Remote monitoring method for state of temporary transition tower of power transmission line

Abstract

The invention discloses a remote monitoring method for the state of a temporary transition tower of a power transmission line, and the method comprises the steps: designing a model selection and installation scheme of a sensing module according to the climate condition and geographical environment of an area where the temporary transition tower is located, and collecting the original data of the state of the tower through the sensing module, the temporary transition pole tower is supported by an assembly type foundation spliced by I-shaped steel; based on the original data, adopting a data preprocessing technology to obtain primarily processed data, and then performing compression processing to obtain compressed data; designing a multi-mode communication transmission strategy according to the communication condition of the area where the temporary transition tower is located, extracting key feature parameters according to the compressed data, judging the change trend of the data, and identifying potential abnormal conditions; if an abnormal condition is recognized, the abnormal condition is transmitted to a monitoring center in real time, and remote monitoring is completed. According to the invention, the problem of state monitoring of the temporary transition tower in a complex environment is effectively solved, and the accuracy and real-time performance of monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, and particularly to a method for remotely monitoring the state of temporary transition poles and towers of transmission lines. Background Art

[0002] With the rapid development of social economy, the scale of power grid construction has grown rapidly, and there have been a large number of projects such as relocation and capacity expansion transformation of transmission lines. The use of temporary transition poles and towers for transmission lines is increasing. The remote monitoring of temporary transition poles and towers of transmission lines also faces many technical challenges. First, the environment where the poles and towers are located is complex and changeable, and the field environment is harsh. The selection and installation location of sensors need to be designed specifically to adapt to different climate conditions and geographical environments. Second, the massive data collected by sensors needs to be effectively preprocessed and compressed to reduce the transmission load and ensure data integrity. Third, the coverage of the field communication network is limited, and it is difficult to guarantee the transmission bandwidth and stability. It is necessary to comprehensively utilize a variety of communication technologies and design a reasonable data transmission strategy to adapt to the communication conditions in different regions. Finally, the monitoring center needs to accurately identify abnormal situations from the massive heterogeneous data, generate early warning information in a timely manner and notify relevant personnel, which puts forward higher requirements for the intelligent level of data analysis algorithms and the reliability of the system. In short, the state monitoring of temporary transition poles and towers of transmission lines involves multiple links such as sensor technology, data processing, communication transmission, and intelligent analysis, and needs to be designed as a whole from a systematic perspective in order to achieve stable, reliable, intelligent and efficient remote monitoring. Summary of the Invention

[0003] To solve the above technical problems existing in the prior art, the present invention proposes a method for remotely monitoring the state of temporary transition poles and towers of transmission lines, which improves the accuracy and real-time performance of monitoring and provides a reliable guarantee for the safe operation of the power system.

[0004] To achieve the above object, the present invention provides a method for remotely monitoring the state of temporary transition poles and towers of transmission lines, including:

[0005] According to the climate conditions and geographical environment of the area where the temporary transition pole and tower are located, design the selection and installation scheme of the sensing module, and collect the original data of the pole and tower state through the sensing module, wherein the temporary transition pole and tower are supported by an assembled foundation spliced with I-beams;

[0006] Based on the original data, adopt data preprocessing technology to obtain the preliminarily processed data, and perform compression processing on the preliminarily processed data to obtain the compressed data;

[0007] According to the communication conditions of the area where the temporary transition pole and tower are located, design a multi-mode communication transmission strategy, extract key characteristic parameters according to the compressed data, and judge the change trend of the data to identify potential abnormal situations;

[0008] If an abnormal situation is recognized, the abnormal situation will be transmitted to the monitoring center in real time to complete remote monitoring.

[0009] Preferably, the selection and installation scheme of the sensing module includes:

[0010] Using a pre-established database of climate and environmental parameters, obtain the climate and geographical information of the area where the current temporary transition tower is located. According to the climate and geographical information, determine the suitable type of sensing module, analyze the geographical information, and judge the specific installation position of the sensing module.

[0011] Preferably, the type and installation position of the sensing module are:

[0012] The types of the sensing module include a pressure sensing module, an inclination sensing module, and a temperature and humidity sensing module; among them, the pressure sensing modules are respectively arranged on the top surface of the bottom plate of the fabricated foundation spliced by I-beams and the bottom surface of the bottom plate of the fabricated foundation spliced by I-beams, and the pressure sensing modules on the top surface and the bottom surface of the bottom plate of the fabricated foundation spliced by I-beams are respectively installed at the web of the corner I-beams; the inclination sensing module is installed on the webs of the I-beams perpendicular to each other at the first and second steps of the fabricated foundation spliced by I-beams; the temperature and humidity sensing module is arranged on one side of the bottom plate of the fabricated foundation spliced by I-beams.

[0013] Preferably, obtaining the preliminarily processed data includes:

[0014] According to a preset noise threshold, remove the noise values from the original data, and judge and eliminate the abnormal values in the original data through an anomaly detection algorithm to obtain the preliminarily processed data.

[0015] Preferably, obtaining the compressed data includes:

[0016] Perform multi-scale decomposition on the preliminarily processed data using wavelet transform to obtain low-frequency components and high-frequency components;

[0017] For the high-frequency components, set a preset threshold for soft threshold filtering to remove noise and redundant data, and perform inverse wavelet transform reconstruction on the filtered components to obtain preliminary compressed data;

[0018] According to the pre-set compression ratio requirement, adjust the size of the preset threshold and re-perform filtering and reconstruction processing. Compare the reconstructed data with the preliminary compressed data, calculate the information loss rate. If the information loss rate exceeds the preset threshold, re-adjust the filtering parameters and compress again to obtain the compressed data, and store the compressed data in the database according to the time series, and record the compression parameter information at the same time.

[0019] Preferably, the design of the multi-mode communication transmission strategy includes:

[0020] Obtain the communication condition parameters of the area where the temporary transition tower is located, determine the coverage range, bandwidth, and signal strength of wired, wireless, and satellite communications, based on the communication condition parameters, divide the applicability levels of different communication technologies within the area, and determine the main communication mode and backup communication mode for each area;

[0021] Preset a multi-mode communication strategy, including a combination of mainly wired, assisted by wireless, and supplemented by satellite, and match the communication strategy with the area applicability level;

[0022] During the transmission process, monitor the quality of the communication link in real time. If the signal strength of the current communication mode is lower than the preset signal strength value, switch to the backup communication mode, and use error correction coding technology to process the transmitted data. According to the stability and real-time requirements of the communication link, adjust the frequency of data transmission and the priority of data packets, record the transmitted data according to the communication mode and time sequence, and generate a transmission log for subsequent analysis and strategy optimization.

[0023] Preferably, judging the change trend of data and identifying potential abnormal situations includes:

[0024] Adopt the sliding window technology to extract feature parameters and change rates from the compressed data to form a parameter set;

[0025] Use the time series analysis method to convert the parameter set into a serialized data stream, generate a trend line, and set the abnormal point monitoring threshold according to the change rate and stability of the trend line;

[0026] If the data at the monitoring point exceeds the preset monitoring threshold, it is judged as an abnormal point, and the abnormal recognition mechanism is triggered.

[0027] Preferably, judging the change trend of data further includes:

[0028] Continuously update the parameter set through the sliding window and dynamically adjust the trend line; combine the compression ratio and eigenvalue of the data stream to optimize the time series analysis algorithm.

[0029] Preferably, if an abnormal situation is identified, use a classification algorithm based on machine learning to classify the abnormal type, obtain the specific description of the abnormal type, and generate a warning message according to the specific description of the abnormal type. Adopt the preset warning level division standard to determine the warning level and obtain the final warning result.

[0030] Preferably, adopting the preset warning level division standard to determine the warning level includes:

[0031] Determine the ultimate state of the prefabricated foundation of the temporary pole tower through numerical simulation. Set 60% - 80% of the ultimate state as the attention state, 80% - 90% as the warning state, and ≥90% as the dangerous state. Identify various states through the edge computing function and automatically change the data acquisition and transmission frequency;

[0032] Among them, the data acquisition and transmission interval in the attention state is 60 seconds, the data acquisition and transmission interval in the warning state is 30 seconds, and the data acquisition and transmission interval in the dangerous state is 10 seconds;

[0033] Below 60% of the ultimate state is the safe state, the acquisition interval is 60 seconds, and the transmission interval is 30 minutes. Below 40% of the ultimate state is the sleep state, the acquisition interval is 300 seconds, and the transmission interval is 12 hours.

[0034] Compared with the prior art, the present invention has the following advantages and technical effects:

[0035] (1) In view of the characteristics of the ever-changing environment where the pole tower is located, the present invention designs a sensor selection scheme with strong adaptability and determines the optimal installation position. Remove noise and outliers through data preprocessing technology, and then use a compression algorithm based on wavelet transform to reduce the data storage and transmission load. To address the problem of limited communication network coverage, a multi-mode communication transmission strategy is designed, comprehensively utilizing wired, wireless, and satellite communication technologies. The present invention also uses time series analysis and machine learning algorithms to extract key features from the compressed data, identify and classify potential abnormal situations, generate warning information and determine the warning level. Finally, transmit the warning results to the monitoring center through real-time push technology. The present invention effectively solves the problem of pole tower state monitoring in complex environments, improves the accuracy and real-time performance of monitoring, and provides a reliable guarantee for the safe operation of the power system.

[0036] (2) The present invention matches the prefabricated foundation of the temporary pole tower, real-time senses the operating state of the prefabricated foundation of the temporary pole tower, and ensures the safe and stable operation of the prefabricated foundation of the temporary pole tower; Since the temporary pole tower foundation needs to be reused, various sensors of the present invention can be assembled and disassembled with the prefabricated foundation, only involving re-wiring. Brief Description of the Drawings

[0037] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0038] Figure 1 It is a flowchart of a method for remotely monitoring the state of a temporary transition pole tower in an embodiment of the present invention;

[0039] Figure 2Schematic diagram of the installation of the sensing module for the temporary tower assembled foundation according to the embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the on-site test for real-time monitoring of the stress of the transmission tower members according to the embodiment of the present invention;

[0041] Among them, 1. Temporary steel pipe pole; 2. Assembled foundation spliced by I-beams; 3. Top soil pressure sensor; 4. Bottom soil pressure sensor; 5. Biaxial inclination sensor; 6. Temperature and humidity sensor. Specific implementation manners

[0042] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0043] It should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0044] The present invention proposes a method for remotely monitoring the state of a temporary transition tower of a transmission line, such as Figure 1 , including:

[0045] According to the climate conditions and geographical environment of the area where the temporary transition tower is located, design the selection and installation scheme of the sensing module, and collect the original data of the tower state through the sensing module, wherein the temporary transition tower is supported by an assembled foundation spliced by I-beams;

[0046] Based on the original data, adopt data preprocessing technology to obtain the preliminarily processed data, and perform compression processing on the preliminarily processed data to obtain the compressed data;

[0047] According to the communication conditions of the area where the temporary transition tower is located, design a multi-mode communication transmission strategy, extract key feature parameters according to the compressed data, and judge the change trend of the data to identify potential abnormal situations;

[0048] If an abnormal situation is identified, the abnormal situation will be transmitted to the monitoring center in real time to complete the remote monitoring.

[0049] Specifically, such as Figure 2, the fabricated foundation 2 for splicing I-beams used in this embodiment is composed of 14 H-shaped steels and steel pipe main columns. The size of the steel pipe main columns is the same as that of the steel pipe poles. A positioning flange is provided at the top for connection with the steel pipe pole, and the bottom is connected to the H-shaped steel of the fabricated foundation through a connecting steel plate. The positioning flange is the same as the bottom connecting flange of the steel pipe pole, and the connecting plate is welded to the main column. Adjacent layers of H-shaped steels are connected by bolts. To ensure the reliable connection between the fabricated foundation and the steel pipe main column, 3 H-shaped steels are used in the first layer, and the number of H-shaped steels in the second and third layers is 2 each. To increase the contact area between the foundation and the ground and improve the uplift, downward pressure, and anti-overturning stability bearing capacity of the foundation, the number of H-shaped steels in the bottom layer is 7. The H-shaped steels in the same layer are arranged in parallel, and the H-shaped steels in adjacent layers are arranged vertically.

[0050] Furthermore, the selection and installation scheme of the sensing module includes:

[0051] Using a pre-established database of climate and environmental parameters, obtain the climate and geographical information of the area where the current temporary transition tower is located. According to the climate and geographical information, determine the suitable type of sensing module, analyze the geographical information, and judge the specific installation location of the sensing module.

[0052] Specifically, in this embodiment, establishing a pre-established database of climate and environmental parameters is the basis of remote monitoring. This database contains meteorological data such as temperature, humidity, wind speed, precipitation, as well as geographical information such as terrain and vegetation coverage. For example, in mountainous areas, the database may record information such as altitude, slope, and soil type. These data are obtained through various channels such as weather stations and satellite remote sensing and are updated regularly. According to the obtained information, suitable sensors can be selected for different environments. For example, in coastal areas with frequent fog, sensors with corrosion resistance and moisture resistance may be selected; in plateau areas, sensors that can withstand low temperatures and strong ultraviolet rays need to be considered. The selection of sensors directly affects the accuracy of data collection and the service life of the equipment. Geographical information analysis helps to determine the optimal installation location of the tower. In complex terrains such as valleys or canyons, the impact of terrain on wireless signal transmission may need to be considered. At this time, it may be installed at a high point, or multiple relay stations may be used to ensure signal coverage. For towers located in areas with unstable geology, additional foundation reinforcement measures are required. The selection of high-stability sensors is particularly important. For example, in earthquake-prone areas, acceleration sensors with seismic resistance functions may be selected; in strong wind areas, tilt sensors with wind pressure compensation functions may be used. These sensors can not only work stably in harsh environments but also provide more accurate data.

[0053] Furthermore, the type and installation location of the sensing module are as Figure 2 shown:

[0054] The types of the sensing modules include a pressure sensing module, an inclination sensing module, and a temperature and humidity sensing module. Among them, the pressure sensing module is respectively arranged on the top surface of the bottom plate of the assembled foundation spliced by I-beams and on the bottom surface of the bottom plate of the assembled foundation spliced by I-beams, and the pressure sensing modules on the top surface and the bottom surface of the bottom plate of the assembled foundation spliced by I-beams are respectively installed at the web of the corner I-beams; the inclination sensing module is installed on the webs of the I-beams perpendicular to each other at the first and second steps of the foundation steps of the assembled foundation spliced by I-beams; the temperature and humidity sensing module is arranged on one side of the bottom plate of the assembled foundation spliced by I-beams. In this embodiment, the inclination sensing module is a biaxial inclination sensor 5, and the temperature and humidity sensing module is a temperature and humidity sensor 6.

[0055] The pressure sensing module includes 4 top surface earth pressure sensors 3 on the top surface of the foundation bottom plate and 4 bottom surface earth pressure sensors 4 on the bottom surface of the foundation bottom plate. The 4 earth pressure sensors on the top surface and the bottom surface are respectively installed at the web of the corner I-beams to accurately obtain the pressure change of the soil. The biaxial inclination sensor 5 is installed on the webs of the I-beams perpendicular to each other at the first and second steps of the foundation steps to accurately obtain the inclination change of the foundation.

[0056] Specifically, the working principle of the sensing module is as follows:

[0057] (1) Overturning perception: For temporary pole towers, most of them adopt the type of steel pipe poles, that is, temporary steel pipe poles 1. This type of pole tower has very high requirements for the anti-overturning performance of the foundation. Since the self-weight of the assembled foundation is small, the anti-overturning performance depends on the interaction between the foundation bottom plate and the soil to provide the anti-overturning force. If the foundation generates an overturning force under the action of lateral forces such as wind on the pole, the value of the top surface earth pressure sensor 3 corresponding to the top surface of the foundation on the inclined side will decrease, and the value of the bottom surface earth pressure sensor 4 will increase. The value of the top surface earth pressure sensor 3 corresponding to the top surface of the foundation on the opposite side will increase, and the value of the bottom surface earth pressure sensor 4 will decrease. In addition, the working state of the foundation under the action of the overturning moment can be judged by combining the value of the biaxial inclination sensor 5.

[0058] (2) Downward pressure perception: The steel pipe pole foundation also has to bear the downward pressure load generated by the self-weight of the transmission line and ice coating. If the soil quality is poor, the foundation will sink under the action of a large vertical force. At this time, the values of the 4 bottom surface earth pressure sensors 4 on the bottom surface of the foundation bottom plate will increase simultaneously, while the values of the 4 top surface earth pressure sensors 3 on the top surface of the foundation will decrease simultaneously. The value of the biaxial inclination sensor 5 may remain unchanged or change slightly.

[0059] (3) Surface scouring or soil taking perception: Once the temporary pole tower position is subjected to surface scouring or illegal soil taking, the anti-overturning force will decrease, and pole falling and pole leaning accidents will occur under the action of horizontal loads. In this case, the data of the 4 top surface earth pressure sensors 3 on the top surface of the foundation bottom plate will decrease simultaneously or partially, and the data of the 4 bottom surface earth pressure sensors 4 on the bottom surface may decrease simultaneously or partially.

[0060] (4) Groundwater level sensing: When the values of the temperature and humidity sensor 6 installed at the bottom of the foundation significantly increase, it indicates that the groundwater level under the pole position is too high or there is waterlogging.

[0061] (5) Frost heaving and thaw settlement sensing of foundation soil: When the temperature and humidity sensor 6 at the bottom of the foundation slab senses that the temperature is below 0°C, and the value of the soil pressure sensor 4 at the bottom of the foundation slab increases while the humidity decreases, it reflects frost heaving of the foundation soil. If the temperature and humidity sensor 6 senses that the temperature gradually rises from below 0°C, and the value of the soil pressure sensor 3 on the top surface of the slab decreases, it indicates thaw settlement of the foundation soil.

[0062] Furthermore, the obtained preliminarily processed data includes:

[0063] According to the preset noise threshold, noise values are removed from the original data, and through an anomaly detection algorithm, anomaly values in the original data are judged and removed to obtain the preliminarily processed data.

[0064] Specifically, real-time collection of tower status data is a key link in monitoring the safe operation of transmission lines. The data collected by sensors contains various physical parameters of the tower. Noise removal is the first step in data processing. The preset noise threshold is usually determined based on historical data statistics and expert experience. For a biaxial inclination sensor, a noise threshold of ±0.1° is set. Data outside this range will be regarded as valid signals, while small fluctuations within this range are filtered out to improve data quality. The anomaly detection algorithm helps identify and remove anomaly values. Common methods include statistical methods (three times the standard deviation method) and machine learning methods (cluster analysis). Extracting valid values is the core of data processing. For the pressure sensor, in this embodiment, the maximum stress value and the average stress value within 24 hours are concerned. By calculating these indicators, key information reflecting the structural state of the tower can be obtained. The introduction of the climate and environment parameter database makes data analysis more accurate. Under strong wind weather, a slight tilt of the tower may be a normal phenomenon; while in sunny and windless weather, the same degree of tilt may indicate a structural problem. By combining real-time meteorological data, the tower status can be judged more accurately.

[0065] Furthermore, the obtained compressed data includes:

[0066] Perform multi-scale decomposition on the preliminarily processed data using wavelet transform to obtain low-frequency components and high-frequency components;

[0067] For the high-frequency components, set a preset threshold for soft threshold filtering to remove noise and redundant data, and perform inverse wavelet transform reconstruction on the filtered components to obtain preliminarily compressed data;

[0068] According to the pre-set compression ratio requirement, adjust the size of the preset threshold and re-perform filtering and reconstruction processing. Compare the reconstructed data with the preliminary compressed data, calculate the information loss rate. If the information loss rate exceeds the preset threshold, re-adjust the filtering parameters and compress again to obtain the compressed data, and store the compressed data in the database according to the time series, while recording the compression parameter information.

[0069] Specifically, wavelet transform is a time-frequency analysis method that can decompose a signal into multiple scales. In the monitoring of tower states, wavelet transform can effectively separate high-frequency noise and low-frequency useful signals. The low-frequency component reflects the overall vibration trend of the tower, while the high-frequency component contains environmental noise and local disturbance information. Performing soft threshold filtering on the high-frequency component is a key step in noise removal. The soft threshold function has better continuity compared to the hard threshold function, which can avoid mutations in the reconstructed signal. Assume that the coefficient range of a certain high-frequency component is [-0.5, 0.5], and the threshold λ = 0.2 is selected. When the absolute value of the coefficient is less than λ, it is set to zero, and when it is greater than λ, (coefficient - λ) is retained. This can effectively suppress noise while retaining the main features of the signal. Inverse wavelet transform reconstruction is the process of synthesizing the filtered components at each scale into a complete signal. Compared with the original signal, the noise in the reconstructed signal is effectively suppressed, while the main features of the tower vibration are retained. For example, there may be a 50Hz power frequency interference in the original signal, which is significantly weakened in the reconstructed signal after wavelet decomposition and soft threshold filtering. The compression ratio is an important indicator to measure the data compression effect. Assume that the original data contains 10,000 sampling points, and 2,000 non-zero coefficients are retained after wavelet transform and threshold processing, then the compression ratio is 5:1. If the pre-set compression ratio requirement is 10:1, then the threshold λ needs to be increased to further reduce the number of non-zero coefficients. This process requires multiple iterations to reach the target compression ratio. The information loss rate reflects the amount of useful information lost during the compression process. Commonly used evaluation indicators are the root mean square error (RMSE) or the signal-to-noise ratio (SNR). In this embodiment, if the root mean square value of the original signal is 1.0 and the root mean square of the difference between the compressed and reconstructed signal and the original signal is 0.05, then the information loss rate can be considered to be 5%. If this loss rate exceeds the preset 10% threshold, the threshold λ needs to be decreased to reduce the information loss. Storing the compressed data in time series is conducive to subsequent rapid retrieval and analysis.

[0070] The compressed data per hour can be stored as a data block in the time series database InfluxDB. Meanwhile, the compression parameter information, such as the type of wavelet basis function, the number of decomposition levels, the threshold size, etc., is recorded. These parameters are crucial for subsequent data decompression and analysis. Through the above steps, the storage space requirement for the tower status monitoring data can be significantly reduced while retaining the key information. This data compression method based on wavelet transform is particularly suitable for processing large-scale and long-term tower monitoring data, and can improve the overall efficiency of the system while ensuring data quality.

[0071] Furthermore, the design of the multi-mode communication transmission strategy includes:

[0072] Obtain the communication condition parameters of the area where the temporary transition tower is located, determine the coverage range, bandwidth, and signal strength of wired, wireless, and satellite communications. Based on the communication condition parameters, divide the applicability levels of different communication technologies within the area, and determine the main communication mode and the backup communication mode for each area;

[0073] Preset a multi-mode communication strategy, including a combination method of mainly wired, assisted by wireless, and supplemented by satellite, and match the communication strategy with the area applicability level;

[0074] During the transmission process, monitor the quality of the communication link in real time. If the signal strength of the current communication mode is lower than the preset signal strength value, switch to the backup communication mode, and use error correction coding technology to process the transmitted data. According to the stability and real-time requirements of the communication link, adjust the frequency of data transmission and the priority of data packets, and record the transmitted data according to the communication mode and time sequence to generate a transmission log for subsequent analysis and strategy optimization.

[0075] Specifically, in the design of a target area communication system, it is first necessary to obtain communication condition parameters. This includes the coverage and bandwidth of wired networks, the signal strength and frequency bands of wireless networks, and the visibility and latency of satellite communications. For example, in the suburban area of a city with complex terrain, the wired network may cover the main residential areas, 4G / 5G wireless networks cover most areas, while remote mountainous areas need to rely on satellite communications. Based on these parameters, the applicability levels of communication technologies can be divided. The target area is divided into three levels: A, B, and C. In level A areas, the wired network has good coverage and can be used as the main communication method; in level B areas, the wireless network has high signal strength and is used as the main communication method; in level C areas, only satellite communication can be relied on. This division helps to formulate a reasonable communication strategy. In the design of the communication strategy, multiple combination methods can be preset. A typical strategy may be: in level A areas, use fiber optic networks as the main, 4G networks as the auxiliary, and satellite communication as the emergency backup; in level B areas, use 5G networks as the main, microwave communication as the auxiliary, and at the same time retain wired network access points; in level C areas, mainly rely on satellite communication, supplemented by long-distance radio communication. This flexible strategy can maximize the utilization of various communication resources. Real-time monitoring of communication quality is the key to ensuring system stability. In this embodiment, a dynamic threshold system can also be set. When the signal strength of the main communication mode drops below 70% of the normal level, the system automatically switches to the standby mode. This mechanism can effectively handle sudden communication interruptions or performance degradation. To improve transmission reliability, it is necessary to adopt error correction coding technology. Using Reed-Solomon codes can effectively reduce the bit error rate in data transmission. In practical applications, different strengths of error correction coding may be adopted according to different communication modes, such as using stronger error correction capabilities in satellite communications. Transmission optimization is an important link to improve system efficiency. Different priorities can be set according to the importance and timeliness of the data. Real-time monitored data may be given the highest priority, and periodic report data can be transmitted when the network load is light. At the same time, the transmission frequency can be dynamically adjusted according to the network conditions, and the transmission frequency of non-critical data can be reduced when the network is congested. Finally, detailed transmission logs are crucial for system optimization. The logs should contain information such as the time of each transmission, the communication mode used, the data volume, and the transmission quality. By analyzing these logs, the bottlenecks of the communication system can be found, resource allocation can be optimized, and even potential communication failures can be predicted. This data-driven approach can continuously improve the overall performance and reliability of data transmission.

[0076] Furthermore, to judge the change trend of the data and identify potential abnormal situations, it includes:

[0077] Adopt the sliding window technique to extract feature parameters and change rates from the compressed data to form a parameter set;

[0078] Using time series analysis method, transform the parameter set into a serialized data stream, generate a trend line, and set an abnormal point monitoring threshold according to the change rate and stability of the trend line;

[0079] If the data at the monitoring point exceeds the preset monitoring threshold, it is determined as an abnormal point, and the abnormal recognition mechanism is triggered.

[0080] Specifically, the sliding window technique plays an important role in data processing. It extracts local features by moving a fixed-size window on the data stream. In the monitoring of temperature and humidity, a 10-minute sliding window is used to extract the characteristic parameters of temperature data. Each time the window moves, parameters such as the average temperature, the highest temperature, and the temperature change rate within this time period are calculated to form a parameter set. The time series analysis method then transforms these parameter sets into a serialized data stream and generates a trend line. The change rate and stability of the trend line are the keys to setting the abnormal point monitoring threshold.

[0081] Adopt the time series analysis method to extract eigenvalue and change rate from the parameter set, generate a serialized data stream, and construct a trend line. Set the abnormal point monitoring threshold according to the change rate of the trend line and the stability of the eigenvalue, and determine the dynamic monitoring range. If the data at the monitoring point exceeds the preset threshold, trigger the abnormal recognition mechanism to judge the existence of the abnormal point. Adopt a classification algorithm based on machine learning to accurately classify the abnormal point data to obtain a specific description of the abnormal type. According to the specific description of the abnormal type, combined with the preset warning level classification standard, determine the corresponding warning level. Generate a warning message according to the warning level and output the final warning result. Continuously update the parameter set through the sliding window, optimize the time series analysis algorithm, and improve the accuracy and real-time performance of abnormal recognition.

[0082] Furthermore, if an abnormal situation is recognized, adopt a classification algorithm based on machine learning to classify the abnormal type, obtain a specific description of the abnormal type, and generate a warning message according to the specific description of the abnormal type. Adopt the preset warning level classification standard to determine the warning level and obtain the final warning result.

[0083] Specifically, after the abnormal recognition mechanism is started in this embodiment, machine learning algorithms such as support vector machine (SVM) are used to classify the abnormal points. Through the training data set, SVM can accurately distinguish different types of power grid faults such as voltage sags and harmonic interferences. This classification method can better handle complex non-linear relationships compared with the traditional rule-based method. Automatically match the preset warning level according to the abnormal type and severity.

[0084] Furthermore, adopt the preset warning level classification standard to determine the warning level (such as Figure 3 ) including:

[0085] Determine the limit state of the temporary tower assembled foundation through numerical simulation. Set 60% - 80% of the limit state as the attention state, 80% - 90% as the warning state, and ≥90% as the dangerous state. Identify various states through the edge computing function and automatically change the data acquisition and transmission frequency;

[0086] Among them, the data acquisition and transmission interval of the attention state is 60 seconds, the data acquisition and transmission interval of the warning state is 30 seconds, and the data acquisition and transmission interval of the dangerous state is 10 seconds;

[0087] Below 60% of the limit state is the safe state, the acquisition interval is 60 seconds, and the transmission interval is 30 minutes. Below 40% of the limit state is the sleep state, the acquisition interval is 300 seconds, and the transmission interval is 12 hours.

[0088] After the early warning information is generated, it is transmitted to the monitoring center through high-speed communication technologies such as 5G networks. At the same time, real-time push technologies such as WebSocket are used to ensure that the duty personnel can receive the early warning in a timely manner on the mobile terminal. This multi-channel push strategy greatly shortens the information transmission time and wins precious time for fault handling. The sliding window technology is also used to continuously update the parameter set. A 24-hour sliding window is used to update the data set once an hour, eliminating old data and incorporating the latest observed values. This dynamic update mechanism enables the system to adapt to the seasonal changes of the power grid load and maintain the timeliness of the algorithm. The entire early warning process forms a closed loop: from data acquisition, anomaly identification, early warning generation to information push, each link is optimized. Through continuous data accumulation and algorithm optimization, its performance will be continuously improved, gradually realizing the transformation from passive response to active prevention, and finally achieving the intelligent and autonomous operation and maintenance goal.

[0089] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for remote monitoring of the status of a temporary transition tower of a transmission line, characterized in that: include: According to the climatic conditions and geographical environment of the area where the temporary transition tower is located, the selection and installation scheme of the sensor module is designed, and the original data of the tower status is collected through the sensor module, wherein the temporary transition tower adopts an assembled foundation support spliced ​​by I-beams; Based on the original data, using data preprocessing technology to obtain preliminarily processed data, and compressing the preliminarily processed data to obtain compressed data; Design a multi-mode communication transmission strategy according to the communication conditions of the area where the temporary transition tower is located, extract key characteristic parameters according to the compressed data, determine the change trend of the data, and identify potential abnormal situations; If an abnormal situation is identified, the abnormal situation will be transmitted to the monitoring center in real time to complete remote monitoring.

2. The method for remote monitoring of the status of a temporary transition tower of a power transmission line according to claim 1, characterized in that: The selection and installation scheme of the design sensor module includes: A pre-established climate and environmental parameter database is used to obtain the climate and geographic information of the area where the current temporary transition tower is located. Based on the climate and geographic information, a suitable sensor module type is determined, and the geographic information is analyzed to determine the specific installation location of the sensor module.

3. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 2 is characterized in that: The type and installation location of the sensor module are: The types of the sensing modules include pressure sensing modules, inclination sensing modules and temperature and humidity sensing modules; wherein the pressure sensing modules are respectively arranged on the top surface of the bottom plate of the assembled foundation spliced ​​by I-beams and the bottom surface of the bottom plate of the assembled foundation spliced ​​by I-beams, and the pressure sensing modules on the top surface and bottom surface of the bottom plate of the assembled foundation spliced ​​by I-beams are respectively installed on the web of the I-beams at the top angle; the inclination sensing module is installed on the web of the I-beams that are perpendicular to each other at the first and second steps of the assembled foundation spliced ​​by I-beams; and the temperature and humidity sensing module is arranged on one side of the bottom plate of the assembled foundation spliced ​​by I-beams.

4. The method for remote monitoring the status of a temporary transition tower of a power transmission line according to claim 1, characterized in that: The data obtained after preliminary processing include: According to a preset noise threshold, noise values ​​are removed from the original data, and through an anomaly detection algorithm, outliers in the original data are determined and removed to obtain the preliminarily processed data.

5. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 2, characterized in that: Obtaining the compressed data includes: Using wavelet transform to perform multi-scale decomposition on the preliminarily processed data to obtain low-frequency components and high-frequency components; For the high-frequency component, a preset threshold is set to perform soft threshold filtering to remove noise and redundant data, and the filtered component is reconstructed by inverse wavelet transform to obtain preliminary compressed data; According to the preset compression ratio requirement, the size of the preset threshold is adjusted and filtering and reconstruction processing is performed again, the reconstructed data is compared with the preliminary compressed data, and the information loss rate is calculated. If the information loss rate exceeds the preset threshold, the filtering parameters are readjusted and compressed again to obtain the compressed data, and the compressed data is stored in a database in time series, and the compression parameter information is recorded at the same time.

6. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 1 is characterized in that: Designing a multimodal communication transmission strategy includes: Obtain communication condition parameters of the area where the temporary transition tower is located, determine the coverage, bandwidth and signal strength of wired, wireless and satellite communications, divide the applicability levels of different communication technologies in the area based on the communication condition parameters, and determine the main communication mode and backup communication mode of each area; Preset multi-mode communication strategies, including a combination of wired-based, wireless-assisted, and satellite-supplemented, matching communication strategies with regional applicability levels; During the transmission process, the quality of the communication link is monitored in real time. If the signal strength of the current communication mode is lower than the preset signal strength value, it switches to the backup communication mode and uses error correction coding technology to process the transmitted data. According to the stability and real-time requirements of the communication link, the frequency of data transmission and the priority of the data packet are adjusted. The transmitted data is recorded according to the communication mode and timing, and a transmission log is generated for subsequent analysis and strategy optimization.

7. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 1, characterized in that: Determine the changing trend of data and identify potential abnormal situations, including: Using sliding window technology, characteristic parameters and change rates are extracted from the compressed data to form a parameter set; Using a time series analysis method, the parameter set is converted into a serialized data stream to generate a trend line, and an abnormal point monitoring threshold is set according to the change rate and stability of the trend line; If the data at a monitoring point exceeds the preset monitoring threshold, it is judged as an abnormal point and the abnormality identification mechanism is triggered.

8. The method for remote monitoring the status of temporary transition towers of power transmission lines according to claim 7, characterized in that: Judging the changing trend of data also includes: The parameter set is continuously updated through a sliding window to dynamically adjust the trend line; the time series analysis algorithm is optimized in combination with the compression ratio and characteristic value of the data stream.

9. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 1, characterized in that: If an abnormal situation is identified, a classification algorithm based on machine learning is used to classify the abnormality type, obtain a specific description of the abnormality type, and generate warning information based on the specific description of the abnormality type. The preset warning level classification standard is used to determine the warning level and obtain the final warning result.

10. The method for remote monitoring of the status of temporary transition towers of power transmission lines according to claim 9, characterized in that: The preset warning level classification standards are used to determine the warning levels, including: The limit state of the temporary tower assembly foundation is determined through numerical simulation, and 60% to 80% of the limit state is set as a caution state, 80% to 90% as a warning state, and ≥90% as a dangerous state. The edge computing function is used to identify various states and automatically change the data collection and transmission frequency; The data collection and transmission interval of the attention state is 60 seconds, the data collection and transmission interval of the warning state is 30 seconds, and the data collection and transmission interval of the danger state is 10 seconds; When the limit state is below 60%, it is in a safe state, with a collection interval of 60 seconds and a transmission interval of 30 minutes. When the limit state is below 40%, it is in a dormant state, with a collection interval of 300 seconds and a transmission interval of 12 hours.

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