Power transmission line inspection data asset management method and system

Abstract

The invention provides a power transmission line inspection data asset management method and system, and the method comprises the steps: obtaining the multi-modal data of a power transmission line through a plurality of sensors disposed on the power transmission line and the surrounding environment, and sensors carried by a helicopter and an unmanned plane; and processing the multi-modal data based on the space-time correlation model to generate a fusion data set. Analyzing the fused data set by using an adaptive feature extraction algorithm, generating structured power transmission line data assets including environmental features, defect features and hidden danger features, and evaluating the possibility of triggering defects by environmental conditions based on the association of the environmental features and the defect features; according to the hidden danger characteristics and the environmental conditions, constructing a graph network model which takes a power transmission line as a node and sensor data as an edge weight and is used for analyzing a defect propagation path and predicting time and space distribution of hidden dangers; defect risk level early warning is generated based on a prediction result, a power transmission line hidden danger distribution diagram is generated in combination with environmental meteorological data, and accurate assistance is provided for patrol and maintenance plans.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a power transmission line patrol data asset management method and system. Background Art

[0002] Transmission lines are key infrastructure in the power system, and their safe and stable operation is of great significance to the reliability and economy of power supply. At present, traditional transmission line inspection methods mainly rely on manual inspection and limited online monitoring technology. These methods usually rely on inspectors to observe the status of transmission lines on site, record the problems found, and use some sensors to monitor equipment parameters such as current, voltage and temperature changes. In addition, helicopters / medium and large drones and satellite remote sensing technology have been introduced in recent years for transmission line inspection.

[0003] Although existing technologies can identify hidden dangers in transmission lines to a certain extent, the following problems are generally encountered: First, the data source is single or incomplete, and it is impossible to comprehensively consider the multimodal information of the transmission line, such as the combined effects of environmental factors, equipment status and appearance defects; second, the data processing and analysis methods are relatively traditional, usually only focusing on a single time point or a single equipment status, and it is difficult to provide a comprehensive analysis of multiple temporal and spatial correlations; third, the hidden danger risk prediction lacks accuracy, especially in the prediction of the hidden danger propagation path and the spatiotemporal distribution of potential risks, and it is impossible to provide an accurate basis for maintenance decisions.

[0004] In response to the above problems, the present application proposes a transmission line patrol data asset management method and system. Summary of the invention

[0005] The present application provides a transmission line inspection data asset management method and system to improve the inspection efficiency and decision-making accuracy of transmission lines.

[0006] The present application provides a transmission line patrol data asset management method, comprising: Through the various sensors installed on the transmission lines and the surrounding environment, the mobile terminals carried by the patrol personnel, and the data collection devices of the environmental monitoring satellite, the multimodal data related to the transmission line patrol is obtained, wherein the multimodal data includes the physical state data of the transmission line, the electrical parameter data, the environmental meteorological data, the appearance photos and video data of the transmission line, the temperature distribution data captured by the infrared camera, the defect data of the transmission line and the hidden danger data of the transmission channel; Based on the spatiotemporal correlation model, the multimodal data are synchronously calibrated and dynamically fused to obtain a fused data set with temporal consistency and spatial accuracy; The fused data set is parsed using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features, wherein the transmission line data asset includes an environmental feature label, a defect feature label, and a hidden danger feature label, wherein the environmental feature label describes the potential impact of weather, temperature and humidity, and wind speed on the operation of the transmission line; According to the association between the environmental characteristic tag and the defect characteristic tag, evaluating the possibility of environmental conditions triggering the transmission line defect; According to the evaluation results of the hidden danger feature labels and environmental conditions, a graph network model is constructed with the transmission line as the node and the sensor data as the edge weight, and the graph network model is used to analyze the defect propagation path of the transmission line and its channel; based on the historical inspection data and environmental dynamic parameters, the graph network model is used to predict the time when the hidden danger occurs; Based on the prediction results of the graph network model, a defect risk level warning is generated, and a transmission line hidden danger distribution map is generated in combination with environmental meteorological data. The hidden danger distribution map is used to assist in formulating inspection and maintenance plans.

[0007] The beneficial effects of the technical solution provided by this application include: (1) By combining multimodal data collected by sensors, mobile terminals and satellites, the paper comprehensively covers multi-dimensional information such as the physical state, electrical parameters, environmental weather and appearance defects of the transmission line, solving the problem of single data source or limited coverage in traditional methods, and improving the comprehensiveness and reliability of hidden danger data collection. (2) The multimodal data are synchronously calibrated and dynamically fused using a spatiotemporal correlation model to ensure that the generated data set has temporal consistency and spatial accuracy. This processing method can effectively combine the data characteristics of different acquisition devices and provide a high-quality data foundation for subsequent feature extraction and analysis. (3) By constructing a graph network model with transmission lines as nodes and sensor data as edge weights, the propagation path of transmission line hidden dangers can be dynamically simulated, and the time and spatial distribution of hidden dangers can be predicted by combining historical data and environmental dynamic parameters, providing an accurate dynamic assessment of hidden danger propagation. (4) Based on the prediction results, a defect risk level warning is generated, and a transmission line hidden danger distribution map is generated by combining environmental meteorological data. The distribution map can intuitively display high-risk areas and their intensity distribution, assisting operation and maintenance personnel to rationally plan patrol and maintenance tasks, and improving patrol efficiency and decision-making accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of a transmission line patrol data asset management method provided in the first embodiment of the present application.

[0009] Figure 2 It is a schematic diagram of a transmission line patrol data asset management system provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0010] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0011] The first embodiment of the present application provides a method for managing transmission line patrol data assets. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A method for managing transmission line patrol data assets is described in detail in the first embodiment of the present application.

[0012] Step S101: Acquire multimodal data related to transmission line inspection through various sensors installed on the transmission line and the surrounding environment, sensors carried by helicopters and drones, mobile terminals carried by patrol personnel, and data acquisition devices of environmental monitoring satellites, wherein the multimodal data includes physical state data of the transmission line, electrical parameter data, three-dimensional laser point cloud data, environmental meteorological data, appearance photos and video data of the transmission line, temperature distribution data and infrared photos captured by infrared cameras, defect data of the transmission line and hidden danger data of the transmission channel.

[0013] Step S101 involves collecting multimodal data from the transmission line and its surrounding environment to comprehensively cover the operating status, environmental conditions and potential hazards of the transmission line. The implementation of this step is described in detail below.

[0014] First, by installing a variety of sensors on the transmission line and its surrounding environment, real-time data related to the operation of the transmission line is collected. These sensors include but are not limited to: vibration sensors for monitoring the mechanical state of the transmission line; temperature sensors for measuring changes in conductor temperature; current and voltage sensors for collecting electrical parameters of the transmission line, such as current, voltage and their fluctuations. In addition, meteorological sensors are used to collect environmental meteorological data in real time, including parameters such as wind speed, humidity, temperature and precipitation.

[0015] The mobile terminals carried by the inspectors are equipped with high-definition cameras, infrared cameras and positioning modules, which are used to take photos and videos of the appearance of the transmission lines during the inspection, and capture the temperature distribution data of the transmission lines and their key components through infrared imaging. These mobile terminals can also upload the collected data to the data processing center in real time through the wireless communication module.

[0016] At the same time, data collection devices of environmental monitoring satellites are deployed to monitor the wide-area environment of the transmission line corridor through high-resolution imaging technology and optical remote sensing technology, and obtain data on terrain changes, vegetation coverage and potential external hazards (such as tree toppling or fire risks) around the transmission line. Satellite data can be collected in a fixed period, and emergency monitoring mode can also be activated according to specific events or needs.

[0017] During the data collection process, each device calibrates the timestamp through a time synchronization protocol (such as the Network Time Protocol NTP) to ensure the consistency of data from different sources in the time dimension. In addition, a unique identifier is used to assign a label to each sensor, patrol terminal, and satellite data collection device so that the data source can be accurately identified in the subsequent data processing and integration stages.

[0018] The multimodal data obtained through the above methods include but are not limited to: environmental parameters such as mechanical vibration frequency of transmission lines, conductor temperature, current fluctuations, wind speed and humidity; visualization features of potential defects (such as cracks, corrosion areas) in photos and videos of the transmission line appearance; abnormal temperature hotspots reflected in infrared imaging; and potential external hidden dangers information in the transmission channel (such as changes in vegetation coverage and the location of fallen trees).

[0019] Finally, all collected data are stored in a unified data management platform and classified and labeled according to time, space and data type, providing high-quality raw input data for subsequent steps (such as data calibration, fusion and feature extraction).

[0020] Step S102: synchronously calibrating and dynamically fusing the multimodal data based on the spatiotemporal correlation model to obtain a fused data set with temporal consistency and spatial accuracy.

[0021] Step S102 is to synchronously calibrate and dynamically fuse the multimodal data acquired by various means to ensure the consistency of the data in time and space, and to provide an accurate basis for subsequent data processing and analysis.

[0022] First, multimodal data needs to be classified and organized for subsequent processing. The acquired data includes time series data (such as physical state data, electrical parameter data, and environmental meteorological data) and spatial data (such as appearance photos, videos, infrared temperature distribution, and transmission channel hidden danger data). These data are preliminarily classified and indexed according to their timestamps, collection equipment identification, and geographic location information to ensure that all data can accurately match their sources and collection times.

[0023] In the time dimension, the dynamic time warping method is used to synchronize and calibrate the time series data. First, the timestamps of all sensors and data acquisition devices are unified to eliminate errors caused by device clock drift. Then, the dynamic time warping algorithm is used to time-align data with different acquisition frequencies. For example, the high-frequency sampled electrical parameter data and the low-frequency sampled environmental meteorological data are matched at the time point to generate a time-consistent data sequence. For image and video data, the timestamp is associated with the time series data to ensure that relevant data from multiple sensors and devices can be integrated at the same time point.

[0024] In the spatial dimension, the data needs to be spatially mapped according to the geographical location of the acquisition equipment and the actual distribution of the transmission lines. By combining the geographical topology of the transmission lines, all data are mapped into a unified geographical coordinate system. For example, for the images and video data taken by the inspection personnel, the geographical location corresponding to the image is calibrated through the longitude and latitude information provided by the positioning device; for the temperature distribution data collected by the infrared camera, according to the installation position and shooting direction of the sensor, combined with the distance correction algorithm, the data is projected onto the spatial model of the transmission line. In this way, the spatial coordinates of all data can accurately reflect the actual location of the transmission line.

[0025] Subsequently, after time consistency and spatial mapping are completed, the multimodal data are dynamically fused. The key steps of fusion include weighted superposition of data and feature extraction. First, a weight is assigned to each data type. For example, the weight of physical state data and electrical parameter data in hidden danger assessment may be higher than that of environmental meteorological data. The fusion process performs weighted calculations on the data based on these weights to obtain a fused data set that can comprehensively reflect the current status of the transmission line. In this process, abnormal data is eliminated, for example, by identifying outliers that deviate significantly from other data through statistical analysis, and smoothing is performed to ensure data consistency.

[0026] Finally, the fused data is stored as a time-space fusion dataset in a unified format, which contains comprehensive status information at each time point and spatial location. This dataset is not only temporally consistent, but also accurately located in the spatial dimension, laying a reliable data foundation for feature extraction and hidden danger assessment in subsequent steps.

[0027] Furthermore, the synchronous calibration and dynamic fusion of the multimodal data based on the spatiotemporal correlation model to obtain a fused data set with temporal consistency and spatial accuracy includes: Acquire multimodal data of transmission lines; According to the timestamp information of the multimodal data, the data is sorted by the acquisition time to generate a preliminary time series; the data in the preliminary time series is calibrated by a dynamic time warping method, and the multimodal data collected at different times are time-aligned to generate time series data with consistent time; Based on the temporally consistent time series data and the geographic location information of the acquisition equipment in the multimodal data, combined with the geographic topology of the transmission line, the data is mapped to a unified geographic grid; the spatial distribution characteristics of the mapped data are interpolated and matched using a geographically weighted regression method to eliminate the spatial deviation caused by the position offset of the acquisition equipment, and generate spatially consistent multimodal data; Assign priorities to physical state data, electrical parameter data, ambient meteorological data, infrared temperature distribution data, and visual feature data based on the source characteristics, historical accuracy, and environmental conditions of each data type; According to the real-time monitoring results of the transmission line operation status, dynamically adjust the priority of the physical status data, electrical parameter data, environmental meteorological data, infrared temperature distribution data and visual feature data; According to the adjusted data priority, the temporally consistent and spatially consistent multimodal data are weightedly superimposed and fused to generate a fused dataset with temporal consistency and spatial accuracy.

[0028] First, multimodal data of the transmission line is obtained from multiple data acquisition devices, including sensors installed on the transmission line, mobile terminals carried by patrol personnel, and environmental monitoring satellites. Multimodal data includes physical state data (such as vibration and stress), electrical parameter data (such as current and voltage), environmental meteorological data (such as temperature, humidity, and wind speed), temperature distribution data captured by infrared cameras, and appearance photos and video data collected by patrol personnel. Each type of data is accompanied by a timestamp and the geographic location information of the acquisition device.

[0029] Next, all collected data are sorted in chronological order according to the timestamp information of the data to generate a preliminary time series. Since the sampling frequency and time synchronization accuracy of different devices may vary, the dynamic time warping method is used to calibrate the time series to ensure the alignment of various types of data in the time dimension. For example, for high-frequency sampled electrical parameter data and low-frequency sampled meteorological data, the time step is adjusted through the dynamic time warping algorithm so that they can be aligned to a common time axis. After calibration, the generated time series data is consistent in the time dimension.

[0030] After completing the time calibration, the multimodal data is mapped to a unified geographic grid using the geographic location information of the acquisition equipment and the geographic topology of the transmission line. The geographic grid is divided into small regional units based on the actual distribution of the transmission line. The boundary and center position of each unit are defined according to the specific spatial layout of the transmission line. For the data within each unit, the spatial distribution characteristics of the data are interpolated and matched using the geographically weighted regression method to compensate for the errors caused by the offset of the acquisition equipment position or the uneven spatial distribution. For example, when there are fewer temperature data sampling points in some areas, the temperature distribution is estimated by the interpolation method to generate spatially consistent multimodal data.

[0031] Based on spatially consistent data, priorities are assigned to each data type. Priorities are determined based on the source characteristics of the data, historical accuracy, and the impact of environmental conditions. For example, under weather conditions with high wind speed and high humidity, the importance of environmental meteorological data for defect assessment increases, and its priority is increased accordingly, while other data such as electrical parameters may have a relatively lower priority. Through statistical analysis of historical monitoring results, data priorities are further adjusted to make the same type of data dynamically adaptable in different operating scenarios.

[0032] Based on the above priority adjustment results, the time-consistent and space-consistent multimodal data are weighted superimposed and fused. The fusion process comprehensively considers the importance of each data type and its impact on specific problems. For example, physical state data and electrical parameter data may have higher weights in mechanical defect analysis, while visual feature data dominates in appearance defect recognition. Through the weighted superposition algorithm, the feature values ​​of each type of data are combined according to their priority, and finally a fused data set with time consistency and spatial accuracy is generated.

[0033] The generated fusion dataset fully reflects the state changes of the transmission line in time and space dimensions, and has the comprehensive characteristics of multimodal data. This dataset provides a high-quality foundation for subsequent feature extraction, hidden danger assessment and risk prediction, ensuring the accuracy and applicability of data processing.

[0034] Furthermore, the priority of the physical state data, electrical parameter data, environmental meteorological data, infrared temperature distribution data and visual feature data is dynamically adjusted according to the real-time monitoring result of the transmission line operation status, including: According to the monitoring results of the transmission line operation status, the transmission line is divided into a normal state and an abnormal state; wherein the normal state means that the transmission line operation parameters are within the set safety threshold range, and the abnormal state means that the transmission line operation parameters are outside the set safety threshold range; wherein, for the situation where the monitoring results cannot be obtained, the defect distribution is considered; when the operation status is normal, the initial priority of the data type is maintained, and data types with higher historical accuracy and credibility are given priority; when the operation status is abnormal, the data priority is dynamically adjusted according to the abnormal category.

[0035] First, the current operating status is classified through the real-time monitoring results of the transmission line operating status. The monitoring data of the transmission line operating status includes physical status data (such as vibration frequency, tension value), electrical parameter data (such as current, voltage), environmental meteorological data (such as wind speed, humidity, temperature), etc. The real-time monitoring system continuously collects this data and compares it with the pre-set safety threshold. If all monitoring parameters are within the set safety threshold range, the operating status is classified as normal; if any parameter exceeds the safety threshold range, the operating status is classified as abnormal.

[0036] Under normal conditions, the system maintains the initial priority of each data type by default. The initial priority is determined based on the historical accuracy and credibility of the data type. For example, physical state data and electrical parameter data may be given a higher priority because they are usually directly derived from high-precision sensor devices, while environmental meteorological data and visual feature data may have a relatively lower priority because they are subject to greater external interference. During data processing, the system prioritizes data with higher priority to ensure that key data is analyzed and used in a timely manner.

[0037] When the operating status is abnormal, the system will dynamically adjust the priority of the data type according to the specific category of the abnormality. For example, if the abnormality category is an electrical parameter abnormality (such as voltage or current outside the normal range), the priority of the electrical parameter data will be significantly increased, and the corresponding physical status data will also be given a higher priority due to possible associated problems (such as the risk of line breakage). For abnormalities that may be caused by environmental conditions (such as excessive wind speed or abnormal temperature), the priority of environmental meteorological data will be increased, and infrared temperature distribution data may be given a higher priority due to the importance of identifying temperature rise areas. In this dynamic adjustment, the priority of visual feature data may remain unchanged unless the abnormality involves appearance defects (such as broken insulators or loose lines).

[0038] Dynamically adjusted priorities will directly affect the order of data processing and resource allocation. Based on the adjusted priorities, the system processes high-priority data first to ensure that key information related to anomalies can be extracted and analyzed in a timely manner. Although low-priority data will still be processed, it may be delayed until the key data analysis is completed, or compressed to reduce the amount of calculation when necessary.

[0039] In this way, the system can efficiently utilize computing resources under normal conditions, and can quickly respond and concentrate resources to process key data under abnormal conditions, ensuring timely identification and processing of abnormal situations.

[0040] Step S103: Analyze the fused data set using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features, wherein the transmission line data asset includes environmental feature labels, defect feature labels, and hidden danger feature labels, wherein the environmental feature labels describe the potential impact of weather, temperature, humidity, and wind speed on the operation of the transmission line.

[0041] The core of step S103 is to use an adaptive feature extraction algorithm to deeply analyze the fused data set to extract and annotate key features of the transmission line operation status, thereby generating structured transmission line data assets. The implementation process of this step is described in detail below.

[0042] First, extract different categories of data from the fused data set, including environmental data, physical state data, electrical parameter data, and image and video data. These data have been time and space calibrated in step S102 and have consistency and accurate geographical mapping. For each type of data, feature extraction is performed according to its characteristics and application scenarios. For example, for environmental data, extract features such as the average wind speed, the change trend of humidity, and the frequency of extreme weather events; for electrical parameter data, extract features including current fluctuation range, voltage abnormality peak, and short-term power change rate.

[0043] Next, for image and video data, a convolutional neural network (CNN) is used to extract key visual features of the image. These features include the crack edges on the surface of the transmission line, the distribution of corrosion areas, and visual information of missing insulators. To improve the extraction accuracy, the original image data can be preprocessed, such as optimizing the image quality through edge enhancement and denoising algorithms, to ensure that the extracted features are accurate and physically meaningful.

[0044] For infrared temperature distribution data, the distribution of hot spots is identified through the thermal imaging feature extraction algorithm. Specifically, the infrared imaging data is associated with the spatial coordinates of the transmission line, and the area, temperature gradient and temperature rise of the hot spot area relative to the normal area are calculated. Through this process, it is possible to determine the areas of high temperature hazards that may exist in the transmission line, such as overloaded wires or heated joints.

[0045] All extracted feature data are further organized into structured data assets. In this process, an adaptive feature extraction algorithm is used to dynamically weight various features to ensure that the most relevant features are extracted first in different scenarios. For example, under strong wind conditions, the weight of wind speed changes in environmental features will be dynamically increased to prioritize the potential impact of wind speed on the operation of transmission lines. The final generated data assets are stored in a multi-dimensional structure, including environmental feature labels, defect feature labels, and hidden danger feature labels. Environmental feature labels mainly describe the potential impact of weather, temperature and humidity, and wind speed on the operation of transmission lines; defect feature labels are used to mark equipment problems such as cracks, corrosion, and missing insulators; hidden danger feature labels include high-risk area identification based on electrical parameters and thermal imaging data.

[0046] The structured data assets generated by the above method cover the multi-dimensional operating status information of the transmission line and can provide high-quality input for hidden danger assessment and risk prediction in subsequent steps. This process not only ensures the accuracy and comprehensiveness of the data, but also improves the applicability and dynamic response capabilities of the feature extraction results.

[0047] Furthermore, the method of parsing the fused data set using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features includes: Extracting multimodal data from the fused data set, including environmental data, physical state data, electrical parameter data, infrared temperature distribution data, and visual feature data, and grouping and preprocessing the multimodal data, wherein the environmental data and the electrical parameter data are normalized, and the visual feature data are image enhanced; Based on the pre-processed multimodal data, environmental features, defect features and hidden danger features are extracted respectively using a feature extraction algorithm, specifically including extracting environmental features that describe the impact of weather, temperature, humidity and wind speed on the operation of the transmission line based on environmental data; extracting defect features of cracks or corrosion areas on the surface of the transmission line based on visual feature data and infrared temperature distribution data; extracting hidden danger features that may cause transmission line operation risks based on physical state data, the conditions of buildings and trees in the transmission line channel and electrical parameter data; The extracted environmental features, defect features and hidden danger features are respectively generated into environmental feature labels, defect feature labels and hidden danger feature labels, and are associated with their time and space information to generate annotated transmission line data labels; According to the generated annotated transmission line data labels, environmental feature labels, defect feature labels and hidden danger feature labels are organized into structured transmission line data assets. The transmission line data assets include time dimension, space dimension and feature dimension, which are used for transmission line operation status assessment and hidden danger prediction.

[0048] Multimodal data is extracted from the fused data set, including environmental data, physical state data, electrical parameter data, infrared temperature distribution data, and visual feature data. These data have been time-space calibrated and dynamically fused in the previous steps, and have temporal consistency and spatial accuracy. In order to improve the accuracy and efficiency of data processing, the extracted data needs to be grouped and preprocessed. Specifically, due to the differences in numerical ranges and units, environmental data and electrical parameter data are normalized to eliminate the influence of dimensions. For example, data such as wind speed, humidity, and voltage are standardized to the range of 0 to 1. The visual feature data is processed by image enhancement, including adjusting the image contrast and brightness, eliminating low-quality images, and using edge enhancement algorithms to improve the recognizability of cracks or corrosion areas.

[0049] Based on the pre-processed multimodal data, the adaptive feature extraction algorithm is used to extract environmental features, defect features and hidden danger features respectively. The extraction of environmental features is mainly based on environmental data. The feature description is generated by analyzing the impact of weather, temperature, humidity and wind speed on the operation of transmission lines. For example, high wind speed may cause the conductor swing amplitude to increase, and increased humidity may accelerate the corrosion of insulators. Defect feature extraction targets visual feature data and infrared temperature distribution data, and uses specific algorithms to detect cracks, corrosion areas and infrared temperature rise hot spots in the image. For example, the length and depth of the crack are extracted through image segmentation technology, and the range of the corrosion area is marked; abnormal hot spots are identified through temperature gradient analysis. The extraction of hidden danger features is based on physical state data and electrical parameter data. For example, potential mechanical structure problems are identified through abnormal changes in vibration frequency, and possible short circuit or overload hazards are detected through current or voltage fluctuations.

[0050] After the extraction is completed, the corresponding labels are generated for the environmental features, defect features and hidden danger features, and associated with the time and space information. Each feature label not only contains the descriptive information of the feature, but also records the time point and spatial location of the feature. For example, the environmental feature label may mark that the wind speed at a certain point in time is 20 meters per second, and the affected area is a specific section of a transmission line; the defect feature label may mark that a crack appears on the insulator of a tower, and the crack length is 2 centimeters; the hidden danger feature label may indicate that the current fluctuation of a certain section of the transmission line exceeds the safety threshold at a specific time point.

[0051] Finally, all feature tags are organized into structured transmission line data assets, which include time dimension, space dimension and feature dimension. For example, the time dimension records the occurrence time of the feature, the space dimension records the geographical location of the feature or the corresponding transmission line segment, and the feature dimension classifies and records detailed descriptions of environmental features, defect features and hidden danger features. This structured data asset provides a basis for the comprehensive evaluation of the operation status of the transmission line, and also supports the accurate prediction of hidden dangers.

[0052] Step S104: Based on the association between the environmental feature tags and the defect feature tags, the possibility of environmental conditions triggering transmission line defects is evaluated.

[0053] The goal of step S104 is to evaluate the possibility of environmental conditions triggering transmission line defects based on the association between the environmental feature tags and the defect feature tags. The following is a detailed description of the specific implementation process.

[0054] First, environmental feature labels and defect feature labels are extracted from the structured transmission line data assets. Environmental feature labels include key parameters such as wind speed, temperature, and humidity. These parameters are collected by sensors or satellites, and after time-space calibration and dynamic fusion, they have temporal and spatial consistency. Defect feature labels include information such as crack length, corrosion area, insulator missing, and temperature rise area identified in infrared thermal imaging on the surface of the transmission line. These labels are generated by feature extraction algorithms in the previous steps.

[0055] Next, establish the correlation between environmental characteristics and defect characteristics. Specifically, by analyzing historical data, determine the degree of influence of each environmental parameter on the triggering of a specific defect. For example, the correlation between wind speed and crack growth can be quantified by the growth rate of crack length with wind speed; the arc effect caused by temperature and insulator loss is represented by the statistical relationship between temperature rise and fault occurrence rate. These correlations can be modeled using regression models, decision trees or other statistical analysis methods to form a complete set of environment-defect correlation rules.

[0056] Then, the environmental characteristic data and defect characteristic data collected in real time are evaluated using the above rules. Specifically, the possibility of each defect triggering under the current environmental conditions is calculated one by one. For example, to calculate the possibility of wind speed causing crack extension under the current conditions, the current wind speed can be substituted into the correlation model and predicted in combination with the initial value of the crack length and its expansion rate. Similarly, for the possibility of insulator loss due to temperature rise, the abnormal degree of the temperature rise area is calculated and combined with the historical defect records of the insulator for analysis. For complex correlation situations, multivariate models or analysis methods based on conditional probability can be introduced to more accurately evaluate the comprehensive impact of the environment on defect triggering.

[0057] After the above calculations are completed, the triggering probability of each defect is standardized to ensure that the probability values ​​of different categories can be compared on the same scale. By comprehensively scoring the probability of each type of defect, an evaluation result of the overall defect triggering risk is generated. This score can be divided into multiple levels as needed, such as low risk, medium risk and high risk, so as to be used for hidden danger propagation path analysis and risk warning in subsequent steps.

[0058] Ultimately, all assessment results will be associated with the time and space information corresponding to the environment and defect characteristics to form a set of detailed defect trigger possibility assessment data. These data can be directly used for subsequent hidden danger propagation path modeling, and can also provide targeted suggestions for operation and maintenance personnel to optimize the inspection and maintenance strategy of the transmission line. The whole process is simple and efficient, ensuring the accuracy and practicality of the assessment results.

[0059] Furthermore, the evaluating the possibility of environmental conditions triggering transmission line defects according to the association between the environmental feature tag and the defect feature tag includes: Obtain environmental feature labels, including key environmental parameters such as wind speed, temperature and humidity, and precipitation, as well as their corresponding spatiotemporal distribution; Obtain defect feature labels, including crack length, corrosion area, and infrared temperature abnormality hotspot distribution on the surface of the transmission line.

[0060] First, extract key parameters from environmental feature labels, including wind speed ,humidity , precipitation and its corresponding temporal and spatial distribution. These data come from meteorological sensors or environmental monitoring satellites installed around the transmission line. Secondly, the physical damage information of the transmission line, such as crack length, is extracted from the defect feature labels. Corrosion area , and hotspot temperatures identified by infrared imaging These feature data have been generated through the previous steps to ensure the consistency of time and space.

[0061] According to the following formula (1), the possibility of crack extension under different environmental conditions is calculated: ; in, is the crack triggering factor, reflecting the possibility of crack extension under different environmental conditions; is the crack length; is the wind speed; , , is a sensitivity parameter obtained through historical data and model training; Crack triggering factor It reflects the possibility of crack extension under the current environmental conditions. It is identified by visual feature extraction algorithm, for example, the initial length of a crack on a transmission line is 10 mm. Derived from real-time meteorological data, for example, the current wind speed is 20 meters per second. Sensitivity parameters It is obtained by regression analysis or model training of historical crack growth data. For example, under similar wind speed conditions, the growth rate will show a certain growth trend for every 1 mm increase in crack length. The term quantifies the coupling effect between wind speed and crack length, that is, the higher the wind speed, the more significant the effect on the growth of long cracks.

[0062] The possibility of corrosion development is calculated according to the following formula (2): ; in, It is a corrosion trigger factor, reflecting the possibility of corrosion development; is the corrosion area; for humidity; is the precipitation; , , is a sensitivity parameter obtained through historical data and model training; Corrosion triggers The accelerating effect of humidity and precipitation on the development of corrosion is quantified. Obtained by visual feature extraction algorithm, for example, the corrosion area of ​​a certain area is 15 square millimeters. Humidity and precipitation It is provided by the meteorological sensor, for example, the current humidity is , the precipitation is 5 mm. Parameters It is obtained by analyzing the expansion data of the corrosion area under different humidity and precipitation conditions. For example, when the humidity is high, the logarithmic growth factor of the corrosion rate will increase significantly, and the effect of precipitation is further amplified through the exponential term.

[0063] The possibility of high temperature aggravating crack growth is calculated according to the following formula (3): ; in, It is the temperature rise trigger factor, reflecting the possibility of high temperature aggravating crack growth; is the temperature value based on the infrared temperature hotspot; is the crack length; , is the influence coefficient, obtained through historical data and model training; Temperature rise trigger factor It reflects the combined effect of infrared temperature hotspot and crack length. Hotspot temperature It is captured by infrared imaging, for example, the temperature at a certain point is 80 degrees Celsius. It indicates the direct and indirect effects of high temperature on crack growth, usually obtained by fitting experimental data. The square root term describes the nonlinear acceleration effect of high temperature on longer cracks, that is, the longer the crack, the greater the potential risk of high temperature to its extension.

[0064] According to the following formula (4), calculate the comprehensive defect trigger probability value : ; in, , , is the weight coefficient, obtained through historical data and model training; if If the value is greater than or equal to the specified threshold, a defect trigger warning is generated and the risk type is recorded; if If it is less than the specified threshold, the low impact of the current environmental conditions on the defect trigger is recorded.

[0065] Step S105: Based on the evaluation results of the hidden danger feature labels and environmental conditions, a graph network model is constructed with transmission lines as nodes and sensor data as edge weights, and the graph network model is used to analyze the defect propagation path of the transmission lines and their channels; based on historical inspection data and environmental dynamic parameters, the graph network model is used to predict the time when the hidden danger will occur.

[0066] The core of step S105 is to build a graph network model with transmission lines as nodes and sensor data as edge weights based on the evaluation results of hidden danger feature labels and environmental conditions, and use this model to analyze the defect propagation path and predict the time when hidden dangers will occur. The following is the detailed implementation process of this step.

[0067] First, the basic structure of the graph network model needs to be defined. Transmission lines and their ancillary facilities (such as towers, insulators, etc.) are used as nodes of the graph network. The attributes of each node include its hidden danger feature label and the evaluation results of environmental conditions, such as crack length, corrosion area, hot spot temperature, and wind speed and humidity in adjacent areas. The edges between nodes represent physical or functional associations, such as electrical connections between transmission lines, geographical proximity, or associations exposed to similar environmental conditions. The weight of the edge is determined by the following factors: first, geographical distance, nodes with closer distances have a higher probability of propagation; second, the correlation of hidden danger features, such as the presence of similar cracks or temperature anomalies at two nodes; third, the similarity of environmental conditions, such as adjacent nodes being affected by the same wind speed or humidity.

[0068] Next, the basic topology of the graph network is constructed by setting the initial properties of the nodes and edges. In the node initialization stage, the hidden danger characteristics and environmental conditions of each node are used as input vectors. For example, the initial hidden danger feature vector of the node may include crack length, temperature rise amplitude, and defect trigger probability calculated based on historical data. The weight initialization of the edge is based on the above-mentioned geographical, hidden danger and environmental associations, and the specific value is calculated by formula. After the initialization of all nodes and edges is completed, a complete weighted graph network is formed.

[0069] Subsequently, the defect propagation path is analyzed on the graph network. The hidden danger propagation state of each node is iteratively updated through the graph network propagation algorithm (such as graph convolutional network, GCN). Specifically, the hidden danger propagation state of each node is weighted by its own characteristics and the characteristics of neighboring nodes through edge weights. For example, the possibility of hidden danger propagation of a node will be affected by the hidden danger characteristics and propagation state of adjacent nodes. At the same time, the role of edge weights is considered, so that nodes with farther distances or lower hidden danger correlation contribute less to propagation. After multiple rounds of propagation iterations, the hidden danger propagation state tends to be stable, and the main paths and high-risk areas of hidden danger propagation can be determined at this time.

[0070] On this basis, the time of hidden danger occurrence is further predicted by combining historical patrol data and environmental dynamic parameters. Time prediction is mainly based on the hidden danger propagation status of the node and the time distribution of hidden danger development under similar conditions in historical data. For example, the crack growth rate of the node combined with the time model of historical data can predict when the crack may develop to the critical value. Similarly, the growth trend of the hot spot temperature combined with the overheating failure time in the historical record can estimate the time point of the failure. Through these calculations, the hidden danger occurrence time of each node is assigned and recorded in the graph network model.

[0071] Finally, the results of the entire graph network model include the analysis of the hidden danger propagation path and the prediction of the hidden danger occurrence time of each node. The propagation path provides the possible hidden danger expansion trend, while the time prediction calibrates the time distribution of hidden danger risk for each node. These results provide a direct basis for generating risk warnings and formulating maintenance plans in subsequent steps, and ensure the accuracy and practicality of the prediction.

[0072] Furthermore, the construction of a graph network model with transmission lines as nodes and sensor data as edge weights based on the hidden danger feature labels and the evaluation results of environmental conditions includes: The transmission lines are regarded as nodes of the graph network, and the attributes of the nodes include hidden danger feature labels, environmental feature labels and historical inspection data features.

[0073] In the construction of the graph network, the transmission lines and their ancillary facilities (such as towers, insulators, etc.) are first defined as nodes of the graph network. The attributes of each node include hidden danger feature labels (such as crack length, corrosion area, temperature anomaly, etc.), environmental feature labels (such as wind speed, humidity, precipitation, etc.), and historical inspection data features (such as the number of defect repairs, historical hidden danger records, etc.). These attributes are obtained through the multimodal data extracted and processed in the previous steps, and have temporal and spatial consistency.

[0074] According to the following formula 5, the physical proximity weight between nodes is calculated: ; in, Representation Node and nodes The physical proximity weight between Representation Node and nodes The physical distance between Representation Node and nodes The electrical transmission distance between is the distance sensitivity coefficient, obtained through historical analysis, and used to adjust the impact of distance on weight; According to the following formula (6), the environmental similarity weight between nodes is calculated: ; in, Representation Node and nodes The weight of the environmental similarity between them; To represent the node and nodes The Euclidean distance between the nodes is calculated based on the similarity of the environmental features between the nodes.

[0075] For example, if the node The environmental characteristics are wind speed 10 m / s, humidity , precipitation 5 mm, node The environmental characteristics are wind speed 12 m / s, humidity , precipitation is 4 mm, then the Euclidean distance is: ; When the environmental conditions are more similar, the Euclidean distance is smaller and the environmental similarity weight is higher, and vice versa.

[0076] According to the following formula (7), the hidden danger correlation weight between nodes is calculated: ; in, Representation Node and nodes The hidden danger correlation weight between them; For Node and nodes The angle between the hidden danger eigenvectors of two nodes.

[0077] Representation Node and nodes The risk relevance weight of . is the angle between the hidden danger feature vectors of two nodes. and The hidden danger features are crack length 10 mm, corrosion area 20 square millimeters, and crack length 8 mm, corrosion area 25 square millimeters. The hidden danger feature vector is and The cosine of the angle can be calculated using the inner product formula: ; The closer the cosine value is to 1, the higher the similarity of the hidden danger characteristics of the two nodes is.

[0078] According to the following formula (8), the edge weights between nodes are calculated: ; in, For Node and nodes The edge weights between ; is the weighting coefficient, determined according to historical data and satisfies .

[0079] Construct a weighted graph based on the nodes and edge weights. , in represents the set of transmission line nodes, Represents the edge set; initializes the attribute vector of each node, combines the node characteristics with the weight distribution of its adjacent nodes, and generates the initial hidden danger propagation state.

[0080] Furthermore, the method of predicting the time of occurrence of hidden dangers by using the graph network model based on historical patrol data and environmental dynamic parameters includes: First, based on the node and edge weights constructed by the graph network model, the propagation process of hidden dangers is simulated. In the graph network, each node represents a part of the transmission line or related facilities, and its attributes include hidden danger feature labels and environmental feature labels. The edge weights reflect the physical distance between nodes, environmental similarity, and hidden danger correlation. The simulation of hidden danger propagation is based on the graph convolutional network (GCN), which updates the hidden danger propagation status of the node through layer-by-layer iterative calculation.

[0081] According to the following formula (9), for each node, the hidden danger propagation state of the node is updated by combining the attribute vector and edge weight of its adjacent nodes: ; in, Indicates Layer propagation node Status; Indicates Layer propagation node Status; For Node The set of neighbor nodes of is the weight matrix obtained through training; is the activation function; Repeat the propagation iterations until the node states converge.

[0082] In this formula, Representation Node In the The updated state after layer propagation. Initial state Is a node Initial hidden danger characteristics, such as the characteristic vector calculated by the crack length, corrosion area and temperature rise value. Neighbor node set Included with node All nodes connected. For example, if the node is connected to three other nodes, whose neighbor set is . Represents neighbor nodes In the The state after layer propagation.

[0083] Weight Is a node and The edge weights between them are obtained by the weight calculation formula (such as the combination of physical proximity weight, environmental similarity weight and hidden danger association weight). The weight matrix is ​​obtained through historical data training and is used to weight the different characteristics of hidden danger propagation. For example, if the historical data shows that crack-related hidden dangers propagate faster, the matrix The weight value corresponding to the crack feature is higher. Activation function It is a nonlinear transformation function. Common choices include ReLU or sigmoid. Its function is to limit the range of state values ​​to make it suitable for subsequent calculations.

[0084] The propagation process is implemented through layer iteration, and each propagation updates the node status until the node status converges. For example, after the first layer of propagation, the node The state of will be updated by integrating the state of its neighboring nodes and edge weight information; in the second layer of propagation, the updated state continues to merge with the information of more distant nodes. The number of iterations of this process is determined by the parameter Control usually depends on the depth of the network and the scope of information dissemination.

[0085] After the hidden danger propagation status is updated, the node is calculated according to the following formula 10 The time of occurrence of potential hazards : ; in, is the time when the initial hidden danger occurred, is the number of propagation iterations; Indicates Layer propagation node status.

[0086] In the formula Is a node The time when potential hazards occur, It is the initial hazard occurrence time, for example, it can be set to the current time or the first discovery time of a known hazard based on historical data. The value after each propagation is mapped to by the sigmoid function The interval reflects the cumulative impact of hidden dangers. The possibility of cumulative hidden dangers is calculated by summing the state values ​​of all propagation layers.

[0087] For example, suppose the node The initial hidden danger state is , after three propagations, the state values ​​are When calculating the time of occurrence of hidden dangers, the initial time , but: ; The specific calculation results for each item are and 0.750, so the hidden danger occurs at , indicating that the node The hidden danger may occur after about 2 units of time.

[0088] Step S106: Based on the prediction results of the graph network model, a defect risk level warning is generated, and a transmission line hidden danger distribution map is generated in combination with environmental meteorological data. The hidden danger distribution map is used to assist in formulating inspection and maintenance plans.

[0089] The goal of step S106 is to generate a defect risk level warning based on the prediction results of the graph network model, and generate a transmission line hidden danger distribution map in combination with environmental meteorological data to assist in formulating an efficient inspection and maintenance plan. The implementation method of this step is described in detail below.

[0090] First, based on the prediction results of the graph network model, a comprehensive analysis is conducted on the hidden danger propagation status, risk probability, and predicted hidden danger occurrence time of each node. For each node, its defect risk score is calculated based on its hidden danger triggering possibility, environmental dynamic parameters, and importance on the propagation path. This score can be determined by combining multiple factors through a weighted formula. For example, the risk score is inversely proportional to the predicted hidden danger occurrence time, that is, the node with the hidden danger occurring earlier has a higher risk score, while considering the impact of hidden danger propagation on the node at other nodes on the propagation path. These scores are divided into multiple levels, such as low risk, medium risk, and high risk, and each node is labeled with the corresponding risk level.

[0091] When generating risk level warnings, high-risk areas are identified as key areas. By analyzing the propagation path of the graph network model, the central nodes of hidden danger propagation and the scope of possible impact are identified. In high-risk areas, the risk level and main risk types are recorded, such as potential disconnection risks caused by crack propagation and overheating hazards caused by temperature rise hot spots. These warning information will be classified and sorted, and the risk level warning data for subsequent analysis will be output.

[0092] Next, combined with environmental meteorological data, the risk information is mapped to geographic space to generate a transmission line hidden danger distribution map. The hidden danger distribution map intuitively presents the overall operational risk status of the transmission line by superimposing environmental dynamic data and risk level information. For example, high-risk areas on the map can be marked with colors, and the darker the color, the higher the risk; in addition, environmental data such as wind speed direction, humidity distribution, etc. can be superimposed on the map to provide contextual information for further analysis. The hidden danger distribution map also includes detailed information on specific nodes, such as the specific location of the node, risk level and risk type. This information can help operators quickly locate high-risk areas.

[0093] Finally, the hidden danger distribution map provides a basis for inspection and maintenance plans. According to the risk level and hidden danger type in the distribution map, inspection personnel are given priority to inspect high-risk areas, such as areas with significant crack extension or abnormal temperature rise. At the same time, maintenance plans can be formulated based on the distribution map, such as repairing hidden danger nodes that may cause serious consequences in advance or optimizing inspection routes to cover high-risk areas. This process provides precise support for operation and maintenance by analyzing the spatial characteristics and risk levels of the hidden danger distribution map, ensuring the rational allocation of inspection and maintenance resources.

[0094] Furthermore, the prediction results based on the graph network model generate a defect risk level warning, and generate a transmission line hidden danger distribution map in combination with environmental meteorological data, including: According to the hidden danger propagation status value of each node in the graph network model, combined with environmental meteorological data and historical inspection records, the defect risk score of each node is calculated, and the nodes are divided into low risk, medium risk and high risk levels according to the preset level standards; Based on the geographical location and risk level of the node, the risk information of the transmission line and its ancillary facilities is mapped to the geographic space to generate a transmission line hidden danger distribution map, wherein the hidden danger distribution map is used to show the distribution range and risk intensity of high-risk areas; According to the hidden danger distribution map, the key nodes and impact range of the high-risk area are identified, defect risk warning information is generated, and corresponding inspection and maintenance suggestions are provided.

[0095] First, the hidden danger propagation status value of each node is obtained according to the prediction results of the graph network model. These values ​​represent the degree of risk accumulation of the node in the hidden danger propagation path. The defect risk score of each node is calculated by combining environmental meteorological data (such as wind speed, humidity, precipitation, etc.) and historical inspection records (such as the number of defect repairs and inspection frequency). The calculation of the risk score combines the influence of the hidden danger propagation status value, the role of the current environmental conditions in promoting the triggering of hidden dangers, and the probability of defect occurrence at the node in historical data. For example, if the hidden danger propagation status value of a node is high, and the wind speed and precipitation in the area where the node is located are at abnormal levels, and historical records show that there are multiple defect repair records for the node, the risk score of the node will be significantly improved.

[0096] The calculated defect risk scores are standardized to a unified range, and the nodes are divided into low risk, medium risk and high risk levels according to the preset risk level standards. The level division can be achieved by setting the risk score threshold, for example, low risk is a score less than 0.4, medium risk is 0.4 to 0.7, and high risk is above 0.7. This level division makes it easy to clarify the risk level of each node and provide a basis for subsequent visualization and decision-making.

[0097] Next, the risk level of the node is associated with its geographic location to show the risk distribution in geographic space. By mapping the location information and risk level of each node to geographic space, a transmission line hidden danger distribution map is generated. In the hidden danger distribution map, high-risk areas are marked with prominent colors or icons, such as red for high-risk areas, yellow for medium-risk areas, and green for low-risk areas. The hidden danger distribution map not only shows the node risk level, but also shows the intensity distribution of high-risk areas in a gradient manner. For example, nodes with higher risk scores and their adjacent areas will appear dark red, while nodes with lower risks will appear light yellow or green. This visualization method intuitively reflects the risk status of the transmission line, making it easier for operators to quickly identify key hidden dangers.

[0098] Based on the hidden danger distribution map, the key nodes and impact range of high-risk areas can be further analyzed. Key nodes refer to nodes with high risk levels and significant impact on hidden danger propagation, such as nodes located on the main propagation path or nodes with strong associations with multiple adjacent nodes. The impact range refers to the potential hidden danger area propagated from high-risk nodes to surrounding nodes. By analyzing key nodes and impact ranges, the main concentrated areas of risks and the possibility of hidden danger propagation can be determined, thereby generating targeted defect risk warning information.

[0099] Finally, combined with the generated warning information, corresponding inspection and maintenance suggestions are provided. For example, for high-risk areas, it can be recommended to arrange priority inspections, check the actual status of key nodes, and conduct detailed inspections of related equipment; for medium-risk areas, regular maintenance can be planned to reduce the possibility of hidden dangers accumulation; for low-risk areas, it is recommended to maintain normal inspection frequency. Such maintenance suggestions can optimize the allocation of inspection resources, improve inspection efficiency, and ensure that high-risk areas receive priority attention.

[0100] Through the above steps, the prediction results based on the graph network model not only realize the accurate assessment of the hidden dangers of the transmission line, but also provide clear guidance for the actual operation and maintenance through the generation of visualization and early warning information.

[0101] Furthermore, the transmission line patrol data asset management method further includes: A visualization model of the transmission line is constructed through laser point cloud data and a three-dimensional geographic information system. The visualization model is used to dynamically display the defect hotspot map and hidden danger distribution map of the transmission line, and supports users to select display dimensions and analysis results as needed.

[0102] First, the spatial structure of the transmission line is modeled using a 3D geographic information system. By collecting geographic coordinate data of the transmission line and its surrounding environment, including information such as tower location, line direction, and height, the basic 3D geometric model of the transmission line is generated in combination with 3D terrain data. The model needs to accurately reflect the distribution of the transmission line in real space, including the starting point and end point of the line, as well as the relative height difference and distance between towers. In addition, the bottom terrain of the transmission line model is rendered using high-precision terrain data to provide a real geographical background.

[0103] On the basis of the three-dimensional model, dynamic data is further superimposed to realize the visualization of temperature flow distribution, defect hotspot map and hidden danger distribution map. The data of temperature flow distribution comes from the surface temperature information of the transmission line collected by infrared cameras or temperature sensors. By interpolating and rendering the temperature data, the temperature distribution is superimposed on the surface of the transmission line in the form of color gradient. The defect hotspot map is generated based on the data of the defect feature label. For example, areas with larger crack lengths can be marked with dark red, and areas with severe corrosion can be marked with orange. The hidden danger distribution map is generated based on the analysis results of the graph network model. The areas with higher risk scores are highlighted, and the corresponding hidden danger types are marked on the map, such as high temperature hotspots, mechanical damage or external threats.

[0104] In order to enhance the user's interactive experience, the visualization model supports users to select display dimensions and analysis results as needed. For example, users can choose to view only the temperature flow distribution of a specific area through the interface, or they can view only areas involving high-risk hidden dangers through filtering conditions. Users can also zoom in to view the detailed information of a tower by adjusting the viewing angle, or zoom out to view the entire transmission line. In addition, the system supports layered display function, and users can choose to overlay the temperature flow distribution and hidden danger distribution map at the same time, or view the defect hotspot map separately.

[0105] The visualization model also supports real-time update. By linking with the transmission line sensors and the background analysis system, the model can dynamically update the status information of the transmission line. For example, when the sensor detects a new temperature anomaly area or the analysis results of the hidden danger propagation path change, the model can re-render and display the latest distribution within seconds, thus ensuring that users obtain the latest transmission line status.

[0106] Through the above implementation, the three-dimensional geographic information system can not only provide spatial visualization of the transmission line, but also intuitively present complex operating status and hidden danger information to users, while providing flexible interactive functions, allowing users to customize analysis and display solutions according to specific needs. This method has important application value in the operation monitoring, hidden danger investigation and maintenance plan formulation of transmission lines.

[0107] In the above-mentioned embodiment, a method for managing transmission line patrol data assets is provided. Correspondingly, the present application also provides a transmission line patrol data asset management system. Figure 2 , which is a schematic diagram of an embodiment of a power line patrol data asset management system of the present application. Since this embodiment, i.e., the second embodiment, is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described below is only illustrative.

[0108] A second embodiment of the present application provides a transmission line patrol data asset management system, comprising: The acquisition unit 201 is used to acquire multimodal data related to the inspection of the transmission line through various sensors installed on the transmission line and the surrounding environment, sensors carried by helicopters and drones, mobile terminals carried by inspection personnel, and data acquisition devices of environmental monitoring satellites, wherein the multimodal data includes physical state data of the transmission line, electrical parameter data, environmental meteorological data, appearance photos and video data of the transmission line, temperature distribution data captured by infrared cameras, defect data of the transmission line, and hidden danger data of the transmission channel; An obtaining unit 202 is used to synchronously calibrate and dynamically fuse the multimodal data based on a spatiotemporal correlation model to obtain a fused data set with temporal consistency and spatial accuracy; A generating unit 203 is used to parse the fused data set using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features, wherein the transmission line data asset includes an environmental feature label, a defect feature label, and a hidden danger feature label, wherein the environmental feature label describes the potential impact of weather, temperature and humidity, and wind speed on the operation of the transmission line; An evaluation unit 204, configured to evaluate the possibility of environmental conditions triggering transmission line defects according to the association between the environmental feature tags and the defect feature tags; A construction unit 205 is used to construct a graph network model with transmission lines as nodes and sensor data as edge weights according to the hidden danger feature labels and the evaluation results of the environmental conditions, wherein the graph network model is used to analyze the defect propagation path of the transmission line and its channel, and predict the time and spatial distribution of hidden danger occurrence based on historical inspection data and environmental dynamic parameters; The combination unit 206 is used to generate a defect risk level warning based on the prediction results of the graph network model, and generate a transmission line hidden danger distribution map in combination with environmental meteorological data. The hidden danger distribution map is used to assist in formulating inspection and maintenance plans.

[0109] A third embodiment of the present application provides an electronic device, the electronic device comprising: processor; The memory is used to store a program, and when the program is read and executed by the processor, it executes a transmission line patrol data asset management method provided in the first embodiment of the present application.

[0110] The fourth embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, a transmission line patrol data asset management method provided in the first embodiment of the present application is executed.

[0111] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A method for managing transmission line patrol data assets, characterized in that: include: Through the use of various sensors installed on the transmission lines and the surrounding environment, sensors carried by helicopters and drones, mobile terminals carried by patrol personnel, and data acquisition devices of environmental monitoring satellites, multimodal data related to the inspection of transmission lines are obtained, wherein the multimodal data includes physical state data of the transmission lines, electrical parameter data, three-dimensional laser point cloud data, environmental meteorological data, appearance photos and video data of the transmission lines, temperature distribution data and infrared photos captured by infrared cameras, defect data of the transmission lines, and hidden danger data of the transmission channels; Based on the spatiotemporal correlation model, the multimodal data are synchronously calibrated and dynamically fused to obtain a fused data set with temporal consistency and spatial accuracy; The fused data set is parsed using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features, wherein the transmission line data asset includes an environmental feature label, a defect feature label, and a hidden danger feature label, wherein the environmental feature label describes the potential impact of weather, temperature and humidity, and wind speed on the operation of the transmission line; According to the association between the environmental characteristic tag and the defect characteristic tag, evaluating the possibility of environmental conditions triggering the transmission line defect; According to the evaluation results of the hidden danger feature labels and environmental conditions, a graph network model is constructed with the transmission line as the node and the sensor data as the edge weight, and the graph network model is used to analyze the defect propagation path of the transmission line and its channel; based on the historical inspection data and environmental dynamic parameters, the graph network model is used to predict the time when the hidden danger occurs; Based on the prediction results of the graph network model, a defect risk level warning is generated, and a transmission line hidden danger distribution map is generated in combination with environmental meteorological data. The hidden danger distribution map is used to assist in formulating inspection and maintenance plans.

2. The power transmission line patrol data asset management method according to claim 1, characterized in that: Also includes: A visualization model of the transmission line is constructed through laser point cloud data and a three-dimensional geographic information system. The visualization model is used to dynamically display the defect hotspot map and hidden danger distribution map of the transmission line, and supports users to select display dimensions and analysis results as needed.

3. The power transmission line patrol data asset management method according to claim 1, characterized in that: The synchronous calibration and dynamic fusion of the multimodal data based on the spatiotemporal correlation model to obtain a fused data set with temporal consistency and spatial accuracy includes: Acquire multimodal data of transmission lines; According to the timestamp information of the multimodal data, the data is sorted by the acquisition time to generate a preliminary time series; the data in the preliminary time series is calibrated by a dynamic time warping method, and the multimodal data collected at different times are time-aligned to generate time series data with consistent time; Based on the temporally consistent time series data and the geographic location information of the acquisition equipment in the multimodal data, combined with the geographic topology of the transmission line, the data is mapped to a unified geographic grid; the spatial distribution characteristics of the mapped data are interpolated and matched using a geographically weighted regression method to eliminate the spatial deviation caused by the position offset of the acquisition equipment, and generate spatially consistent multimodal data; Assign priorities to physical state data, electrical parameter data, ambient meteorological data, infrared temperature distribution data, and visual feature data based on the source characteristics, historical accuracy, and environmental conditions of each data type; According to the monitoring results of the transmission line operation status, dynamically adjust the priorities of the physical status data, electrical parameter data, environmental meteorological data, infrared temperature distribution data and visual feature data; According to the adjusted data priority, the temporally consistent and spatially consistent multimodal data are weightedly superimposed and fused to generate a fused dataset with temporal consistency and spatial accuracy.

4. The transmission line patrol data asset management method according to claim 3, characterized in that: The method of dynamically adjusting the priorities of the physical state data, electrical parameter data, environmental meteorological data, infrared temperature distribution data and visual feature data according to the real-time monitoring results of the transmission line operation status includes: According to the monitoring results of the transmission line operation status, the transmission line is divided into a normal state and an abnormal state; wherein the normal state means that the transmission line operation parameters are within the set safety threshold range, and the abnormal state means that the transmission line operation parameters are outside the set safety threshold range; wherein, in the case where the monitoring results cannot be obtained, the defect distribution is considered; When the operating status is normal, the initial priority of the data type is maintained, and data types with higher historical accuracy and credibility are given priority; when the operating status is abnormal, the data priority is dynamically adjusted according to the abnormal category.

5. The power transmission line patrol data asset management method according to claim 1, characterized in that: The method of parsing the fused data set using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features includes: Extracting multimodal data from the fused data set, including environmental data, physical state data, electrical parameter data, infrared temperature distribution data, and visual feature data, and grouping and preprocessing the multimodal data, wherein the environmental data and the electrical parameter data are normalized, and the visual feature data are image enhanced; Based on the pre-processed multimodal data, environmental features, defect features and hidden danger features are extracted respectively using a feature extraction algorithm, specifically including extracting environmental features that describe the impact of weather, temperature, humidity and wind speed on the operation of the transmission line based on environmental data; extracting defect features of cracks or corrosion areas on the surface of the transmission line based on visual feature data and infrared temperature distribution data; extracting hidden danger features that may cause transmission line operation risks based on physical state data, the conditions of buildings and trees in the transmission line channel and electrical parameter data; The extracted environmental features, defect features and hidden danger features are respectively generated into environmental feature labels, defect feature labels and hidden danger feature labels, and are associated with their time and space information to generate annotated transmission line data labels; According to the generated annotated transmission line data labels, environmental feature labels, defect feature labels and hidden danger feature labels are organized into structured transmission line data assets. The transmission line data assets include time dimension, space dimension and feature dimension, which are used for transmission line operation status assessment and hidden danger prediction.

6. The power transmission line patrol data asset management method according to claim 1, characterized in that: The step of evaluating the possibility of environmental conditions triggering transmission line defects according to the association between the environmental feature tag and the defect feature tag includes: Obtain environmental feature labels, including key environmental parameters such as wind speed, temperature and humidity, and precipitation, as well as their corresponding spatiotemporal distribution; Obtain defect feature labels, including crack length, corrosion area, and infrared temperature abnormality hotspot distribution on the surface of the transmission line; According to the following formula (1), the possibility of crack extension under different environmental conditions is calculated: ； in, is the crack triggering factor, reflecting the possibility of crack extension under different environmental conditions; is the crack length; is the wind speed; , , is a sensitivity parameter obtained through historical data and model training; The possibility of corrosion development is calculated according to the following formula (2): ； in, It is a corrosion trigger factor, reflecting the possibility of corrosion development; is the corrosion area; for humidity; is the precipitation; , , is a sensitivity parameter obtained through historical data and model training; The possibility of high temperature aggravating crack growth is calculated according to the following formula (3): ； in, It is the temperature rise trigger factor, reflecting the possibility of high temperature aggravating crack growth; is the temperature value based on the infrared temperature hotspot; is the crack length; , is the influence coefficient, obtained through historical data and model training; According to the following formula (4), calculate the comprehensive defect trigger probability value : ； in, , , is the weight coefficient, obtained through historical data and model training; if If it is greater than or equal to the specified threshold, a defect trigger warning is generated and the risk type is recorded; if If it is less than the specified threshold, the low impact of the current environmental conditions on the defect trigger is recorded.

7. The power transmission line patrol data asset management method according to claim 1, characterized in that: The method of constructing a graph network model with transmission lines as nodes and sensor data as edge weights according to the evaluation results of the hidden danger feature labels and environmental conditions includes: The transmission line is regarded as a node of the graph network, and the attributes of the node include a hidden danger feature label, an environmental feature label, and a historical inspection data feature; According to the following formula 5, the physical proximity weight between nodes is calculated: ； in, Representation Node and nodes The physical proximity weight between Representation Node and nodes The physical distance between Representation Node and nodes The electrical transmission distance between is the distance sensitivity coefficient; According to the following formula (6), the environmental similarity weight between nodes is calculated: ； in, Representation Node and nodes The weight of the environmental similarity between them; To represent the node and nodes The Euclidean distance between the nodes is calculated based on the similarity of the environmental characteristics between the nodes; According to the following formula (7), the hidden danger correlation weight between nodes is calculated: ； in, Representation Node and nodes The hidden danger correlation weight between them; For Node and nodes The angle between the hidden danger feature vectors of two nodes; According to the following formula (8), the edge weights between nodes are calculated: ； in, For Node and nodes The edge weights between is the weighting coefficient, determined according to historical data and satisfies ; Construct a weighted graph based on the nodes and edge weights. , in represents the set of transmission line nodes, Represents the edge set; initializes the attribute vector of each node, combines the node characteristics with the weight distribution of its adjacent nodes, and generates the initial hidden danger propagation state.

8. The power transmission line patrol data asset management method according to claim 7, characterized in that: The method of predicting the time of occurrence of hidden dangers by using the graph network model based on historical patrol data and environmental dynamic parameters includes: The hidden danger propagation simulation is carried out on the graph network, and the hidden danger propagation probability of the node is calculated based on the graph convolutional network, including: According to the following formula (9), for each node, the hidden danger propagation state of the node is updated by combining the attribute vector and edge weight of its adjacent nodes: ； in, Indicates Layer propagation node Status; Indicates Layer propagation node Status; For Node The set of neighbor nodes of is the weight matrix obtained through training; is the activation function; Repeat the propagation iteration until the node status converges; According to the following formula 10, calculate the node The time of occurrence of potential hazards : ； in, is the time when the initial hidden danger occurred, is the number of propagation iterations; Indicates Layer propagation node status.

9. The power transmission line patrol data asset management method according to claim 1, characterized in that: The prediction results based on the graph network model generate a defect risk level warning, and generate a transmission line hidden danger distribution map in combination with environmental meteorological data, including: According to the hidden danger propagation status value of each node in the graph network model, combined with environmental meteorological data and historical inspection records, the defect risk score of each node is calculated, and the nodes are divided into low risk, medium risk and high risk levels according to the preset level standards; Based on the geographical location and risk level of the node, the risk information of the transmission line and its ancillary facilities is mapped to the geographic space to generate a transmission line hidden danger distribution map, wherein the hidden danger distribution map is used to show the distribution range and risk intensity of high-risk areas; According to the hidden danger distribution map, the key nodes and impact range of the high-risk area are identified, defect risk warning information is generated, and corresponding inspection and maintenance suggestions are provided.

10. A transmission line patrol data asset management system, characterized in that: include: An acquisition unit is used to acquire multimodal data related to the inspection of the transmission line through various sensors installed on the transmission line and the surrounding environment, sensors carried by helicopters and drones, mobile terminals carried by inspection personnel, and data acquisition devices of environmental monitoring satellites, wherein the multimodal data includes physical state data of the transmission line, electrical parameter data, environmental meteorological data, appearance photos and video data of the transmission line, temperature distribution data captured by infrared cameras, defect data of the transmission line, and hidden danger data of the transmission channel; An acquisition unit, used for synchronously calibrating and dynamically fusing the multimodal data based on a spatiotemporal correlation model to obtain a fused data set with temporal consistency and spatial accuracy; A generating unit, configured to parse the fused data set using an adaptive feature extraction algorithm to generate a transmission line data asset with structured features, wherein the transmission line data asset includes an environmental feature label, a defect feature label, and a hidden danger feature label, wherein the environmental feature label describes the potential impact of weather, temperature and humidity, and wind speed on the operation of the transmission line; An evaluation unit, configured to evaluate the possibility of environmental conditions triggering transmission line defects based on the association between the environmental feature tags and the defect feature tags; A construction unit, configured to construct a graph network model with transmission lines as nodes and sensor data as edge weights according to the hidden danger feature labels and the evaluation results of the environmental conditions, wherein the graph network model is used to analyze the defect propagation path of the transmission line and its channel, and to predict the time and spatial distribution of hidden danger occurrence based on historical inspection data and environmental dynamic parameters; The combining unit is used to generate a defect risk level warning based on the prediction results of the graph network model, and to generate a transmission line hidden danger distribution map in combination with environmental meteorological data, wherein the hidden danger distribution map is used to assist in formulating inspection and maintenance plans.

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