A transmission line monitoring method based on edge computing

Abstract

The present invention provides a transmission line monitoring method based on edge computing. By deploying intelligent edge computing units at key nodes of the transmission line, multi-source data such as ice thickness, ambient temperature and humidity, and cable tension are collected in real time. The edge computing fusion algorithm is used to process the data to generate real-time warning signals and abnormal data sets. In combination with the historical environment, an adaptive neural network optimization algorithm is used to calculate the risk assessment index. When the risk assessment index reaches the warning threshold, the data is uploaded to the remote monitoring center through the communication module, and the fault point is quickly located, and a fault diagnosis report is generated, thereby realizing real-time monitoring of the transmission line status and intelligent management of safe operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a power transmission line monitoring method based on edge computing. Background Art

[0002] Transmission lines are an important component of power intelligent edge computing units, responsible for transmitting electricity from power plants to user terminals. Their safe and stable operation is crucial to ensuring power supply. Traditional transmission line monitoring intelligent edge computing units usually use periodic manual inspections or simple sensor monitoring, which have obvious limitations. Manual inspections are costly and inefficient, and cannot achieve continuous real-time monitoring of line status. Simple sensor monitoring often only provides limited data and lacks the ability to comprehensively analyze complex environmental factors, making it difficult to accurately predict and assess the safety risks of transmission lines. When a transmission line fails, the fault location technology is not accurate enough, resulting in time-consuming and inefficient maintenance work.

[0003] Existing technologies also suffer from slow response and low efficiency when handling sudden environmental events such as icing and lightning strikes that affect power transmission lines. Therefore, there is an urgent need for intelligent edge computing units that can monitor power line status in real time, quickly respond, and accurately handle various complex situations, thereby improving the reliability of power transmission lines and the overall security of the power grid. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a transmission line monitoring method based on edge computing, which solves the problem of how to achieve real-time monitoring of transmission lines, accurately assess risk status and quickly respond to abnormal events.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a transmission line monitoring method based on edge computing, comprising:

[0008] S1. Deploy intelligent edge computing units at key nodes along the transmission lines. These units include a multimodal sensing module, a data processing module, and a low-power communication module. The multimodal sensing modules collect multi-source data, including ice thickness, ambient temperature and humidity, cable tension, vibration frequency, and lightning current. The collected data is timestamped for time synchronization and dynamic analysis.

[0009] S2. The intelligent edge computing unit processes the collected multi-source data in real time and uses edge computing fusion algorithms to generate real-time warning signals and abnormal data sets;

[0010] S3. In the edge computing unit, based on the generated warning signals, abnormal data sets, and multimodal data streams, combined with historical environmental data, an adaptive neural network optimization algorithm is used to calculate a risk assessment index reflecting the overall operating status of the transmission line.

[0011] S4. When the risk assessment index reaches the warning threshold, the risk assessment index, warning signal, and abnormal data set are uploaded to the remote monitoring center via the low-power communication module. Simultaneously, an emergency signal is transmitted to surrounding edge computing units to achieve a distributed collaborative response.

[0012] S5. When an abnormal line interruption is detected, the abnormal data set and risk assessment index are combined to quickly locate the fault point, generate a fault type diagnosis report, and upload it to the remote monitoring center;

[0013] S6. In the remote monitoring center, a comprehensive safety picture of the transmission line operation is constructed by combining risk assessment indices, early warning signals, and abnormal data sets, and the power supply path is dynamically adjusted based on the safety picture.

[0014] Preferably, in the intelligent edge computing unit for monitoring transmission lines, key nodes are located at the branch points of the line, areas susceptible to environmental influences, and areas with a history of frequent faults; for example, at the branch points where the transmission line connects to the substation or distribution network, these locations are prone to faults due to the complex structure and stress concentration; mountainous, coastal or forest areas susceptible to environmental influences will also become key nodes for monitoring due to harsh natural conditions such as strong winds, lightning strikes, and salt spray erosion; in addition, through historical data analysis, areas where frequent faults occur are identified, and the transmission lines in these areas require more detailed monitoring; at these key nodes, the intelligent edge computing unit is deployed on the tower or pole of the transmission line, installed in a position that is easy to maintain and can monitor the line status; a multimodal sensing module is integrated inside the unit, including but not limited to temperature and humidity sensors, vibration sensors, tension sensors, and current sensors, to collect various status information of the line in real time; the data collected by each sensor will be given a timestamp to record the exact time when the data was collected, ensuring the time consistency of the data and the accuracy of subsequent analysis.

[0015] Preferably, the processing process of the intelligent edge computing unit includes: dynamically synchronizing multi-source data with different timestamps through an adaptive time weighting factor to generate a multimodal data stream with a unified time series for feature extraction and modeling; based on the multimodal data stream after dynamic synchronization, using Gaussian variational distribution modeling technology to convert the nonlinear relationship of multi-source data into a joint probability distribution model, and outputting a dynamic probability matrix to represent the real-time risk status of the transmission line; utilizing the risk status data in the dynamic probability matrix, combined with sliding window analysis to extract local fluctuation characteristics, and dynamically identify sudden lightning strikes or abnormal ice cover changes; when the local fluctuation exceeds a preset threshold, a real-time warning signal is generated, and an abnormal data set related to the abnormality is output at the same time.

[0016] Preferably, in the transmission line monitoring intelligent edge computing unit, the edge computing unit is responsible for analyzing the timestamps of each data source; the intelligent edge computing unit collects data from different sensors, such as temperature, humidity and cable tension, each of which carries a timestamp indicating the time when the data was collected, and the intelligent edge computing unit identifies the relative position of each data point in the time dimension by calculating the difference between these timestamps, i.e., the deviation; for example, if the data timestamp of a temperature sensor is 0.5 seconds earlier than the data timestamp of a humidity sensor, the intelligent edge computing unit will record this deviation; the intelligent edge computing unit calculates an adaptive time weighting factor based on the degree of these deviations and the characteristics of each data source; the calculation of the weighting factor takes into account the time correlation and acquisition frequency of the data, and its core is to dynamically adjust the weights of different data sources to alleviate the time differences caused by acquisition speed or Alignment problems caused by different accuracies; for example, if the data acquisition frequency of the tension sensor is low but the real-time requirement is high, the intelligent edge computing unit will give it a larger weighting factor to ensure that it has a higher influence in the data synchronization process; after completing the weighted calculation, the intelligent edge computing unit maps these multi-source data to a unified time series through interpolation and time window adjustment methods to form a multimodal data stream; such as linear interpolation or polynomial interpolation, and dynamic adjustment of the time window to ensure the continuity and consistency of the data on the time axis; for example, if a sensor is missing data within a time window, the intelligent edge computing unit will estimate the data value at the missing time through interpolation based on the previous and next timestamps and data values ​​to ensure the integrity of the data stream; convert heterogeneous data from different sensors into a synchronized, unified format data stream.

[0017] Preferably, the Gaussian variational distribution modeling technique makes a Gaussian distribution assumption for each data source to obtain the probability density function of each data source. ; where i is the number of data sources, x is the collection of multi-source data, is the mean of the ith data source, is the variance; through the Gaussian process regression method, the nonlinear relationship of x is modeled as a joint probability distribution , where θ is the model parameter, including the mean and variance of all data sources; through the kernel function Represents the similarity between data points; the covariance matrix is ​​calculated by the kernel function ,in is the noise level, I is the identity matrix; combine the joint probability distribution model with the covariance matrix to output the dynamic probability matrix , where |C| is the determinant of the covariance matrix, It is the inverse of the covariance matrix; it converts the nonlinear relationship of multi-source data into a joint probability distribution model and outputs a dynamic probability matrix.

[0018] By monitoring the data in the dynamic probability matrix in real time, local fluctuation characteristics are extracted through sliding window analysis; the data change trend of the transmission line within a certain period of time is captured to identify abnormal behavior; when the monitored local fluctuation characteristics exceed the preset safety threshold, the intelligent edge computing unit will immediately generate a real-time warning signal and send the signal to the remote monitoring center and surrounding intelligent edge computing units through the low-power communication module. The intelligent edge computing unit extracts data related to the anomaly to form an abnormal data set.

[0019] In the intelligent edge computing unit for monitoring transmission lines, the intelligent edge computing unit sets a time window, which slides in the data stream to capture and analyze local data fluctuations within the window; for example, the intelligent edge computing unit sets a 30-second sliding window, which slides forward every 5 seconds to continuously monitor small changes in the line status; by calculating the mean and variance statistical characteristics of the data in the window, the intelligent edge computing unit identifies the trend of data changes; if the variance of cable tension suddenly increases within a certain period of time, it indicates that there may be line abnormalities caused by icing or strong winds; the intelligent edge computing unit presets a series of safety thresholds based on historical data and expert experience. For temperature data, it is assumed that the normal operating temperature range of the transmission line is -10 degrees Celsius to 40 degrees Celsius; by analyzing historical data, the intelligent edge computing unit sets the threshold The upper and lower boundaries of this range are defined as follows: if the temperature of a certain line section suddenly drops to -15 degrees Celsius or rises to 45 degrees Celsius, indicating extreme weather or equipment failure, the intelligent edge computing unit will trigger an early warning signal; when local fluctuation characteristics, such as the temperature mutation rate, exceed these thresholds, the intelligent edge computing unit will consider the transmission line to be abnormal and immediately trigger a real-time early warning signal; for example, if the tension variance of three consecutive sliding windows exceeds the preset threshold, the intelligent edge computing unit will generate an early warning signal; this warning signal and data related to the anomaly, such as tension value, timestamp and location information, are quickly sent to the remote monitoring center and surrounding intelligent edge computing units via the low-power communication module, so that timely countermeasures can be taken; the status of the transmission line is monitored in real time, potential safety hazards can be quickly responded to, and the stable operation of the power grid can be guaranteed.

[0020] The generated warning signals, datasets of abnormal characteristics, and multimodal data streams are input into the adaptive neural network optimization algorithm. The input layer of the neural network receives the trigger frequency of the warning signal, the risk state parameters in the dynamic probability matrix, and a collection of historical multi-source data; these data are input into the neural network and converted through the activation function to output a comprehensive risk assessment index. , where f is the triggering frequency of the warning signal, H is the collection of historical multi-source data, and R reflects the real-time risk status of the transmission line.

[0021] When an abnormal interruption occurs in a transmission line, the remote monitoring center mobilizes various intelligent edge computing units. Suppose that after a strong storm, an abnormality is detected at a key node of the transmission line. The intelligent edge computing unit immediately begins to analyze the multi-source data at the time of the abnormality, including ice thickness, cable tension, vibration frequency, etc. By analyzing these data and comparing them with the risk status in the dynamic probability matrix, it is found that the readings of ice thickness and cable tension rise sharply when the abnormality occurs, and the vibration frequency also shows an abnormal peak. The data and results will be comprehensively analyzed. If the increase in ice thickness and abnormal changes in cable tension are found to be significantly different from the normal state, this indicates that the fault is likely due to line overload caused by ice. By collecting abnormal data from specific intelligent edge computing units, the data related to the fault is quickly locked, and the fault point is determined to be located near a branch point of the transmission line.

[0022] The intelligent edge computing unit generates a detailed fault type diagnosis report, which includes the nature, location, possible causes, and recommended repair measures. This report is uploaded to the remote monitoring center via a low-power communication module. At the monitoring center, operators combine risk assessment indices, early warning signals, and abnormal data sets to construct a panoramic view of the transmission line's operational safety. Through this panoramic view, operators can intuitively see the operating status of the entire transmission line and dynamically adjust the power supply path based on the safety panoramic view, such as switching to a backup line, to ensure the continuity and safety of the power supply.

[0023] (3) Beneficial effects

[0024] The present invention provides a transmission line monitoring method based on edge computing, which has the following beneficial effects:

[0025] 1. The present invention deploys intelligent edge computing units at key nodes of transmission lines to achieve real-time monitoring of line status, and uses Gaussian variational distribution modeling technology to construct a joint probability distribution model, output a dynamic probability matrix, and accurately assess the real-time risk status of transmission lines. It provides more comprehensive and accurate risk assessment results, providing reliable protection for the safe operation of power intelligent edge computing units.

[0026] 2. The present invention identifies sudden lightning strikes or abnormal ice cover changes and generates real-time warning signals; calculates a risk assessment index that reflects the overall operating status of the transmission line; when the risk assessment index reaches the warning threshold, the intelligent edge computing unit will immediately upload the risk assessment index, warning signal and related abnormal data set to the remote monitoring center, and transmit emergency signals to surrounding edge computing units to achieve distributed collaborative response, thereby quickly responding to abnormal events and avoiding accidents.

[0027] 3. The present invention quickly locates the fault point based on the abnormal data set and risk assessment index. In the remote monitoring center, it combines the risk assessment index, early warning signal and abnormal data set to build a panoramic view of the transmission line operation safety, and dynamically adjusts the power supply path according to the safety panoramic view, thereby improving the intelligence level of the intelligent edge computing unit and providing more accurate decision-making support for the safe operation of the power intelligent edge computing unit. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] The embodiment of the present invention provides a transmission line monitoring method based on edge computing to achieve comprehensive and accurate monitoring and effective management of the transmission line status.

[0030] Deploying intelligent edge computing units at key nodes along transmission lines is fundamental to the entire monitoring system. These key nodes are carefully selected. For example, branch points within a transmission line experience complex stress distribution due to variations in current distribution and line direction, making them prone to problems such as loose connections or electrical discharges. Areas susceptible to environmental impacts, such as lines crossing mountainous areas, often face severe weather conditions such as strong winds, heavy rain, and freezing temperatures, as well as areas with frequent lightning activity, resulting in a higher probability of lightning strikes. Furthermore, areas with a history of frequent failures, as identified through in-depth analysis of historical data, harbor potential, yet-to-be-resolved, risk factors. On a transmission line section in a mountainous area, a branch point located at the windward edge of a valley, surrounded by dense trees and prone to frequent lightning activity, was identified as a key node. The intelligent edge computing unit was installed on the transmission line tower at this branch point, approximately 15 meters above the ground. This facilitates routine inspections and equipment maintenance for maintenance personnel while ensuring that sensors accurately collect line status information.

[0031] The multimodal sensing module integrated within the intelligent edge computing unit starts working, collecting multi-source data; the temperature and humidity sensor collects ambient temperature and humidity data every 10 minutes. For example, during the hot summer period, the ambient temperature is recorded to be 40 degrees Celsius and the humidity is 60%. This data can reflect the thermal and humidity conditions of the line environment, which is important for determining whether there is a risk of overheating of the line and the risk of insulation moisture; the vibration sensor monitors the vibration frequency of the line in real time. When strong winds pass by, the vibration frequency is detected to increase significantly, from the normal 5Hz to 15Hz, which indicates that the transmission line is subject to a large external force and is at risk of dancing or fatigue damage; the tension sensor continuously measures the cable tension, and the tension is maintained at 5000N during normal operation. If the tension changes suddenly due to icing or other factors, such as if the tension rises to 6000N after a slight icing, it needs to be paid attention to; the current sensor closely monitors the current size during lightning weather. In a certain lightning strike, the peak lightning current was detected to reach 100kA. This data helps to assess the impact of the lightning strike on the line; each piece of data collected is attached with a timestamp to ensure the time synchronization of the data in subsequent processing and the accuracy of dynamic analysis.

[0032] The intelligent edge computing unit processes the collected multi-source data in real time. First, the data with different timestamps are dynamically synchronized through an adaptive time weighting factor. Assuming that the timestamp of the temperature sensor data lags behind the timestamp of the tension sensor data by 2 seconds, according to the characteristics of the data sources and the acquisition frequency of the two, the weighting factor of the temperature sensor data is calculated to be 0.3, and the weighting factor of the tension sensor data is calculated to be 0.7. After weighted calculation and interpolation processing, they are mapped to a unified time series to form a multimodal data stream. Then, based on the multimodal data stream after dynamic synchronization, the Gaussian variational distribution modeling technology is used to The technique uses a Gaussian distribution assumption for each data source and calculates a probability density function. For example, for cable tension data, assume its mean is 5000N and its variance is 500, and calculate its probability density function according to the formula. The nonlinear relationship between multi-source data is then modeled as a joint probability distribution using the Gaussian process regression method. The covariance matrix is ​​calculated using the kernel function, and the dynamic probability matrix is ​​ultimately output. If an element in the dynamic probability matrix shows a sudden increase in the risk status value, exceeding the normal range, it indicates that the line may be abnormal. At the same time, local fluctuation characteristics are extracted by combining sliding window analysis. The sliding window is set to 20 seconds and slides every 4 seconds. The mean and variance statistical characteristics of the data within the window are calculated. When the variance of the cable tension within a sliding window increases from the normal 100 to 500, exceeding the preset threshold, the intelligent edge computing unit immediately generates a real-time warning signal and extracts the tension data, timestamp, and location information related to the anomaly to form an abnormal data set.

[0033] In the edge computing unit, an adaptive neural network optimization algorithm is used to calculate the risk assessment index based on the generated warning signals, abnormal data sets and multimodal data streams, combined with historical environmental data. The input layer of the neural network receives the triggering frequency of the warning signal, such as whether the warning signal was triggered 3 times in the past hour; the risk status parameters in the dynamic probability matrix, such as the risk status value of a key node is 0.8; and a collection of historical multi-source data, such as the line operation data of the area under similar conditions in the past week. After these data are input into the neural network, they are converted by the activation function and output as a comprehensive risk assessment index. Assuming the calculated risk assessment index is 0.75, it indicates that the transmission line is at a high risk and timely measures need to be taken.

[0034] When the risk assessment index reaches the warning threshold, assuming it is set at 0.7, the intelligent edge computing unit uploads the risk assessment index, warning signal and related abnormal data sets to the remote monitoring center through the low-power communication module, and at the same time transmits an emergency signal to the surrounding intelligent edge computing units; for example, during a rainstorm, the risk assessment index reaches 0.8, and the intelligent edge computing unit quickly uploads the data. After receiving the emergency signal, the surrounding intelligent edge computing units strengthen monitoring of their own areas and prepare for coordinated response; after receiving the data, the remote monitoring center has a more comprehensive understanding of the operating status of the entire transmission line.

[0035] When an abnormal line interruption is detected, the fault point is quickly located by combining the abnormal data set and risk assessment index. For example, the intelligent edge computing unit at a key node detects an abnormal interruption. By analyzing the abnormal data set, it is found that the ice thickness data increases rapidly before the interruption, and the cable tension rises sharply. Combined with the risk assessment index, it shows that the area is in a high-risk state, and it is judged that the fault point may be located near this node. Further analysis shows that the ice thickness in this area reaches 10mm, which exceeds the normal range, causing the cable tension to exceed the limit, and ultimately causing the line to be interrupted. A detailed fault type diagnosis report is generated and uploaded to the remote monitoring center, including the nature of the fault being ice overload causing line interruption, the location being near a specific branch point, the possible cause being bad weather causing rapid ice growth, and the recommended maintenance measure of clearing the ice before checking the line damage.

[0036] In the remote monitoring center, a panoramic view of the transmission line operation safety is constructed by combining risk assessment indices, early warning signals and abnormal data sets. Through the panoramic view, operators can intuitively see the operating status of each key node of the entire transmission line, including which areas are at high risk and where early warnings have been issued. The power supply path is dynamically adjusted according to the safety panoramic view. For example, when a high-risk early warning appears on a major transmission line, part of the load is promptly switched to the backup line to ensure the continuity and safety of the power supply, realize the intelligent management of the transmission line, and effectively improve the reliability and stability of the transmission line operation.

[0037] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A transmission line monitoring method based on edge computing, characterized in that; The following steps are involved: S1. Deploy intelligent edge computing units at key nodes along the transmission lines. These units include a multimodal sensing module, a data processing module, and a low-power communication module. The multimodal sensing modules collect multi-source data, including ice thickness, ambient temperature and humidity, cable tension, vibration frequency, and lightning current. The collected data is timestamped for time synchronization and dynamic analysis. S2. The intelligent edge computing unit processes the collected multi-source data in real time. Using an edge computing fusion algorithm, it generates a dynamic probability matrix based on the real-time risk status data of the transmission line. The matrix's rows represent time series, and its columns represent risk status parameters. The unit then generates real-time warning signals and anomaly datasets based on the dynamic probability matrix. S3. In the edge computing unit, based on the generated warning signals, abnormal data sets, and multimodal data streams, combined with historical environmental data, an adaptive neural network optimization algorithm is used to calculate a risk assessment index reflecting the overall operating status of the transmission line. S4. When the risk assessment index reaches the warning threshold, the risk assessment index, warning signal, and abnormal data set are uploaded to the remote monitoring center via the low-power communication module. Simultaneously, an emergency signal is transmitted to surrounding edge computing units to achieve a distributed collaborative response. S5. When an abnormal line interruption is detected, the abnormal data set and risk assessment index are combined to quickly locate the fault point, generate a fault type diagnosis report, and upload it to the remote monitoring center; S6. In the remote monitoring center, a comprehensive safety picture of the transmission line operation is constructed by combining risk assessment indices, early warning signals, and abnormal data sets, and the power supply path is dynamically adjusted based on the safety picture.

2. The power transmission line monitoring method based on edge computing according to claim 1, characterized in that: The key nodes of the transmission line are branch points of the line, sections susceptible to environmental impacts, and areas with a history of frequent faults. Intelligent edge computing units are deployed at the selected key nodes. The intelligent edge computing units integrate a multimodal sensing module, a data processing module, and a low-power communication module. The multimodal sensing module collects data from multiple sources and adds a timestamp to each piece of data collected.

3. The power transmission line monitoring method based on edge computing according to claim 1, characterized in that: The processing process of the edge computing fusion algorithm in the intelligent edge computing unit includes: S2.1: Dynamically synchronize multi-source data with different timestamps through adaptive time weighting factors to generate a multimodal data stream with a unified time series for feature extraction and modeling; S2.2: Based on the dynamically synchronized multimodal data stream, Gaussian variational distribution modeling technology is used to transform the nonlinear relationship of multi-source data into a joint probability distribution model, and a dynamic probability matrix is ​​output to represent the real-time risk status of the transmission line; S2.3: By combining the risk status in the dynamic probability matrix with sliding window analysis to extract local fluctuation characteristics, sudden lightning strikes or abnormal ice cover changes can be dynamically identified; when the local fluctuation exceeds the preset threshold, a real-time warning signal is generated and an abnormal data set is output.

4. The power transmission line monitoring method based on edge computing according to claim 3, characterized in that: The edge computing unit performs a preliminary analysis of the timestamps of each data source, identifies the relative positions of different data in the time dimension by calculating the deviation between the timestamps, and calculates an adaptive time weighting factor based on the degree of deviation and the characteristics of the data source; The weighting factor dynamically adjusts the weights of different data sources according to the time of the data; after completing the weighted calculation, the multi-source data is mapped to a multimodal data stream with a unified time series through interpolation and time window adjustment.

5. The power transmission line monitoring method based on edge computing according to claim 3, characterized in that: The Gaussian variational distribution modeling technique makes a Gaussian distribution assumption on each data source to obtain the probability density function of each data source. ; Where i is the number of data sources, x is the collection of multi-source data, is the mean of the ith data source, is the variance; through the Gaussian process regression method, the nonlinear relationship of x is modeled as a joint probability distribution , where θ is the model parameter, including the mean and variance of all data sources; through the kernel function Represents the similarity between data points; the covariance matrix is ​​calculated by the kernel function ,in is the noise level, I is the identity matrix; combine the joint probability distribution model with the covariance matrix to output the dynamic probability matrix , where |C| is the determinant of the covariance matrix, It is the inverse of the covariance matrix; it converts the nonlinear relationship of multi-source data into a joint probability distribution model and outputs a dynamic probability matrix.

6. The power transmission line monitoring method based on edge computing according to claim 5, characterized in that: The dynamic probability matrix is ​​analyzed through sliding windows to extract local fluctuation characteristics; the data change trend of the transmission line within a certain period of time is captured to identify abnormal behavior; when the monitored local fluctuation characteristics exceed the preset safety threshold, the intelligent edge computing unit immediately generates a real-time warning signal and sends the signal to the remote monitoring center and surrounding edge computing units through a low-power communication module. The intelligent edge computing unit extracts data related to the anomaly and forms an abnormal data set.

7. The power transmission line monitoring method based on edge computing according to claim 1, characterized in that: Warning signals, abnormal data sets, and multimodal data streams are input into the adaptive neural network optimization algorithm. The input layer of the neural network receives the trigger frequency of the warning signal, the risk state parameters in the dynamic probability matrix, and the collection of historical multi-source data. The data is input into the neural network and converted through the activation function to output a comprehensive risk assessment index. , where f is the trigger frequency of the warning signal, H is the collection of historical multi-source data, R reflects the real-time risk status of the transmission line, and P ij The parameter value representing the risk state in the i-th row and j-th column of the dynamic probability matrix.

8. The power transmission line monitoring method based on edge computing according to claim 1, characterized in that: When an abnormal line interruption is detected, a time synchronization analysis is performed based on the timestamps attached to the multi-source data collected by the multimodal sensing module according to the abnormal data set and risk assessment index, combined with the real-time risk status reflected by the dynamic probability matrix. Through a comprehensive analysis of the data and results, based on the characteristics of different data at the time of the fault and its differences from the normal state, the data related to the fault is quickly locked in, thereby locating the fault point, and then generating a fault type diagnosis report and uploading it to the remote monitoring center.