point, high conductivity and good corrosion resistance. The electrode shape is a needle-plate structure, which is conducive to the generation of stable plasma under high voltage. The gas supply system can accurately control the gas flow rate at 0.1-1L / min, which is determined according to the needs of plasma generation and regulation. According to different environmental conditions, argon can be selected in areas with high humidity, and helium and other gases can be selected in areas with relatively dry air and more dust. A generator is installed every 10-20 meters around the transmission line. It is determined according to the line length and wind deviation and dancing risk assessment. The gas supply system can accurately control the gas flow rate and type, select argon, helium and other gases according to different environmental conditions, and install multiple generators around the transmission line.

[0062] The superconducting coil of the dynamic magnetic field suppression module is made of high-temperature superconducting materials, such as yttrium barium copper oxide superconducting materials, with high critical current density (greater than 10 5A / cm²) and low AC loss (less than 10⁻³W / kg). It is installed on the insulating bracket around the transmission line, keeping a distance of 0.5-1 meter from the line to avoid electromagnetic interference to the normal operation of the line while ensuring the effect of the magnetic field. It is connected to the superconducting current controller and equipped with a low-temperature cooling system, such as a liquid helium cooling system, which can maintain the temperature below the critical temperature of the superconducting material, about 77K-90K. The superconducting current controller can adjust the current to generate the corresponding magnetic field according to the dancing parameters in less than 0.1 second.

[0063] The intelligent magnetic material of the dynamic magnetic field suppression module uses new materials with magnetostrictive effect and controllable magnetism, such as Terfenol-D alloy, etc. Through nanotechnology and material composite technology, it is made into a film with a thickness of 0.1-0.5 mm or tiny particles with a diameter of 0.5-2 mm, and evenly coated on the surface of the transmission line or embedded in a specific protective casing. The protective casing uses insulating, high-strength composite materials, such as glass fiber reinforced epoxy resin). Magnetic field sensors and stress sensors are installed every 2-5 meters on the transmission line, which are determined according to the line length and the requirements for data accuracy and are connected to the magnetic material control system.

[0064] In the IoT monitoring network of the data monitoring and processing module, the sensor is connected to the local data acquisition node through ZigBee, LoRa wireless communication modules or fiber-optic wired communication. The local data acquisition node pre-processes the data according to a unified data acquisition protocol. The data acquisition protocol specifies the data format, sampling frequency, timestamp and other information. The pre-processed data is transmitted through the TCP / IP protocol, and data encryption and error correction technology are used.

[0065] In the establishment of big data analysis and early warning models, the cloud data center uses the distributed storage system HDFS to store data, classifies and stores data according to dimensions such as time series, sensor type, and line location, establishes a data index and metadata management mechanism, and uses Spark, Flink and other technologies to clean data and extract features, remove noise, outliers and duplicate data, and extract key features such as wind speed change rate, wind direction change frequency, line vibration spectrum, and magnetic field intensity change trend as input for the machine learning model.

[0066] The edge computing nodes of the data collaborative processing module use high-performance embedded computing devices with multi-core processors, large-capacity memory and high-speed storage devices. They have low power consumption, high reliability and can adapt to harsh outdoor environments. They can perform simple data analysis tasks such as local data screening, local anomaly detection, and rapid equipment status assessment. They can monitor and make preliminary adjustments to the pressure of the jet device, the temperature of the superconducting coil and other conditions, and upload key data and local decision-making results to the cloud computing platform.

[0067] The cloud computing platform in the data collaborative processing module uses the Kubernetes distributed computing architecture to manage and schedule resources. After receiving the data from the edge computing nodes, it combines it with more extensive meteorological data and power grid operation data for a more comprehensive and in-depth analysis. It uses machine learning models to update and optimize the wind deviation and swing prediction model and control strategy, and sends collaborative control instructions to the edge computing nodes, including precise control parameters of equipment such as jet devices, superconducting coils, and magnetic material control systems.

[0068] The main structure of the micro crawling robot is made of carbon fiber material, with a length of about 10-20 cm, a width of 3-5 cm, and a height of 2-4 cm. The bottom adsorption device is based on the principle of negative pressure adsorption or electromagnetic adsorption (negative pressure adsorption can generate a negative pressure of 10-20 kPa, and the electromagnetic adsorption force can reach 5-10N to ensure that the robot is stably attached to the surface of different lines). The micro camera is a wide-angle lens with a resolution of not less than 1080p (viewing angle range of 120°-150°), and the welding equipment adopts micro laser welding technology (laser power of 5-10W, spot diameter of 0.1-0.3 mm). The sealing material storage tank is filled with high-performance sealant and is applied by a micro nozzle with a diameter of 0.5-1 mm. The robot's built-in autonomous navigation system realizes autonomous positioning and path planning by identifying line markings (marker intervals are 1-2 meters) or inertial navigation technology;

[0069] The body of the flying microrobot is made of aviation aluminum alloy and plastic materials, with a wingspan of about 20-30 cm and a weight of less than 50 grams. It obtains energy through an electromagnetic induction coil (induction voltage is 5-10V) or a wireless charging receiving device (receiving power is 2-5W) and has a built-in small rechargeable battery with a capacity of 10-20Wh as a backup energy source. The flying microrobot cluster collaborates through a wireless communication network (communication frequency band is 2.4GHz-5.8GHz), carries repair pieces with an area of ​​2-5 square centimeters, glue with a capacity of 5-10ml and other materials, and uses a high-precision robotic arm with an accuracy of up to 0.1 mm to perform hovering operations and repairs in complex wind field environments (it can stably hover when the wind speed does not exceed 15m / s).

[0070] Pulse jet device, sensor: the wind direction sensor adopts three-dimensional ultrasonic wind direction measurement technology, and the wind speed sensor uses the laser Doppler speed measurement principle, with a measurement accuracy of ±0.1m / s; internal structure: equipped with a pressure regulating valve and a pulse controller; the pressure regulating valve adjusts the jet pressure according to the command, and the pulse controller accurately controls the jet frequency and duration; installation layout: a group is set every 5-10 meters along the transmission line, and the specific spacing depends on the line voltage level and the wind conditions in the area; each group contains 3-5 jet nozzles, and the number of jet nozzles is adjusted according to the line diameter, and the number is appropriately increased for thick lines; the direction of the jet nozzle is at an angle of 30°-60° to the direction of the line. This angle is obtained through wind tunnel experiments and simulation calculations, and can effectively destroy the aerodynamic conditions of wind deviation and dancing.

[0071] Plasma flow generator, power module: adopts high-frequency high-voltage power supply, outputs electrical energy with frequency of 10kHz-50kHz and voltage of 5kV-20kV; electrode system: the electrode material is special materials such as tungsten-copper alloy, which has high melting point, high conductivity and good corrosion resistance. The electrode shape is a needle-plate structure, which is conducive to the generation of stable plasma under high voltage; gas supply system: the gas flow rate can be accurately controlled at 0.1-1L / min, and the gas is selected according to the plasma generation and regulation requirements and different environmental conditions, such as argon in areas with high humidity, helium in dry and dusty areas, etc.; one is installed every 10-20 meters around the transmission line, and the number of installations is determined according to the line length and wind yaw risk assessment.

[0072] Superconducting coil magnetic field control, superconducting coil: made of high-temperature superconducting materials (such as yttrium barium copper oxide superconducting materials), with high critical current density (greater than 10 5 A / cm²) and low AC loss (less than 10⁻³W / kg); it is installed on the insulating bracket around the transmission line, keeping a distance of 0.5-1 meter from the line, connected to the superconducting current controller, and equipped with a low-temperature cooling system (such as liquid helium cooling system), which can maintain the temperature below the critical temperature of the superconducting material (about 77K-90K). The superconducting current controller can adjust the current according to the dancing parameters in less than 0.1 second to generate the corresponding magnetic field.

[0073] Intelligent distribution of magnetic materials: Intelligent magnetic materials: New materials with magnetostrictive effect and controllable magnetism (such as Terfenol-D alloy) are selected, and made into thin films with a thickness of 0.1-0.5 mm or tiny particles with a diameter of 0.5-2 mm through nanotechnology and material composite technology; these materials are evenly coated on the surface of transmission lines or embedded in specific protective casings (using insulating, high-strength composite materials such as glass fiber reinforced epoxy resin).

[0074] Sensor and control system: Magnetic field sensors and stress sensors are installed every 2-5 meters on the transmission line (depending on the line length and data accuracy requirements). They are connected to the magnetic material control system to intelligently adjust the magnetism of the magnetic material according to the external magnetic field and line stress conditions, change the magnetic field environment around the line, and adjust the line stress state in the wind.

[0075] Construction of the Internet of Things monitoring network, sensor connection method: a variety of sensors (wind direction, wind speed, temperature, humidity, line displacement, superconducting coil temperature, jet device pressure, plasma generator power, magnetic field sensor and stress sensor, etc.) are connected to the local data acquisition node through ZigBee, LoRa wireless communication modules or optical fiber wired communication methods; Data acquisition node function: The local data acquisition node pre-processes the data according to a unified data acquisition protocol. The protocol specifies data format, sampling frequency, timestamp and other information. The pre-processed data is transmitted through the TCP / IP protocol, and data encryption and error correction technology are used to ensure data security and accuracy.

[0076] Big data analysis and early warning model establishment, data storage: the cloud data center uses the distributed storage system HDFS to store data, and classifies and stores data according to dimensions such as time series, sensor type, and line location, and establishes a data index and metadata management mechanism; data processing: Spark, Flink and other technologies are used to clean data and extract features, remove noise, outliers and duplicate data, and extract key features such as wind speed change rate, wind direction change frequency, line vibration spectrum, and magnetic field intensity change trend as machine learning model input, so as to establish a more accurate wind swing prediction model, combine meteorological data and transmission line operation data to predict the probability, amplitude and time of wind swing in advance, and issue early warning information when it may occur.

[0077] Edge computing node settings, equipment features: using high-performance embedded computing equipment, equipped with multi-core processors, large-capacity memory and high-speed storage devices, with low power consumption and high reliability, and can adapt to harsh outdoor environments; Functional implementation: Perform simple data analysis tasks such as local data screening, local anomaly detection, and rapid evaluation of equipment status. It can monitor and make preliminary adjustments to the jet device pressure, superconducting coil temperature, etc., and upload key data and local decision results to the cloud computing platform.

[0078] Cloud computing platform collaboration and architecture management: Kubernetes distributed computing architecture is used to manage and schedule resources; functional operation: after receiving edge computing node data, it is combined with more extensive meteorological data and power grid operation data for more comprehensive and in-depth analysis, and machine learning models are used to update and optimize wind deviation and swing prediction models and control strategies, and to send collaborative control instructions to edge computing nodes, including precise control parameters of equipment such as jet devices, superconducting coils, and magnetic material control systems.

[0079] Micro crawling robot, main structure: made of carbon fiber material, about 10-20 cm in length, 3-5 cm in width, 2-4 cm in height; adsorption device: the bottom adsorption device is based on the principle of negative pressure adsorption or electromagnetic adsorption. Negative pressure adsorption can generate a negative pressure of 10-20 kPa, and the electromagnetic adsorption force can reach 5-10N, ensuring stable attachment on different line surfaces;

[0080] Detection and repair tools: The micro camera is a wide-angle lens with a resolution of not less than 1080p (viewing angle range 120°-150°). The welding equipment adopts micro laser welding technology (laser power 5-10W, spot diameter 0.1-0.3 mm). The sealing material storage tank is filled with high-performance sealant, which is applied by a micro nozzle with a diameter of 0.5-1 mm. Navigation system: The built-in autonomous navigation system realizes autonomous positioning and path planning by identifying line markings (marker intervals 1-2 meters) or inertial navigation technology, and can crawl on the surface of the transmission line to inspect and repair local damage.

[0081] Flying micro-robot cluster, fuselage material and size: the fuselage is made of aviation aluminum alloy and plastic materials, with a wingspan of about 20-30 cm and a weight of less than 50 grams; energy acquisition and storage: energy is obtained through electromagnetic induction coils (induction voltage 5-10V) or wireless charging receivers (receiving power 2-5W), and a small rechargeable battery with a built-in capacity of 10-20Wh is used as a backup energy source; collaboration and repair functions: through wireless communication networks (communication frequency band is 2.4GHz-5.8GHz), it collaborates, carries repair pieces with an area of ​​2-5 square centimeters, glue with a capacity of 5-10ml and other materials, and uses a high-precision robotic arm with an accuracy of up to 0.1 mm. It can stably hover and operate repairs in complex wind field environments (when the wind speed does not exceed 15m / s), which is used to deal with large-area damage or damage to multiple parts.

[0082] like Figure 4-6 As shown, Figure 4 This is a schematic diagram of the installation of the entire device structure on the transmission line, describing the approximate location of the installation. The transmission line is supported by towers. Figure 5 It can be seen that the flying micro-robots on the surface of the transmission line are flying micro-robots, and multiple robots form a cluster. The spring-shaped structures on the surface of the transmission line are superconducting coils, the small rectangular structures are various sensors, the long strips are magnetic materials, and the waist-shaped structures on the surface are crawling robots. Multiple pulse jet devices are installed on the side of the tower. Figure 6 It can be seen that the pulse jet device emits gas from the jet nozzle to limit the dancing of the transmission line, and the plasma flow generator is installed on the top of the tower through a platform. Figure 4-5 It is a simple schematic structure diagram to facilitate the understanding of the entire device architecture.

[0083] Since the types of equipment used in the entire device architecture can be found in the currently disclosed technologies, and combined with the above statements, it can be clearly understood that the corresponding functions, structures, materials, installation locations, etc. of the corresponding equipment, such as the body size, materials, surface equipment, etc. of the flying microrobot, can be explained in detail through the relevant knowledge in this field and the disclosed technical documents, combined with the above description. Therefore, this application protects the architectural integrity of the entire device, and does not impose further restrictions on specific device structures, such as the shape of the flying microrobot, and the structure through which the rechargeable battery is connected. On the premise of meeting the above functions and basic architecture, further detailed architecture can be supplemented in actual use in combination with relevant knowledge in this field.

[0084] Specific embodiment 4: Figure 1-6 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0085] In the above-mentioned entire device architecture, the algorithm content and steps for data processing are as follows:

[0086] Machine learning algorithms used in big data analysis and early warning model building

[0087] In the process of big data analysis and early warning model establishment, the neural network algorithm of the machine learning algorithm is used to establish a wind deviation and dancing prediction model. The forward propagation formula of the multi-layer perceptron can be expressed as:

[0088] For input layer to hidden layer:

[0089] ;

[0090] ;

[0091] in:

[0092] : is the first Input features, representing the characteristic variables in the input data, such as wind speed value, wind direction angle value, etc. These are the basic data used to analyze and predict wind swing;

[0093] is the number of input layer features;

[0094] Is the connection input layer neurons to the hidden layer The weight of each neuron indicates the strength of the connection between the input layer neurons and the hidden layer neurons. Different weight values ​​will affect the transmission and processing of information in the network. The weight is continuously adjusted through training to achieve the best prediction effect.

[0095] : Hidden layer The bias of each neuron provides an adjustable offset for each hidden layer neuron, which helps the neural network to better fit the data and make it more expressive.

[0096] :It is the hidden layer The weighted sum of the inputs of each neuron is the weighted sum of the input values ​​received by the neurons in the hidden layer. It combines the information transmitted by each feature of the input layer through the corresponding weights and the influence of the bias.

[0097] :It is the hidden layer The neurons are activated by the function The output after is the output value of the hidden layer neurons after being processed by the activation function. The activation function (such as the commonly used Sigmoid, ReLU, etc.) can introduce nonlinear factors, enabling the neural network to handle complex nonlinear relationships, thereby better modeling complex phenomena such as wind yaw and flutter.

[0098] From the hidden layer to the output layer, the output layer has only one neuron for predicting the probability of wind yaw, etc.:

[0099] ;

[0100] ;

[0101] in, is the number of neurons in the hidden layer, is the connection hidden layer neurons to the output layer The weight of a neuron, The output layer The bias of a neuron, The output layer The weighted sum of the inputs of the neurons, is the final prediction output, such as the probability of wind yaw. :The final prediction result, such as the predicted probability value of wind yaw dance, is the judgment of the entire neural network on the wind yaw dance-related situation after a series of calculations and processing based on the input data.

[0102] The implementation steps are as follows:

[0103] Sp1: Data collection and preprocessing: Collect various monitoring data including wind speed, wind direction, line vibration, etc., and store them according to time series, sensor type, line location and other dimensions. Use Spark, Flink and other technologies to clean the data and extract features, remove noise, outliers and duplicate data, and extract key features as input for the neural network.

[0104] Sp2: Network structure design: determine the number of layers of the neural network (such as the setting of input layer, hidden layer, output layer), the number of neurons in each layer, and the selection of activation function, etc.;

[0105] Sp3: Initialize weights and biases: Randomly initialize all weights in the network , and bias , ;

[0106] Sp4: Forward propagation calculation: According to the above formula, the preprocessed input data is calculated through the input layer and hidden layer in turn, and finally the prediction result of the output layer is obtained. ;

[0107] Sp5: Calculate the loss function: based on the prediction results and the actual wind yaw and actual situation (through actual observations or known label data; calculate the loss function value; such as mean square error MSE, etc.; the loss function is used to measure the gap between the predicted result and the actual situation;

[0108] Sp6: Back propagation update: Using optimization algorithms such as gradient descent, back propagation updates the weights and biases in the network according to the gradient information of the loss function to reduce the loss function value and make the prediction results more accurate;

[0109] Sp7: Repeat training: Repeat steps Sp4-Sp6 until the predetermined training stop condition is reached, such as reaching a certain number of training rounds, the loss function value is less than a certain threshold, etc.

[0110] Operation logic: The input layer receives various pre-processed monitoring data features. After these data are multiplied by the corresponding weights and added with the bias, they are weighted summed and activated in the hidden layer to obtain the output of the hidden layer. The output of the hidden layer is then used as the input of the next layer. After a similar calculation process, the prediction result of the wind yaw is finally obtained in the output layer. Then, the loss function is calculated by comparing with the actual result, and the weight and bias are adjusted in reverse according to the gradient information of the loss function, so as to continuously optimize the prediction ability of the network, so that it can more accurately predict the probability, amplitude and time of wind yaw.

[0111] Able to handle complex nonlinear relationships: There is a complex nonlinear relationship between wind fluctuations and a variety of factors (wind speed, wind direction, line status, etc.). The neural network introduces nonlinear factors through the activation function, which can well model and predict this complex relationship. By continuously training and updating weights and biases, the neural network can automatically adapt to different data distributions and characteristics, so as to better fit the actual situation and improve the accuracy of the prediction. A fully trained neural network can make reasonable predictions for unseen data (new monitoring data) and has a certain generalization ability, which helps to give early warning of wind fluctuations and provide a scientific basis for taking prevention and control measures.

[0112] The cloud computing platform in the data collaborative processing module uses the gradient descent algorithm for model update and optimization:

[0113] When conducting more comprehensive and in-depth analysis on the cloud computing platform and using machine learning models to update and optimize the wind yaw and gallop prediction model and control strategy, optimization algorithms such as the gradient descent algorithm are used to adjust the model parameters;

[0114] For a parameter vector with parameters, such as weights and biases in a neural network, the loss function is , the formula for updating parameters is:

[0115] ;

[0116] in, , : represent the updated and current parameter vectors respectively. In a neural network, the parameter vector contains all adjustable parameters such as weights and biases. The performance of the model is optimized by continuously updating these parameters.

[0117] : Learning rate, which determines the step size of each parameter update. If the learning rate is too large, it may cause the parameters to update too quickly, miss the optimal solution, or even increase the loss function value; if the learning rate is too small, the parameter update will be very slow and the training time will be very long. Therefore, it is necessary to choose a reasonable learning rate;

[0118] : The gradient of the loss function at the current parameter vector. The gradient indicates the direction in which the loss function changes fastest at a certain point. By updating the parameters in the opposite direction of the gradient, the loss function value can be gradually reduced, thereby optimizing the model.

[0119] For the weight of a layer in a neural network (Take the update from input layer to hidden layer as an example), let the loss function be , then its gradient calculation can be expressed as:

[0120] ;

[0121] in, is the output layer outputs (such as predicted windage-related indicators), is the number of outputs of the output layer, is the hidden layer The weighted sum of the inputs of the neurons, as in the previous formula ;

[0122] : represents the partial derivative of the loss function with respect to a particular weight, which measures the effect of changing the weight on the loss function value. By calculating the partial derivatives of each weight, we can know how to adjust these weights to reduce the loss function value;

[0123] :This is a formula for the derivation of a composite function, which is used to calculate the loss function for a certain weight The partial derivative of

[0124] in, Represents the loss function for the output layer The partial derivative of the output, which reflects the impact of the change in the output result on the loss function;

[0125] Represents the output layer The output pairs of the hidden layer The partial derivative of the weighted sum of the inputs of neurons, which describes the impact of information transfer from the hidden layer to the output layer on the output result;

[0126] Represents the hidden layer The weighted sum of the inputs of neurons to a certain weight The partial derivative of Impact on the weighted sum of hidden layer inputs.

[0127] Implementation steps:

[0128] Sp1: Initialization parameters: First determine the initial parameters of the model (such as a neural network) (including all weights and biases, etc.), which can be initialized randomly;

[0129] Sp2: Calculate loss function: Calculate loss function based on current parameter values, input monitoring data, and corresponding real wind yaw and waving conditions (such as through actual observations or known label data);

[0130] Sp3: Calculate the gradient: Calculate the gradient of the loss function at the current parameter vector according to the above formula. This may involve calculating the derivative of a composite function, such as calculating the partial derivatives of each weight and bias in a neural network.

[0131] Sp4: Update parameters: Update the parameter vector according to the calculated gradient and the set learning rate according to the formula;

[0132] Sp5: Repeat Sp2-Sp4, and keep repeating the above steps until the predetermined training stop condition is reached (such as reaching a certain number of training rounds, the loss function value is less than a certain threshold, etc.).

[0133] Operation logic: First, the input monitoring data is predicted according to the current model parameters to obtain the prediction results. Then, by comparing with the actual wind yaw and flutter situation, the loss function value is calculated to measure the gap between the prediction result and the actual situation. Next, the gradient of the loss function at the current parameter vector is calculated. This gradient indicates how the parameters should be adjusted to reduce the loss function value. Finally, the parameters are updated according to the gradient and learning rate to continuously improve the prediction ability of the model, thereby more accurately optimizing the wind yaw and flutter prediction model and control strategy, and effectively optimizing the model: By continuously updating the parameters in the opposite direction of the loss function gradient, the prediction ability of the model can be gradually improved, thereby more accurately predicting the relevant situations of wind yaw and flutter, such as the probability, amplitude and time of occurrence. There is no need to manually adjust the various parameters of the model, but automatically adjust them according to the data and loss function, saving labor costs and being able to adapt to different data situations and prediction needs. By optimizing the model, it can better fit the training data, and at the same time improve the prediction ability of unseen data, that is, the generalization ability, which helps to more accurately warn and control wind yaw and flutter in practical applications.

[0134] Specific embodiment 5: Figure 1-6 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0135] In the entire device architecture, the data flow is as follows:

[0136] Data sources mainly include sensor monitoring data, line parameters and geographic meteorological information data. Among them, sensor monitoring data includes meteorological related sensors, line status related sensors, equipment status related sensors, and further includes the following contents:

[0137] Meteorological related sensors: including wind direction sensors (using three-dimensional ultrasonic wind direction measurement technology) and wind speed sensors (using the laser Doppler speed measurement principle, with a measurement accuracy of ±0.1m / s) distributed on the transmission lines. These sensors monitor the meteorological conditions around the transmission lines in real time and are an important source of data reflecting external environmental factors such as wind deflection and dancing;

[0138] Line status related sensors: such as line vibration sensors, superconducting coil temperature sensors, jet device pressure sensors, plasma generator power sensors, magnetic field sensors and stress sensors, etc. They respectively monitor the vibration of the transmission line itself, the temperature of the superconducting coil, the pressure of the jet device, the power of the plasma generator, the magnetic field changes and the stress of the line in real time, providing data support for understanding the operating status of the line and each device.

[0139] Equipment status related sensors: such as jet device status sensors, magnetic material status sensors, etc., are used to monitor the specific working status of pulse jet devices, magnetic materials and other equipment, so as to timely understand whether the equipment is operating normally.

[0140] Line parameters and geographic meteorological information: In the early preparation stage (Sp1), the relevant parameters of the transmission line are measured and recorded in detail, including line length, tower location and height, conductor model and specifications, etc. At the same time, combined with the geographical environment (topography, landform, distribution of surrounding buildings, etc.) and meteorological data (year-round wind speed, wind direction distribution, extreme weather conditions) of the line, these data provide a basic basis for subsequent assessment of the risk level of wind yaw and swing, determination of the installation location of the device, etc. Although they are not real-time dynamic data, they are used as important reference information in the relevant analysis and processing during the operation of the entire system.

[0141] The input data includes IoT monitoring network input data and edge computing node input data, and further includes the following:

[0142] IoT monitoring network input data: including real-time data collected by various sensors, such as the wind direction, wind speed, line vibration, superconducting coil temperature, jet device pressure and other data mentioned above. These data are transmitted to the corresponding local data acquisition nodes through ZigBee, LoRa wireless communication modules or optical fiber wired communication methods. The local data acquisition nodes pre-process the data according to the unified data acquisition protocol, and stipulate the data format, sampling frequency, timestamp and other information. The pre-processed data is transmitted through the TCP / IP protocol, and data encryption and error correction technology are used to ensure that the data is accurately input into the subsequent data processing links.

[0143] Edge computing node input data: some real-time data pre-processed by the local data acquisition node will be transmitted to the edge computing node. These data are mainly related to the simple data analysis tasks performed by the edge computing node, such as the pressure of the jet device, the temperature of the superconducting coil, etc., so that the edge computing node can quickly and preliminarily process the real-time collected data, including data screening, local anomaly detection, and rapid evaluation of equipment status.

[0144] Data flow and processing process: IoT monitoring network construction and data flow: First, various sensors transmit the collected real-time data to the local data acquisition node through the corresponding communication method (ZigBee, LoRa or optical fiber). The local data acquisition node pre-processes the data according to the unified data acquisition protocol, including the specification of the data format, sampling according to the set sampling frequency, and adding timestamps. Then, the pre-processed data is transmitted to the subsequent data processing link through the TCP / IP protocol. The data here will flow to the edge computing node for preliminary processing and will also be transmitted to the cloud computing platform for more comprehensive and in-depth analysis.

[0145] Data flow and processing of edge computing nodes: Receive some real-time data after pre-processing from local data acquisition nodes, such as jet device pressure, superconducting coil temperature and other related data. Inside the edge computing node, use its high-performance embedded computing device (with multi-core processor, large-capacity memory and high-speed storage device, low power consumption, high reliability and adaptability to harsh outdoor environment) to perform simple data analysis tasks on these data, including data screening, local anomaly detection, rapid evaluation of equipment status, etc. After preliminary processing, the edge computing node uploads key data (such as preliminary risk assessment results obtained through evaluation, etc.) and local decision results to the cloud computing platform so that the cloud computing platform can perform more complex model calculations and data mining.

[0146] Data flow and processing on the cloud computing platform: Receive key data uploaded by edge computing nodes, as well as more extensive meteorological data (such as more comprehensive meteorological information obtained from the meteorological department), and power grid operation data (data about the operating status of the entire power grid), and use the Kubernetes distributed computing architecture to manage and schedule resources to conduct a more comprehensive and in-depth analysis of these data, including using Spark, Flink and other technologies for data cleaning and feature extraction, removing noise, outliers and duplicate data, extracting key features such as wind speed change rate, wind direction change frequency, line vibration spectrum, and magnetic field intensity change trend as machine learning model input, updating and optimizing the wind swing prediction model and control strategy through the machine learning model, and sending collaborative control instructions to the edge computing nodes based on the evaluation results, including precise control parameters of equipment such as jet devices, superconducting coils, and magnetic material control systems, so as to achieve efficient control of the entire system, dynamically adjust the parameters of each device, and effectively suppress wind swing.

[0147] Output data, warning information output: In the big data analysis and warning model establishment link, through deep mining and analysis of massive monitoring data, a more accurate wind yaw and flutter prediction model is established using machine learning algorithms. Combined with meteorological data and transmission line operation data, the probability, amplitude and time of wind yaw and flutter are predicted in advance, and warning information is issued when it may occur. These warning information can be sent to the terminal equipment of the relevant monitoring center or operation and maintenance personnel so that prevention and control measures can be taken in time.

[0148] Control command output: After a comprehensive and in-depth analysis, the cloud computing platform sends coordinated control commands to the edge computing nodes, including precise control parameters of the jet device, superconducting coil, magnetic material control system, etc. The edge computing nodes precisely control the corresponding equipment according to these commands, realize efficient control of the entire system, and dynamically adjust the parameters of the active interference airflow system and the dynamic magnetic field suppression system to effectively suppress wind yaw.

[0149] Equipment status and line repair related output: For the line repair module, when performing repair tasks, the micro crawler robot and the flying micro robot cluster can feed back the relevant status information of the repair process (such as repair progress, whether the repair is completed, etc.) to the control center through the communication module they carry, so as to monitor the entire repair process. At the same time, when the sensor detects damage to the line or wind deflection protection device, the relevant damage information will also be fed back to the control system as output data to start the repair module for repair operations.

[0150] In summary, the data in the device architecture comes from a wide range of sources. After collection and transmission by the Internet of Things monitoring network, preliminary processing by the edge computing nodes, and comprehensive and in-depth analysis by the cloud computing platform, it finally outputs early warning information, control instructions, equipment status, and line repair-related information, etc., realizing effective monitoring, prediction, and control of wind-induced swing of high-altitude power transmission lines, thereby ensuring the stable operation of transmission lines.

[0151] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device for limiting wind deviation and dancing of high-altitude power transmission lines, characterized by: It includes line suppression module, data monitoring and processing module, data collaborative processing module and line repair module, among which: The line suppression module includes an active airflow interference module and a dynamic magnetic field suppression module. The active airflow interference module is used to disturb the airflow around the line and destroy the aerodynamic conditions of wind deviation dancing to reduce line dancing. The dynamic magnetic field suppression module is used to generate a dynamically changing magnetic field to offset the force generated by wind deviation to suppress line dancing. The data monitoring and processing module includes an Internet of Things monitoring network construction module and a big data analysis and early warning model establishment module. The Internet of Things monitoring network construction module is used to connect sensors distributed on the transmission line into a network to realize real-time data collection and transmission. The big data analysis and early warning model establishment module is used to mine and analyze monitoring data through a big data analysis platform, establish a wind yaw prediction model using a machine learning algorithm, combine meteorological data and transmission line operation data to predict the probability, amplitude and time of wind yaw, and issue early warning information when wind yaw occurs. The data collaborative processing module includes an edge computing node setting module and a cloud computing platform collaborative module. The edge computing node setting module is used to set a computing node at the edge end of the wind deviation protection device to perform preliminary processing on the real-time collected wind deviation data, including feature extraction and risk assessment. The cloud computing platform collaborative module is used to upload the key data processed by the edge computing node to the cloud computing platform, perform model calculation and data mining in the cloud, and issue control instructions to the edge computing node according to the evaluation results. The line repair module is used to repair the line or the wind deflection protection device when the sensor detects that there is damage. The line repair module includes a micro crawling robot and a flying micro robot cluster. The micro crawling robot crawls on the surface of the transmission line to inspect and repair the damage. The flying micro robot cluster obtains energy through electromagnetic induction or wireless charging technology, repairs the damaged position when repair is needed, and cooperates with the communication module; The active airflow interference module includes a pulse jet device and a plasma flow generator. The pulse jet device is connected to a wind direction sensor and a wind speed sensor to spray high-speed gas into the airflow according to the jet angle and frequency under different wind speed and wind direction conditions. The plasma flow generator excites gas to generate plasma through a high-frequency power supply, and adjusts the power and direction of the plasma according to environmental conditions and line conditions to change the flow direction and pressure distribution of the wind around the transmission line.

2. The device for limiting windage swing of high-altitude power transmission lines according to claim 1 is characterized in that: The dynamic magnetic field suppression module includes a superconducting coil magnetic field control module and a magnetic material intelligent distribution module. The superconducting coil magnetic field control module controls the current in the superconducting coil to generate a dynamically changing magnetic field by installing a superconducting coil near the line, and adjusts the magnetic field strength and direction according to the direction and amplitude of the dancing. The magnetic material intelligent distribution module coats the intelligent magnetic material on the surface of the transmission line or embeds it into the equipment casing, adjusts the magnetism of the magnetic material according to the external magnetic field and the line stress conditions, and changes the magnetic field environment around the line to adjust the line stress state in the wind.

3. The device for limiting windage swing of high-altitude power transmission lines according to claim 2 is characterized in that: The superconducting coil is installed on an insulating bracket around the transmission line, connected to a superconducting current controller, and equipped with a cryogenic cooling system.

4. The device for limiting windage swing of high-altitude power transmission lines according to claim 2 is characterized in that: The smart magnetic material is made into a film or particles, and the control system of the smart magnetic material is connected to a magnetic field sensor and a stress sensor installed on the transmission line.

5. The device for limiting windage swing of high-altitude power transmission lines according to claim 1 is characterized in that: The sensors of the Internet of Things monitoring network building module include a wind direction sensor, a wind speed sensor, a line vibration sensor, a superconducting coil current sensor, a jet device state sensor, and a magnetic material state sensor.

6. The device for limiting windage swing of high-altitude power transmission lines according to claim 1 is characterized in that: The big data analysis and early warning model establishment module adopts the distributed storage system HDFS to store data in the cloud data center, classifies and stores data according to time series, sensor type, and line location dimensions, establishes a data index and metadata management mechanism, and performs data cleaning and feature extraction to remove noise, outliers, and duplicate data, and extracts key features such as wind speed change rate, wind direction change frequency, line vibration spectrum, and magnetic field intensity change trend as input to the machine learning model.

7. The device for limiting windage swing of high-altitude power transmission lines according to claim 1 is characterized in that: The edge computing nodes of the data collaborative processing module use embedded computing devices to perform data screening, local anomaly detection, and data analysis tasks for equipment status evaluation locally, monitor and make preliminary adjustments to the pressure of the jet device and the temperature of the superconducting coil, and upload the data and local decision results to the cloud computing platform; After receiving the data from the edge computing nodes, the cloud computing platform combines meteorological data and power grid operation data for analysis, uses machine learning models to update and optimize the wind yaw and swing prediction model and control strategy, and sends collaborative control instructions for equipment control parameters to the edge computing nodes.

8. The method for using the device for limiting wind deviation and swaying of high-altitude power transmission lines according to claim 1 is characterized in that: Here are the steps: Sp1: Measure transmission line parameters and assess wind yaw risk in combination with geographic and meteorological data to determine the number, type and location of installations; Sp2: Install the pulse jet device, debug and check the jet pressure, frequency and angle adjustment functions; install the plasma flow generator, and test its plasma generation and power regulation functions; Sp3: Install superconducting coils and test magnetic field changes; connect sensors and control systems with intelligent magnetic materials, calibrate sensors and test control system functions; Sp4: Install various sensors on the transmission line and connect them to local data acquisition nodes, and configure the data acquisition protocol, sampling frequency and communication parameters of the acquisition nodes; Sp5: Install edge computing nodes in towers or substations, deploy cloud computing platforms in the cloud, and establish data transmission channels; Sp6: Place the micro crawler robot at the beginning of the transmission line or at a designated placement point; Assemble and debug a swarm of flying micro-robots, and set up take-off and landing platforms around power transmission lines; Sp7: After the system is running, the Internet of Things continuously collects data and transmits it to the edge computing nodes and cloud computing platform. The edge computing nodes perform preliminary analysis in real time, and the cloud computing platform globally analyzes and updates the model, dynamically adjusting the airflow and magnetic field system parameters accordingly, and regularly checking and repairing the system status.

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