Ice-skating risk assessment method, system and device based on entropy weight method weight

Abstract

This invention discloses a method, system, and apparatus for assessing the risk of de-icing jumps based on entropy weighting, relating to the field of transmission line icing disaster prevention and control technology. The method includes: acquiring multiple sample data and preprocessing them; standardizing the preprocessed sample data to obtain a dimensionless standardized dataset, wherein each sample data contains a set of values ​​related to influencing factors of de-icing jumps; calculating the weight of each sample data under each influencing factor based on the standardized dataset; calculating the information entropy of each influencing factor based on the weight value; calculating the objective weight of each influencing factor based on the information entropy; constructing a comprehensive risk assessment model based on the objective weights and the standardized dataset; and assessing the risk of de-icing jumps based on the comprehensive risk assessment model. This invention can effectively improve the accuracy of transmission line icing disaster prevention and control, providing a scientific basis and technical support for the safe operation of power systems.

Description

Technical Field

[0001] This invention relates to the field of transmission line icing disaster prevention and control technology, specifically to a method, system and device for assessing de-icing jump risk based on entropy weight method. Background Technology

[0002] Transmission lines are a crucial component of the power system, and their safe and stable operation directly impacts the reliability of power supply. In cold regions or during winter, transmission line surfaces are highly susceptible to icing. Icing increases conductor weight and sag, potentially leading to line breaks, tower collapses, and other accidents. When ambient temperatures rise or weather conditions change (e.g., strong winds), the ice on the transmission line surface can detach, a phenomenon known as de-icing. During de-icing, the conductor's weight suddenly decreases, causing violent vibrations, a phenomenon known as de-icing jumps. De-icing jumps not only lead to flashover discharges between conductors but also generate enormous impact loads on towers, hardware, and other components, seriously threatening the safe operation of transmission lines.

[0003] To reduce the hazards of ice-breaking tripping to transmission lines, researchers and engineers in related technical fields have conducted extensive research and proposed various methods for assessing the risk of ice-breaking tripping. The core idea of ​​these methods is to analyze various factors affecting ice-breaking tripping, establish corresponding assessment systems, and quantitatively evaluate the risk of ice-breaking tripping, thereby providing a basis for decision-making in the operation and maintenance management of transmission lines.

[0004] Currently, existing risk assessment methods for de-icing jumps mainly employ subjective weighting methods such as expert scoring and the analytic hierarchy process (AHP). However, in the complex physical process of transmission line de-icing jumps, which involves multiple coupled factors and exhibits significant nonlinear characteristics, subjective weighting methods struggle to accurately reflect the true contribution of each factor to the risk, leading to biased risk assessment results and potentially causing mismatches in protective measures or failure of early warnings. Specifically, existing de-icing jump risk assessment methods have the following drawbacks: (1) High subjectivity: Over-reliance on expert experience, with significant differences in the weighting results given by different experts; (2) Poor stability: When the influencing factors or sample data change, the weighting results need to be re-evaluated by experts; (3) Poor scalability: It is difficult to adapt to the analysis needs of large-scale, multi-dimensional influencing factors; (4) Ignoring the inherent patterns of the data: failing to fully utilize the discrete characteristics of the data itself. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, and addressing the drawbacks of current de-icing jump risk assessment methods that primarily employ subjective weighting, resulting in high subjectivity, poor stability, poor scalability, and neglect of inherent data patterns, this invention provides a de-icing jump risk assessment method based on entropy weighting, comprising: Multiple sample data were acquired and preprocessed. The preprocessed sample data were then standardized to obtain a dimensionless standardized dataset, in which each sample data contained a set of values ​​related to the influencing factors of the ice-breaking jump. The proportion of each sample data under each influencing factor is calculated based on the standardized dataset. The information entropy of each influencing factor is calculated based on the proportion. The objective weight of each influencing factor is calculated based on the information entropy. A comprehensive risk assessment model is constructed based on the objective weights and the standardized dataset, and the risk of ice-breaking jumps is assessed based on this model. Preprocessing and standardization can eliminate differences in dimensions and ensure data comparability; the entropy weight method calculates objective weights, avoiding subjective bias and accurately reflecting the true contribution of influencing factors; by constructing a complete comprehensive risk assessment model, risk quantification assessment can be achieved, improving the systematicity and reliability of the assessment, compatible with multi-source heterogeneous data, realizing data association and integration, and ensuring the comprehensiveness of the assessment.

[0006] Optionally, the influencing factors include at least line structure parameters, icing characteristic parameters, and climate environment parameters; The line structure parameters include at least one or more of the following: span, elevation difference, splitting method, and conductor diameter; The icing characteristic parameters include at least one or more of the following: icing type, icing thickness, de-icing location, and de-icing amount; The climate and environmental parameters include at least one or more of temperature, rate of warming, irradiance, and wind speed. Systematically classifying influencing factors comprehensively covers the core dimensions affecting de-icing and skipping, avoiding omissions of key factors. By clearly defining specific influencing parameters, clear input standards are provided for subsequent data processing and weight calculations, improving the standardization of the assessment.

[0007] Optionally, the standardization process for the preprocessed sample data to obtain a dimensionless standardized dataset includes: If the larger the value of a certain influencing factor, the higher the risk of ice break jump, then the minimum value of the influencing factor in all sample data is mapped to 0 and the maximum value is mapped to 1, and the remaining sample values ​​of the influencing factor are linearly mapped to the [0,1] interval according to the proportion of the difference between the minimum value and the maximum value to the difference between the minimum value and the maximum value. If a larger value for a certain influencing factor corresponds to a lower risk of "freezing jump," then the maximum value of that influencing factor in all sample data is mapped to 0, and the minimum value is mapped to 1. The remaining sample values ​​of that influencing factor are then linearly mapped to the [0,1] interval according to the proportion of the difference between the maximum and minimum values. By standardizing based on risk correlation differences, it is possible to ensure that the standardized values ​​accurately reflect the risk contribution of the factors, linearly map different influencing factors to a unified interval, eliminate differences in units and orders of magnitude, and ensure the accuracy of weight calculation.

[0008] Optionally, the standardization process of the preprocessed sample data to obtain a dimensionless standardized dataset further includes: The different categories of the splitting modes are mapped to different dimensionless values, wherein the categories of the splitting modes include at least one or more of single splitting, binary splitting, tetrasplitting, and octetlitting; Different categories of icing types are mapped to different dimensionless values, wherein the categories of icing types include at least one or more of snow frost, rain frost, mixed frost, and hoarfrost. By mapping categorical variables to dimensionless values, they can be incorporated into quantitative processing, completing the influencing factor system and ensuring the comprehensiveness of the assessment; it also allows categorical variables to be adapted to entropy weight calculation, quantifying the contribution of category differences to risk and improving the accuracy of the assessment.

[0009] Optionally, calculating the weight of each sample data under each influencing factor based on the standardized dataset includes: Real-time weather warnings and line status data are matched with a dynamic risk scenario rule base. If the match is successful, the values ​​of the core influencing factors in the standardized dataset are enhanced according to the weight adjustment logic of the scenario to obtain the standardized dataset after scenario adjustment. The weight of each sample data under each influencing factor is calculated based on the standardized dataset adjusted for the described scenario. Dynamic scenario adjustment makes the weight calculation more relevant to the real-time scenario, improving the timeliness and relevance of the evaluation.

[0010] Optionally, the dynamic risk scenario rule base includes at least one or more dynamic risk scenarios from the following: strong sunlight melting ice scenario, cold wave path coverage scenario, uneven icing scenario, and gust warning scenario. Each dynamic risk scenario is pre-defined with a set of quantifiable trigger thresholds and a set of core influencing factors associated with that dynamic risk scenario. The trigger threshold is used to determine whether real-time weather warnings and line status data meet the triggering conditions of the corresponding dynamic risk scenarios. The core influencing factor set consists of the components that play a dominant role in the risk of ice-breaking jumps in specific scenarios. This set is used to execute corresponding weight adjustment logic after a successful matching of a dynamic risk scenario. Standardized dynamic risk scenario rules enable precise scenario matching and improve the effectiveness of scenario adjustments; clearly defined trigger thresholds and the core influencing factor set provide a reliable basis for dynamic adjustments and ensure the accuracy of the assessment.

[0011] Optionally, the step of acquiring and preprocessing multiple sample data includes: Multi-source heterogeneous data undergoes time alignment, spatial matching, missing value handling, and outlier cleaning to obtain preprocessed sample data. This multi-source heterogeneous data includes at least one or more of the following: real-time data from online monitoring systems, historical operation and maintenance data, numerical simulation data, geographic information system data, and weather forecast data. Multi-source heterogeneous data fusion enriches the dimensions of the evaluation data and improves the comprehensiveness of the evaluation; standardized preprocessing steps improve data quality, providing high-quality input for subsequent evaluations.

[0012] Optionally, the risk assessment of the off-ice jump based on the comprehensive risk assessment model includes: The comprehensive risk assessment value corresponding to each sample data is calculated based on the comprehensive risk assessment model, and then compared with multiple preset risk thresholds to classify each sample data into the corresponding risk level. Based on the risk level assigned to the sample data, the system retrieves the corresponding control measures from a pre-defined differentiated control measure library, and then associates these control measures with the transmission line scenario represented by the corresponding sample. Risk level classification makes risk assessment results more intuitive and facilitates identification and management by maintenance personnel; differentiated control measures provide targeted protection solutions, helping to improve maintenance management efficiency.

[0013] Preferably, the method for assessing the risk of ice-breaking jumps further includes: The elasticity coefficient of the objective weight of each influencing factor to its standardized numerical change is calculated using the numerical perturbation method; Factors with elastic coefficients greater than the sensitivity threshold are classified as highly sensitive factors, while factors with elastic coefficients not greater than the sensitivity threshold are classified as low-sensitive factors. A sensitivity list of these factors is then generated. Based on the sensitivity list of influencing factors, a monitoring strategy adjustment instruction is generated and sent to the online monitoring system of the transmission line. This increases the data sampling frequency of the online monitoring system for highly sensitive influencing factors and decreases the data sampling frequency for low-sensitivity influencing factors. Sensitivity analysis can accurately identify highly sensitive factors, providing a basis for adjusting the monitoring strategy; optimizing the allocation of monitoring resources can improve the accuracy of data collection for highly sensitive factors and reduce the cost of ineffective monitoring; and a closed loop of "assessment-monitoring-reassessment" is formed to continuously improve the accuracy of assessment and the effectiveness of monitoring.

[0014] Preferably, the method for assessing the risk of ice-breaking jumps further includes: When the update trigger condition of the comprehensive risk assessment model is met, new sample data from the last construction of the comprehensive risk assessment model to the present is obtained, and the comprehensive risk assessment model is updated accordingly. The update triggering conditions include at least one or more of the following: time-based triggering, event-based triggering, data accumulation triggering, and environmental change triggering. Preset triggering conditions enable timely model updates, adapting to data accumulation and changes in the network or environment; ensuring the long-term accuracy and adaptability of the evaluation model and avoiding evaluation biases caused by static models.

[0015] Preferably, the method for assessing the risk of ice-breaking jumps further includes: After each update of the comprehensive risk assessment model is triggered, the standardized dataset used in this update and the objective weights of each influencing factor are recorded and compared with the historical records to generate a weight evolution sequence. Based on the weight evolution sequence, the influencing factors that stabilize the weight change trend and the influencing factors that cause continuous fluctuations in weight change are identified; To address the influencing factors of the continuous fluctuations in weight changes, a smoothing factor is used in subsequent updates of the comprehensive risk assessment model to adjust the fusion ratio of the old and new weights, generating smoothed weights. These smoothed weights are then applied to the next round of updates to the comprehensive risk assessment model. Weight evolution sequence analysis can accurately identify stable / volatile factors, providing a basis for weight optimization; smoothing the weights of volatile factors can avoid bias in assessment results caused by drastic weight fluctuations, thus improving model stability.

[0016] Based on the same inventive concept, this invention also provides a de-icing jump risk assessment system based on entropy weight method, including: The data acquisition and fusion module is used to acquire multiple sample data and preprocess them. The preprocessed sample data is then standardized to obtain a dimensionless standardized dataset. Each sample data contains a set of values ​​related to the influencing factors of ice-breaking jumps. The entropy weight calculation module is used to calculate the proportion of each sample data under each influencing factor based on the standardized dataset, calculate the information entropy of each influencing factor based on the proportion, and calculate the objective weight of each influencing factor based on the information entropy. The risk assessment module is used to construct a comprehensive risk assessment model based on the objective weights and the standardized dataset, and to assess the risk of ice-breaking jumps based on the comprehensive risk assessment model.

[0017] Optionally, the ice-breaking jump risk assessment system further includes: The dynamic scene recognition submodule is used to match real-time weather warnings and line status data with a dynamic risk scene rule base. If the match is successful, the values ​​of the associated core influencing factors in the standardized dataset are enhanced according to the weight adjustment logic of the scene to obtain a scene-adjusted standardized dataset. The proportion of each sample data under each influencing factor is calculated based on the scene-adjusted standardized dataset. The sensitivity analysis submodule is used to calculate the elasticity coefficient of the objective weight of each influencing factor to its standardized numerical change using the numerical perturbation method; influencing factors with elasticity coefficients greater than the sensitivity threshold are identified as highly sensitive influencing factors, and influencing factors with elasticity coefficients not greater than the sensitivity threshold are identified as low-sensitive influencing factors, and a sensitivity list of influencing factors is generated accordingly; based on the sensitivity list of influencing factors, a monitoring strategy adjustment instruction is generated and sent to the online monitoring system of the transmission line to increase the data sampling frequency of the online monitoring system for the highly sensitive influencing factors and decrease the data sampling frequency of the online monitoring system for the low-sensitive influencing factors; The control decision output submodule is used to retrieve control measures corresponding to the risk level from the preset differentiated control measures library according to the risk level classified by the sample data, and associate the control measures with the transmission line scenario represented by the corresponding sample and output them. The model update management submodule is used to record the standardized dataset and objective weights of each influencing factor used in each update of the comprehensive risk assessment model, and compare them with historical records to generate a weight evolution sequence. Based on the weight evolution sequence, it identifies influencing factors with stable weight change trends and influencing factors with continuous weight fluctuations. For the influencing factors with continuous weight fluctuations, a smoothing factor is used in subsequent updates of the comprehensive risk assessment model to adjust the fusion ratio of the old and new weights to generate smoothed weights, and the smoothed weights are applied to the next round of updates of the comprehensive risk assessment model.

[0018] Based on the same inventive concept, this invention also provides a de-icing jump risk assessment device based on entropy weight method, comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the de-icing jump risk assessment method.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a risk assessment method for de-icing jumps based on entropy weighting, comprising: acquiring multiple sample data and preprocessing them; standardizing the preprocessed sample data to obtain a dimensionless standardized dataset, wherein each sample data contains a set of values ​​related to the influencing factors of de-icing jumps; calculating the weight of each sample data under each influencing factor according to the standardized dataset; calculating the information entropy of each influencing factor according to the weight value; and calculating the objective weight of each influencing factor according to the information entropy; constructing a comprehensive risk assessment model based on the objective weight and the standardized dataset; and assessing the risk of de-icing jumps based on the comprehensive risk assessment model.

[0020] Based on the de-icing jump risk assessment of this invention, the weighted results are objective, accurately reflecting the true contribution of each influencing factor to the de-icing jump risk and improving the accuracy of the risk assessment results. The assessment process is stable; when influencing factors or sample data change, only recalculation according to a unified process is required, without the need to reorganize expert assessments, thus improving the efficiency of the assessment work. The assessment method is scalable, compatible with any number and dimension of influencing factors, meeting the needs of large-scale, multi-dimensional analysis, and adapting to the assessment needs of different regions and types of transmission lines. It can fully explore the inherent laws of the data itself, improve the reliability of the assessment results, provide accurate and reliable decision-making basis for the operation and maintenance management of transmission lines, and reduce the harm of de-icing jumps to transmission lines. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0022] Figure 1 This is a flowchart of the de-icing jump risk assessment method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a dynamic risk scenario rule base according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the risk level classification rules for de-icing jumps according to an embodiment of the present invention; Figure 4 This is a schematic diagram of differentiated control measures according to an embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are provided to better understand the present invention and are not intended to limit the preferred embodiments described. They do not constitute a limitation on the content and scope of protection of the present invention. Any product that is the same as or similar to the present invention, derived by any person under the guidance of the present invention or by combining the features of the present invention with other prior art, falls within the protection scope of the present invention.

[0024] Example 1 like Figures 1-4 As shown, the de-icing jump risk assessment method based on entropy weight method includes: Multiple sample data were acquired and preprocessed. The preprocessed sample data were then standardized to obtain a dimensionless standardized dataset, in which each sample data contained a set of values ​​related to the influencing factors of the ice-breaking jump. The proportion of each sample data under each influencing factor is calculated based on the standardized dataset. The information entropy of each influencing factor is calculated based on the proportion. The objective weight of each influencing factor is calculated based on the information entropy. A comprehensive risk assessment model is constructed based on the objective weights and the standardized dataset, and the risk of ice-breaking jumps is assessed based on the comprehensive risk assessment model.

[0025] In practical implementation, when conducting risk assessment for ice-breaking jumps, this invention first preprocesses and standardizes multi-source data to eliminate dimensional differences and data noise, providing a unified input for subsequent calculations. Then, based on the mathematical logic of the entropy weight method, it extracts the objective weights of each influencing factor from the standardized data. The weights are determined by the discrete nature of the data itself, without relying on manual intervention. Finally, the objective weights are combined with the standardized dataset to construct a comprehensive risk assessment model, achieving a quantitative assessment of the risk of ice-breaking jumps. The working principle of this technical solution follows a linear logic and closed-loop design of "data input - weight calculation - model construction - risk assessment," ensuring the integrity and coherence of the process.

[0026] Existing methods for assessing the risk of ice-breaking jumps primarily employ subjective weighting, which suffers from high subjectivity, poor stability, poor scalability, and neglect of the inherent patterns in the data, leading to biased assessment results. To address the issue of high subjectivity in existing methods: This invention uses the entropy weighting method to calculate objective weights. The weighting results are determined by the discrete characteristics of the data itself, completely eliminating subjective interventions such as expert scoring. To address the issue of poor stability in existing methods: This invention establishes a standardized calculation process and comprehensive risk assessment model. When influencing factors or sample data change, only recalculation according to the unified process is required, without the need to reorganize expert assessments. To address the issue of poor scalability in existing methods: The preprocessing and standardization process of this invention is compatible with any number and dimension of influencing factors. The calculation logic of the entropy weighting method does not fundamentally change with the increase of influencing factors, meeting the needs of large-scale, multi-dimensional analysis. To address the issue of existing methods neglecting the inherent patterns in the data: The core logic of the entropy weighting method used in this invention is to utilize the discrete characteristics (information entropy) of the data to measure the importance of influencing factors, thereby fully exploring the inherent patterns in the data itself.

[0027] For example, the number of samples is That is, it exists Sample data from monitoring points or monitoring periods of each transmission line; number of influencing factors: That is, each sample data contains One influencing factor related to ice-breaking jump; the original sample dataset is... ,in Indicates the first The first sample The original values ​​of each influencing factor; the standardized dataset is ,in Indicates the first The first sample The standardized values ​​of each influencing factor; the following formula can be used for specific calculations: Specific gravity calculation:

[0028] in, Indicates the first The sample at the th The proportion of each influencing factor; Information entropy calculation:

[0029] in, Indicates the first The information entropy of each influencing factor is specified, and if Then let ; Calculation of the coefficient of difference:

[0030] in, Indicates the first The coefficient of difference of each influencing factor; Objective weight calculation:

[0031] in, Indicates the first The objective weight of each influencing factor; The comprehensive risk assessment model can use the following formula:

[0032] in, Indicates the first The comprehensive risk assessment value of each sample. Furthermore, it can be based on... The risk is ranked by size, and the icing scenarios of the corresponding transmission line monitoring points or monitoring periods are divided into different risk levels.

[0033] Preprocessing of sample data can be achieved by temporal alignment and spatial matching, which can associate heterogeneous data from different sources, times, and spaces with the same monitoring period or monitoring point of the same transmission line, thus realizing the spatial and temporal correlation of data. Standardization processing can be achieved by linear mapping, which can map heterogeneous data of different dimensions and orders of magnitude to a unified interval (e.g., [0,1]), thus realizing the unification of data dimensions. On this basis, the calculation process of entropy weight method and the comprehensive risk assessment model can incorporate these correlated and unified multi-source heterogeneous data into the same evaluation system, fully explore the inherent relationship between the data, and realize a comprehensive and accurate assessment of the risk of ice-breaking jump.

[0034] Existing methods for assessing the risk of ice-breaking jumps primarily employ subjective weighting methods such as expert scoring and the analytic hierarchy process (AHP). Their core lies in the human judgment of the importance of influencing factors, thereby determining their weights. This invention, however, uses the entropy weighting method to calculate objective weights. Its core lies in extracting the importance of influencing factors from the discrete nature of the data itself, completely eliminating human intervention. Existing assessment methods lack a complete and systematic comprehensive risk assessment model, only achieving simple weighted summation calculations. In contrast, this invention establishes a complete comprehensive risk assessment model encompassing data preprocessing, standardization, weight calculation, and risk assessment, enabling standardized and systematic processes.

[0035] Optionally, the influencing factors include at least line structure parameters, icing characteristic parameters, and climate environment parameters; The line structure parameters include at least one or more of the following: span, elevation difference, splitting method, and conductor diameter; The icing characteristic parameters include at least one or more of the following: icing type, icing thickness, de-icing location, and de-icing amount; The climate environmental parameters include at least one or more of the following: temperature, rate of temperature rise, irradiance, and wind speed.

[0036] In practical implementation, this invention divides the influencing factors of de-icing jump into three categories: line structure parameters, icing characteristic parameters, and climate environment parameters, and lists the specific influencing factors in each category, which may include at least the following specific influencing factors: Line structure parameters: span X1, elevation difference X2, splitting method X3, conductor diameter X4; Icing characteristics parameters: Icing type X5, Icing thickness X6, De-icing location X7, De-icing amount X8 Climate and environmental parameters: Temperature X9, rate of temperature rise X 10 Irradiation intensity X 11 Wind speed X 12 Ice removal jumping is a complex physical process involving the combined effects of three factors: the line's own structure, the physical characteristics of icing, and the external climate environment. Dividing the influencing factors into these three categories can comprehensively and systematically cover all the major influencing factors of ice removal jumping, providing a complete input parameter system for subsequent weight calculation and risk assessment.

[0037] The structure of the transmission line itself is a fundamental factor affecting ice-breaking jumps. Different line structures lead to differences in the physical properties of the conductor, such as stiffness, strength, and sag, which in turn affect the amplitude and impact load of the ice-breaking jump. Among these factors, span and height difference can affect the conductor's sag and tension, the splitting method can affect the conductor's stiffness and ice distribution, and the conductor diameter can affect the conductor's weight and the amount of ice accumulation.

[0038] The physical properties of icing are a direct factor affecting de-icing jumps. Different icing properties lead to differences in the timing, speed, and amount of de-icing, thus affecting the intensity of the de-icing jump. Among these factors, the type of icing affects the density and adhesion of the icing, the thickness of the icing affects the weight of the icing, the location of de-icing affects the stress distribution on the conductor, and the amount of de-icing affects the range of change in the conductor's weight.

[0039] External climate conditions are the triggering factors for ice break-off jumps. Different climate conditions lead to differences in ice formation and shedding, thus affecting the probability and intensity of ice break-off jumps. Among these factors, temperature and the rate of temperature rise can affect the melting rate of ice, irradiance is the main energy source for ice melting, and wind speed is also one of the main triggering factors for ice break-off jumps.

[0040] Existing risk assessment methods for de-icing jumps often involve random and one-sided selection of influencing factors, covering only a portion of them and failing to form a systematic and complete system of influencing factors. Furthermore, because existing assessment methods largely employ subjective weighting, they cannot objectively reflect the true impact of each factor on de-icing jumps. Therefore, even with a sufficient number of influencing factors included in the assessment system, accurate assessment results are difficult to obtain. This invention, however, uses the entropy weight method to objectively determine the weights of each factor. Its advantage lies in its ability to include a sufficient number of influencing factors while objectively reflecting the true importance of each factor, thereby significantly improving the accuracy and reliability of risk assessment and providing a scientific basis for the safe operation of transmission lines.

[0041] Optionally, the standardization process for the preprocessed sample data to obtain a dimensionless standardized dataset includes: If the larger the value of a certain influencing factor, the higher the risk of ice break jump, then the minimum value of the influencing factor in all sample data is mapped to 0 and the maximum value is mapped to 1, and the remaining sample values ​​of the influencing factor are linearly mapped to the [0,1] interval according to the proportion of the difference between the minimum value and the maximum value to the difference between the minimum value and the maximum value. If the larger the value of a certain influencing factor, the lower the risk of ice break jump, then the maximum value of the influencing factor in all sample data is mapped to 0 and the minimum value is mapped to 1, and the remaining sample values ​​of the influencing factor are linearly mapped to the [0,1] interval according to the proportion of the difference between the maximum value and the minimum value.

[0042] In practical implementation, this invention proposes at least two different standardization methods for different influencing factors of ice-breaking jumps. By using linear mapping, the original numerical values ​​are mapped to the [0,1] interval, achieving dimensionless data. Different influencing factors have varying correlations with the risk of ice-breaking jumps. Employing different standardization methods ensures that the standardized values ​​accurately reflect the contribution of each influencing factor to the risk. Simultaneously, mapping all influencing factor values ​​to a unified interval eliminates dimensional differences and data noise, providing a unified input for subsequent weight calculations.

[0043] For example, the The original values ​​of the influencing factors are ;No. The standardized values ​​of the influencing factors are: ;No. Each influencing factor in all of them m The minimum value of the original values ​​in the samples is ;No. Each influencing factor in all of them m The maximum value of the original values ​​in the samples is The specific standardization process can be achieved using the following formula: Standardized formula for positively correlated factors (the higher the value, the higher the risk of ice break jump):

[0044] This formula can be used to... The original values ​​of each influencing factor are mapped to the interval [0,1], where the minimum value is... Mapped to 0, maximum value The value is mapped to 1, and the remaining values ​​are mapped to the interval according to a linear proportion. The larger the standardized value, the greater the contribution of the influencing factor to the risk of ice break jumps; Standardized formula for negatively correlated factors (the larger the value, the lower the risk of ice break jump):

[0045] This formula can be used to... The original values ​​of each influencing factor are mapped to the interval [0,1], where the maximum value is... Mapped to 0, minimum value The value is mapped to 1, and the remaining values ​​are mapped to the interval according to a linear proportion. The larger the standardized value, the greater the contribution of the influencing factor to the risk of ice break jumps.

[0046] Based on the standardization processing method of this invention, different standardization formulas can be used according to the correlation between influencing factors and risks to ensure that the standardized values ​​can accurately reflect the contribution of the influencing factors of the ice-breaking jump to the risk; the standardized values ​​are dimensionless, which can eliminate the dimensional differences and data noise between different influencing factors, and provide a unified input for subsequent weight calculation; the standardization formula has linear characteristics, which can maintain the distribution characteristics of the original data, avoid data distortion caused by nonlinear transformation, and improve the accuracy and reliability of the standardized data.

[0047] Optionally, the standardization process of the preprocessed sample data to obtain a dimensionless standardized dataset further includes: The different categories of the splitting modes are mapped to different dimensionless values, wherein the categories of the splitting modes include at least one or more of single splitting, binary splitting, tetrasplitting, and octetlitting; The different categories of the icing type are mapped to different dimensionless values, wherein the categories of the icing type include at least one or more of snow frost, rain frost, mixed frost, and hoarfrost.

[0048] In practical implementation, this invention proposes a targeted standardization method for the two categorical variables, splitting method and icing type, mapping different categories to different dimensionless values ​​to achieve quantification of categorical variables. Splitting method and icing type are important influencing factors for ice-breaking jumps, but both are categorical variables and cannot be directly calculated numerically. By mapping different categories to different dimensionless values, the categorical variables can be quantified and incorporated into the standardized dataset, providing complete input for subsequent weight calculations.

[0049] For the two categorical variables, splitting method and icing type, the following standardization methods can be used: Direct mapping to integers: Different categories of splitting methods (single splitting, two splitting, four splitting, eight splitting) are directly mapped to 1, 2, 3, 4; different categories of icing types (snow rime, rain rime, mixed rime, hoarfrost) are directly mapped to 1, 2, 3, 4; that is, for splitting method X3: 1=single splitting, 2=two splitting, 3=four splitting, 4=eight splitting; for icing type X5: 1=snow rime, 2=rain rime, 3=mixed rime, 4=hoarfrost.

[0050] Numbers directly mapped to the [0,1] interval: Different categories of splitting methods are directly mapped to numbers in the [0,1] interval. For example, single splitting is mapped to 0.25, two splitting to 0.5, four splitting to 0.75, and eight splitting to 1. Different categories of icing types are directly mapped to numbers in the [0,1] interval. For example, snow frost is mapped to 0.25, rain frost to 0.5, mixed frost to 0.75, and hoarfrost to 1.

[0051] First map to integers and then linearly map to the [0,1] interval: First, map the different categories of splitting mode and icing type to integers (such as 1, 2, 3, 4), and then linearly map these integers to the [0,1] interval according to the standardization formula (such as the standardization formula for positively correlated factors or the standardization formula for negatively correlated factors).

[0052] The splitting pattern and icing type are important influencing factors of ice-breaking jumps. Incorporating them into the entropy weight method calculation can complete the influencing factor system and avoid bias in assessment results due to the omission of important categorical variables. The entropy weight method can measure the differentiated contribution of different categories to risk through the discrete characteristics of data. For example, if the proportion of ice-breaking accidents corresponding to rime ice is high in the historical data of a certain line, the entropy weight method will identify the degree of variation of the data of that category, assign it a higher weight, and objectively reflect its actual impact on risk. After quantifying the categorical variables, they can be incorporated into a standardized dataset, adapting to the subsequent entropy weight method calculation process and comprehensive risk assessment model, and achieving a comprehensive and accurate assessment of the risk of ice-breaking jumps.

[0053] Existing methods for assessing the risk of ice break-off jumping often neglect two categories of categorical variables: splitting mode and icing type, or can only perform qualitative analysis on them and cannot incorporate them into quantitative calculations. This invention proposes a standardized processing method for these two categories of categorical variables, which can realize the quantitative processing of categorical variables and incorporate them into the quantitative calculation process of the entropy weight method, thereby completing the influencing factor system and improving the comprehensiveness of the assessment results.

[0054] Optionally, calculating the weight of each sample data under each influencing factor based on the standardized dataset includes: Real-time weather warnings and line status data are matched with a dynamic risk scenario rule base. If the match is successful, the values ​​of the core influencing factors in the standardized dataset are enhanced according to the weight adjustment logic of the scenario to obtain the standardized dataset after scenario adjustment. The weight of each sample data under each influencing factor is calculated based on the standardized dataset adjusted according to the scenario.

[0055] In practice, real-time weather warnings and line status data can be matched with a dynamic risk scenario rule base first. If a match is successful, the values ​​of the core influencing factors in the standardized dataset are enhanced to obtain a scenario-adjusted standardized dataset; then, the weight values ​​are calculated based on this dataset. The risk of de-icing jumps is greatly affected by real-time scenarios, and the importance of each influencing factor varies in different scenarios. Through dynamic scenario adjustment, the values ​​of the core influencing factors can be enhanced according to changes in real-time scenarios, making the weight calculation results more in line with the actual needs of real-time scenarios and improving the accuracy and timeliness of risk assessment results. If no risk scenario is matched, the original standardized dataset can be used as the scenario-adjusted standardized dataset, and then the weight values ​​of each sample data under each influencing factor can be calculated based on the original standardized dataset. This processing method ensures the integrity and consistency of the process, and the weight values ​​can be calculated regardless of whether a risk scenario is matched.

[0056] Existing methods for assessing de-icing jump risks employ static weight calculations, failing to consider the impact of real-time scenario changes on the importance of influencing factors. This invention, however, uses a dynamic weight calculation method. By dynamically adjusting the scenario, the weight calculation results better reflect the actual needs of the real-time scenario. By combining dynamic scenario adjustment with standardization, static standardized data can be transformed into dynamically adjusted data. Based on this, this invention can enhance the values ​​of relevant core influencing factors according to changes in the real-time scenario, making the weight calculation results more aligned with the actual needs of the real-time scenario. It can promptly reflect the impact of real-time scenario changes on de-icing jump risks, providing timely and accurate decision-making basis for transmission line operation and maintenance management. Furthermore, it can select different relevant core influencing factors for enhancement processing for different dynamic risk scenarios, thereby improving the accuracy and effectiveness of scenario adjustments.

[0057] Optionally, the dynamic risk scenario rule base includes at least one or more dynamic risk scenarios from the following: strong sunlight melting ice scenario, cold wave path coverage scenario, uneven icing scenario, and gust warning scenario. Each dynamic risk scenario is pre-defined with a set of quantifiable trigger thresholds and a set of core influencing factors associated with that dynamic risk scenario. The trigger threshold is used to determine whether real-time weather warnings and line status data meet the triggering conditions of the corresponding dynamic risk scenarios. The core influencing factor set consists of the part of the influencing factors that plays a dominant role in the risk of de-icing and jumping in a specific scenario, and is used to execute the corresponding weight adjustment logic after a successful matching of a dynamic risk scenario.

[0058] In practice, Figure 2 A specific example of a dynamic risk scenario rule base is provided. If multiple scenario conditions are met simultaneously, processing can be performed according to preset priorities (e.g., "cold wave" takes precedence over "gusts") or by using weighted stacking rules. The dynamic risk scenario rule base is the foundation for dynamic scenario adjustments. By clearly defining the scenario types included in the rule base, the trigger thresholds for each scenario, and the set of associated core influencing factors, accurate matching of real-time scenarios and accurate selection of associated core influencing factors can be achieved, providing a reliable basis for dynamic scenario adjustments.

[0059] Existing methods for assessing the risk of de-icing jumps do not consider the impact of dynamic risk scenarios, nor do they establish a rule base for dynamic risk scenarios. This invention, however, establishes a dynamic risk scenario rule base, clearly defining the scenario types included in the rule base, the trigger thresholds for each scenario, and the set of associated core influencing factors, providing a reliable basis for dynamic scenario adjustments. The dynamic risk scenario rule base of this invention is standardized, providing a unified standard for dynamic scenario adjustments across different regions and types of transmission lines, improving the accuracy and effectiveness of scenario matching. The trigger thresholds of this invention's dynamic risk scenario rule base are quantifiable, enabling accurate real-time scenario matching and avoiding scenario matching deviations caused by qualitative judgments. The set of associated core influencing factors in this invention is targeted, allowing for the selection and enhancement of different associated core influencing factors for different dynamic risk scenarios, further improving the accuracy and effectiveness of scenario adjustments.

[0060] Optionally, the step of acquiring and preprocessing multiple sample data includes: Time alignment, spatial matching, missing value processing, and outlier cleaning are performed on multi-source heterogeneous data to obtain preprocessed sample data. The multi-source heterogeneous data includes at least one or more of the following: real-time data from online monitoring systems, historical operation and maintenance and archive data, numerical simulation data, geographic information system data, and meteorological forecast data.

[0061] In practice, the quality of sample data directly affects the accuracy of the assessment results. Preprocessing steps can improve the quality of sample data, providing high-quality input for subsequent standardization and weight calculation. The introduction of multi-source heterogeneous data can provide more comprehensive and richer information for risk assessment, improving the accuracy and reliability of the assessment results.

[0062] Real-time data from the online monitoring system can be obtained from sensors deployed on towers or conductors, including micro-weather stations (collecting temperature, humidity, wind speed, wind direction, air pressure, rainfall, and irradiance), image monitoring devices (using image recognition algorithms to invert ice thickness, type, and distribution), and conductor status sensors (collecting tilt angle, curvature, tension, acceleration, etc.).

[0063] Historical operation and maintenance data and archives: can be extracted from databases such as production management system (PMS) and enterprise resource planning (ERP), including line design parameters (span, elevation difference, conductor type, number of splits, diameter, elastic modulus) and text reports of historical icing and de-icing events (key information needs to be extracted in a structured manner).

[0064] Numerical simulation data: Software such as finite element analysis (FEA) and computational fluid dynamics (CFD) can be used to perform dynamic simulations on specific lines under preset combinations of icing, de-icing, and meteorological parameters to obtain response data such as amplitude, frequency, and unbalanced tension of de-icing jumps, which can be used as a supplement to insufficient measured data or as a pre-simulation of extreme working conditions.

[0065] Geographic Information System (GIS) data: Information such as elevation, topographic relief, slope aspect, and vegetation cover of the route can be obtained to assist in the analysis of micro-topographic climate effects and inspection accessibility.

[0066] Meteorological forecast data: Meteorological forecast data for the area where the transmission line is located can be obtained from meteorological departments, such as temperature, humidity, wind speed, wind direction, precipitation, and radiation intensity.

[0067] Specifically, multi-source heterogeneous data can be collected using different acquisition devices and systems and stored in a unified data warehouse. The collected multi-source heterogeneous data undergoes preprocessing, including time alignment, spatial matching, missing value handling, and outlier cleaning, to improve data quality. Based on temporal and spatial information, the preprocessed multi-source heterogeneous data is correlated to form a unified sample data with transmission line monitoring points as the core and time sequence as the data. The correlated multi-source heterogeneous data is then fused using methods such as weighted fusion and feature fusion to integrate data from different sources into a unified sample data, providing input for subsequent standardization processing and weight calculation. The fused sample data is then output and stored in a unified dataset, providing input for subsequent evaluation processes.

[0068] Based on this, the sources of sample data can be diversified, providing more comprehensive and richer information for risk assessment and improving the accuracy and reliability of assessment results; the preprocessing steps are standardized, which can improve the quality of sample data, eliminate data noise and errors, and provide high-quality input for subsequent assessment processes; the data fusion method is systematic, which can integrate multi-source heterogeneous data into a unified sample data, realize the spatial and temporal correlation of data, and improve the utilization and value of data.

[0069] Optionally, the risk assessment of the off-ice jump based on the comprehensive risk assessment model includes: The comprehensive risk assessment value corresponding to each sample data is calculated based on the comprehensive risk assessment model, and then compared with multiple preset risk thresholds to classify each sample data into the corresponding risk level. Based on the risk level assigned to the sample data, control measures corresponding to the risk level are retrieved from the preset differentiated control measures library, and the control measures are associated with the transmission line scenario represented by the corresponding sample and output.

[0070] In practice, Figure 3 A specific example of a rule for classifying the risk level of ice break jumps is provided. Figure 4 A specific example of differentiated control measures is provided. The comprehensive risk assessment value is a quantitative representation of the risk of ice break jump. By comparing it with a preset risk threshold, the risk can be divided into different levels, making it easier for operation and maintenance personnel to identify and manage the risk. The associated output of the differentiated control measures can provide operation and maintenance personnel with targeted protective measures, improving the efficiency and effectiveness of operation and maintenance management.

[0071] The specific method of output association involves linking the transmission line scenario information represented by the sample data (such as line name, monitoring point location, monitoring time, etc.) with the risk level and corresponding control measures of the sample data to form a complete risk assessment report. This report is then sent to the transmission line operation and maintenance personnel via a visual interface, SMS, email, or other means. Specifically, the output association may include: transmission line scenario information (line name, monitoring point location, monitoring time, line structural parameters, icing characteristic parameters, climate and environmental parameters, etc.), risk assessment results (comprehensive risk assessment value, risk level), and corresponding differentiated control measures.

[0072] Based on this, the risk level classification of the present invention is standardized, providing a unified standard for risk assessment of different regions and types of transmission lines, thereby improving the accuracy and effectiveness of risk identification; the differentiated control measures are targeted, providing different control measures for different risk levels, thereby improving the efficiency and effectiveness of operation and maintenance management; the associated output is intuitive, enabling the associated output of transmission line scenario information, risk assessment results, and control measures, providing operation and maintenance personnel with clear and explicit decision-making basis, and improving the convenience of operation and maintenance management.

[0073] Preferably, the method for assessing the risk of ice-breaking jumps further includes: The elasticity coefficient of the objective weight of each influencing factor to its standardized numerical change is calculated using the numerical perturbation method; Factors with elastic coefficients greater than the sensitivity threshold are classified as highly sensitive factors, while factors with elastic coefficients not greater than the sensitivity threshold are classified as low-sensitive factors. A sensitivity list of these factors is then generated. Based on the sensitivity list of influencing factors, a monitoring strategy adjustment instruction is generated and sent to the online monitoring system of the transmission line to increase the data sampling frequency of the online monitoring system for the highly sensitive influencing factors and decrease the data sampling frequency of the online monitoring system for the low-sensitivity influencing factors.

[0074] In practice, different influencing factors have varying sensitivities to the weight calculation results. Changes in the values ​​of highly sensitive influencing factors can lead to significant changes in the weight calculation results, while changes in the values ​​of low-sensitivity influencing factors have a smaller impact on the weight calculation results. Through sensitivity analysis, highly sensitive influencing factors can be identified, allowing for adjustments to the monitoring strategy. This involves increasing the sampling frequency of highly sensitive influencing factors and decreasing the sampling frequency of low-sensitivity influencing factors, thereby optimizing the allocation of monitoring resources.

[0075] When calculating the elasticity coefficient, local sensitivity analysis can be performed to calculate the weight of each index. elasticity coefficient of its own standardized value An approximate calculation formula can be expressed as examining minute changes in the index value. Caused weight changes The relative ratio. This can be achieved through the numerical perturbation method: slightly change the value of a certain indicator in a sample (e.g., increase it by 1%), quickly recalculate the weight, and observe the percentage change in the weight of that indicator.

[0076] For example, the The standardized values ​​of the influencing factors are: ;No. Small changes in each influencing factor That is, to make a small perturbation to the standardized value, the perturbed value is ;No. The original objective weights of the influencing factors are: ;No. The objective weights of the influencing factors after perturbation are: ;No. The percentage change in the weight of each influencing factor is ;No. The elasticity coefficients of the influencing factors are The sensitivity threshold is The following formula can be used for specific calculations: Calculation of percentage change in weight:

[0077] Calculation of elasticity coefficient (numerical perturbation method):

[0078] Among them, the elastic coefficient Indicates the first When the standardized value of an influencing factor changes by 1%, the percentage change in its objective weight is indicated by the elasticity coefficient. The larger the elasticity coefficient, the more sensitive the influencing factor is to the weight calculation results.

[0079] When identifying highly sensitive indicators, a sensitivity threshold can be set. (For example, 0.1). If the elasticity coefficient of a certain indicator... > If the value of the indicator is within a certain range, it is classified as a "highly sensitive indicator." This means that even small fluctuations in the monitored value of this indicator may have a significant impact on the final risk assessment result.

[0080] Furthermore, based on the identified list of highly sensitive indicators, monitoring strategy optimization instructions can be generated. For example, instructions can be sent to the online monitoring system to increase the data sampling frequency of highly sensitive indicators (such as de-icing amount x8, icing thickness x6) from the usual 1 hour / time to 15 minutes / time or 5 minutes / time. For indicators with lower sensitivity, the sampling frequency can be appropriately reduced to save communication and storage resources. This achieves a closed-loop optimization of "assessment guiding monitoring, and monitoring serving assessment".

[0081] Preferably, the method for assessing the risk of ice-breaking jumps further includes: When the update trigger condition of the comprehensive risk assessment model is met, new sample data from the last construction of the comprehensive risk assessment model to the present is obtained, and the comprehensive risk assessment model is updated accordingly. The update triggering conditions include at least one or more of the following: time-triggered, event-triggered, data accumulation-triggered, and environmental change-triggered.

[0082] In practice, the accuracy of the comprehensive risk assessment model depends on the quality and quantity of sample data. As time goes on, sample data accumulates, and the operating status of transmission lines and the surrounding environment also change. By pre-setting trigger conditions for model updates, the comprehensive risk assessment model can be updated in a timely manner, ensuring that the model always maintains high accuracy and reliability.

[0083] Time-triggered events can be triggered when the cumulative safe operating time of a transmission line reaches a set limit. This means the transmission line has accumulated a predetermined number of years of safe operation since its commissioning, such as 5 or 10 years. As the line's operating time increases, considering long-term effects such as material aging and creep, the line's structural performance will change, and its icing and de-icing characteristics will also change, thus requiring model updates.

[0084] Event triggering can be triggered by faults or abnormal events related to ice-breaking tripping on transmission lines. These events include tripping, hardware damage, line breakage, tower collapse, and flashover discharge. These events provide new sample data for the model, helping to improve its accuracy.

[0085] Data accumulation triggers an update when the amount of newly added valid sample data reaches a predetermined proportion of the original sample dataset. This can be achieved at various thresholds, such as 20%, 30%, or 50%. As sample data accumulates, the model's accuracy continuously improves; therefore, when the amount of newly added sample data reaches a certain proportion, the model needs to be updated.

[0086] Environmental changes can trigger an event when the long-term trend of key meteorological parameters in the area where the transmission line is located exceeds a preset threshold. This means that the long-term trend of key meteorological parameters (such as temperature, wind speed, irradiance, annual average temperature, and annual extreme minimum temperature) in the area exceeds the preset threshold; for example, the annual average temperature increases by 1°C, or the annual average wind speed increases by 1 m / s. These long-term changes in meteorological parameters affect the characteristics of icing and de-icing, thus requiring model updates.

[0087] Based on this, the model update mechanism of the present invention is standardized, providing a unified standard for model updates of different regions and different types of transmission lines, thereby improving the accuracy and effectiveness of model updates; the update triggering conditions are explicit, enabling timely model updates and ensuring that the model always maintains high accuracy and reliability; the model update is continuous, allowing the model to be updated continuously as sample data accumulates and the operating status of transmission lines changes, thereby improving the adaptability and reliability of the model.

[0088] Preferably, the method for assessing the risk of ice-breaking jumps further includes: After each update of the comprehensive risk assessment model is triggered, the standardized dataset used in this update and the objective weights of each influencing factor are recorded and compared with the historical records to generate a weight evolution sequence. Based on the weight evolution sequence, the influencing factors that stabilize the weight change trend and the influencing factors that cause continuous fluctuations in weight change are identified; In response to the influencing factors of the continuous fluctuation of the weight changes, a smoothing factor is used in the subsequent update of the comprehensive risk assessment model to adjust the fusion ratio of the old and new weights to generate smoothed weights, and the smoothed weights are applied to the next round of updates to the comprehensive risk assessment model.

[0089] In practice, the weight changes of different influencing factors exhibit varying trends. Stable influencing factors show relatively stable weight changes, while fluctuating influencing factors show more volatile weight changes. By analyzing the weight evolution sequence, these influencing factors can be identified. Furthermore, for fluctuating influencing factors, smoothing techniques can be applied to avoid biases in the evaluation results caused by drastic weight fluctuations, thereby improving the model's stability.

[0090] For example, the During the model update, the first... The objective weights of the influencing factors are: ;No. During the model update, the first... The objective weights of the influencing factors are: The smoothing factor is , The weights after smoothing are ; Weight calculation after smoothing:

[0091] Smoothing factor This indicates the proportion of the old weights in the merged weights. This indicates the proportion of the new weight in the merged weight. By adjusting the smoothing factor, the fusion ratio of the old and new weights can be controlled, thus smoothing out factors that influence fluctuations. When, the smoothed weights are equal to the new weights; when When the weights are smoothed, the weights are equal to the old weights; when When the value is 0.3, the weight after smoothing is the weighted sum of the old and new weights.

[0092] Based on this, the weight evolution sequence analysis of the present invention is accurate, capable of identifying stable and fluctuating influencing factors, providing a reliable basis for smoothing processing; the smoothing processing is targeted, capable of adjusting the fusion ratio of old and new weights for fluctuating influencing factors using a smoothing factor, avoiding deviations in evaluation results caused by drastic fluctuations in weights, and improving the stability of the model; the smoothing factor is adjustable, capable of adjusting the size of the smoothing factor according to actual needs, controlling the fusion ratio of old and new weights, and improving the flexibility and effectiveness of smoothing processing.

[0093] Example 2 Taking an important 750kV ultra-high voltage transmission line in the frigid Northwest region as an example, the de-icing jump risk assessment method of this invention is applied to conduct real-time assessment and control of de-icing jump risk in winter.

[0094] Data preparation: The system integrates data from 5 micro-weather stations and 3 sets of image monitoring devices along the line, accesses information from the provincial power meteorological early warning platform, and retrieves line design files. The current time is January 15th, and the meteorological observatory has issued a blue cold wave warning for the next 48 hours.

[0095] Dynamic scenario trigger: The system monitors cold wave warning information in real time, and the forecast shows that the minimum temperature in the area along the route will drop to -15℃ and last for more than 12 hours. This condition triggers the "cold wave path coverage scenario".

[0096] Evaluation Calculation: The system selects forecast data from the past 24 hours and the next 24 hours to form 10 evaluation samples. First, the data is standardized. Then, due to the cold wave scenario, the standardized values ​​of three related indicators—temperature (X9), ice thickness (X6), and ice shedding (X8)—are assigned an enhancement coefficient of 1.15. Next, the entropy weight method is used to calculate the weights under the current dynamic scenario. The calculation reveals that the weights of ice thickness (X6) and ice shedding (X8) are significantly higher than the historical average.

[0097] Sensitivity analysis: Calculations show that the elasticity coefficient of icing thickness (X6) is as high as 0.18, which is identified as a highly sensitive indicator. The system automatically issues an instruction to the monitoring system to shorten the sampling interval for icing identification of the line image from 1 hour to 20 minutes.

[0098] Risk Calculation and Control: Calculate the risk for each sample. Value. Among them, one sample located in a windy area with a large elevation difference has... With a value of 0.72, and considering the importance of the line, it was classified as "high risk (Level II)".

[0099] Results and Actions: The system immediately generated and pushed an orange alert report to the dispatch center and the line maintenance team. The report stated: "L750-XX line #105-#106 span is currently assessed as high risk (Level II). Dominant factors: Ice thickness is expected to increase rapidly under cold weather (weight 25%), with a large potential amount of de-icing (weight 22%). Recommended measures: Immediately increase the monitoring frequency to 15 minutes / time; notify the emergency de-icing team to be on standby; arrange for drones to conduct a detailed inspection of this span tomorrow." Follow-up update: After this cold wave ends, the system will archive all monitoring and assessment data generated during this period. If no line accidents occurred during this process, the data will be included in the database as valid samples. When similar scenarios occur again in the future or the cumulative data volume reaches a certain level, it will be used to fine-tune the model weights.

[0100] Entropy weighting, as an objective weighting method based on information entropy, has the core idea that: if the information entropy of a certain indicator... The smaller the value, the greater the variability of the indicator's value, the more information it provides, and the greater its role in the comprehensive evaluation; therefore, its weight should also be greater. Conversely, the larger the information entropy of an indicator... The larger the value, the smaller the degree of variation in its indicator value, the less information it provides, and the smaller its role in the comprehensive evaluation; therefore, its weight should also be smaller. This is the principle of the entropy weight method.

[0101] Although the entropy weight method has been applied to evaluation methods in other fields, no technical solution has yet been developed to apply it to the field of ice-breaking jump risk assessment. This invention is the first to introduce the entropy weight method into the field of ice-breaking jump risk assessment, and, combined with the actual needs of this field, establishes a complete comprehensive risk assessment model and forms a standardized assessment process.

[0102] Furthermore, directly applying the standard entropy weight method to the risk assessment of transmission line de-icing and jumping—a complex physical process with high spatiotemporal variability and multi-factor nonlinearity—still has significant shortcomings: First, the quality, consistency, and representativeness of the input data highly depend on the prior data collection and fusion processing, which itself presents technical challenges; second, the standard entropy weight method deals with static datasets and cannot reflect the characteristics of risk impact mechanisms changing with external dynamic scenarios (such as specific weather processes); third, existing solutions lack a systematic design for how the calculated weight results can be further used to optimize monitoring resource allocation, how to trigger model self-updates, and how to accurately connect with different levels of emergency response actions.

[0103] In this regard, the de-icing jump risk assessment method of the present invention can achieve the following objectives: (1) Automated and objective weight calculation of all relevant influencing factors to eliminate human subjectivity and arbitrariness; (2) Standardized multi-source heterogeneous data (online monitoring, historical archives, numerical simulation, geographic information, weather forecast) are accessed, aligned, cleaned and fused to lay a high-quality data foundation for objective evaluation; (3) Determine the weight determination mechanism that integrates "dynamic scene recognition" and "entropy weight method core calculation" so that the weight system can be adaptively adjusted based on real-time meteorological warning and line status perception information, thereby improving the evaluation model's response capability to sudden working conditions and the foresight of risk assessment. (4) Based on the weights calculated by the entropy weight method, the “index sensitivity analysis” step is introduced to identify key influencing factors and dynamically optimize the sampling frequency configuration of the monitoring system accordingly, forming a closed-loop optimization of “evaluation feedback to monitoring”. (5) Establish a precise mapping relationship between “comprehensive risk index - risk level - differentiated control measures” to realize the automatic conversion of risk assessment results into specific and operable operation and maintenance instructions, and improve the pertinence and timeliness of disaster prevention and mitigation measures; (6) Construct a dynamic update and self-learning mechanism for the weight calculation model, set clear triggering conditions (such as long time, after an accident, or environmental change), and ensure that the evaluation method can continuously evolve with the line life cycle and changes in the external environment to maintain long-term effectiveness; (7) The above evaluation methods are integrated into a software-implementable system that is applicable to transmission lines of different voltage levels, different geographical climate zones and different structures, and has good engineering portability and scalability.

[0104] The de-icing jump risk assessment method of the present invention has the following advantages: (1) Strong objectivity: The weights are determined entirely based on the dispersion of the data itself, which completely avoids human subjective bias and makes the weight results more scientific and convincing; (2) Dynamic intelligence, responding to real-time changes: The innovative introduction of the "dynamic scene recognition" module enables the static entropy weight model to perceive and respond to external sudden weather and line status changes, greatly improving the timeliness and foresight of risk assessment. (3) Closed-loop optimization to improve monitoring efficiency: Through the "sensitivity analysis-monitoring frequency optimization" closed loop, the evaluation results are used to guide the allocation of monitoring resources, so that the limited monitoring resources are focused on the key parameters most sensitive to the impact of risks, thereby improving the economy and intelligence of the entire monitoring and evaluation system. (4) Precise control and strong decision support: A direct and automated mapping from risk index to specific and differentiated operation and maintenance control measures has been established. The output is no longer an abstract score, but a directly executable suggestion for inspection, monitoring, early warning and disposal, which greatly enhances the engineering practical value of risk assessment results; (5) The system is self-evolving and maintains long-term effectiveness: A multi-condition triggered model update mechanism is designed to enable the system to absorb new data and new experience, self-iterate and optimize, adapt to the long-term changes in the line life cycle and external environment, and ensure the long-term vitality of the method. (6) Strong scalability and broad application prospects: The methodology has a clear framework and is not only applicable to the risk assessment of de-icing jumps. After adaptive adjustments, it can also be extended to the risk assessment of external disasters such as wind deflection, galloping, and wildfires of transmission lines, as well as the condition assessment of other power equipment (such as substation equipment).

[0105] Example 3 Based on the same inventive concept, this invention also provides a de-icing jump risk assessment system based on entropy weight method, including: The data acquisition and fusion module is used to acquire multiple sample data and preprocess them. The preprocessed sample data is then standardized to obtain a dimensionless standardized dataset. Each sample data contains a set of values ​​related to the influencing factors of ice-breaking jumps. The entropy weight calculation module is used to calculate the proportion of each sample data under each influencing factor based on the standardized dataset, calculate the information entropy of each influencing factor based on the proportion, and calculate the objective weight of each influencing factor based on the information entropy. The risk assessment module is used to construct a comprehensive risk assessment model based on the objective weights and the standardized dataset, and to assess the risk of ice-breaking jumps based on the comprehensive risk assessment model.

[0106] Preferably, the ice-breaking jump risk assessment system further includes: The dynamic scene recognition submodule is used to match real-time weather warnings and line status data with a dynamic risk scene rule base. If the match is successful, the values ​​of the associated core influencing factors in the standardized dataset are enhanced according to the weight adjustment logic of the scene to obtain a scene-adjusted standardized dataset. The proportion of each sample data under each influencing factor is calculated based on the scene-adjusted standardized dataset. The sensitivity analysis submodule is used to calculate the elasticity coefficient of the objective weight of each influencing factor to its standardized numerical change using the numerical perturbation method; influencing factors with elasticity coefficients greater than the sensitivity threshold are identified as highly sensitive influencing factors, and influencing factors with elasticity coefficients not greater than the sensitivity threshold are identified as low-sensitive influencing factors, and a sensitivity list of influencing factors is generated accordingly; based on the sensitivity list of influencing factors, a monitoring strategy adjustment instruction is generated and sent to the online monitoring system of the transmission line to increase the data sampling frequency of the online monitoring system for the highly sensitive influencing factors and decrease the data sampling frequency of the online monitoring system for the low-sensitive influencing factors; The control decision output submodule is used to retrieve control measures corresponding to the risk level from the preset differentiated control measures library according to the risk level classified by the sample data, and associate the control measures with the transmission line scenario represented by the corresponding sample and output them. The model update management submodule is used to record the standardized dataset and objective weights of each influencing factor used in each update of the comprehensive risk assessment model, and compare them with historical records to generate a weight evolution sequence. Based on the weight evolution sequence, it identifies influencing factors with stable weight change trends and influencing factors with continuous weight fluctuations. For the influencing factors with continuous weight fluctuations, a smoothing factor is used in subsequent updates of the comprehensive risk assessment model to adjust the fusion ratio of the old and new weights to generate smoothed weights, and the smoothed weights are applied to the next round of updates of the comprehensive risk assessment model.

[0107] Example 4 Based on the same inventive concept, this invention also provides a de-icing jump risk assessment device based on entropy weight method, comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the de-icing jump risk assessment method.

[0108] In practice, those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0109] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention.

Claims

1. A method for assessing the risk of ice-breaking jumps based on entropy weighting, characterized in that, include: Multiple sample data were acquired and preprocessed. The preprocessed sample data were then standardized to obtain a dimensionless standardized dataset, in which each sample data contained a set of values ​​related to the influencing factors of the ice-breaking jump. The proportion of each sample data under each influencing factor is calculated based on the standardized dataset. The information entropy of each influencing factor is calculated based on the proportion. The objective weight of each influencing factor is calculated based on the information entropy. A comprehensive risk assessment model is constructed based on the objective weights and the standardized dataset, and the risk of ice-breaking jumps is assessed based on the comprehensive risk assessment model.

2. The method for assessing the risk of ice-breaking jumps according to claim 1, characterized in that: The influencing factors include at least line structure parameters, icing characteristics parameters, and climate environment parameters; The line structure parameters include at least one or more of the following: span, elevation difference, splitting method, and conductor diameter; The icing characteristic parameters include at least one or more of the following: icing type, icing thickness, de-icing location, and de-icing amount; The climate environmental parameters include at least one or more of the following: temperature, rate of temperature rise, irradiance, and wind speed.

3. The method for assessing the risk of ice-breaking jumps according to claim 2, characterized in that, The standardization process for the preprocessed sample data to obtain a dimensionless standardized dataset includes: If the larger the value of a certain influencing factor, the higher the risk of ice break jump, then the minimum value of the influencing factor in all sample data is mapped to 0 and the maximum value is mapped to 1, and the remaining sample values ​​of the influencing factor are linearly mapped to the [0,1] interval according to the proportion of the difference between the minimum value and the maximum value to the difference between the minimum value and the maximum value. If the larger the value of a certain influencing factor, the lower the risk of ice break jump, then the maximum value of the influencing factor in all sample data is mapped to 0 and the minimum value is mapped to 1, and the remaining sample values ​​of the influencing factor are linearly mapped to the [0,1] interval according to the proportion of the difference between the maximum value and the minimum value.

4. The method for assessing the risk of ice-breaking jumps according to claim 3, characterized in that, The standardization process for the preprocessed sample data to obtain a dimensionless standardized dataset further includes: The different categories of the splitting modes are mapped to different dimensionless values, wherein the categories of the splitting modes include at least one or more of single splitting, binary splitting, tetrasplitting, and octetlitting; The different categories of the icing type are mapped to different dimensionless values, wherein the categories of the icing type include at least one or more of snow frost, rain frost, mixed frost, and hoarfrost.

5. The method for assessing the risk of ice-breaking jumps according to claim 1 or 3, characterized in that, The step of calculating the weight of each sample data under each influencing factor based on the standardized dataset includes: Real-time weather warnings and line status data are matched with a dynamic risk scenario rule base. If the match is successful, the values ​​of the core influencing factors in the standardized dataset are enhanced according to the weight adjustment logic of the scenario to obtain the standardized dataset after scenario adjustment. The weight of each sample data under each influencing factor is calculated based on the standardized dataset adjusted according to the scenario.

6. The method for assessing the risk of ice-breaking jumps according to claim 5, characterized in that: The dynamic risk scenario rule base includes at least one or more dynamic risk scenarios such as strong sunlight melting ice scenario, cold wave path coverage scenario, uneven icing scenario, and gust warning scenario. Each dynamic risk scenario is pre-defined with a set of quantifiable trigger thresholds and a set of core influencing factors associated with that dynamic risk scenario. The trigger threshold is used to determine whether real-time weather warnings and line status data meet the triggering conditions of the corresponding dynamic risk scenarios. The core influencing factor set consists of the part of the influencing factors that plays a dominant role in the risk of de-icing and jumping in a specific scenario, and is used to execute the corresponding weight adjustment logic after a successful matching of a dynamic risk scenario.

7. The method for assessing the risk of ice-breaking jumps according to claim 1, characterized in that, The process of acquiring and preprocessing multiple sample data includes: Time alignment, spatial matching, missing value processing, and outlier cleaning are performed on multi-source heterogeneous data to obtain preprocessed sample data. The multi-source heterogeneous data includes at least one or more of the following: real-time data from online monitoring systems, historical operation and maintenance and archive data, numerical simulation data, geographic information system data, and meteorological forecast data.

8. The method for assessing the risk of ice-breaking jumps according to claim 1, characterized in that, The risk assessment of the off-ice jump based on the comprehensive risk assessment model includes: The comprehensive risk assessment value corresponding to each sample data is calculated based on the comprehensive risk assessment model, and then compared with multiple preset risk thresholds to classify each sample data into the corresponding risk level. Based on the risk level assigned to the sample data, control measures corresponding to the risk level are retrieved from the preset differentiated control measures library, and the control measures are associated with the transmission line scenario represented by the corresponding sample and output.

9. A de-icing jump risk assessment system based on entropy weight method, characterized in that, include: The data acquisition and fusion module is used to acquire multiple sample data and preprocess them. The preprocessed sample data is then standardized to obtain a dimensionless standardized dataset. Each sample data contains a set of values ​​related to the influencing factors of ice-breaking jumps. The entropy weight calculation module is used to calculate the proportion of each sample data under each influencing factor based on the standardized dataset, calculate the information entropy of each influencing factor based on the proportion, and calculate the objective weight of each influencing factor based on the information entropy. The risk assessment module is used to construct a comprehensive risk assessment model based on the objective weights and the standardized dataset, and to assess the risk of ice-breaking jumps based on the comprehensive risk assessment model.

10. A de-icing jump risk assessment device based on entropy weight method, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the de-icing jump risk assessment method as described in any one of claims 1 to 8.

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