A power transmission line early warning method, device, equipment and medium considering the superposition of wind and ice coupling factors

Abstract

The application discloses a power transmission line early warning method and device considering wind-ice coupling factors, an equipment and a medium, relates to the technical field of overhead line risk prediction and early warning, better adapts to complex and changeable meteorological environment, has better accuracy and timeliness of early warning, and improves the disaster prevention and reduction ability of power grid in response to ice shedding jump disasters. The method comprises the following steps: when it is detected that the power transmission line starts to appear icing conditions, collecting real-time meteorological data of the environment where the power transmission line is located and inputting the real-time meteorological data into an icing thickness prediction model to predict the icing thickness, and obtaining the future icing thickness change trend; the ice shedding jump numerical simulation of the power transmission line is carried out by using a plurality of preset working condition information, and an icing thickness threshold result set is generated; based on the real-time meteorological data, a target icing thickness threshold is extracted from the icing thickness threshold result set, the future icing thickness change trend is evaluated by using the target icing thickness threshold, and the ice shedding jump risk warning of the power transmission line is carried out according to the obtained evaluation result.

Description

Technical Field

[0001] This application relates to the field of risk prediction and early warning technology for overhead lines, and in particular to a method, device, equipment and medium for early warning of transmission lines that considers the superposition of wind and ice coupling factors. Background Technology

[0002] In power systems, transmission lines serve as crucial channels for power transmission, and their safe and stable operation directly impacts the reliability of the entire power grid. However, when transmission lines traverse areas with complex terrain and variable weather, the low temperatures and high humidity of winter can easily cause ice to form on the surface of the conductors. This not only significantly increases the vertical load on the conductors, leading to mechanical and electrical problems such as increased sag and insufficient clearance, but more seriously, when weather conditions change due to rising temperatures, increased wind speeds, or enhanced sunlight, the ice may suddenly detach, triggering a violent "ice-shedding jump" phenomenon. This can then lead to serious accidents such as flashover, short circuits, line breaks, and even tower collapses, posing a significant threat to the safe and stable operation of the power grid.

[0003] To address the risks posed by ice-induced jumps in transmission lines, relevant technologies primarily employ finite element simulation or a single threshold-based method based on ice thickness for early warning. Finite element simulation establishes a mathematical model of the conductor icing and de-icing process, simulating the dynamic response of the conductor under different operating conditions to predict the height and range of the ice-induced jump. In contrast, the ice thickness-based early warning method sets a fixed ice thickness threshold; when the ice thickness on the conductor exceeds this threshold, an early warning mechanism is triggered.

[0004] However, the applicant recognizes that the relevant technology has at least the following technical problems in its implementation: Treating wind speed and ice thickness as independent parameters can easily lead to deviations in the setting of warning thresholds, making it impossible to accurately reflect the risk level under actual operating conditions. Furthermore, relying on fixed ice thickness thresholds for judgment in terms of warning mechanisms makes it difficult to adapt to complex and ever-changing meteorological environments, resulting in insufficient accuracy and timeliness of warnings and failing to effectively improve the power grid's disaster prevention and mitigation capabilities. Summary of the Invention

[0005] In view of this, this application provides a transmission line early warning method, device, equipment and medium that considers the superposition of wind and ice coupling factors. The main purpose is to solve the problems that the current method of treating wind speed and ice thickness as independent parameters is prone to deviations in the setting of early warning thresholds, which cannot accurately reflect the risk level under actual operating conditions. In addition, the early warning mechanism relies on a fixed ice thickness threshold for judgment, which is difficult to adapt to complex and changeable meteorological environments, resulting in insufficient accuracy and timeliness of early warnings, and failing to effectively improve the disaster prevention and mitigation capabilities of the power grid.

[0006] According to the first aspect of this application, a transmission line early warning method considering the superposition of wind and ice coupling factors is provided, the method comprising: When icing conditions are detected on the transmission line, real-time meteorological data of the environment where the transmission line is located is collected, and the real-time meteorological data is input into a pre-trained icing thickness prediction model to predict the icing thickness and obtain the future trend of icing thickness change. Numerical simulation of de-icing jump of transmission lines is performed using multiple preset operating condition information, and an ice thickness threshold result set is generated based on the simulation results. Each preset operating condition information includes a preset ice thickness level and a preset wind speed level, and the ice thickness threshold result set includes multiple ice thickness thresholds and each ice thickness threshold corresponds to a preset wind speed level. Based on the real-time meteorological data, a target icing thickness threshold is extracted from the icing thickness threshold result set. The target icing thickness threshold is used to evaluate the future icing thickness change trend, and the risk warning of de-icing jump of transmission lines is given based on the evaluation results.

[0007] According to a second aspect of this application, a transmission line early warning device considering the superposition of wind and ice coupling factors is provided, the device comprising: The prediction module is used to collect real-time meteorological data of the environment where the transmission line is located when it is detected that the transmission line begins to be covered with ice. The real-time meteorological data is then input into a pre-trained ice thickness prediction model to predict the ice thickness and obtain the future trend of ice thickness changes. The simulation module is used to perform numerical simulation of de-icing jump of transmission lines using multiple preset operating condition information, and to generate an ice thickness threshold result set based on the simulation results. Each preset operating condition information includes a preset ice thickness level and a preset wind speed level. The ice thickness threshold result set includes multiple ice thickness thresholds and each ice thickness threshold corresponds to a preset wind speed level. The early warning module is used to extract a target icing thickness threshold from the icing thickness threshold result set based on the real-time meteorological data, use the target icing thickness threshold to evaluate the future icing thickness change trend, and issue an early warning of the risk of de-icing jump of the transmission line based on the evaluation results.

[0008] According to a third aspect of this application, an apparatus is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described in any of the first aspects above.

[0009] According to a fourth aspect of this application, a medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0010] By utilizing the above technical solutions, this application provides a transmission line early warning method, device, equipment, and medium that considers the superposition of wind and ice coupling factors. Before the ice thickness of the transmission line reaches the risk threshold, this application achieves accurate early warning of the risk of de-icing jump by comprehensively analyzing the coupling effect of wind speed and ice thickness. Furthermore, it dynamically adjusts the ice thickness threshold for different line structure parameters and meteorological conditions, and maintains the accuracy of risk assessment by combining it with an internally established hierarchical assessment system, preventing false alarms or missed alarms caused by judgments based on a single factor. It can better adapt to complex and changeable meteorological environments, and the accuracy and timeliness of the early warning are good, thereby improving the power grid's ability to prevent and mitigate de-icing jump disasters.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This paper presents a schematic flowchart of a transmission line early warning method considering the superposition of wind and ice coupling factors according to an embodiment of this application. Figure 2 This illustration shows a schematic diagram of the correlation between the maximum ice-removal jump height and the ice thickness under different wind speed conditions provided in the embodiments of this application; Figure 3 This illustration shows a structural schematic diagram of a transmission line early warning device considering the superposition of wind and ice coupling factors provided in an embodiment of this application; Figure 4 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] This application provides a transmission line early warning method that considers the superposition of wind and ice coupling factors, such as... Figure 1 As shown, the method includes: S10: When icing conditions are detected on the transmission line, real-time meteorological data of the environment where the transmission line is located is collected, and the real-time meteorological data is input into the pre-trained icing thickness prediction model to predict the icing thickness and obtain the future trend of icing thickness change.

[0015] The technical solution of this application embodiment can be applied to an early warning system. When icing conditions are detected on a transmission line, the early warning system collects real-time meteorological data of the environment where the transmission line is located. This data includes, but is not limited to, key parameters such as temperature, wind speed, and humidity. Subsequently, this real-time meteorological data is input into a pre-trained LSTM (Long Short-Term Memory) icing thickness prediction model. The LSTM model is a deep learning model suitable for processing time-series data and can capture long-term dependencies in the data, thereby achieving accurate prediction of future icing thickness trends. Through the processing of the LSTM model, the early warning system can generate future icing thickness trends for transmission lines over a period of time, providing data support for subsequent risk assessment.

[0016] In this way, predicting the trend of ice thickness change using LSTM models allows for the early assessment of potential risks in the early stages of icing, avoiding the limitations of traditional methods that rely on a single ice thickness threshold or finite element simulation, thus improving the lead time and accuracy of early warnings. For example, suppose in a 500kV transmission line area, the early warning system detects that the temperature has dropped below zero and the humidity is high, determining that icing conditions have occurred. The system then collects real-time meteorological data of -5℃ temperature, 8m / s wind speed, and 90% humidity, and inputs this data into the LSTM model. The model prediction results show that the ice thickness will increase at a rate of 0.2mm per hour over the next 24 hours, providing important early warning information for maintenance personnel.

[0017] In step S10, when icing conditions are detected on the transmission line, real-time meteorological data of the environment where the transmission line is located is collected. The real-time meteorological data is then input into a pre-trained icing thickness prediction model to predict the icing thickness and obtain the future trend of icing thickness changes. This includes the following steps: S11: Continuously monitor the line parameters of the transmission line. When the monitored line parameters indicate that the ambient temperature and ambient humidity have reached the temperature threshold and humidity threshold respectively, it is determined that the transmission line has started to experience icing conditions.

[0018] In this embodiment, the early warning system continuously monitors the line parameters of the transmission line, including but not limited to ambient temperature and ambient humidity. When the monitored ambient temperature drops to a preset temperature threshold (e.g., below 0°C) and the ambient humidity reaches or exceeds a preset humidity threshold (e.g., above 85%), the early warning system determines that the transmission line has begun to experience icing conditions. This determination is based on the physical conditions for icing formation, namely, a low-temperature and high-humidity environment is a necessary condition for icing on the conductor surface.

[0019] In this way, by precisely setting temperature and humidity thresholds, the early warning system can accurately detect icing conditions at the initial stage of icing, providing timely information support for subsequent risk warnings and avoiding the lag of issuing warnings only when icing has developed to a severe stage. For example, suppose that the temperature in a certain region drops sharply in winter, and the early warning system detects that the ambient temperature in the area where a 500kV transmission line is located drops to -3℃, while the ambient humidity rises to 90%. Based on the preset threshold conditions, the early warning system immediately determines that icing conditions have begun to appear on the line and initiates the subsequent data collection and early warning process.

[0020] S12: Real-time meteorological data is collected on the environment where the transmission line is located to obtain real-time meteorological data.

[0021] In this embodiment, the early warning system deploys various types of sensors at key locations along the transmission line, including but not limited to temperature sensors, humidity sensors, wind speed sensors, and wind direction sensors. These sensors can capture raw meteorological data of the environment where the transmission line is located in real time. Specifically, the temperature sensor is used to accurately measure the ambient temperature, typically in degrees Celsius (°C); the humidity sensor measures the relative humidity, expressed as a percentage (%); the wind speed sensor measures the wind speed, in meters per second (m / s); and the wind direction sensor indicates the direction of the wind.

[0022] Meanwhile, the early warning system has a built-in Geographic Information System (GIS) module, which stores detailed geographic feature data of the area where the transmission line is located, including but not limited to altitude information (representing the height of a point on the line relative to sea level, in meters), slope aspect information (representing the direction of the terrain slope, such as east slope, west slope, etc.), and terrain information (such as plains, mountains, hills, etc.). Geographic feature data is crucial for accurately correcting meteorological data, because geographic features can significantly affect local climate conditions.

[0023] After collecting the raw meteorological data, the early warning system corrects the data by referring to geographical feature data. For example, considering the impact of altitude on temperature, the system adjusts the temperature data for high-altitude areas accordingly, based on the principle that temperature decreases with increasing altitude. For humidity data, the system considers the influence of terrain on humidity distribution; for instance, humidity may be higher in valleys and lower in open areas. Wind speed data is corrected based on slope aspect and terrain, as mountains may block or accelerate wind flow, thus affecting local wind speed. This correction process can employ specific algorithmic models built upon meteorological principles and field observation data, accurately quantifying the impact of geographical features on meteorological elements. In this embodiment, the corrected raw meteorological data is considered more accurate and representative real-time meteorological data, which will be directly used in subsequent icing thickness prediction models to improve prediction accuracy and reliability.

[0024] In this way, by combining raw meteorological data collected by sensors with geographical feature data to correct the meteorological data, the early warning system can eliminate or reduce the influence of geographical factors on the meteorological data, thereby obtaining more accurate and representative real-time meteorological data. This is crucial for subsequent icing thickness prediction, as accurate meteorological data is the foundation for the effective operation of the prediction model. Through data correction, the early warning system can significantly improve prediction accuracy, providing maintenance personnel with more reliable early warning information, and thus enhancing the power grid's ability to cope with de-icing jump disasters. For example, in a 500kV transmission line case, the early warning system collected raw meteorological data through sensors deployed on the tower: temperature -2℃, humidity 88%, and wind speed 6m / s. At the same time, it was found that the tower's location had an altitude of 800 meters, a north-facing slope, and mountainous terrain. The early warning system corrected the raw meteorological data based on these geographical feature data: considering the altitude effect, the temperature was lowered to -2.5℃; considering that the mountainous terrain might increase local humidity, the humidity was increased to 90%; and considering that the north-facing slope might block some wind flow, the wind speed was slightly adjusted to 5.8m / s. The revised real-time meteorological data more accurately reflects the actual situation at the location of the line, providing a reliable data basis for subsequent icing thickness prediction.

[0025] S13: Obtain the pre-trained icing thickness prediction model, input real-time meteorological data into the icing thickness prediction model, predict the icing thickness of the transmission line in multiple future time periods based on the icing thickness prediction model, and organize the predicted icing thicknesses according to the time sequence of multiple future time periods to obtain the future icing thickness change trend.

[0026] In this embodiment, the early warning system acquires a pre-trained icing thickness prediction model. This model, built based on deep learning algorithms (such as LSTM), can capture the complex nonlinear relationship between meteorological data and icing thickness. Specifically, in constructing the icing thickness prediction model, historical meteorological data (including temperature, humidity, wind speed, etc.) and corresponding observed icing thickness values ​​for transmission lines are first collected to form a training dataset. Subsequently, the data undergoes preprocessing, including missing value imputation, outlier handling, and data standardization, to ensure data quality. Next, an LSTM network structure is designed, including an input layer, hidden layers (containing LSTM units), and an output layer. The LSTM units in the hidden layers effectively manage the information flow through a gating mechanism, solving the gradient vanishing or exploding problem in traditional RNNs (Recurrent Neural Networks). During the model training phase, the LSTM model is iteratively trained using the training dataset. The network weights are adjusted using the backpropagation algorithm to minimize the error between the predicted and actual observed values. During training, methods such as cross-validation are used to prevent overfitting and ensure the model's generalization ability. Ultimately, a well-trained LSTM model can capture the complex nonlinear relationship between meteorological conditions and icing thickness, enabling accurate prediction of icing thickness on transmission lines in the future.

[0027] The early warning system will input real-time meteorological data into the icing thickness prediction model. Based on the input data, the model will predict the icing thickness of the transmission line in multiple future time periods. The prediction results will be organized in the time sequence of multiple future time periods to obtain the future icing thickness change trend, which can be in the form of a graph or a data table.

[0028] In this way, by utilizing deep learning models for icing thickness prediction, the early warning system can achieve high-precision predictions in complex and ever-changing meteorological environments, providing maintenance personnel with sufficient early warning time and decision-making basis. Simultaneously, the time-series characteristics of the prediction results allow maintenance personnel to clearly understand the development trend of icing thickness, further optimizing maintenance strategies. For example, continuing with the 500kV transmission line case mentioned above, the early warning system inputs the corrected real-time meteorological data into the pre-trained LSTM icing thickness prediction model. The model prediction results show that the icing thickness of the line will increase at a rate of 0.15 mm per hour over the next 24 hours. Based on this prediction, the early warning system generates a future icing thickness change trend.

[0029] S20: Perform numerical simulation of de-icing jump of transmission lines using multiple preset operating condition information, and generate a set of icing thickness threshold results based on the simulation results.

[0030] In this embodiment, the early warning system utilizes multiple preset operating condition information to perform numerical simulations of transmission line de-icing jumps. Each preset operating condition information includes a preset icing thickness level (e.g., 5mm, 10mm, 15mm, etc.) and a preset wind speed level (e.g., 0m / s, 10m / s, 20m / s, etc.). Through numerical simulation, the early warning system can calculate the maximum height of the transmission line de-icing jump under different combinations of icing thickness and wind speed, thereby generating an icing thickness threshold result set. This set contains multiple icing thickness thresholds, each corresponding to a preset wind speed level, providing a grading standard for subsequent risk assessment.

[0031] In this way, by generating a set of ice thickness threshold results through numerical simulation, a quantitative assessment of the risk of ice break jump under different meteorological conditions is achieved, providing a scientific basis for dynamically adjusting the warning threshold and improving the pertinence and effectiveness of the warning. Continuing with the example of the aforementioned 500kV transmission line, the warning system preset three ice thickness levels (10mm, 15mm, 20mm) and three wind speed levels (5m / s, 10m / s, 15m / s) for numerical simulation. The simulation results show that when the ice thickness is 15mm and the wind speed is 10m / s, the maximum height of the ice break jump reaches the critical value of the line's electrical safety clearance. Therefore, the warning system uses the ice thickness of 15mm under this combination as the warning threshold under this wind speed condition and includes it in the ice thickness threshold result set.

[0032] In step S20, which involves performing numerical simulations of de-icing jumps of transmission lines using multiple preset operating condition information, and generating an ice thickness threshold result set based on the simulation results, the steps include: S21: Obtain multiple preset icing thickness levels and multiple preset wind speed levels.

[0033] In this embodiment, the early warning system acquires multiple preset icing thickness levels and multiple preset wind speed levels. The preset icing thickness levels are based on the icing conditions that the transmission line may encounter, dividing the icing thickness into several intervals, such as 5mm, 10mm, 15mm, 20mm, 30mm, etc., with each interval representing an icing thickness level. The preset wind speed levels are based on meteorological conditions, dividing wind speeds into intervals, such as 0m / s, 5m / s, 10m / s, 15m / s, 20m / s, etc., with each value representing a wind speed level. These preset levels provide basic data for subsequent pairwise combinations.

[0034] In practical applications, the early warning system can analyze historical meteorological data and transmission line icing records, combining meteorological principles and engineering experience to scientifically and rationally set the classification standards for icing thickness and wind speed. This ensures the comprehensiveness and accuracy of subsequent simulations, covering various meteorological conditions that transmission lines may encounter. For example, in a certain region, based on historical meteorological data, the early warning system sets icing thickness levels at 5mm, 10mm, 15mm, 20mm, and 30mm, and wind speed levels at 0m / s, 10m / s, and 20m / s. This setting considers both common icing conditions and extreme weather conditions, providing rich basic data for subsequent simulations.

[0035] S22: Combine multiple preset icing thickness levels and multiple preset wind speed levels in pairs to obtain multiple data sets, and use each data set as a preset operating condition information to obtain multiple preset operating condition information.

[0036] In this embodiment, after acquiring preset icing thickness levels and wind speed levels, the early warning system combines these levels in pairs to form multiple data groups. Each data group contains a preset icing thickness level and a preset wind speed level, representing a specific combination of meteorological conditions. The early warning system treats each data group as a preset operating condition information to obtain multiple preset operating condition information. For example, when there are 5 preset icing thickness levels and 3 preset wind speed levels, 15 different data groups will be generated.

[0037] In this way, by combining pairs of data, the early warning system can comprehensively consider the risk of ice break-off jumps under different icing thicknesses and wind speeds, providing rich operational information for subsequent numerical simulations. Continuing the previous example, the early warning system combines 5 preset icing thickness levels and 3 preset wind speed levels to generate 15 data sets, such as (5mm, 0m / s), (5mm, 10m / s), (5mm, 20m / s), and (10mm, 0m / s). Each data set represents a specific meteorological condition and serves as a preset operational condition information.

[0038] S23: Collect the line structure parameters of the transmission line, use numerical simulation tools to simulate the de-icing jump process of the line under each preset working condition as indicated by the line structure parameters, and record the maximum height of the line's vertical vibration under each preset working condition as the maximum de-icing jump height, so as to obtain multiple maximum de-icing jump heights corresponding to multiple preset working conditions as simulation results.

[0039] In this embodiment, after obtaining multiple preset operating condition information, the early warning system needs to collect the line structure parameters of the transmission line, such as span, elevation difference, and altitude, and use numerical simulation tools to simulate the de-icing jump process under each preset operating condition. Specifically, the early warning system first collects the line structure parameters of the transmission line through sensors or manual methods; then, using finite element analysis software or a dedicated de-icing jump simulation tool, it inputs the line structure parameters and preset operating condition information to simulate the de-icing jump process of the transmission line under specific meteorological conditions. During the simulation, the early warning system records the maximum height of the vertical vibration of the line, i.e., the maximum de-icing jump height. Specifically, the correlation between the maximum de-icing jump height and the ice thickness under different wind speed conditions during the simulation is as follows: Figure 2 As shown, from Figure 2 It can be seen that as the icing thickness gradually increases from 5mm to 30mm, the de-icing jump height under various wind speed conditions shows a continuous upward trend, and the amplification effect of wind speed on the jump height is significant: under calm wind conditions (v=0m / s), the jump height increases relatively slowly with icing thickness, reaching only about 6 meters with 30mm of icing; when the wind speed increases to 10m / s, the jump height under the same icing thickness increases to about 21 meters; while when the wind speed reaches 20m / s, the jump height increases sharply to nearly 28 meters with 30mm of icing. The data shows that there is a coupling enhancement effect between wind speed and icing thickness—the higher the wind speed, the stronger the driving effect of increased icing thickness on jump height, especially when the icing is thicker, the increase in jump height under high wind speed conditions is far greater than that under low wind speed scenarios. This nonlinear growth characteristic reveals the amplification effect of the combined effect of wind and ice on the risk of line de-icing jump, providing a quantitative basis for dynamically adjusting threshold parameters in power grid disaster prevention and early warning.

[0040] In addition, in practical applications, simulation results can be recorded in tabular form, as shown in Table 1 below: Table 1

[0041] Table 1 records the parameter settings and corresponding de-icing rate results for transmission line de-icing jump simulations under 15 different operating conditions. Each operating condition was formed by combining preset icing thickness levels (range 5mm to 30mm) and preset wind speed levels (range 0m / s to 20m / s). Data shows that in all combinations of scenarios with icing thickness increasing in 5mm increments and wind speed varying in 10m / s intervals, the de-icing rate for each operating condition remained consistently at 100%. This result indicates that within the range covered by the simulation parameters, regardless of changes in icing thickness and wind speed, the line de-icing process exhibits complete de-icing, without any abnormalities of partial or no de-icing, providing fundamental data support for subsequent threshold analysis.

[0042] In this way, through numerical simulation, the early warning system can intuitively display the de-icing and jumping process of transmission lines under different meteorological conditions, providing important data support for subsequent risk assessment and early warning. For example, in a case of a 500kV transmission line, the early warning system collected parameters such as the span, elevation difference, and altitude of the line and input them into the numerical simulation tool. For the 15 data sets generated previously, the early warning system performed de-icing and jumping simulations and recorded the maximum de-icing and jumping height under each operating condition.

[0043] S24: Determine the preset safe operation standards, analyze the simulation results based on the preset safe operation standards, determine the icing thickness threshold corresponding to each preset wind speed level, and integrate the icing thickness thresholds corresponding to each preset wind speed level to obtain the icing thickness threshold result set.

[0044] In this embodiment of the application, after obtaining the simulation results, the early warning system will determine the preset safe operation standard, analyze the simulation results in combination with the preset safe operation standard, determine the icing thickness threshold corresponding to each preset wind speed level, and integrate the icing thickness thresholds corresponding to each preset wind speed level to generate a scientific and reasonable icing thickness threshold result set.

[0045] Specifically, firstly, the early warning system will determine the maximum allowable height for ice-breaking jumps based on the design specifications, safety requirements, and historical operating experience of the transmission line. For example, for a certain 500kV transmission line, a safety benchmark of 2 meters may be set to ensure that the line can still maintain safe operation during ice-breaking jumps and avoid accidents such as flashover and line breakage.

[0046] Subsequently, for each preset wind speed level, the early warning system identifies multiple target preset operating condition information items, including the preset wind speed level, from a pool of preset operating condition information. These target preset operating condition information items all focus on a specific preset wind speed level. Based on this, the early warning system extracts the target maximum de-icing jump height corresponding to each target preset operating condition information item from the simulation results, thus obtaining multiple target maximum de-icing jump heights corresponding to multiple target preset operating condition information items. The early warning system compares each target maximum de-icing jump height with the de-icing jump height threshold indicated by the preset safe operation standard, to determine at least one specified maximum de-icing jump height that reaches the de-icing jump height threshold from the multiple target maximum de-icing jump heights. The early warning system continues to query the target preset icing thickness level included in the preset operating condition information corresponding to each specified maximum de-icing jump height in the simulation results, to obtain at least one target preset icing thickness level. In at least one target preset icing thickness level, the early warning system will determine the lowest target preset icing thickness level and use the icing thickness indicated by the lowest target preset icing thickness level as the icing thickness threshold corresponding to the preset wind speed level, so as to ensure that when the icing thickness reaches or exceeds this threshold, the icing jump height may exceed the safety standard and pose a potential threat to line safety.

[0047] Ultimately, the early warning system integrates the icing thickness thresholds determined under all preset wind speed levels, constructing a complete and systematic icing thickness threshold result set. This result set is indexed by wind speed level, with each index clearly labeled with the corresponding icing thickness threshold, providing an intuitive and easy-to-use reference framework for subsequent early warning systems. This process not only demonstrates scientific rationality, avoiding the limitations of single-factor judgment by comprehensively considering safety standards and simulation results; it also exhibits dynamic adaptability, flexibly adjusting the icing thickness thresholds according to changes in the meteorological environment, improving the accuracy and timeliness of early warnings; furthermore, the presentation of the result set is concise and clear, facilitating quick understanding and application by maintenance personnel, providing solid technical support for the power grid to cope with de-icing jump disasters. For example, if the maximum allowable height for ice removal jumps is set to 2 meters, for a wind speed of 10 m / s, the early warning system will filter all data with a wind speed of 10 m / s in the simulation results and find the data points where the maximum ice removal jump height reaches or exceeds 2 meters. Suppose it is found that when the ice thickness is 15 mm, the maximum ice removal jump height in some simulation data exceeds 2 meters, while when the ice thickness is 10 mm, none of them exceed this limit. The early warning system will then determine 15 mm as the ice thickness threshold for a wind speed of 10 m / s. Similarly, the early warning system will determine the ice thickness threshold for other wind speed levels one by one and finally integrate them to form an ice thickness threshold result set.

[0048] S30: Based on real-time meteorological data, extract the target icing thickness threshold from the icing thickness threshold result set, use the target icing thickness threshold to assess the future trend of icing thickness change, and provide early warning of the risk of de-icing jump of transmission lines based on the assessment results.

[0049] In this embodiment, based on real-time meteorological data, the early warning system extracts a target icing thickness threshold that matches the current wind speed level from the icing thickness threshold result set. Subsequently, this target icing thickness threshold is used to evaluate the future icing thickness change trend predicted by the LSTM model. Specifically, if the prediction results show that the icing thickness at a future time will exceed the target icing thickness threshold, and the overall risk level reaches a preset "high" or "extremely high" level, the early warning system automatically triggers a de-icing jump risk warning, sending warning information to maintenance personnel, including the risk level, expected occurrence time, and suggested handling measures.

[0050] In this way, by comprehensively analyzing the coupling effect of wind speed and icing thickness, and combining it with the internally established hierarchical assessment system, the early warning system can achieve accurate early warning of the risk of de-icing jump. Simultaneously, by dynamically adjusting the icing thickness threshold for different line structure parameters and meteorological conditions, the system maintains the accuracy of risk assessment, effectively preventing false alarms or missed alarms caused by a single factor, and improving the power grid's ability to prevent and mitigate de-icing jump disasters. In the aforementioned 500kV transmission line case, assuming real-time meteorological data shows a wind speed of 10m / s, the early warning system extracts the corresponding target icing thickness threshold of 15mm from the icing thickness threshold results set. Meanwhile, the LSTM model prediction results show that the icing thickness will reach 16mm within the next 12 hours. After comprehensive assessment, the early warning system considers there to be a high risk of de-icing jump and immediately sends a "high" risk level warning to the maintenance personnel, recommending increased inspection efforts and preparation of emergency resources. This allows the maintenance personnel to deploy inspection forces in advance based on the warning information, successfully preventing a potential de-icing jump accident.

[0051] In step S30, which involves extracting the target icing thickness threshold from the icing thickness threshold result set based on real-time meteorological data, the following steps are included: S31: Determine the real-time wind speed level based on the wind speed information in the real-time meteorological data.

[0052] In this embodiment, the early warning system determines the real-time wind speed level based on the wind speed information in the real-time meteorological data, ensuring that the corresponding icing thickness threshold can be dynamically selected according to the actual wind speed conditions, flexibly responding to icing conditions under different wind speed conditions, avoiding misjudgments caused by fixed thresholds, and improving the accuracy and adaptability of the early warning.

[0053] For example, assuming real-time weather data shows that the current wind speed is 12 m / s, according to the preset wind speed level classification standard, the early warning system determines that the current wind speed belongs to the transition range from medium to high wind speed, but is closer to the lower limit of the high wind speed level. Therefore, the early warning system classifies it as the starting range of the high wind speed level (such as the part close to 10 m / s within 10 m / s to 15 m / s, the actual classification may be more refined) for subsequent processing.

[0054] S32: Query the specified preset wind speed level that the real-time wind speed level matches in the ice thickness threshold result set, and extract the ice thickness threshold corresponding to the specified preset wind speed level in the ice thickness threshold result set as the target ice thickness threshold.

[0055] In this embodiment, as described above, the icing thickness threshold result set is generated in advance through simulation analysis of de-icing jumps under different wind speed and icing thickness combinations. It includes the corresponding icing thickness thresholds for each wind speed level. The early warning system queries the icing thickness threshold result set based on the real-time wind speed level to find a specified preset wind speed level entry that matches the real-time wind speed level. Subsequently, the early warning system extracts the icing thickness threshold corresponding to the specified preset wind speed level as the target icing thickness threshold under the current meteorological conditions. This ensures that the early warning threshold for icing thickness can be dynamically adjusted according to the actual wind speed conditions, achieving accurate early warning under the wind-ice coupling effect. By combining the coupling relationship between wind speed and icing thickness, the system's adaptability to complex meteorological environments is improved, effectively preventing false alarms or missed alarms caused by a single factor, and enhancing the accuracy and reliability of the early warning.

[0056] Continuing with the previous example, suppose the early warning system finds an ice thickness threshold of 18mm for a high wind speed level (e.g., the range of 10m / s to 15m / s, specifically the preset level closest to 10m / s) in the ice thickness threshold result set. This means that under the current wind speed conditions, if the predicted or measured ice thickness reaches or exceeds 18mm, the early warning system will trigger an early warning of the risk of ice shedding and jumping, prompting maintenance personnel to take corresponding measures to effectively prevent potential safety hazards.

[0057] In step S30, which involves assessing the future trend of icing thickness using the target icing thickness threshold and issuing a warning for the risk of de-icing jumps in transmission lines based on the assessment results, the steps include: S33: Compare the predicted icing thickness corresponding to each future time period indicated by the future icing thickness change trend with the target icing thickness threshold, and determine the comprehensive risk level of the transmission line based on the number of future time periods in which the predicted icing thickness indicated by the future icing thickness change trend exceeds the target icing thickness threshold.

[0058] In this embodiment of the application, the early warning system will compare the predicted ice thickness corresponding to each future time period indicated by the future ice thickness change trend with the target ice thickness threshold one by one, and count the number of time periods in which the predicted ice thickness exceeds the target threshold. This number serves as an important indicator for assessing the risk of ice-breaking jumps.

[0059] The early warning system classifies the comprehensive risk level of transmission lines into four levels: low, medium, high, and extremely high, based on the number of times the line exceeds the specified time period. This enables a quantitative assessment of the risk and ensures that the system can adjust the risk level in a timely manner according to the dynamic changes in ice thickness. This improves the accuracy and foresight of the early warning system, allows for a more precise judgment of the possibility of ice breakage and jumping, and provides a scientific basis for subsequent early warning and response.

[0060] For example, suppose the system predicts that icing thickness will exceed the target threshold in three time periods within the next 24 hours, and the total predicted time periods are 12. This represents 25% of the time periods exceeding the target threshold. According to preset rules, the early warning system might classify this situation as a "medium" risk level, prompting maintenance personnel to pay attention but not necessarily to take immediate emergency measures. As another example, if the percentage of periods exceeding the target threshold is 80% on the second day, the early warning system might classify this situation as a "high" risk level, prompting maintenance personnel to pay close attention.

[0061] S34: Determine the comprehensive risk level threshold. If the comprehensive risk level reaches the comprehensive risk level threshold, construct the expected occurrence time by using the future period of time when the predicted icing thickness exceeds the target icing thickness threshold, which is indicated by the future trend of icing thickness change.

[0062] In this embodiment, the early warning system pre-sets a comprehensive risk level threshold, which is determined based on the requirements for safe operation of the power grid and historical experience data, and is used to distinguish different levels of risk response measures. When the comprehensive risk level reaches or exceeds the preset threshold, the early warning system enters the early warning preparation phase. At this time, the early warning system utilizes information on time periods exceeding the target threshold in the future trend of icing thickness changes to further analyze and construct the predicted time range for ice-breaking jumps. This considers not only the absolute value of the icing thickness but also its changing trend, making the predicted time more accurate and reliable.

[0063] In this way, by providing specific estimated occurrence times, the early warning system can help maintenance personnel prepare in advance, rationally allocate inspection and emergency resources, and effectively prevent ice-breaking and jumping accidents. Continuing the previous example, if the early warning system sets the "high" risk level threshold to be 80% of the time period exceeding the target threshold, and the current risk level has reached "medium" and is close to "high," further analysis by the early warning system reveals that the time period exceeding the target threshold is mainly concentrated within the next 6 to 12 hours. Therefore, the early warning system constructs an estimated occurrence time within the next 6 to 12 hours, prompting maintenance personnel to strengthen monitoring and preparation during this period.

[0064] S35: Match recommended handling measures, generate a de-icing jump risk warning by using the comprehensive risk level, expected occurrence time and recommended handling measures, and push the de-icing jump risk warning to the operation and maintenance personnel of the transmission line.

[0065] In this embodiment, the early warning system matches corresponding suggested measures from a preset suggested measures library based on the comprehensive risk level and the expected occurrence time. The preset suggested measures include, but are not limited to, strengthening inspections, preparing emergency resources, and adjusting operating modes, in order to reduce the impact of de-icing jumps on power grid safety.

[0066] The early warning system integrates comprehensive risk levels, estimated occurrence times, and recommended handling measures into a de-icing jump risk warning message, which is promptly sent to transmission line maintenance personnel through various means such as SMS and APP push notifications. This enables rapid transmission and effective response of early warning information, improves the power grid's ability to cope with de-icing jump disasters, and helps maintenance personnel react quickly and effectively control accident risks by providing specific and actionable early warning information and handling suggestions, thus ensuring the safe and stable operation of the power grid.

[0067] For example, upon receiving an early warning message indicating a "high" risk level and an expected occurrence time within the next 6 to 12 hours, maintenance personnel immediately organize inspection teams to conduct special inspections of the relevant lines based on the suggested measures provided by the system, and prepare emergency repair materials and personnel. At the same time, they adjust the line operation mode and reduce the load to mitigate the impact of ice-breaking jumps on the power grid. Through the implementation of these measures, the occurrence of ice-breaking jump accidents is effectively prevented, ensuring the safe operation of the power grid.

[0068] As can be seen from the above description of the technical solution of this application, the technical solution of this application has the characteristics of universality. It can be applied on ordinary lines without the installation of dedicated monitoring equipment. It can also dynamically adjust the early warning threshold according to different line structure parameters and meteorological conditions. The internally established hierarchical assessment system can always maintain the accuracy of risk assessment and prevent false alarms or missed alarms caused by a single factor. Moreover, it can output hierarchical early warning signals and achieve continuous monitoring. It can also combine meteorological forecast data to predict risks in advance and improve the power grid's ability to prevent and mitigate disasters such as de-icing jumps.

[0069] The method provided in this application provides an accurate early warning of the risk of de-icing jumps by comprehensively analyzing the coupling effect between wind speed and icing thickness before the icing thickness of the transmission line reaches the risk threshold. Furthermore, it dynamically adjusts the icing thickness threshold for different line structure parameters and meteorological conditions, and maintains the accuracy of risk assessment by combining it with an internally established hierarchical assessment system. This prevents false alarms or missed alarms caused by a single factor, and can adapt well to complex and changeable meteorological environments. The accuracy and timeliness of the early warning are good, thereby improving the power grid's ability to prevent and mitigate de-icing jump disasters.

[0070] Furthermore, as Figure 1 In a specific implementation of the method, this application provides a transmission line early warning device that considers the superposition of wind and ice coupling factors, such as... Figure 3 As shown, the device includes: a prediction module 301, a simulation module 302, and an early warning module 303.

[0071] The prediction module 301 is used to collect real-time meteorological data of the environment where the transmission line is located when it is detected that the transmission line begins to be covered with ice, and input the real-time meteorological data into a pre-trained ice thickness prediction model to predict the ice thickness and obtain the future trend of ice thickness change. The simulation module 302 is used to perform numerical simulation of the de-icing jump of the transmission line using multiple preset operating condition information, and to generate an ice thickness threshold result set based on the simulation results. Each preset operating condition information includes a preset ice thickness level and a preset wind speed level. The ice thickness threshold result set includes multiple ice thickness thresholds and each ice thickness threshold corresponds to a preset wind speed level. The early warning module 303 is used to extract a target icing thickness threshold from the icing thickness threshold result set based on the real-time meteorological data, evaluate the future icing thickness change trend using the target icing thickness threshold, and issue an early warning of the risk of de-icing jump of the transmission line based on the obtained evaluation results.

[0072] In a specific application scenario, the prediction module 301 is used to continuously monitor the line parameters of the transmission line. When the monitored line parameters indicate that the ambient temperature reaches a temperature threshold and the ambient humidity reaches a humidity threshold, it is determined that the transmission line has started to experience icing conditions. The module also collects meteorological data of the environment where the transmission line is located in real time to obtain the real-time meteorological data. It acquires the pre-trained icing thickness prediction model, inputs the real-time meteorological data into the icing thickness prediction model, and predicts the icing thickness of the transmission line in multiple future time periods based on the icing thickness prediction model. The module then organizes the predicted icing thicknesses according to the time sequence of the multiple future time periods to obtain the future icing thickness change trend.

[0073] In a specific application scenario, the prediction module 301 is used to collect raw meteorological data of the environment where the transmission line is located in real time based on multiple sensors installed in the environment where the transmission line is located; query the geographical feature data of the environment where the transmission line is located, including altitude information, slope information, and terrain information; correct the raw meteorological data with reference to the geographical feature data; and use the corrected raw meteorological data as the real-time meteorological data.

[0074] In specific application scenarios, the simulation module 302 is used to acquire multiple preset icing thickness levels and multiple preset wind speed levels; combine the multiple preset icing thickness levels and multiple preset wind speed levels in pairs to obtain multiple data groups, and use each data group as a preset operating condition information to obtain the multiple preset operating condition information, wherein each data group includes a preset icing thickness level and a preset wind speed level; collect the line structure parameters of the transmission line, use numerical simulation tools to simulate the de-icing jump process of the line indicated by the line structure parameters under each preset operating condition information, and record the maximum height of the line's vertical vibration under each preset operating condition information during the simulation process as the maximum de-icing jump height, so as to obtain multiple maximum de-icing jump heights corresponding to the multiple preset operating condition information as the simulation results; determine preset safe operation standards, analyze the simulation results in conjunction with the preset safe operation standards, determine the icing thickness threshold corresponding to each preset wind speed level, and integrate the icing thickness thresholds corresponding to each preset wind speed level to obtain the icing thickness threshold result set.

[0075] In a specific application scenario, the simulation module 302 is used to: for each preset wind speed level, determine multiple target preset operating condition information including the preset wind speed level from the multiple preset operating condition information; extract the target maximum de-icing jump height corresponding to each target preset operating condition information from the simulation results to obtain multiple target maximum de-icing jump heights corresponding to the multiple target preset operating condition information; compare each target maximum de-icing jump height with the de-icing jump height threshold indicated by the preset safe operation standard to determine at least one specified maximum de-icing jump height that reaches the de-icing jump height threshold from the multiple target maximum de-icing jump heights; query the target preset icing thickness level included in the preset operating condition information corresponding to each specified maximum de-icing jump height from the simulation results to obtain at least one target preset icing thickness level; determine the lowest target preset icing thickness level from the at least one target preset icing thickness level, and use the icing thickness indicated by the lowest target preset icing thickness level as the icing thickness threshold corresponding to the preset wind speed level.

[0076] In a specific application scenario, the early warning module 303 is used to determine the real-time wind speed level based on the wind speed information in the real-time meteorological data; query the specified preset wind speed level that the real-time wind speed level matches in the ice thickness threshold result set; and extract the ice thickness threshold corresponding to the specified preset wind speed level in the ice thickness threshold result set as the target ice thickness threshold.

[0077] In specific application scenarios, the early warning module 303 is used to compare the predicted icing thickness corresponding to each future time period indicated by the future icing thickness change trend with the target icing thickness threshold; determine the comprehensive risk level of the transmission line based on the number of future time periods where the predicted icing thickness exceeds the target icing thickness threshold; determine the comprehensive risk level threshold; if the comprehensive risk level reaches the comprehensive risk level threshold, construct the expected occurrence time using the future time periods where the predicted icing thickness exceeds the target icing thickness threshold; match suggested handling measures; generate a de-icing jump risk warning using the comprehensive risk level, the expected occurrence time, and the suggested handling measures; and push the de-icing jump risk warning to the operation and maintenance personnel of the transmission line.

[0078] The device provided in this application embodiment can accurately warn of the risk of de-icing jump by comprehensively analyzing the coupling effect of wind speed and de-icing thickness before the ice thickness of the transmission line reaches the risk threshold. It can also dynamically adjust the ice thickness threshold according to different line structure parameters and meteorological conditions, and maintain the accuracy of risk assessment by combining the internally established hierarchical assessment system, preventing false alarms or missed alarms caused by a single factor. It can adapt well to complex and changeable meteorological environments, and the accuracy and timeliness of the early warning are good, thereby improving the power grid's ability to prevent and mitigate de-icing jump disasters.

[0079] It should be noted that other corresponding descriptions of the functional units involved in the transmission line early warning device considering the superposition of wind and ice coupling factors provided in the embodiments of this application can be found in the following references. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0080] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0081] The above embodiments and the technical features in the embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0083] In an exemplary embodiment, see Figure 4 The invention also provides a computer device including a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the transmission line early warning method considering the superposition of wind and ice coupling factors described in the above embodiments.

[0084] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the transmission line early warning method considering the superposition of wind and ice coupling factors.

[0085] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0086] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0087] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0088] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0089] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A power transmission line early warning method considering wind-ice coupling factors superimposed, characterized in that, include: When icing conditions are detected on the transmission line, real-time meteorological data of the environment where the transmission line is located is collected, and the real-time meteorological data is input into a pre-trained icing thickness prediction model to predict the icing thickness and obtain the future trend of icing thickness change. Numerical simulations of de-icing jumps in transmission lines are performed using multiple preset operating condition information. Based on the simulation results, an ice thickness threshold result set is generated. Each preset operating condition information includes a preset ice thickness level and a preset wind speed level. The ice thickness threshold result set includes multiple ice thickness thresholds, and each ice thickness threshold corresponds to a preset wind speed level. When determining the ice thickness threshold corresponding to each preset wind speed level, for each preset wind speed level, multiple target preset operating condition information including the preset wind speed level is determined from the multiple preset operating condition information. The target maximum de-icing jump height corresponding to each target preset operating condition information is extracted from the simulation results to obtain the multiple target preset operating condition information. The simulation results are used to determine the maximum target de-icing jump heights corresponding to the operating conditions. Each maximum target de-icing jump height is compared with a de-icing jump height threshold indicated by a preset safe operation standard to identify at least one specified maximum de-icing jump height that reaches the threshold. The simulation results are then used to query the preset operating conditions information corresponding to each specified maximum de-icing jump height, including the target preset icing thickness level, to obtain at least one target preset icing thickness level. The lowest target preset icing thickness level is identified among the at least one target preset icing thickness level, and the icing thickness indicated by the lowest target preset icing thickness level is used as the icing thickness threshold corresponding to the preset wind speed level. Based on the real-time meteorological data, a target icing thickness threshold is extracted from the icing thickness threshold result set. The target icing thickness threshold is used to evaluate the future icing thickness change trend, and the risk warning of de-icing jump of transmission lines is given based on the evaluation results.

2. The method of claim 1, wherein, When icing conditions are detected on the transmission line, real-time meteorological data of the environment where the transmission line is located is collected. This real-time meteorological data is then input into a pre-trained icing thickness prediction model to predict the future trend of icing thickness changes, including: The line parameters of the transmission line are continuously monitored. When the monitored line parameters indicate that the ambient temperature reaches the temperature threshold and the ambient humidity reaches the humidity threshold, it is determined that the transmission line has started to experience icing conditions. The meteorological data of the environment in which the transmission line is located is collected in real time to obtain the real-time meteorological data; The pre-trained icing thickness prediction model is obtained, the real-time meteorological data is input into the icing thickness prediction model, and the icing thickness of the transmission line in multiple future time periods is predicted based on the icing thickness prediction model. The predicted icing thicknesses are then organized according to the time sequence of the multiple future time periods to obtain the future icing thickness change trend.

3. The method of claim 2, wherein, The real-time meteorological data collection of the environment where the transmission line is located, to obtain the real-time meteorological data, includes: Based on multiple sensors installed in the environment where the transmission line is located, raw meteorological data of the environment where the transmission line is located are collected in real time. Query the geographical feature data of the environment where the transmission line is located, including altitude information, slope information, and terrain information; The original meteorological data is corrected based on the geographical feature data, and the corrected original meteorological data is used as the real-time meteorological data.

4. The method of claim 1, wherein, The process of performing numerical simulations of de-icing jumps on transmission lines using multiple preset operating condition information, and generating an ice thickness threshold result set based on the simulation results, includes: Obtain multiple preset icing thickness levels and multiple preset wind speed levels; The multiple preset icing thickness levels and multiple preset wind speed levels are combined in pairs to obtain multiple data groups, and each data group is used as a preset operating condition information to obtain the multiple preset operating condition information, wherein each data group includes a preset icing thickness level and a preset wind speed level. The line structure parameters of the transmission line are collected, and numerical simulation tools are used to simulate the de-icing jump process of the line indicated by the line structure parameters under each preset working condition. The maximum height of the line's vertical vibration under each preset working condition during the simulation is recorded as the maximum de-icing jump height, so as to obtain multiple maximum de-icing jump heights corresponding to the multiple preset working conditions as the simulation results. A preset safe operation standard is determined. Based on the preset safe operation standard, the simulation results are analyzed to determine the icing thickness threshold corresponding to each preset wind speed level. The icing thickness thresholds corresponding to each preset wind speed level are then integrated to obtain the icing thickness threshold result set.

5. The method of claim 1, wherein, The step of extracting the target icing thickness threshold from the icing thickness threshold result set based on the real-time meteorological data includes: The real-time wind speed level is determined based on the wind speed information in the real-time meteorological data; Query the specified preset wind speed level that the real-time wind speed level matches in the ice thickness threshold result set, and extract the ice thickness threshold corresponding to the specified preset wind speed level in the ice thickness threshold result set as the target ice thickness threshold.

6. The method of claim 1, wherein, The process of assessing the future trend of icing thickness using the target icing thickness threshold and issuing a warning of transmission line de-icing jump risk based on the assessment results includes: The predicted icing thickness corresponding to each future time period indicated by the future icing thickness change trend is compared with the target icing thickness threshold. Based on the number of future time periods in which the predicted icing thickness indicated by the future icing thickness change trend exceeds the target icing thickness threshold, the comprehensive risk level of the transmission line is determined. Determine the comprehensive risk level threshold, and when the comprehensive risk level reaches the comprehensive risk level threshold, construct the expected occurrence time by using the future period of predicted ice thickness exceeding the target ice thickness threshold indicated by the future ice thickness change trend. Match the recommended handling measures, and use the comprehensive risk level, the expected occurrence time and the recommended handling measures to generate a de-icing jump risk warning, and push the de-icing jump risk warning to the operation and maintenance personnel of the transmission line.

7. A power transmission line early warning device considering wind-ice coupling factors superposition, characterized in that, include: The prediction module is used to collect real-time meteorological data of the environment where the transmission line is located when it is detected that the transmission line begins to be covered with ice. The real-time meteorological data is then input into a pre-trained ice thickness prediction model to predict the ice thickness and obtain the future trend of ice thickness changes. The simulation module is used to perform numerical simulations of de-icing jumps of transmission lines using multiple preset operating condition information, and to generate an ice thickness threshold result set based on the simulation results. Each preset operating condition information includes a preset ice thickness level and a preset wind speed level. The ice thickness threshold result set includes multiple ice thickness thresholds, and each ice thickness threshold corresponds to a preset wind speed level. When determining the ice thickness threshold corresponding to each preset wind speed level, for each preset wind speed level, multiple target preset operating condition information including the preset wind speed level is determined from the multiple preset operating condition information. The target maximum de-icing jump height corresponding to each target preset operating condition information is extracted from the simulation results to obtain the multiple target... The system identifies multiple target maximum de-icing jump heights corresponding to preset operating conditions; compares each target maximum de-icing jump height with a de-icing jump height threshold indicated by a preset safe operation standard to determine at least one specified maximum de-icing jump height that reaches the de-icing jump height threshold among the multiple target maximum de-icing jump heights; queries the preset operating conditions information corresponding to each specified maximum de-icing jump height in the simulation results to obtain at least one target preset icing thickness level; determines the lowest target preset icing thickness level among the at least one target preset icing thickness level, and uses the icing thickness indicated by the lowest target preset icing thickness level as the icing thickness threshold corresponding to the preset wind speed level; The early warning module is used to extract a target icing thickness threshold from the icing thickness threshold result set based on the real-time meteorological data, use the target icing thickness threshold to evaluate the future icing thickness change trend, and issue an early warning of the risk of de-icing jump of the transmission line based on the evaluation results.

8. A device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A medium having stored thereon a computer program, characterized in that When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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