Power transmission line tower state early warning method and device based on multi-technology fusion

Abstract

The invention relates to the technical field of power grid data processing, in particular to a power transmission line tower state early warning method and device based on multi-technology fusion, and the method comprises the steps: obtaining tower state data and micrometeorological monitoring data through a tower state monitoring and collection unit in the power transmission line tower state early warning device; the data processing unit analyzes the change characteristics of the micrometeorological monitoring data, and divides the time interval of early warning analysis of the state of the power transmission line tower; analyzing the windage yaw coefficient of the tower state data in the early warning analysis process through the divided time intervals; a digital twinborn model is established, early warning information of the pole and tower state is obtained through the digital twinborn model, and the state alarm generates alarm information according to the early warning information of the pole and tower state. According to the invention, the corresponding digital twinborn model is built through the power transmission line tower early warning device, accurate tower state early warning information is generated, and the state alarm is used for carrying out accurate early warning on the tower state.

Description

Technical Field

[0001] The present application relates to the field of power grid data processing technology, and specifically to a method and device for early warning of transmission line tower status based on multi-technology integration. Background Art

[0002] With the rapid development of power technology, transmission line operation and maintenance, as well as technological transformation, have undergone significant changes in both concepts and approaches. Transmission line tower condition assessment models based on digital twin technology have gradually become mainstream in the industry. These models, in conjunction with drone tower inspections, rely on parameter data collected by real-time monitoring equipment and employ intelligent monitoring algorithms to analyze the condition of transmission line towers, thereby achieving the goal of providing early warning of conditions.

[0003] However, faced with the growing demand for intelligent processing, the limitations of traditional monitoring methods are becoming increasingly apparent. During data collection and transmission, factors such as sensor accuracy and stability, as well as power supply and communication methods, all restrict data quality. Furthermore, existing data analysis and processing capabilities struggle to cope with the complex and massive data volumes. Furthermore, due to the varying operating conditions of actual monitoring and early warning processes, the efficient and accurate extraction of valuable information is hindered.

[0004] For example, the invention patent with publication number CN111223276A proposes a transmission line tower tilt warning method and device based on the ubiquitous Internet of Things. Although the specific implementation process carries out real-time collection of tilt angles and comparison of warning values, when facing tower status warnings in complex environments, it is difficult to avoid the impact of different working conditions on the warning results during the actual monitoring and warning process. Summary of the Invention

[0005] In view of the above, it is necessary to provide a transmission line tower status early warning method and device based on multi-technology integration to solve the above problems.

[0006] In a first aspect, the present application provides a transmission line tower status early warning method based on multi-technology integration, the method comprising: Obtain various tower status data and various micro-meteorological monitoring data at each collection moment; Feature extraction is performed on tower status data and micro-meteorological monitoring data, specifically: A1: For micrometeorological monitoring data, based on the numerical variation characteristics of each sampling moment and adjacent sampling moments, the working condition evaluation samples at each sampling moment are obtained, and the working condition evaluation samples at all moments are clustered; A2: Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained; A3: Analyze the distribution of the operating response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals; A4: Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval; A digital twin model is established based on all collected data and feature extraction results to provide early warning of the status of transmission line towers.

[0007] Preferably, the obtaining of the working condition evaluation sample at each sampling moment is specifically as follows: The change in each micrometeorological monitoring data between each collection moment and the previous moment is calculated, and the vector composed of the change in all micrometeorological monitoring data at each collection moment is used as the working condition evaluation sample at each collection moment.

[0008] Preferably, the working condition response value of each micrometeorological monitoring data of each cluster is specifically the mean value of the change amount of each micrometeorological monitoring data of all working condition evaluation samples in each cluster.

[0009] Preferably, obtaining the control sample set is specifically: For each type of micrometeorological monitoring data, obtain the normalized value of the working condition response value of each type of micrometeorological monitoring data in all clusters; calculate the normalized value mean of the working condition response values ​​of all micrometeorological monitoring data in each cluster, and determine the cluster with the largest normalized mean as the control sample set.

[0010] Preferably, the obtained multiple time intervals are specifically: The collection time of all working condition evaluation samples in the control sample set is taken as the working condition response time, and the working condition response time is used as the time division point to obtain several time intervals.

[0011] Preferably, the characteristic vector is determined by the trend statistics, variance and deviation value of each tower status data in each time interval.

[0012] Preferably, the deviation value is specifically the average value of the numerical differences of each tower status data at all adjacent moments in each time interval.

[0013] Preferably, the wind deviation coefficient for each time interval is obtained by the following formula: ,in, For the The wind deviation coefficient of the time interval; Indicates the The time interval and Normalized results of the differences in wind angle data within a time interval; Indicates the The time interval and Normalized results of the differences in tension data within a time interval; Indicates the number of time intervals; among them, wind angle data and tension data are both tower status data.

[0014] Preferably, the condition for issuing an early warning on the status of the transmission line tower is that the digital twin model monitors that the wind angle, tension and wind coefficient exceed the corresponding early warning values.

[0015] In a second aspect, an embodiment of the present application further provides a transmission line tower status warning device based on multi-technology integration, which implements the transmission line tower status warning method based on multi-technology integration as described in any one of the above, and the device includes: Tower status monitoring and acquisition unit: used to obtain various tower status data and various micro-meteorological monitoring data at each acquisition moment; Transmission line tower monitoring system in high-altitude and cold regions: used to transmit data acquired by the tower status monitoring acquisition unit to the substation; Substation: used to pre-process the acquired data and transmit the pre-processed data to the data processing unit; Data processing unit: for micrometeorological monitoring data, based on the numerical change characteristics of each sampling moment and adjacent sampling moments, obtains the working condition evaluation samples at each sampling moment, and clusters the working condition evaluation samples at all moments; Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained; Analyze the distribution of the working condition response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals; Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval; Status alarm: used to establish a digital twin model based on all collected data and feature extraction results, and to provide early warning of the status of transmission line towers.

[0016] The above scheme first analyzes the different working condition characteristics of the micro-meteorological monitoring data collected during the tower status warning process. Due to the performance of the micro-meteorological monitoring data under different working conditions, the working condition evaluation samples of each sampling moment are obtained based on the numerical change characteristics of each collection moment and the adjacent collection moments. The working condition evaluation samples of all moments are clustered, and a large amount of working condition data are clustered into several main categories, which simplifies the subsequent processing of the data and makes the monitoring and maintenance of the system more efficient. Based on the numerical distribution of all working condition samples of each cluster under the same micro-meteorological monitoring data, the working condition response value of each micro-meteorological monitoring data of each cluster is obtained. By analyzing the characteristics of each micro-meteorological monitoring data of each cluster, it is possible to predict Predict future working condition performance to help maintenance personnel prepare in advance or optimize equipment; then divide the time intervals under different working condition characteristics. According to the time interval division under different working conditions, operation and maintenance personnel can more accurately grasp the equipment status of each time period, providing a basis for equipment maintenance and inspection; analyze the distribution trend and discreteness of various tower status data in each time interval to obtain feature vectors; compare the difference between the feature vectors of the same tower status data in each time interval and the rest of the time intervals to obtain the wind deviation coefficient of each time interval, which helps to more accurately grasp and predict the changes in wind force; this application constructs a digital twin model of the tower through feature extraction technology and realizes the status warning function. This method can accurately extract key information from complex data. At the same time, the establishment of the digital twin model makes it possible to simulate and warn the status of the tower in real time. This application uses a variety of technical means to significantly improve the accuracy of the status warning of the transmission line tower, greatly promote the intelligent process of transmission line operation and maintenance, and effectively ensure the safe and stable operation of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of the steps of a transmission line tower status early warning method based on multi-technology integration provided in one embodiment of the present application; Figure 2 A schematic diagram of obtaining a windage coefficient according to an embodiment of the present application; Figure 3 A block diagram of a transmission line tower status early warning device based on multi-technology integration provided in one embodiment of the present application. DETAILED DESCRIPTION

[0018] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0020] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of protection of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0021] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0022] The specific scheme of the transmission line tower status early warning method and device based on multi-technology integration provided by this application is described in detail below with reference to the accompanying drawings.

[0023] See also Figure 1 , which shows a flowchart of a method for early warning of a transmission line tower status based on multi-technology integration provided by an embodiment of the present application, the method comprising the following steps: The first step: obtain various tower status data and various micro-meteorological monitoring data at each collection moment.

[0024] The transmission line tower status early warning system of the present application is mainly composed of a tower status monitoring and acquisition unit, a substation, a transmission line tower monitoring system in high-cold areas, a data processing unit, and a status alarm. The tower status monitoring and acquisition unit and the substation are both installed on the transmission line tower, and the tower status monitoring acquisition unit is used to collect the transmission line tower status data. The acquisition unit includes a conductor / insulator wind deflection angle acquisition unit, a tension monitoring acquisition unit, and an intelligent micro-meteorological acquisition unit, which are respectively used to collect the conductor / insulator wind deflection angle data, tension data, and micro-meteorological monitoring data of the transmission line tower, wherein the micro-meteorological monitoring data includes temperature, humidity, wind speed, wind direction, and air pressure; the conductor / insulator wind deflection angle data and tension data are both tower status data.

[0025] The transmission line tower monitoring system for high-altitude and cold regions utilizes wireless radio frequency technology and SMS / GPRS / 5G wireless communication technologies to transmit data acquired by the tower status monitoring and acquisition unit to a substation. This combination of multiple communication technologies ensures data accuracy, stability, timeliness, and integrity. Given that the data acquisition and transmission process is susceptible to environmental changes and the stability of the acquisition device, which can lead to poor data quality, a Wiener filter is used to reduce noise in the collected data. The substation then transmits the filtered and de-noised tower status data to the data processing module.

[0026] The second step: feature extraction of tower status data and micrometeorological monitoring data.

[0027] Due to the complex environment in which transmission line towers operate, the instantaneous characteristics of tower status data collected under different operating conditions vary significantly. Comparing status assessment and early warning based solely on real-time data with warning values ​​can result in significant errors. Therefore, a data processing module is required to extract features based on real-time data at different time periods and operating conditions during the status early warning monitoring process.

[0028] A1: For micrometeorological monitoring data, based on the numerical variation characteristics between each sampling moment and adjacent sampling moments, the working condition evaluation samples at each sampling moment are obtained, and the working condition evaluation samples at all moments are clustered.

[0029] The tower status data collected under different working conditions exhibit significant differences in instantaneous characteristics, which may cause the instantaneous characteristics reflected by the real-time tower status data to deviate significantly from the actual state, leading to large errors in the subsequent tower status warning using the digital twin model. Therefore, using micro-meteorological monitoring data, the change in each type of micro-meteorological monitoring data at each collection moment compared to the previous moment is calculated. The vector composed of the changes in all micro-meteorological monitoring data at the same moment is used as the instantaneous state data change vector. This vector is used as the working condition assessment sample, and the agglomerative hierarchical clustering algorithm is used to cluster and divide all working condition assessment samples. The agglomerative hierarchical clustering algorithm is a well-known prior art and will not be described in detail in this application.

[0030] A2: Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained.

[0031] Considering the dramatic changes in tower status data when the tower's environment transitions under different operating conditions, a comparative analysis of the clustered operating condition assessment samples was conducted. Specifically, the mean change in each microclimate monitoring data point across all operating condition assessment samples within each cluster was calculated as the operating condition response value for each microclimate monitoring data point within each cluster. A larger operating condition response value indicates a higher likelihood that the corresponding data within the cluster has changed state due to the change in operating conditions.

[0032] A3: Analyze the distribution of the operating response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals.

[0033] To further integrate the comprehensive variation characteristics of micrometeorological data under different operating conditions and accurately determine the status data characteristics of tower status warning analysis in different time intervals, for each type of micrometeorological monitoring data, the operating condition response values ​​of the same micrometeorological monitoring data in all clusters were used as input and normalized using the Z-score algorithm. The mean of the normalized operating condition response values ​​of all micrometeorological monitoring data in each cluster was calculated, and the cluster with the largest mean was determined as the control sample set for tower status feature extraction.

[0034] The collection time of all working condition evaluation samples in the control sample set is used as the working condition response time, and the working condition response time is used as the time division point to obtain several time intervals for tower early warning monitoring.

[0035] A4: Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval.

[0036] For each time interval, the trend statistics, variance, and deviation of the wind angle data and tension data are calculated. The trend statistics are calculated using a trend verification algorithm, which is a well-known technique. The deviation is calculated by arranging a type of tower status data in a time series within a time interval, calculating the numerical differences between adjacent moments, and taking the mean of all numerical differences as the deviation of the corresponding tower status data within that time interval. The vector consisting of the trend statistics, variance, and deviation of the wind angle data and tension data in each time interval is used as the feature vector of each type of tower status data in each time interval, thereby accurately extracting tower status features under different working conditions based on micrometeorological monitoring data.

[0037] Furthermore, the state characteristic differences of the characteristic vectors for different operating conditions are compared. The core purpose is to accurately demonstrate the differences in the state change responses of transmission line towers under different operating conditions. The greater the difference in state characteristics, the more obvious the characteristic response of the state data to tower status warning when the operating condition characteristic intervals of overall state changes are divided based on micrometeorological monitoring data.

[0038] Specifically, the normalized difference between the eigenvector corresponding to each tower status data in each time interval and the eigenvectors corresponding to the tower status data in other time intervals is calculated. When calculating the difference, methods that can be used include, but are not limited to, Manhattan distance and Euclidean distance. This embodiment uses Euclidean distance, and the normalization method uses the maximum and minimum value normalization method. It should be understood that the larger the calculated difference value, the more significant the characteristic response of the tower status warning. This data change can keenly reflect the sensitive characteristics of tower status changes under different working conditions.

[0039] Based on the comparison results of different tower condition monitoring data under different working conditions, the windage coefficient in each time interval is calculated. The formula is: ,in, For the The wind deviation coefficient of the time interval; Indicates the The time interval and Normalized results of the differences in wind angle data within a time interval; Indicates the The time interval and Normalized results of the differences in tension data within a time interval; Indicates the number of time intervals; it should be understood that the larger the wind deviation coefficient is, the more significant the response to the change of the status data in the divided time interval under the corresponding working condition characteristics during the tower warning process is, and the more helpful it is for the accurate analysis of the status warning.

[0040] The schematic diagram of obtaining the wind deviation coefficient is as follows: Figure 2 shown.

[0041] The third step: Establish a digital twin model based on all collected data and feature extraction results to provide early warning of the status of transmission line towers.

[0042] Based on the feature extraction results, a digital twin model is constructed to provide early warning of the status of transmission line towers. The construction of the digital twin model is the core link in achieving accurate early warning. The specific process is as follows: B1: Data Preparation: Summarize the preprocessed data obtained from all the above steps, including micrometeorological monitoring data, conductor / insulator windage data, tension data, and the windage coefficients for windage data and tension data within each time interval. Use the Z-score algorithm to standardize the dimensions of this data, remove outliers, and ensure that the data quality meets modeling requirements.

[0043] B2: Determine the modeling technology: Select appropriate modeling technology based on the physical characteristics, operating principles and data characteristics of the transmission line tower. Specifically, in terms of structural mechanics, use finite element analysis technology to simulate the stress and strain distribution of the tower under different stress conditions. For electrical performance simulation, build a model with the help of circuit theory and electromagnetic principles. When describing the overall dynamic behavior of the system, it is determined specifically based on multi-body dynamics and control theory.

[0044] B3: Model Construction: Build a digital twin model from three dimensions: geometric model, physical model, and behavioral model. Based on the tower's design drawings and actual measurement data, a precise 3D geometric model is constructed to restore the tower's shape, dimensions, and the relative positions of its components. Combined with a mathematical model of the tower's 3D stress state and a mathematical model of conductor stress variations, physical laws and parameters are incorporated to establish a physical model to simulate the tower's physical processes under various operating conditions. Based on historical data and real-time monitoring data, the changing patterns and laws of the tower's operating state are explored, and a behavioral model is established to predict the tower's future operating state. Finally, the geometric, physical, and behavioral models are organically integrated to form a complete digital twin model framework.

[0045] B4: Status Warning: Preprocessed transmission line tower status data, as well as wind angle data and tension data with wind coefficients at different time intervals, are input into the digital twin model. The model simulates the actual state of the tower to map the tower's status in real time. When the model detects that wind angle, tension, and wind coefficient exceed the corresponding warning values, the system automatically triggers the warning mechanism and promptly sends warning information to operation and maintenance personnel, alerting them to potential safety hazards so that appropriate measures can be taken to ensure the safe and stable operation of the transmission line. The warning values ​​are composed of the maximum wind angle data value, maximum tension data value, and maximum wind coefficient value set for the transmission line tower status.

[0046] Based on the same inventive concept as the above method, the embodiment of the present application further provides a transmission line tower status early warning device based on multi-technology integration, the device comprising: Tower status monitoring and acquisition unit: used to obtain various tower status data and various micro-meteorological monitoring data at each acquisition moment; Transmission line tower monitoring system in high-altitude and cold regions: used to transmit data acquired by the tower status monitoring acquisition unit to the substation; Substation: used to pre-process the acquired data and transmit the pre-processed data to the data processing unit; Data processing unit: for micrometeorological monitoring data, based on the numerical change characteristics of each sampling moment and adjacent sampling moments, obtains the working condition evaluation samples at each sampling moment, and clusters the working condition evaluation samples at all moments; Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained; Analyze the distribution of the working condition response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals; Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval; Status alarm: used to establish a digital twin model based on all collected data and feature extraction results, and to provide early warning of the status of transmission line towers.

[0047] Among them, the block diagram of the transmission line tower status early warning device based on multi-technology integration is as follows: Figure 3 shown.

[0048] In summary, the present application first analyzes the different working condition characteristics of the micrometeorological monitoring data collected during the tower status warning process. Due to the performance of the micrometeorological monitoring data under different working conditions, the working condition evaluation samples of each sampling moment are obtained based on the numerical change characteristics of each collection moment and the adjacent collection moments. The working condition evaluation samples of all moments are clustered, and a large amount of working condition data are clustered into several main categories, which simplifies the subsequent processing of the data and makes the monitoring and maintenance of the system more efficient. Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained. By analyzing the characteristics of each micrometeorological monitoring data of each cluster, it can be Predict future working conditions to help maintenance personnel prepare in advance or optimize equipment; then divide the time intervals under different working conditions. According to the time interval division under different working conditions, operation and maintenance personnel can more accurately grasp the equipment status of each time period, providing a basis for equipment maintenance and inspection; analyze the distribution trend and discreteness of various tower status data in each time interval to obtain feature vectors; compare the difference between the feature vectors of the same tower status data in each time interval and the rest of the time intervals to obtain the wind deviation coefficient of each time interval, which helps to more accurately grasp and predict the changes in wind force; this application constructs a digital twin model of the tower through feature extraction technology and realizes the status warning function. This method can accurately extract key information from complex data. At the same time, the establishment of the digital twin model makes it possible to simulate and warn the status of the tower in real time. This application uses a variety of technical means to significantly improve the accuracy of the transmission line tower status warning, greatly promote the intelligent process of transmission line operation and maintenance, and effectively ensure the safe and stable operation of the transmission line.

[0049] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0050] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifications to the technical solutions described in the above embodiments, or equivalent replacement of some of the technical features therein, do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application.

Claims

1. A transmission line tower status early warning method based on multi-technology integration is characterized by: The method comprises the following steps: Obtain various tower status data and various micro-meteorological monitoring data at each collection moment; Feature extraction is performed on tower status data and micro-meteorological monitoring data, specifically: A1: For micrometeorological monitoring data, based on the numerical variation characteristics of each sampling moment and adjacent sampling moments, the working condition evaluation samples at each sampling moment are obtained, and the working condition evaluation samples at all moments are clustered; A2: Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained; A3: Analyze the distribution of the operating response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals; A4: Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval; A digital twin model is established based on all collected data and feature extraction results to provide early warning of the status of transmission line towers.

2. The transmission line tower status early warning method based on multi-technology integration according to claim 1 is characterized in that: The working condition evaluation samples at each sampling moment are obtained as follows: The change in each micrometeorological monitoring data between each collection moment and the previous moment is calculated, and the vector composed of the change in all micrometeorological monitoring data at each collection moment is used as the working condition evaluation sample at each collection moment.

3. The transmission line tower status early warning method based on multi-technology integration according to claim 2 is characterized in that: The working condition response value of each micrometeorological monitoring data of each cluster is specifically the mean value of the change amount of each micrometeorological monitoring data of all working condition evaluation samples in each cluster.

4. The transmission line tower status early warning method based on multi-technology integration according to claim 1 is characterized in that: The control sample set is obtained as follows: For each type of micrometeorological monitoring data, obtain the normalized value of the working condition response value of each type of micrometeorological monitoring data in all clusters; calculate the normalized value mean of the working condition response values ​​of all micrometeorological monitoring data in each cluster, and determine the cluster with the largest normalized mean as the control sample set.

5. The transmission line tower status early warning method based on multi-technology integration according to claim 1 is characterized in that: The obtained time intervals are specifically: The collection time of all working condition evaluation samples in the control sample set is taken as the working condition response time, and the working condition response time is used as the time division point to obtain several time intervals.

6. The transmission line tower status early warning method based on multi-technology integration according to claim 1 is characterized in that: The characteristic vector is determined by the trend statistics, variance and deviation value of each tower status data in each time interval.

7. The transmission line tower status early warning method based on multi-technology integration according to claim 6 is characterized in that: The deviation value is specifically the average value of the difference between the values ​​of each tower status data at all adjacent moments in each time interval.

8. The transmission line tower status early warning method based on multi-technology integration according to claim 1 is characterized in that: The wind deviation coefficient for each time interval is obtained as follows: ,in, For the The wind deviation coefficient of the time interval; Indicates the The time interval and Normalized results of the differences in wind angle data within a time interval; Indicates the The time interval and Normalized results of the differences in tension data within a time interval; Indicates the number of time intervals; among them, wind angle data and tension data are both tower status data.

9. The transmission line tower status early warning method based on multi-technology integration according to claim 8, characterized in that: The condition for issuing an early warning on the status of the transmission line tower is that the digital twin model monitors that the wind angle, tension and wind coefficient exceed the corresponding early warning values.

10. A transmission line tower status early warning device based on multi-technology integration, implementing the transmission line tower status early warning method based on multi-technology integration as described in any one of claims 1 to 9, the device comprising: Tower status monitoring and acquisition unit: used to obtain various tower status data and various micro-meteorological monitoring data at each acquisition moment; Transmission line tower monitoring system in high-altitude and cold regions: used to transmit data acquired by the tower status monitoring acquisition unit to the substation; Substation: used to pre-process the acquired data and transmit the pre-processed data to the data processing unit; Data processing unit: for micrometeorological monitoring data, based on the numerical change characteristics of each sampling moment and adjacent sampling moments, obtains the working condition evaluation samples at each sampling moment, and clusters the working condition evaluation samples at all moments; Based on the numerical distribution of all working condition samples of each cluster under the same micrometeorological monitoring data, the working condition response value of each micrometeorological monitoring data of each cluster is obtained; Analyze the distribution of the working condition response values ​​of all micrometeorological monitoring data in all clusters to obtain a control sample set; based on the sampling time corresponding to each element in the control sample set, obtain several time intervals; Analyze the distribution trend and dispersion of various tower status data in each time interval to obtain the characteristic vector; compare the difference between the characteristic vectors of the same tower status data in each time interval and other time intervals to obtain the windage coefficient of each time interval; Status alarm: used to establish a digital twin model based on all collected data and feature extraction results, and to provide early warning of the status of transmission line towers.

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