A method and system for detecting icing state of a power transmission line based on multi-source monitoring

Abstract

The present application belongs to the technical field of transmission line icing state detection, and particularly relates to a transmission line icing state detection method and system based on multi-source monitoring. By setting an icing density parameter feedback correction mechanism between an image recognition model and a catenary mechanics model, as well as a conductor vibration frequency spectrum separation and environmental interference stripping mechanism, visual qualitative and mechanical quantitative complementary detection is realized to solve the problem that the total weight of icing cannot be accurately quantified and single data source failure leads to unreliable detection results in the prior art. A multi-source data adaptive fusion weight dynamic calculation and icing danger evaluation value four-level classification judgment mechanism is set to realize data source credibility driven fusion and differentiated operation and maintenance strategy generation. Differentiated operation and maintenance strategies containing specific quantitative indicators such as inspection cycle, ice melting power calculation, ice melting opportunity window and power-off protection judgment can be generated for different danger levels.

Description

Technical Field

[0001] This invention belongs to the field of transmission line icing status detection technology, specifically relating to a method and system for detecting transmission line icing status based on multi-source monitoring. Background Technology

[0002] Currently, transmission line icing detection mainly relies on data from a single type of sensor for estimation. Common detection methods include: calculating equivalent icing thickness based on conductor tension and tower tilt sensors combined with a catenary mechanical model; estimating icing thickness using environmental parameters such as temperature, humidity, and wind speed collected by micro-meteorological sensors and then using an icing growth prediction model; and obtaining icing morphology and thickness by capturing images of conductor icing using image recognition models. However, existing technologies have the following shortcomings: First, existing solutions typically rely on a single data source. When sensors malfunction or data becomes abnormal, the system cannot accurately determine the icing status, which can easily lead to unreliable overall detection results. Moreover, while image recognition can provide an intuitive view of the shape and type of icing, its accuracy in estimating icing thickness, especially the total weight of icing, is limited due to factors such as shooting angle, lighting conditions, and irregular icing shape. Mechanical sensors can sense the stress state of the conductor, but traditional methods only substitute tension and tilt angle into a simplified static equilibrium equation, failing to fully utilize the dynamic characteristics of conductor vibration frequency and neglecting corrections for environmental factors such as temperature and wind load. Therefore, the inversion accuracy needs to be improved. Secondly, existing solutions typically use fixed weights for data fusion when employing multi-source data, which cannot be dynamically adjusted according to the real-time operating status of each data source. When the performance of a certain sensor deteriorates, its low-quality data still participates in the calculation with the original weight, resulting in a decrease in overall detection accuracy and even generating false alarms or missed alarms. Furthermore, they usually only perform simple classification based on ice thickness without comprehensively considering multi-dimensional factors such as total ice weight, ice growth rate, and actual line load-bearing capacity. This leads to a lack of targeted operation and maintenance decisions and cannot effectively guide on-site inspections, de-icing, and power outage protection operations.

[0003] To address the aforementioned issues, this application presents a method and system for detecting the icing status of transmission lines based on multi-source monitoring. Summary of the Invention

[0004] To address the shortcomings of existing technologies mentioned in the background section, this application proposes a method and system for detecting the icing status of transmission lines based on multi-source monitoring. By setting up a feedback correction mechanism for icing density parameters between an image recognition model and a catenary mechanical model, as well as a mechanism for separating the conductor vibration frequency spectrum and removing environmental interference, complementary detection of visual qualitative and mechanical quantitative methods is achieved. This solves the problems in existing technologies where the total weight of icing cannot be accurately quantified and the detection results are unreliable due to the failure of a single data source. By setting up a dynamic calculation mechanism for adaptive fusion weights of multi-source data and a four-level classification and judgment mechanism for icing hazard assessment values, data source credibility-driven fusion and differentiated operation and maintenance strategy generation are achieved, thus solving the problem of the lack of quantitative support for operation and maintenance strategies in existing technologies.

[0005] Firstly, to achieve the above objectives, this application provides a method for detecting the icing status of transmission lines based on multi-source monitoring, which includes the following specific steps: Step S1: Real-time acquisition of line mechanical parameters, environmental meteorological parameters, and conductor icing image data to construct a standardized time-series monitoring dataset. The line mechanical parameters include conductor tension, tower tilt angle, and conductor vibration frequency. Step S2: Perform spectrum separation on the conductor vibration frequency, combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent ice thickness and total ice weight, obtain the second equivalent ice thickness through the ice growth prediction model, obtain the third equivalent ice thickness and ice density parameters through the image recognition model, and use the ice density parameters to correct the total ice weight to achieve complementary detection of visual qualitative and mechanical quantitative methods. Step S3: Extract confidence assessment feature parameters within the sliding time window from the line mechanical data source, micro-meteorological data source, and image data source respectively, input them into the adaptive threshold confidence assessment model, output the real-time confidence score of each type of data source, and at the same time, based on the historical sequence of confidence scores of each data source, identify the confidence decay pattern, distinguish between normal aging decay and sudden fault decay, and estimate the remaining effective working time. Step S4: Based on the real-time confidence scores of each data source output in Step S3, dynamically calculate the adaptive fusion weight of each data source. When the confidence score of any data source is lower than the preset minimum threshold, reset the adaptive fusion weight of that data source to zero, automatically isolate the data source and start deep diagnosis, and use the calculation results of the other normal data sources to cross-validate the key output parameters of the isolated data source. Step S5: Based on the adaptive fusion weights of each data source, the first equivalent ice thickness, the second equivalent ice thickness, and the third equivalent ice thickness are weighted and fused to obtain the comprehensive equivalent ice thickness. Combined with the finally determined total ice weight and ice type, the comprehensive judgment result of the ice status is output, and the credibility rating of the comprehensive judgment result is calculated. When all data sources are isolated, a data source failure alarm is generated. Short-term prediction is made based on the last effective state and the ice growth prediction model, and the credibility rating is set to the lowest level. Step S6: Obtain the comprehensive equivalent icing thickness and the final determined total icing weight output in step S5. Combine the icing growth rate and the line importance level to calculate the icing hazard assessment value. Compare the icing hazard assessment value with the classification threshold to determine the icing hazard level and generate a quantitative differentiated operation and maintenance strategy. Step S7: Based on the confidence decay pattern and remaining effective working time identified in step S3, generate planned maintenance work orders for normal aging and decaying data sources, and generate emergency repair alarms for data sources experiencing sudden failures and decay.

[0006] Based on the above-mentioned preferred scheme, step S2 specifically includes the following steps: Step S21: Perform spectral analysis on the conductor vibration frequency in the time-series monitoring dataset, use bandpass filtering to separate the low-frequency vibration component and the high-frequency vibration component, based on conductor vibration theory, use the frequency and amplitude of the low-frequency vibration component to infer the additional tension caused by icing, and use the spectral characteristics of the high-frequency vibration component combined with the wind speed frequency response function to estimate the equivalent wind load. Step S22: Input the conductor tension and tower tilt angle from the time-series monitoring dataset, as well as the icing additional tension and equivalent wind load output from step S21, into the pre-constructed catenary mechanical model of the transmission line. Introduce temperature correction terms and wind load correction terms into the catenary mechanical model. Invert the vertical comprehensive load per unit length of the conductor by solving the conductor state equation. The wind load is only used for horizontal tension correction. Subtract the conductor self-weight load from the comprehensive load to obtain the icing load. Multiply the icing load by the conductor length to obtain the initial total icing weight. Calculate the initial value of the first equivalent icing thickness based on the initial total icing weight and the preset icing density. Step S23: Input the environmental meteorological parameters in the time series monitoring dataset into the icing growth prediction model to calculate the second equivalent icing thickness; Step S24: Input the icing image data of the conductor in the time series monitoring dataset into the image recognition model to identify the icing type and icing morphology, output the third equivalent icing thickness, and output the icing density parameter corresponding to the icing type, where the icing type includes rime, hoarfrost and mixed rime. Step S25: When the image data source is available, the ice density parameter output in step S24 is fed back to step S22 to correct the initial total ice weight and obtain the corrected total ice weight. Based on the corrected total ice weight and ice density parameter, the final first equivalent ice thickness is calculated by combining the wire diameter and ice morphology parameters. When the image data source is unavailable, the first equivalent ice thickness is calculated using the preset default ice density and marked as an uncorrected value. The final determined total icing weight is: the corrected total icing weight when the image data source is available, and the total icing weight calculated based on the preset default icing density and marked as uncorrected when the image data source is unavailable.

[0007] Based on the above scheme, the preferred embodiment of step S22 introduces temperature correction terms and wind load correction terms into the catenary mechanical model of the transmission line. Specifically, a temperature additional term is added to the conductor state equation. The additional term for wind load is defined as follows: α is the thermal expansion coefficient of the conductor, E is the elastic modulus of the conductor, A is the cross-sectional area of ​​the conductor, and ΔT is the difference between the measured temperature and the reference temperature. When temperature data is missing, ΔT is taken as zero or the historical statistical average. The additional term for wind load is the equivalent wind load estimated in step S21, which is introduced into the conductor state equation as a uniformly distributed load in the form of a specific load. The tower tilt angle is converted into the change in the height difference of the conductor suspension point, and substituted into the catenary equation to correct the span parameters. The corrected conductor state equation is obtained, and the comprehensive specific load is solved.

[0008] Based on the above scheme, the confidence assessment feature parameters in step S3 specifically include: The confidence assessment features of the line mechanics data source include the catenary model fitting residual rate within the sliding time window, the tension-tilt consistency deviation rate, the conductor vibration frequency anomaly rate, and the icing-induced tension fluctuation rate. The confidence assessment features of micro-meteorological data sources include the data missing rate, data jump rate, multi-sensor cross-calibration deviation rate, and the deviation rate between meteorological data and physical laws within the sliding time window. Among them, the deviation rate between meteorological data and physical laws is the degree of consistency deviation between the ice thickness variation trend estimated by the micro-meteorological data source and the ice thickness variation trend estimated by the line mechanics data source. The confidence evaluation features of the image data source include the image quality degradation rate within the sliding time window, the volatility of the icing identification results, the consistency deviation rate of the icing thickness calculated from the image data source and the line mechanical data source, and the stability of the icing density output.

[0009] Based on the above scheme, the preferred method for identifying the confidence decline pattern in step S3 is as follows: when the decline slope of the confidence score within a preset statistical period is less than the flat threshold and maintains a continuous downward trend, it is determined to be normal aging decline. When the confidence score drops more than the mutation threshold within a preset short period of time, it is judged as a sudden failure and degradation. For data sources that are experiencing normal aging and decline, the remaining effective working time is estimated based on the historical decline slope. For data sources that experience sudden failure and decline, in-depth diagnosis is triggered immediately, without relying on the decline slope to estimate the remaining effective working time.

[0010] Based on the above scheme, the preferred method for calculating the adaptive fusion weight in step S4 is as follows: subtract the preset minimum threshold from the real-time confidence score of each type of data source and take the maximum value of the result with zero. Then normalize the maximum value result of each data source so that the sum of the adaptive fusion weights of each type of data source is always 1. When the confidence score of a certain type of data source is lower than the preset minimum threshold, its corresponding maximum value result is zero, the adaptive fusion weight is automatically set to zero, and the weights of the remaining data sources are automatically re-normalized.

[0011] Based on the above-mentioned preferred scheme, step S5 specifically includes the following steps: Step S51: Based on the adaptive fusion weights of each data source determined in step S4, the first equivalent icing thickness, second equivalent icing thickness, and third equivalent icing thickness obtained in step S2 are weighted and fused to obtain the comprehensive equivalent icing thickness. When a data source is isolated, its corresponding weight is zero, and the corresponding thickness item is naturally excluded. The calculation formula is as follows:

[0012] In the formula, Dc represents the comprehensive equivalent icing thickness. For adaptive fusion weights of line mechanics data sources, For adaptive fusion weights of micro-meteorological data sources, Let D1 be the first equivalent ice thickness, D2 be the second equivalent ice thickness, and D3 be the third equivalent ice thickness, and satisfy the following conditions: When all data sources are isolated, Dc takes the last valid value and triggers a full failure alarm. Step S52: Combining the final determined total icing weight and icing type obtained in step S2, output the comprehensive judgment result of icing status. The comprehensive judgment result of icing status includes icing type, comprehensive equivalent icing thickness and final determined total icing weight. When the image data source is unavailable, the icing type is marked as unknown or inferred based on meteorological and mechanical parameters and an inferred label is added. Step S53: Based on the confidence scores of each data source and the adaptive fusion weights, calculate the credibility rating score of the comprehensive judgment result. The calculation formula is as follows:

[0013] In the formula, Rc is the confidence rating score, Cm is the real-time confidence score of the line mechanics data source, Cw is the real-time confidence score of the micro-meteorological data source, and Ci is the real-time confidence score of the image data source. The assessment was rated as highly credible. The time rating is rated as medium credibility. The time rating is set to low confidence, where Rh is the high confidence threshold and Rm is the medium confidence threshold. When all data sources are isolated, Rc is forced to take a value lower than Rm.

[0014] Based on the above scheme, the preferred method for calculating the icing hazard assessment value in step S6 is as follows: The icing hazard assessment value is obtained by weighted summing the ratio of the comprehensive equivalent icing thickness to the design icing thickness limit, the ratio of the final determined total icing weight to the maximum allowable load-bearing weight of the line, and the ratio of the icing growth rate to the preset rate threshold. The icing growth rate is taken as a non-negative value, and is zero when the icing growth rate is negative. The calculation formula is:

[0015] In the formula, Vr represents the icing hazard assessment value, and Dc represents the comprehensive equivalent icing thickness. To design the icing thickness limit, Gc is the final determined total weight of the icing. The maximum allowable load capacity of the line is given by: v = maximum icing growth rate, v0 = preset rate threshold, Ka and Kb are basic weighting coefficients, and γ(L) is a dynamic weighting coefficient related to the importance level L of the line. The higher the importance level of the line, the larger the value of γ(L). The preset rate threshold v0 is a reference value for the icing growth rate per unit time set according to the historical icing statistics of the area where the line is located.

[0016] Based on the above scheme, the preferred method for determining the icing hazard level in step S6 is as follows: when At that time, it was determined to be slight icing; when At that time, if Then it is determined to be a continuous increase in icing, if It is then judged as slight icing and marked as slow growth; when At that time, if Then it is judged as moderately stable icing. It is then classified as moderate icing and marked as trending towards stability; when At that time, it was determined to be a high-risk overload with icing. Where V1 is the first danger threshold, V2 is the second danger threshold, and V3 is the third danger threshold; The quantitative differentiated operation and maintenance strategy includes: generating early warning information for minor icing, shortening the inspection cycle from the standard cycle to a preset multiple, and attaching an icing growth trend prediction report; For the continuous increase of icing, the thermal power required for de-icing is calculated based on the icing growth rate and the rated current of the line, and a suggestion on the timing window for starting de-icing is given in combination with meteorological forecast data; For moderately stable icing, generate enhanced monitoring instructions and shorten the inspection cycle; For high-risk overload icing, calculate the ratio of the current total weight of icing to the line's ultimate load-bearing capacity. When the ratio exceeds the emergency threshold, generate an immediate power outage protection command. When the ratio is below the emergency threshold but exceeds the danger threshold, generate a load transfer scheme.

[0017] Secondly, this application provides a transmission line icing status detection system based on multi-source monitoring, which specifically includes: a data acquisition and preprocessing module, a multi-source icing status calculation module, a confidence assessment and decay pattern recognition module, an adaptive fusion weight calculation module, an icing status fusion judgment module, an icing hazard level determination and operation and maintenance strategy generation module, and a sensor maintenance early warning module. The data acquisition and preprocessing module is used to perform step S1, which collects line mechanical parameters, environmental meteorological parameters and conductor icing image data in real time, and constructs a standardized time-series monitoring dataset. The multi-source icing state calculation module has its input end connected to the output end of the data acquisition and preprocessing module. It is used to execute step S2, perform spectrum separation on the conductor vibration frequency, and combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent icing thickness and total icing weight. The second equivalent icing thickness is obtained through the icing growth prediction model, and the third equivalent icing thickness and icing density parameter are obtained through the image recognition model. The total icing weight is corrected using the icing density parameter, thus achieving complementary detection of visual qualitative and mechanical quantitative methods. The multi-source icing state calculation module includes a vibration spectrum separation submodule, a mechanical-catenary icing calculation submodule, a micro-meteorological icing estimation submodule, and an image icing recognition submodule. The vibration spectrum separation submodule is used to separate the frequency spectrum of conductor vibration and output the icing additional tension and equivalent wind load. The mechanical-catenary icing solution module is used to invert the total icing weight and the first equivalent icing thickness using the mechanical model of the transmission line catenary, and to receive icing density parameters to correct the total icing weight. The micro-meteorological icing estimation submodule is used to calculate the second equivalent icing thickness based on environmental meteorological parameters; The image icing recognition submodule is used to identify the icing type and icing morphology and output the third equivalent icing thickness and icing density parameters. The confidence assessment and decay pattern recognition module has its input terminals connected to the output terminals of the data acquisition and preprocessing module, the vibration spectrum separation submodule, the mechanical-catenary icing calculation submodule, the micro-meteorological icing estimation submodule, and the image icing recognition submodule, respectively. It is used to execute step S3, output the real-time confidence scores of each data source, identify the confidence decay pattern, and estimate the remaining effective working time. The input of the adaptive fusion weight calculation module is connected to the output of the confidence assessment and decay pattern recognition module, and is used to execute step S4. The input end of the icing state fusion judgment module is connected to the output end of the adaptive fusion weight calculation module and the multi-source icing state solution module, respectively, and is used to execute step S5; The input end of the icing hazard level determination and operation and maintenance strategy generation module is connected to the output end of the icing state fusion judgment module, and is used to execute step S6; The sensor maintenance early warning module has its input terminal connected to the output terminal of the confidence assessment and decay pattern recognition module, and is used to execute step S7.

[0018] Compared with the prior art, the beneficial effects of the present invention are: First, the image recognition model identifies the type of icing and outputs the corresponding icing density parameter. This icing density parameter is then fed back to the catenary mechanical model to correct the total weight of icing derived from the mechanical model. This overcomes the shortcomings of the mechanical model relying solely on empirical density assumptions. When the image data source is unavailable, the mechanical model can continue to work using a preset default icing density, thus improving the robustness of the system.

[0019] Secondly, by performing spectral separation on the conductor vibration frequency, the low-frequency vibration component caused by icing and the high-frequency vibration component caused by wind excitation are extracted. The low-frequency vibration component is used to infer the additional tension caused by icing for model input, and the high-frequency vibration component is used to estimate the equivalent wind load as a model correction term. This effectively eliminates the interference of environmental factors such as wind on the accuracy of mechanical inversion. At the same time, temperature correction term and wind load correction term are introduced into the catenary mechanical model to further improve the inversion accuracy of icing thickness and total icing weight.

[0020] Third, multidimensional confidence assessment feature parameters are extracted from each data source, and a real-time confidence score is output through an adaptive threshold confidence assessment model. Based on this, adaptive fusion weights are dynamically calculated. When the confidence score of a data source is lower than the preset minimum threshold, its weight is automatically reset to zero and isolated, effectively avoiding low-quality data from polluting the fusion results. At the same time, it can identify confidence decay patterns, distinguish between normal aging decay and sudden failure decay of sensors, and provide a basis for differentiated sensor maintenance strategies.

[0021] Fourth, by comprehensively considering the three factors of ice thickness, total ice weight, and ice growth rate, and combining them with the dynamic weight of the line's importance level, an ice hazard assessment value is calculated. The ice hazard status is divided into four levels: slight ice hazard, continuously growing ice hazard, moderately stable ice hazard, and high-risk overload ice hazard. For different hazard levels, differentiated operation and maintenance strategies are generated, including specific quantitative indicators such as inspection cycle, ice melting power calculation, ice melting timing window, and power outage protection judgment. These strategies can effectively guide on-site operation and maintenance work and improve the power grid's ability to cope with ice hazard disasters. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall process of a method for detecting the icing status of transmission lines based on multi-source monitoring according to the present invention. Figure 2 This is a flowchart illustrating step S2 in a multi-source monitoring-based method for detecting icing status of transmission lines according to the present invention. Figure 3 This is a schematic diagram of the framework of a transmission line icing status detection system based on multi-source monitoring according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: To solve the technical problems mentioned in the background art, this application provides a preferred embodiment: as follows Figures 1-3 As shown, a method for detecting the icing status of transmission lines based on multi-source monitoring includes the following specific steps: Step S1: Real-time acquisition of line mechanical parameters, environmental meteorological parameters, and conductor icing image data to construct a standardized time-series monitoring dataset. The line mechanical parameters include conductor tension, tower tilt angle, and conductor vibration frequency. Step S2: Perform spectrum separation on the conductor vibration frequency, combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent ice thickness and total ice weight, obtain the second equivalent ice thickness through the ice growth prediction model, obtain the third equivalent ice thickness and ice density parameters through the image recognition model, and use the ice density parameters to correct the total ice weight to achieve complementary detection of visual qualitative and mechanical quantitative methods. Step S3: Extract confidence assessment feature parameters within the sliding time window from the line mechanical data source, micro-meteorological data source, and image data source respectively, input them into the adaptive threshold confidence assessment model, output the real-time confidence score of each type of data source, and at the same time, based on the historical sequence of confidence scores of each data source, identify the confidence decay pattern, distinguish between normal aging decay and sudden fault decay, and estimate the remaining effective working time. Step S4: Based on the real-time confidence scores of each data source output in Step S3, dynamically calculate the adaptive fusion weight of each data source. When the confidence score of any data source is lower than the preset minimum threshold, reset the adaptive fusion weight of that data source to zero, automatically isolate the data source and start deep diagnosis, and use the calculation results of the other normal data sources to cross-validate the key output parameters of the isolated data source. Step S5: Based on the adaptive fusion weights of each data source, the first equivalent ice thickness, the second equivalent ice thickness, and the third equivalent ice thickness are weighted and fused to obtain the comprehensive equivalent ice thickness. Combined with the finally determined total ice weight and ice type, the comprehensive judgment result of the ice status is output, and the credibility rating of the comprehensive judgment result is calculated. When all data sources are isolated, a data source failure alarm is generated. Short-term prediction is made based on the last effective state and the ice growth prediction model, and the credibility rating is set to the lowest level. Step S6: Obtain the comprehensive equivalent icing thickness and the final determined total icing weight output in step S5. Combine the icing growth rate and the line importance level to calculate the icing hazard assessment value. Compare the icing hazard assessment value with the classification threshold to determine the icing hazard level and generate a quantitative differentiated operation and maintenance strategy. Step S7: Based on the confidence decay pattern and remaining effective working time identified in step S3, generate planned maintenance work orders for normal aging and decaying data sources, and generate emergency repair alarms for data sources experiencing sudden failures and decay.

[0025] The advantages of this embodiment compared to the prior art are as follows: It identifies the icing type using an image recognition model and outputs the corresponding icing density parameter. This icing density parameter is then fed back to the catenary mechanical model to correct the total icing weight derived from the mechanical model. This overcomes the shortcomings of a purely mechanical model relying on empirical density assumptions. When the image data source is unavailable, the mechanical model can continue operating using a preset default icing density, improving the system's robustness. Simultaneously, by performing spectral separation on the conductor vibration frequency, it extracts the low-frequency vibration components caused by icing and the high-frequency vibration components caused by wind excitation. The low-frequency vibration components are used to infer the additional tension caused by icing for model input, while the high-frequency vibration components are used to estimate the equivalent wind load as a model correction term. This effectively eliminates the interference of environmental factors such as wind on the accuracy of the mechanical inversion. Furthermore, the introduction of temperature and wind load correction terms into the catenary mechanical model further improves the accuracy of icing thickness and total icing weight retrieval.

[0026] Furthermore: In an optional embodiment, step S2 specifically includes the following steps: Step S21: Perform spectral analysis on the conductor vibration frequency in the time-series monitoring dataset, use bandpass filtering to separate the low-frequency vibration component and the high-frequency vibration component, based on conductor vibration theory, use the frequency and amplitude of the low-frequency vibration component to infer the additional tension caused by icing, and use the spectral characteristics of the high-frequency vibration component combined with the wind speed frequency response function to estimate the equivalent wind load. Step S22: Input the conductor tension and tower tilt angle from the time-series monitoring dataset, as well as the icing additional tension and equivalent wind load output from step S21, into the pre-constructed catenary mechanical model of the transmission line. Introduce temperature correction terms and wind load correction terms into the catenary mechanical model. Invert the vertical comprehensive load per unit length of the conductor by solving the conductor state equation. The wind load is only used for horizontal tension correction. Subtract the conductor self-weight load from the comprehensive load to obtain the icing load. Multiply the icing load by the conductor length to obtain the initial total icing weight. Calculate the initial value of the first equivalent icing thickness based on the initial total icing weight and the preset icing density. Step S23: Input the environmental meteorological parameters in the time series monitoring dataset into the icing growth prediction model to calculate the second equivalent icing thickness; Step S24: Input the icing image data of the conductor in the time series monitoring dataset into the image recognition model to identify the icing type and icing morphology, output the third equivalent icing thickness, and output the icing density parameter corresponding to the icing type, where the icing type includes rime, hoarfrost and mixed rime. Step S25: When the image data source is available, the ice density parameter output in step S24 is fed back to step S22 to correct the initial total ice weight and obtain the corrected total ice weight. Based on the corrected total ice weight and ice density parameter, the final first equivalent ice thickness is calculated by combining the wire diameter and ice morphology parameters. When the image data source is unavailable, the first equivalent ice thickness is calculated using the preset default ice density and marked as an uncorrected value. The final determined total icing weight is: the corrected total icing weight when the image data source is available, and the total icing weight calculated based on the preset default icing density and marked as uncorrected when the image data source is unavailable.

[0027] In an optional embodiment, the catenary mechanical model of the transmission line described in step S22 introduces temperature correction terms and wind load correction terms, specifically by adding a temperature additional term to the conductor state equation. The additional term for wind load is defined as follows: α is the thermal expansion coefficient of the conductor, E is the elastic modulus of the conductor, A is the cross-sectional area of ​​the conductor, and ΔT is the difference between the measured temperature and the reference temperature. When temperature data is missing, ΔT is taken as zero or the historical statistical average. The additional term for wind load is the equivalent wind load estimated in step S21, which is introduced into the conductor state equation as a uniformly distributed load in the form of a specific load. The tower tilt angle is converted into the change in the height difference of the conductor suspension point, and substituted into the catenary equation to correct the span parameters. The corrected conductor state equation is obtained, and the comprehensive specific load is solved.

[0028] It needs to be further explained that: Regarding step S21: In the theory of conductor string vibration, the mathematical relationship between the natural frequency of the conductor and the tension is as follows: Where fn is the nth natural frequency of the conductor, L is the span length of the conductor, T is the horizontal tension of the conductor, and m is the mass per unit length of the conductor. After icing, the mass and tension of the conductor increase, causing the natural frequency to shift. The specific implementation method of the vibration spectrum separation submodule in step S21 to back-calculate the additional tension caused by icing using the low-frequency vibration component is as follows: by comparing the change in the natural frequency of the conductor before and after icing, the tension increment is calculated by substituting it into the above frequency-tension relationship, and the tension increment is output as the additional tension caused by icing.

[0029] The method for obtaining the wind speed frequency response function used by the vibration spectrum separation submodule in step S21 is as follows: In the initial stage of the deployment of the icing monitoring system or during the known non-icing operation period, wind speed sensor data and high-frequency vibration component spectrum characteristic data of the conductor vibration sensor are collected simultaneously. The transfer function relationship between wind speed and high-frequency vibration component is established through the system identification method. This transfer function is the wind speed frequency response function. In the subsequent icing monitoring process, the vibration spectrum separation submodule calculates the equivalent wind load based on the real-time high-frequency vibration component spectrum characteristics and this transfer function.

[0030] Regarding step S22: In step S22, the mechanics-caten icing solution module superimposes the additional tension caused by icing as the incremental component of the conductor tension caused by the vertical load of icing. This additional tension is algebraically superimposed with the original conductor tension under the horizontal tension boundary conditions of the conductor's equation of state, together forming the horizontal tension input for solving the conductor's equation of state. The equivalent wind load is introduced into the horizontal load term of the conductor's equation of state as a uniformly distributed load. It only participates in the horizontal force balance calculation and does not affect the solution of the comprehensive load ratio in the vertical direction. After obtaining the comprehensive load ratio per unit length of the conductor in the vertical direction from the solution of the conductor's equation of state, the known conductor self-weight load ratio is subtracted, and the remaining part is the icing load ratio.

[0031] Regarding step S23: The icing growth prediction model used in the micro-meteorological icing estimation submodule in step S23 is a regression model trained based on environmental meteorological parameters and measured icing thickness data from historical icing events. The input parameters include environmental meteorological parameters such as temperature, humidity, wind speed, and precipitation. The output is the predicted icing thickness per unit length of the conductor under the current meteorological conditions, i.e., the second equivalent icing thickness. This model can be trained separately according to different micro-topographical area types and historical icing data to adapt to the icing characteristics of different line sections.

[0032] Regarding step S24: The image recognition model used in the image icing recognition submodule in step S24 adopts a deep convolutional neural network structure. It is pre-trained using images of conductor icing with different icing types and icing morphologies. The icing types include rime, hoarfrost, and mixed rime. Different icing types correspond to different ranges of icing density parameters. The image icing recognition submodule outputs the third equivalent icing thickness by: calculating the radial thickness of the icing area based on the pixel-level segmentation results of the conductor contour and the icing contour in the image, and converting it into the actual icing thickness by combining the calibration parameters of the image acquisition device. The icing density parameter is obtained from the preset density parameter mapping table according to the identified icing type.

[0033] Regarding step S25: The specific implementation method of the mechanics-catenary icing calculation submodule in step S25, which calculates the final first equivalent icing thickness based on the corrected total icing weight and icing density parameters, is as follows: the corrected total icing weight is divided by the conductor length to obtain the icing mass per unit length, the icing volume per unit length is calculated in combination with the icing density parameters, and the equivalent icing thickness is obtained by geometric conversion based on the conductor diameter and icing morphology parameters. The icing morphology parameters are determined by the icing morphology output by the image icing recognition submodule and are used to describe the distribution characteristics of icing on the conductor cross section. When the image data source is unavailable, the preset default icing density used by the mechanics-catenary icing calculation submodule is the density value corresponding to the common icing types statistically obtained from historical icing events in the area where the line is located.

[0034] In an optional embodiment, the confidence assessment feature parameters in step S3 specifically include: The confidence assessment features of the line mechanics data source include the catenary model fitting residual rate within the sliding time window, the tension-tilt consistency deviation rate, the conductor vibration frequency anomaly rate, and the icing-induced tension fluctuation rate. It should be noted that: the catenary model fitting residual rate is the root mean square deviation between the horizontal tension calculated from the conductor tension and tower inclination angle within the sliding time window and the horizontal tension synthesized from the icing-induced additional tension derived from the separated low-frequency vibration components, relative to the reference tension under the ice-free healthy state of the line. The oscillation rate of icing-induced tension is the ratio of the standard deviation to the mean of the icing-induced tension derived from the low-frequency vibration components within the sliding time window. The confidence assessment features of micro-meteorological data sources include the data missing rate, data jump rate, multi-sensor cross-calibration deviation rate, and the deviation rate between meteorological data and physical laws within the sliding time window. Among them, the deviation rate between meteorological data and physical laws is the degree of consistency deviation between the ice thickness variation trend estimated by the micro-meteorological data source and the ice thickness variation trend estimated by the line mechanics data source. The confidence evaluation features of the image data source include the image quality degradation rate within the sliding time window, the volatility of the icing identification results, the consistency deviation rate of the icing thickness calculated from the image data source and the line mechanical data source, and the stability of the icing density output.

[0035] In an optional embodiment, the method for identifying the confidence decline pattern in step S3 is as follows: when the decline slope of the confidence score within a preset statistical period is less than the flat threshold and maintains a continuous downward trend, it is determined to be normal aging decline. When the confidence score drops more than the mutation threshold within a preset short period of time, it is judged as a sudden failure and degradation. For data sources that are aging and declining normally, the remaining effective working time is estimated based on the historical decline slope. For data sources that are declining due to sudden failures, in-depth diagnosis is triggered immediately, without relying on the decline slope to estimate the remaining effective working time. It should be noted that in step S3, the adaptive threshold confidence assessment model uses a regression model to dynamically generate thresholds for each confidence assessment feature parameter based on the voltage level of different lines, the type of micro-topography area they are located in, and historical icing fault data, replacing the fixed threshold judgment.

[0036] Furthermore: In an optional embodiment, the adaptive fusion weight in step S4 is calculated as follows: the real-time confidence score of each type of data source is subtracted from the preset minimum threshold and the maximum value is taken from zero. Then, the maximum value result of each data source is normalized so that the sum of the adaptive fusion weights of each type of data source is always 1. When the confidence score of a certain type of data source is lower than the preset minimum threshold, the corresponding maximum value result is zero, the adaptive fusion weight is automatically set to zero, and the weights of the remaining data sources are automatically re-normalized.

[0037] In an optional embodiment, step S5 specifically includes the following steps: Step S51: Based on the adaptive fusion weights of each data source determined in step S4, the first equivalent icing thickness, second equivalent icing thickness, and third equivalent icing thickness obtained in step S2 are weighted and fused to obtain the comprehensive equivalent icing thickness. When a data source is isolated, its corresponding weight is zero, and the corresponding thickness item is naturally excluded. The calculation formula is as follows:

[0038] In the formula, Dc represents the comprehensive equivalent icing thickness. For adaptive fusion weights of line mechanics data sources, For adaptive fusion weights of micro-meteorological data sources, Let D1 be the first equivalent ice thickness, D2 be the second equivalent ice thickness, and D3 be the third equivalent ice thickness, and satisfy the following conditions: When all data sources are isolated, Dc takes the last valid value and triggers a full failure alarm. Step S52: Combining the final determined total icing weight and icing type obtained in step S2, output the comprehensive judgment result of icing status. The comprehensive judgment result of icing status includes icing type, comprehensive equivalent icing thickness and final determined total icing weight. When the image data source is unavailable, the icing type is marked as unknown or inferred based on meteorological and mechanical parameters and an inferred label is added. Step S53: Based on the confidence scores of each data source and the adaptive fusion weights, calculate the credibility rating score of the comprehensive judgment result. The calculation formula is as follows:

[0039] In the formula, Rc is the confidence rating score, Cm is the real-time confidence score of the line mechanics data source, Cw is the real-time confidence score of the micro-meteorological data source, and Ci is the real-time confidence score of the image data source. The assessment was rated as highly credible. The assessment is rated as medium credibility. The time rating is set to low confidence, where Rh is the high confidence threshold and Rm is the medium confidence threshold. When all data sources are isolated, Rc is forced to take a value lower than Rm.

[0040] In an optional embodiment, the specific method for calculating the icing hazard assessment value in step S6 is as follows: The icing hazard assessment value is obtained by weighted summing the ratio of the comprehensive equivalent icing thickness to the design icing thickness limit, the ratio of the final determined total icing weight to the maximum allowable load-bearing weight of the line, and the ratio of the icing growth rate to a preset rate threshold. The icing growth rate is taken as a non-negative value, and is zero when the icing growth rate is negative. The calculation formula is:

[0041] In the formula, Vr represents the icing hazard assessment value, and Dc represents the comprehensive equivalent icing thickness. To design the icing thickness limit, Gc is the final determined total weight of the icing. The maximum allowable load capacity of the line is given by: v = maximum icing growth rate, v0 = preset rate threshold, Ka and Kb are basic weighting coefficients, and γ(L) is a dynamic weighting coefficient related to the importance level L of the line. The higher the importance level of the line, the larger the value of γ(L). The preset rate threshold v0 is a reference value for the icing growth rate per unit time set according to the historical icing statistics of the area where the line is located.

[0042] In an optional embodiment, the method for determining the icing hazard level in step S6 is as follows: when At that time, it was determined to be slight icing; when At that time, if Then it is determined to be a continuous increase in icing, if It is then judged as slight icing and marked as slow growth; when At that time, if Then it is judged as moderately stable icing. It is then classified as moderate icing and marked as trending towards stability; when At that time, it was determined to be a high-risk overload with icing. Where V1 is the first danger threshold, V2 is the second danger threshold, and V3 is the third danger threshold; The quantitative differentiated operation and maintenance strategy includes: generating early warning information for minor icing, shortening the inspection cycle from the standard cycle to a preset multiple, and attaching an icing growth trend prediction report; For the continuous increase of icing, the thermal power required for de-icing is calculated based on the icing growth rate and the rated current of the line, and a window suggestion for the timing of de-icing is given in combination with meteorological forecast data; For moderately stable icing, generate enhanced monitoring instructions and shorten the inspection cycle; For high-risk overload icing, calculate the ratio of the current total weight of icing to the line's ultimate load-bearing capacity. When the ratio exceeds the emergency threshold, generate an immediate power outage protection command. When the ratio is below the emergency threshold but exceeds the danger threshold, generate a load transfer scheme.

[0043] The advantages of the above embodiments compared to the prior art are as follows: Multi-dimensional confidence assessment feature parameters are extracted from each data source; a real-time confidence score is output through an adaptive threshold confidence assessment model; and adaptive fusion weights are dynamically calculated based on this score. When the confidence score of a data source is lower than a preset minimum threshold, its weight is automatically reset to zero and isolated, effectively avoiding low-quality data contaminating the fusion results. Simultaneously, it can identify confidence decay patterns, distinguishing between normal aging decay and sudden fault decay of sensors, providing a basis for differentiated sensor maintenance strategies. Furthermore, by comprehensively considering three factors—ice thickness, total ice weight, and ice growth rate—and combining them with the dynamic weight of line importance levels, an ice accretion hazard assessment value is calculated, classifying ice conditions into four levels: slight ice accumulation, continuously growing ice accumulation, moderately stable ice accumulation, and high-risk overload ice accumulation. For different hazard levels, differentiated operation and maintenance strategies are generated, including specific quantitative indicators such as inspection cycle, ice melting power calculation, ice melting timing window, and power outage protection judgment. This can effectively guide on-site operation and maintenance work and improve the power grid's ability to cope with ice disasters.

[0044] Example 2: Based on the same inventive concept as Example 1, such as Figure 3 As shown, this embodiment provides a transmission line icing status detection system based on multi-source monitoring, which specifically includes: a data acquisition and preprocessing module, a multi-source icing status calculation module, a confidence assessment and decay pattern recognition module, an adaptive fusion weight calculation module, an icing status fusion judgment module, an icing hazard level judgment and operation and maintenance strategy generation module, and a sensor maintenance early warning module. The data acquisition and preprocessing module is used to perform step S1, which collects line mechanical parameters, environmental meteorological parameters and conductor icing image data in real time, and constructs a standardized time-series monitoring dataset. The multi-source icing state calculation module has its input end connected to the output end of the data acquisition and preprocessing module. It is used to execute step S2, perform spectrum separation on the conductor vibration frequency, and combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent icing thickness and total icing weight. The second equivalent icing thickness is obtained through the icing growth prediction model, and the third equivalent icing thickness and icing density parameters are obtained through the image recognition model. The total icing weight is corrected using the icing density parameters, thus achieving complementary detection of visual qualitative and mechanical quantitative methods. The multi-source icing state calculation module includes a vibration spectrum separation submodule, a mechanical-catenary icing calculation submodule, a micro-meteorological icing estimation submodule, and an image icing recognition submodule; The vibration spectrum separation submodule is used to separate the frequency spectrum of conductor vibration and output the additional tension of icing and the equivalent wind load. The mechanics-catenary icing solution module is used to invert the total icing weight and the first equivalent icing thickness using the mechanics model of the catenary of the transmission line, and to receive the icing density parameter to correct the total icing weight. The micro-meteorological icing estimation submodule is used to calculate the second equivalent icing thickness based on environmental meteorological parameters; The image icing recognition submodule is used to identify the type and shape of icing and output the third equivalent icing thickness and icing density parameters; The confidence assessment and decay pattern recognition module has its input terminals connected to the output terminals of the data acquisition and preprocessing module, the vibration spectrum separation submodule, the mechanical-catenary icing calculation submodule, the micro-meteorological icing estimation submodule, and the image icing recognition submodule, respectively. It is used to execute step S3, output the real-time confidence scores of each data source, identify the confidence decay pattern, and estimate the remaining effective working time. The input of the adaptive fusion weight calculation module is connected to the output of the confidence assessment and decay pattern recognition module, and is used to execute step S4. The input of the icing state fusion judgment module is connected to the output of the adaptive fusion weight calculation module and the multi-source icing state solution module, respectively, and is used to execute step S5. The input end of the icing hazard level determination and operation and maintenance strategy generation module is connected to the output end of the icing state fusion determination module, and is used to execute step S6; The sensor maintenance early warning module has its input terminal connected to the output terminal of the confidence assessment and decay pattern recognition module, and is used to execute step S7.

[0045] The parameters and steps for implementing the corresponding functions of each unit module in the multi-source monitoring-based transmission line icing status detection system of the present invention described above can be referred to the parameters and steps in the embodiments of the multi-source monitoring-based transmission line icing status detection method described above, and will not be repeated here.

[0046] Taking the monitoring and handling of icing on power transmission lines during continuous freezing rain in winter as an example, the technical solution of the present invention will be described in detail below, and the specific implementation method is as follows: First, the data acquisition and preprocessing module collects line mechanical parameters, environmental meteorological parameters, and conductor icing image data in real time to construct a standardized time-series monitoring dataset.

[0047] Then, the multi-source icing state solution module performs multi-source parallel solution of the icing state. The vibration spectrum separation submodule performs spectral analysis on the conductor vibration frequency, using bandpass filtering to separate low-frequency and high-frequency vibration components. The low-frequency vibration components are used to infer the additional icing tension, and the high-frequency vibration components are combined with the wind speed frequency response function to estimate the equivalent wind load. The mechanics-catenary icing solution submodule inputs the conductor tension, tower tilt angle, additional icing tension, and equivalent wind load into the catenary mechanics model of the transmission line. This model introduces temperature and wind load correction terms, and converts the tower tilt angle into a correction span parameter for the change in conductor suspension point height. This is achieved by calculating... The vertical composite load is obtained by solving the conductor state equation and subtracting the conductor's self-weight load. The icing load is then multiplied by the conductor length to obtain the initial total icing weight. The micro-meteorological icing estimation submodule inputs environmental meteorological parameters into the icing growth prediction model to calculate the second equivalent icing thickness. The image icing recognition submodule inputs conductor icing image data into the image recognition model to identify the icing type and output the third equivalent icing thickness and icing density parameters. The mechanics-catenary icing solution submodule receives the icing density parameters to correct the initial total icing weight. Based on the corrected total icing weight and icing density parameters, combined with the conductor diameter, the final first equivalent icing thickness is calculated.

[0048] While the multi-source icing status calculation module is running, the confidence assessment and decay pattern recognition module performs confidence assessments on each data source. It extracts confidence assessment feature parameters from the line mechanical data source, micro-meteorological data source, and image data source, respectively, and inputs them into the adaptive threshold confidence assessment model. This model dynamically generates thresholds for each feature parameter based on the line voltage level, the type of micro-topography area, and historical icing fault data, and outputs real-time confidence scores for each data source. At the same time, it identifies decay patterns based on the historical sequence of confidence scores, distinguishes between normal aging decay and sudden fault decay, and estimates the remaining effective working time for data sources with normal aging decay. In this scenario, the micro-meteorological data source is judged to be in normal aging decay due to normal aging decay of the temperature sensor, which causes the overall confidence score of the data source to continuously decline. The remaining effective working time is estimated.

[0049] Secondly, through the adaptive fusion weight calculation module, the real-time confidence scores of various data sources are subtracted from the preset minimum threshold and the maximum value is taken from zero. Then, the maximum value result is normalized so that the sum of the fusion weights of various data sources is always 1. In this scenario, the micro-meteorological data source is automatically set to zero and isolated because its confidence score is lower than the preset minimum threshold. The weights of the line mechanics data source and the image data source are automatically re-normalized. At the same time, the system uses the calculation results of the other normal data sources to cross-validate the second equivalent icing thickness of the isolated data source.

[0050] The icing state fusion judgment module performs weighted fusion of the first equivalent icing thickness, the second equivalent icing thickness, and the third equivalent icing thickness based on adaptive fusion weights to obtain the comprehensive equivalent icing thickness. Combined with the finally determined total icing weight and icing type, it outputs the comprehensive judgment result of icing state and calculates the credibility rating score. In this scenario, the scores of the line mechanics data source and the image data source are both high, and the credibility is rated as high.

[0051] Finally, through the icing hazard level determination and operation and maintenance strategy generation module, the comprehensive equivalent icing thickness and the final determined total icing weight are obtained, the icing growth rate is calculated, and the growth rate weight coefficient is dynamically adjusted in combination with the line importance level to calculate the icing hazard assessment value. According to the calculation, in this scenario, the hazard assessment value is between the second and third hazard thresholds and the growth rate exceeds the preset rate threshold, which is judged as moderate stable icing. The system generates an enhanced monitoring instruction and shortens the inspection cycle. If the hazard assessment value exceeds the third hazard threshold, it is judged as high-risk overload icing. The ratio of the current total icing weight to the line's ultimate load-bearing weight is calculated. When the ratio exceeds the emergency threshold, an immediate power outage protection instruction is generated. When the ratio is lower than the emergency threshold but exceeds the hazard threshold, a load transfer scheme is generated.

[0052] Meanwhile, based on the output of the confidence assessment and degradation pattern recognition modules, the sensor maintenance early warning module generates planned maintenance work orders for normal aging and degradation data sources, arranges maintenance personnel to calibrate or replace sensors, and generates emergency repair alarms for sudden failure degradation data sources, immediately notifying the maintenance team to rush to the scene for handling.

[0053] Example 3: Based on the same inventive concept as Example 1, this example provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-described method for detecting the icing status of transmission lines based on multi-source monitoring by calling the computer program stored in the memory.

[0054] It should be noted that all computer programs for a method for detecting the icing status of transmission lines based on multi-source monitoring are implemented in C language.

[0055] Example 4: Based on the same inventive concept as Example 1, this example proposes a computer-readable storage medium storing an erasable and rewritable computer program thereon; When the computer program runs on the computer device, it causes the computer device to execute the above-mentioned method for detecting the icing status of transmission lines based on multi-source monitoring.

[0056] For example, computer-readable storage media can be read-only memory, random access memory, read-only optical disc, magnetic tape, floppy disk, and optical data storage devices.

[0057] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for IoT devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0058] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0064] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0065] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0066] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for detecting the icing status of transmission lines based on multi-source monitoring, characterized in that, Includes the following steps: Step S1: Real-time acquisition of line mechanical parameters, environmental meteorological parameters, and conductor icing image data to construct a standardized time-series monitoring dataset. The line mechanical parameters include conductor tension, tower tilt angle, and conductor vibration frequency. Step S2: Perform spectrum separation on the conductor vibration frequency, combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent ice thickness and total ice weight, obtain the second equivalent ice thickness through the ice growth prediction model, obtain the third equivalent ice thickness and ice density parameters through the image recognition model, and use the ice density parameters to correct the total ice weight to achieve complementary detection of visual qualitative and mechanical quantitative methods. Step S3: Extract confidence assessment feature parameters within the sliding time window from the line mechanical data source, micro-meteorological data source, and image data source respectively, input them into the adaptive threshold confidence assessment model, output the real-time confidence score of each type of data source, and at the same time, based on the historical sequence of confidence scores of each data source, identify the confidence decay pattern, distinguish between normal aging decay and sudden fault decay, and estimate the remaining effective working time. Step S4: Based on the real-time confidence scores of each data source output in Step S3, dynamically calculate the adaptive fusion weight of each data source. When the confidence score of any data source is lower than the preset minimum threshold, reset the adaptive fusion weight of that data source to zero, automatically isolate the data source and start deep diagnosis, and use the calculation results of the other normal data sources to cross-validate the key output parameters of the isolated data source. Step S5: Based on the adaptive fusion weights of each data source, the first equivalent ice thickness, the second equivalent ice thickness, and the third equivalent ice thickness are weighted and fused to obtain the comprehensive equivalent ice thickness. Combined with the finally determined total ice weight and ice type, the comprehensive judgment result of the ice status is output, and the credibility rating of the comprehensive judgment result is calculated. When all data sources are isolated, a data source failure alarm is generated. Short-term prediction is made based on the last effective state and the ice growth prediction model, and the credibility rating is set to the lowest level. Step S6: Obtain the comprehensive equivalent icing thickness and the final determined total icing weight output in step S5. Combine the icing growth rate and the line importance level to calculate the icing hazard assessment value. Compare the icing hazard assessment value with the classification threshold to determine the icing hazard level and generate a quantitative differentiated operation and maintenance strategy. Step S7: Based on the confidence decay pattern and remaining effective working time identified in step S3, generate planned maintenance work orders for normal aging and decaying data sources, and generate emergency repair alarms for data sources experiencing sudden failures and decay.

2. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 1, characterized in that: Step S2 specifically includes the following steps: Step S21: Perform spectral analysis on the conductor vibration frequency in the time-series monitoring dataset, use bandpass filtering to separate the low-frequency vibration component and the high-frequency vibration component, based on conductor vibration theory, use the frequency and amplitude of the low-frequency vibration component to infer the additional tension caused by icing, and use the spectral characteristics of the high-frequency vibration component combined with the wind speed frequency response function to estimate the equivalent wind load. Step S22: Input the conductor tension and tower tilt angle from the time-series monitoring dataset, as well as the icing additional tension and equivalent wind load output from step S21, into the pre-constructed catenary mechanical model of the transmission line. Introduce temperature correction terms and wind load correction terms into the catenary mechanical model. Invert the vertical comprehensive load per unit length of the conductor by solving the conductor state equation. The wind load is only used for horizontal tension correction. Subtract the conductor self-weight load from the comprehensive load to obtain the icing load. Multiply the icing load by the conductor length to obtain the initial total icing weight. Calculate the initial value of the first equivalent icing thickness based on the initial total icing weight and the preset icing density. Step S23: Input the environmental meteorological parameters in the time series monitoring dataset into the icing growth prediction model to calculate the second equivalent icing thickness; Step S24: Input the icing image data of the conductor in the time series monitoring dataset into the image recognition model to identify the icing type and icing morphology, output the third equivalent icing thickness, and output the icing density parameter corresponding to the icing type, where the icing type includes rime, hoarfrost and mixed rime. Step S25: When the image data source is available, the ice density parameter output in step S24 is fed back to step S22 to correct the initial total ice weight and obtain the corrected total ice weight. Based on the corrected total ice weight and ice density parameter, the final first equivalent ice thickness is calculated by combining the wire diameter and ice morphology parameters. When the image data source is unavailable, the first equivalent ice thickness is calculated using the preset default ice density and marked as an uncorrected value. The final determined total icing weight is: the corrected total icing weight when the image data source is available, and the total icing weight calculated based on the preset default icing density and marked as uncorrected when the image data source is unavailable.

3. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 2, characterized in that: The introduction of temperature and wind load correction terms into the catenary mechanical model of the transmission line in step S22 specifically involves adding an additional temperature term to the conductor's state equation. The additional term for wind load is defined as follows: α is the thermal expansion coefficient of the conductor, E is the elastic modulus of the conductor, A is the cross-sectional area of ​​the conductor, and ΔT is the difference between the measured temperature and the reference temperature. When temperature data is missing, ΔT is taken as zero or the historical statistical average. The additional term for wind load is the equivalent wind load estimated in step S21, which is introduced into the conductor state equation as a uniformly distributed load in the form of a specific load. The tower tilt angle is converted into the change in the height difference of the conductor suspension point, and substituted into the catenary equation to correct the span parameters. The corrected conductor state equation is obtained, and the comprehensive specific load is solved.

4. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 3, characterized in that: The confidence assessment feature parameters mentioned in step S3 specifically include: The confidence assessment features of the line mechanics data source include the catenary model fitting residual rate within the sliding time window, the tension-tilt consistency deviation rate, the conductor vibration frequency anomaly rate, and the icing-induced tension fluctuation rate. The confidence assessment features of micro-meteorological data sources include the data missing rate, data jump rate, multi-sensor cross-calibration deviation rate, and the deviation rate between meteorological data and physical laws within the sliding time window. Among them, the deviation rate between meteorological data and physical laws is the degree of consistency deviation between the ice thickness variation trend estimated by the micro-meteorological data source and the ice thickness variation trend estimated by the line mechanics data source. The confidence evaluation features of the image data source include the image quality degradation rate within the sliding time window, the volatility of the icing identification results, the consistency deviation rate of the icing thickness calculated from the image data source and the line mechanical data source, and the stability of the icing density output.

5. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 4, characterized in that: The method for identifying the confidence decline pattern in step S3 is as follows: when the decline slope of the confidence score within a preset statistical period is less than the flat threshold and maintains a continuous downward trend, it is determined to be normal aging decline. When the confidence score drops more than the mutation threshold within a preset short period of time, it is judged as a sudden failure and degradation. For data sources that are experiencing normal aging and decline, the remaining effective working time is estimated based on the historical decline slope. For data sources that experience sudden failure and decline, in-depth diagnosis is triggered immediately, without relying on the decline slope to estimate the remaining effective working time.

6. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 5, characterized in that: The adaptive fusion weights in step S4 are calculated as follows: the real-time confidence scores of various data sources are subtracted from the preset minimum threshold and the maximum value is taken from zero. Then, the maximum value results of each data source are normalized so that the sum of the adaptive fusion weights of various data sources is always 1. When the confidence score of a certain data source is lower than the preset minimum threshold, its corresponding maximum value result is zero, the adaptive fusion weight is automatically set to zero, and the weights of the remaining data sources are automatically re-normalized.

7. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 6, characterized in that: Step S5 specifically includes the following steps: Step S51: Based on the adaptive fusion weights of each data source determined in step S4, the first equivalent icing thickness, second equivalent icing thickness, and third equivalent icing thickness obtained in step S2 are weighted and fused to obtain the comprehensive equivalent icing thickness. When a data source is isolated, its corresponding weight is zero, and the corresponding thickness item is naturally excluded. The calculation formula is as follows: ； In the formula, Dc represents the comprehensive equivalent icing thickness. For adaptive fusion weights of line mechanics data sources, For adaptive fusion weights of micro-meteorological data sources, Let D1 be the first equivalent ice thickness, D2 be the second equivalent ice thickness, and D3 be the third equivalent ice thickness, and satisfy the following conditions: When all data sources are isolated, Dc takes the last valid value and triggers a full failure alarm. Step S52: Combining the final determined total icing weight and icing type obtained in step S2, output the comprehensive judgment result of icing status. The comprehensive judgment result of icing status includes icing type, comprehensive equivalent icing thickness and final determined total icing weight. When the image data source is unavailable, the icing type is marked as unknown or inferred based on meteorological and mechanical parameters and an inferred label is added. Step S53: Based on the confidence scores of each data source and the adaptive fusion weights, calculate the credibility rating score of the comprehensive judgment result. The calculation formula is as follows: ； In the formula, Rc is the confidence rating score, Cm is the real-time confidence score of the line mechanics data source, Cw is the real-time confidence score of the micro-meteorological data source, and Ci is the real-time confidence score of the image data source. The assessment was rated as highly credible. The assessment is rated as medium credibility. The time rating is set to low confidence, where Rh is the high confidence threshold and Rm is the medium confidence threshold. When all data sources are isolated, Rc is forced to be a value lower than Rm.

8. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 7, characterized in that: The specific method for calculating the icing hazard assessment value in step S6 is as follows: The icing hazard assessment value is obtained by weighted summing the ratio of the comprehensive equivalent icing thickness to the design icing thickness limit, the ratio of the final determined total icing weight to the maximum allowable load-bearing weight of the line, and the ratio of the icing growth rate to the preset rate threshold. The icing growth rate is taken as a non-negative value, and is zero when it is negative. The calculation formula is as follows: ； In the formula, Vr represents the icing hazard assessment value, and Dc represents the comprehensive equivalent icing thickness. To design the icing thickness limit, Gc is the final determined total weight of the icing. The maximum allowable load capacity of the line is given by: v = maximum icing growth rate, v0 = preset rate threshold, Ka and Kb are basic weighting coefficients, and γ(L) is a dynamic weighting coefficient related to the importance level L of the line. The higher the importance level of the line, the larger the value of γ(L). The preset rate threshold v0 is a reference value for the icing growth rate per unit time set according to the historical icing statistics of the area where the line is located.

9. The method for detecting icing status of transmission lines based on multi-source monitoring according to claim 8, characterized in that: The method for determining the icing hazard level in step S6 is as follows: when At that time, it was determined to be slight icing; when At that time, if Then it is determined to be a continuous increase in icing, if It is then judged as slight icing and marked as slow growth; when At that time, if Then it is judged as moderately stable icing. It is then classified as moderate icing and marked as trending towards stability; when At that time, it was determined to be a high-risk overload with icing. Where V1 is the first danger threshold, V2 is the second danger threshold, and V3 is the third danger threshold; The quantitative differentiated operation and maintenance strategy includes: generating early warning information for minor icing, shortening the inspection cycle from the standard cycle to a preset multiple, and attaching an icing growth trend prediction report; For the continuous increase of icing, the thermal power required for de-icing is calculated based on the icing growth rate and the rated current of the line, and a window suggestion for the timing of de-icing is given in combination with meteorological forecast data; For moderately stable icing, generate enhanced monitoring instructions and shorten the inspection cycle; For high-risk overload icing, calculate the ratio of the current total weight of icing to the line's ultimate load-bearing capacity. When the ratio exceeds the emergency threshold, generate an immediate power outage protection command. When the ratio is below the emergency threshold but exceeds the danger threshold, generate a load transfer scheme.

10. A transmission line icing status detection system based on multi-source monitoring, implemented based on the transmission line icing status detection method based on multi-source monitoring as described in any one of claims 1-9, characterized in that, Specifically, it includes: a data acquisition and preprocessing module, a multi-source icing state calculation module, a confidence assessment and decay pattern recognition module, an adaptive fusion weight calculation module, an icing state fusion judgment module, an icing hazard level determination and operation and maintenance strategy generation module, and a sensor maintenance early warning module. The data acquisition and preprocessing module is used to perform step S1, which collects line mechanical parameters, environmental meteorological parameters and conductor icing image data in real time, and constructs a standardized time-series monitoring dataset. The multi-source icing state calculation module has its input end connected to the output end of the data acquisition and preprocessing module. It is used to execute step S2, perform spectrum separation on the conductor vibration frequency, and combine the conductor tension and tower tilt angle into the catenary mechanical model to obtain the first equivalent icing thickness and total icing weight. The second equivalent icing thickness is obtained through the icing growth prediction model, and the third equivalent icing thickness and icing density parameter are obtained through the image recognition model. The total icing weight is corrected using the icing density parameter, thus achieving complementary detection of visual qualitative and mechanical quantitative methods. The multi-source icing state calculation module includes a vibration spectrum separation submodule, a mechanical-catenary icing calculation submodule, a micro-meteorological icing estimation submodule, and an image icing recognition submodule. The vibration spectrum separation submodule is used to separate the frequency spectrum of conductor vibration and output the additional tension of icing and the equivalent wind load. The mechanical-catenary icing solution module is used to invert the total icing weight and the first equivalent icing thickness using the mechanical model of the transmission line catenary, and to receive icing density parameters to correct the total icing weight. The micro-meteorological icing estimation submodule is used to calculate the second equivalent icing thickness based on environmental meteorological parameters; The image icing recognition submodule is used to identify the icing type and icing morphology and output the third equivalent icing thickness and icing density parameters. The confidence assessment and decay pattern recognition module has its input terminals connected to the output terminals of the data acquisition and preprocessing module, the vibration spectrum separation submodule, the mechanical-catenary icing calculation submodule, the micro-meteorological icing estimation submodule, and the image icing recognition submodule, respectively. It is used to execute step S3, output the real-time confidence scores of each data source, identify the confidence decay pattern, and estimate the remaining effective working time. The input of the adaptive fusion weight calculation module is connected to the output of the confidence assessment and decay pattern recognition module, and is used to execute step S4. The input end of the icing state fusion judgment module is connected to the output end of the adaptive fusion weight calculation module and the multi-source icing state solution module, respectively, and is used to execute step S5; The input end of the icing hazard level determination and operation and maintenance strategy generation module is connected to the output end of the icing state fusion judgment module, and is used to execute step S6; The sensor maintenance early warning module has its input terminal connected to the output terminal of the confidence assessment and decay pattern recognition module, and is used to execute step S7.

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