Cross arm capacitive stress data precision optimization method and system

Abstract

The invention discloses a cross-arm capacitive stress data precision optimization method and system, and relates to the technical field of power transmission line mechanical monitoring, and the method comprises the steps: collecting a cross-arm capacitance change signal through a capacitive sensor, carrying out the temperature compensation, building a multi-measurement-point mutual verification mechanism based on the compensated data, and carrying out the calculation of the cross-arm capacitance change signal. And constructing a constraint relation between the measuring points by using a cross arm mechanical equilibrium condition to calculate a theoretical capacitance value. And after the deviation between the actual capacitance value and the theoretical capacitance value is used as a correction value for compensation, stress data are obtained by adopting a segmented conversion strategy and are reconstructed. By calculating the residual error of the reconstruction data and the mutual verification data, the spatial gradient is extracted to identify the abrupt change measuring point and correct the abrupt change measuring point, finally the optimized stress data is obtained, and the evaluation of the bearing state of the cross arm and the abnormal early warning are realized. According to the method, the stress monitoring precision is improved, and the method has important engineering application value.

Description

Technical Field

[0001] This invention relates to the field of mechanical monitoring technology for power transmission lines, specifically to a method and system for optimizing the accuracy of crossarm capacitive stress data. Background Technology

[0002] With the continuous expansion of power systems, the stress monitoring of crossarms, as important load-bearing components of transmission lines, is of great significance for ensuring the safe operation of the power grid. Capacitive sensors are widely used in crossarm stress monitoring due to their advantages such as high sensitivity and strong anti-interference ability.

[0003] The crossarm stress monitoring method mainly relies on the direct conversion of single-point capacitance data to obtain stress values. This method is easily affected by factors such as temperature changes and measurement errors, resulting in insufficient accuracy of stress data.

[0004] Current stress data processing methods often use a uniform conversion factor, failing to differentiate between regions of the crossarm based on their stress characteristics. This results in discrepancies between the conversion results and the actual stress state. Furthermore, existing methods lack effective mechanisms for identifying and correcting anomalous data, impacting the accuracy of stress monitoring results. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the accuracy of crossarm capacitive stress data, aiming to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of this invention is: a method for optimizing the accuracy of crossarm capacitive stress data, comprising the following steps: The capacitance change signal of the crossarm is collected by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence. A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is then calculated. The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction amount to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence. The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. The preliminary stress data sequence is then converted back into capacitance data to obtain a reconstructed capacitance data sequence. The residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point position is extracted, abrupt measurement points are identified, and the abrupt measurement points are corrected by the transformation relationship between adjacent measurement points to obtain the optimized crossarm stress data sequence. The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated.

[0007] The capacitance change signal of the crossarm is acquired by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence, which includes: The capacitance change signal at a preset measuring point on the crossarm surface is collected by a capacitive sensor, and the temperature data at the preset measuring point is collected simultaneously to form an original capacitance data sequence containing capacitance change signal and temperature data. Calculate the temperature distribution difference between preset measuring points based on temperature data, and determine the temperature gradient change of preset measuring points based on the temperature distribution difference. A temperature compensation coefficient is generated based on the mapping relationship between the temperature gradient change and the capacitance change signal, and the temperature compensation coefficient is used to construct a temperature compensation correction matrix according to the positional relationship of the preset measurement points. Calculate the temperature change trends of the preset measuring points in the horizontal and vertical directions respectively, and combine the temperature change trends with the temperature compensation correction matrix to construct a two-way compensation factor; The capacitance change signal in the original capacitance data sequence is compensated in layers according to the bidirectional compensation factor to obtain the temperature-compensated capacitance data sequence.

[0008] A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is calculated, including: Trend analysis is performed on the temperature-compensated capacitance data sequence to calculate the capacitance value change trend between adjacent measuring points on the crossarm, extract the capacitance change amplitude and direction between measuring points, generate the capacitance change correlation value between measuring points, and construct the capacitance change correlation matrix. Based on the capacitance change correlation matrix analysis, the capacitance change pattern among the measurement points is analyzed, the mutual influence between the measurement points is extracted, the measurement point capacitance data verification criteria are established, and a multi-measurement point mutual verification mechanism is formed. Based on the multi-point mutual verification mechanism, multiple measuring points that meet the stress transfer requirements are selected on the crossarm, and the distribution position of each measuring point is determined. Based on the mechanical equilibrium condition of the crossarm and the distribution of each measuring point, the stress transfer relationship between multiple measuring points is calculated, and the constraint relationship between the capacitance data of multiple measuring points is constructed. The distribution locations of multiple measuring points are substituted into the constraint relationship between the capacitance data of multiple measuring points to construct a set of constraint equations. The set of constraint equations is then solved to calculate the theoretical capacitance value of each measuring point.

[0009] The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction factor to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence including: Obtain the actual capacitance value at each measuring point, calculate the difference sequence between the actual capacitance value and the theoretical capacitance value, and generate a measuring point capacitance deviation sequence based on the difference sequence. Based on the stress characteristics of the crossarm, the stress transmission path is determined. The variation characteristics of the capacitance deviation sequence of the measuring points are analyzed along the stress transmission path. The deviation change and transmission law between adjacent measuring points are extracted, and the measuring point compensation parameters are constructed. Spatial distribution correction of deviation variation is performed using measurement point compensation parameters, and dynamic compensation coefficients are generated based on the transmission law. The dynamic compensation coefficient is used to correct the actual capacitance measurement value of each measuring point according to the stress transmission path sequence, so as to obtain the capacitance data sequence after mutual verification.

[0010] The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. This preliminary stress data sequence is then converted back into capacitance data to obtain the reconstructed capacitance data sequence, which includes: Analyze the capacitance change trend of the measurement points in the capacitance data sequence after mutual verification, calculate the capacitance change amplitude between adjacent measurement points, identify the abrupt change position based on the capacitance change amplitude, and determine the data segmentation interval. Extract the stress-deformation characteristics of the measuring points within the data segment intervals, analyze the correspondence between the stress-deformation characteristics and capacitance changes, generate the capacitance-stress conversion coefficient for each data segment interval, and construct a segmented mapping relationship from capacitance to stress. The segmented mapping relationship is used to transform and calculate the cross-validated capacitance data sequence, and the preliminary stress data sequence is obtained by smoothing the numerical connection at the segment boundaries. The capacitance-stress conversion coefficients are reversed to establish a reverse calculation relationship between stress and capacitance. The preliminary stress data sequence is then reversed to obtain a reconstructed capacitance data sequence.

[0011] The residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point location is extracted, abrupt measurement points are identified, and the abrupt measurement points are corrected using the transformation relationship between adjacent measurement points. The optimized crossarm stress data sequence includes: Calculate the numerical difference between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence at the measurement point location, generate measurement point residual data, and calculate the residual change between measurement points based on the measurement point residual data; A residual distribution function is constructed using the residual data and residual change at the measurement points. The spatial directional derivative of the residual distribution function at the measurement point is calculated to obtain the spatial gradient value of the residual. Analyze the locations of measurement points where the residual space gradient value exceeds the preset gradient range to determine abrupt measurement points, extract the capacitance changes of measurement points on both sides of the abrupt measurement point, and generate a measurement point transformation function; The measurement point conversion function is applied to the stress data at the abrupt measurement point for correction and compensation, resulting in an optimized crossarm stress data sequence.

[0012] The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated, including: Analyze the stress distribution characteristics of the measuring points in the optimized crossarm stress data sequence, extract the stress change trend of the measuring points, and construct the crossarm stress distribution map. Calculate the stress concentration in each region of the crossarm based on the stress distribution map of the crossarm, evaluate the stress state of the crossarm, and generate the crossarm bearing state evaluation result. Based on the crossarm bearing condition assessment results, stress over-limit areas are identified, the stress concentration degree within the stress over-limit areas is calculated, and stress risk assessment indicators are constructed. The stress risk assessment indicators are combined with the degree of stress concentration for graded judgment, generating early warning information that includes risk level and regional location.

[0013] This invention provides a crossarm capacitive stress data accuracy optimization system, the system comprising: The data acquisition and temperature compensation unit is used to acquire the capacitance change signal of the crossarm through a capacitive sensor to obtain the original capacitance data sequence, and to perform temperature compensation processing on the original capacitance data sequence to obtain the temperature-compensated capacitance data sequence. The multi-point mutual verification unit is used to establish a multi-point mutual verification mechanism based on the temperature-compensated capacitance data sequence. It selects multiple measurement points on the crossarm, constructs the constraint relationship between the capacitance data of each measurement point according to the mechanical equilibrium condition of the crossarm, and calculates the theoretical capacitance value of each measurement point. The data compensation unit is used to compensate for the deviation between the actual capacitance value and the theoretical capacitance value as a correction amount to obtain the capacitance data sequence after mutual verification. The data conversion unit is used to segment the cross-validated capacitance data sequence into stress values ​​to obtain a preliminary stress data sequence, and then convert the preliminary stress data sequence back into capacitance data to obtain a reconstructed capacitance data sequence. The abrupt change measurement point correction unit is used to calculate the residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence, extract the spatial gradient of the residual along the measurement point position, identify abrupt change measurement points, and correct the abrupt change measurement points using the transformation relationship of adjacent measurement points to obtain the optimized crossarm stress data sequence. The early warning unit is used to assess the load-bearing status of the crossarm based on the optimized crossarm stress data, identify areas of abnormal stress, and generate early warning information.

[0014] The beneficial effects of this invention are: This invention significantly improves the accuracy of crossarm stress monitoring data by establishing a multi-point mutual verification mechanism, combined with temperature compensation processing and a segmented conversion strategy. The residual spatial gradient analysis method accurately identifies abrupt change points and effectively corrects them through the conversion relationship between adjacent points, ensuring the reliability of the stress data. By evaluating the load-bearing state of the optimized stress data, timely detection and early warning of stress anomaly areas are achieved. This not only solves the problem of traditional single-point monitoring methods being susceptible to external interference but also overcomes the accuracy deviation caused by uniform conversion coefficients, providing more reliable data support for the safe operation monitoring of crossarms and possessing significant engineering application value. Attached Figure Description

[0015] Figure 1 A flowchart of a method for optimizing the accuracy of crossarm capacitive stress data is provided in an embodiment of the present invention; Figure 2 This is a flowchart of the crossarm stress data anomaly detection and correction process according to an embodiment of the present invention. Detailed Implementation

[0016] like Figure 1 As shown, Figure 1 A flowchart of a method for optimizing the accuracy of crossarm capacitive stress data provided in an embodiment of the present invention is included, the method comprising the following steps: The capacitance change signal of the crossarm is collected by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence. A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is then calculated. The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction amount to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence. The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. The preliminary stress data sequence is then converted back into capacitance data to obtain a reconstructed capacitance data sequence. The residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point position is extracted, abrupt measurement points are identified, and the abrupt measurement points are corrected by the transformation relationship between adjacent measurement points to obtain the optimized crossarm stress data sequence. The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated.

[0017] The capacitance change signal of the crossarm is acquired by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence, which includes: The capacitance change signal at a preset measuring point on the crossarm surface is collected by a capacitive sensor, and the temperature data at the preset measuring point is collected simultaneously to form an original capacitance data sequence containing capacitance change signal and temperature data. Calculate the temperature distribution difference between preset measuring points based on temperature data, and determine the temperature gradient change of preset measuring points based on the temperature distribution difference. A temperature compensation coefficient is generated based on the mapping relationship between the temperature gradient change and the capacitance change signal, and the temperature compensation coefficient is used to construct a temperature compensation correction matrix according to the positional relationship of the preset measurement points. Calculate the temperature change trends of the preset measuring points in the horizontal and vertical directions respectively, and combine the temperature change trends with the temperature compensation correction matrix to construct a two-way compensation factor; The capacitance change signal in the original capacitance data sequence is compensated in layers according to the bidirectional compensation factor to obtain the temperature-compensated capacitance data sequence.

[0018] A capacitive sensor is mounted on the surface of the crossarm to collect the capacitance change signal. The sensor uses a grid arrangement to form multiple preset measuring points on the crossarm surface. Each preset measuring point is equipped with a temperature sensor to achieve synchronous acquisition of capacitance change signal and temperature data. The sensitivity of the capacitive sensor is set to 0.01pF, and the sampling frequency is 100Hz. The accuracy of the temperature sensor is ±0.1℃, and its sampling frequency is consistent with that of the capacitive sensor. The acquired data includes the capacitance change signal value and the corresponding temperature value, forming a raw capacitance data sequence, which is stored in a data buffer.

[0019] The raw capacitance data sequence underwent preprocessing to filter out high-frequency noise and outliers. Median filtering was used in the preprocessing, with a filter window size of 5 sampling points. Outliers were defined as those exceeding three times the standard deviation of the average of the preceding and following 10 sampling points. The preprocessed data was then used for subsequent temperature compensation processing.

[0020] Temperature compensation processing first calculates the temperature distribution difference between preset measuring points. For adjacent measuring points p1 and p2, the temperature distribution difference is the difference between the two point temperature values. When the number of measuring points is n, (n-1) temperature distribution difference values ​​are formed. Based on these difference values, the temperature gradient change at each preset measuring point is determined using a spatial interpolation method. The temperature gradient change is expressed as the rate of temperature change per unit distance. The calculation of the temperature gradient change considers the two-dimensional spatial distribution of the measuring points, calculating the lateral and longitudinal temperature gradients separately.

[0021] A mapping relationship between temperature gradient changes and capacitance changes is established using a lookup table. The lookup table ranges from -5℃ / cm to 5℃ / cm with a step size of 0.1℃ / cm, and the corresponding capacitance change compensation coefficient ranges from 0.8 to 1.2. When the temperature gradient change exceeds the lookup table range, boundary values ​​are used for limitation. Temperature compensation coefficients are generated based on this mapping relationship. For each preset measurement point, the corresponding compensation coefficient is looked up based on its temperature gradient change.

[0022] The generated temperature compensation coefficients are used to construct a temperature compensation correction matrix based on the positional relationship of the preset measuring points. Assuming the preset measuring points on the crossarm surface form an m x n matrix, the temperature compensation correction matrix also has an m x n dimension, with each element corresponding to the temperature compensation coefficient of a preset measuring point. The temperature compensation correction matrix considers the spatial correlation between measuring points and smooths the compensation coefficients of adjacent measuring points with a smoothing radius equal to the distance between two measuring points.

[0023] The surface temperature distribution of the crossarm exhibits a spatial variation trend, requiring separate calculations of the temperature variation trends at preset measuring points in both the transverse and longitudinal directions. The transverse temperature variation trend is obtained by performing linear regression on the temperature data from measuring points in the same row; the regression coefficient represents the transverse temperature change rate. Similarly, the longitudinal temperature variation trend is obtained by performing linear regression on the temperature data from measuring points in the same column; the regression coefficient represents the longitudinal temperature change rate. The linear regression employs the least squares method, and outliers are removed before recalculating the regression coefficients to improve regression accuracy.

[0024] A two-way compensation factor is constructed by combining the horizontal and vertical temperature change trends with a temperature compensation correction matrix. This factor considers the combined effects of temperature in both the horizontal and vertical directions. For a preset measuring point at position (i, j), the two-way compensation factor is a weighted average of the horizontal and vertical compensation factors, with the weights dynamically adjusted according to the rate of temperature change. When the horizontal temperature change rate is greater than the vertical temperature change rate, the weight of the horizontal compensation factor increases; conversely, the weight of the vertical compensation factor increases. The weights range from 0 to 1, and the sum of the two is 1.

[0025] The capacitance change signal in the original capacitance data sequence is compensated hierarchically using a bidirectional compensation factor. Hierarchical compensation considers the nonlinear effect of temperature on the capacitance signal and is divided into two levels: coarse compensation and fine compensation. In the coarse compensation stage, the bidirectional compensation factor is used to directly correct the capacitance change signal, eliminating systematic deviations caused by uneven temperature distribution. In the fine compensation stage, the compensation result is fine-tuned based on local temperature fluctuations to adapt to dynamic temperature changes. The compensation process adopts an iterative approach, evaluating the compensation effect after each iteration. The iteration ends when the difference between two adjacent iterations is less than a preset threshold. The preset threshold is set to 0.5% of the capacitance change signal amplitude.

[0026] The capacitance data sequence obtained after temperature compensation processing eliminates interference caused by temperature changes compared to the original capacitance data sequence, improving the accuracy and reliability of the signal. In the compensated capacitance data sequence, the capacitance change signal more accurately reflects the actual state of the crossarm. The compensation results are evaluated through both offline analysis and online verification. Offline analysis compares the capacitance change signals generated by the same displacement under different temperature conditions, while online verification verifies the compensation effect by introducing a known displacement.

[0027] This invention effectively solves the problem of interference from ambient temperature changes on the capacitance signal by acquiring the capacitance change signal of the crossarm using a capacitive sensor and performing temperature compensation processing. Based on the temperature distribution difference, the temperature gradient change is calculated, and a compensation coefficient is generated by combining it with the mapping relationship of the capacitance change signal. A temperature compensation correction matrix is ​​constructed, and then the transverse and longitudinal temperature change trends are analyzed to form a bidirectional compensation factor, achieving hierarchical compensation of the capacitance signal. By comprehensively considering the spatial distribution characteristics and dynamic change laws of temperature, this method achieves a more refined and comprehensive temperature compensation effect, providing more reliable technical support for crossarm condition monitoring.

[0028] A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is calculated, including: Trend analysis is performed on the temperature-compensated capacitance data sequence to calculate the capacitance value change trend between adjacent measuring points on the crossarm, extract the capacitance change amplitude and direction between measuring points, generate the capacitance change correlation value between measuring points, and construct the capacitance change correlation matrix. Based on the capacitance change correlation matrix analysis, the capacitance change pattern among the measurement points is analyzed, the mutual influence between the measurement points is extracted, the measurement point capacitance data verification criteria are established, and a multi-measurement point mutual verification mechanism is formed. Based on the multi-point mutual verification mechanism, multiple measuring points that meet the stress transfer requirements are selected on the crossarm, and the distribution position of each measuring point is determined. Based on the mechanical equilibrium condition of the crossarm and the distribution of each measuring point, the stress transfer relationship between multiple measuring points is calculated, and the constraint relationship between the capacitance data of multiple measuring points is constructed. The distribution locations of multiple measuring points are substituted into the constraint relationship between the capacitance data of multiple measuring points to construct a set of constraint equations. The set of constraint equations is then solved to calculate the theoretical capacitance value of each measuring point.

[0029] When performing trend analysis on the temperature-compensated capacitance data sequence, a sliding window method was used, with a window length of 30 sampling points and an overlap rate of 50%. Within each window, the capacitance difference between adjacent measuring points on the crossarm was calculated to obtain the capacitance change trend. The change trend includes two characteristic parameters: the magnitude of change and the direction of change. The magnitude of change is represented by the difference in capacitance values ​​between adjacent measuring points, and the direction of change is represented by the positive or negative sign of this difference.

[0030] The capacitance change trend was extracted using a dual-threshold discrimination method, with a high threshold of 0.5 pF and a low threshold of 0.1 pF. A capacitance change exceeding the high threshold was considered a significant change; a change between the high and low thresholds was considered a slight change; and a change below the low threshold was considered no significant change. The direction of change was determined by the sign of the difference: a positive value indicated an increase in capacitance, and a negative value indicated a decrease. Based on the extracted change magnitude and direction, the correlation degree between measurement points was calculated. The correlation degree calculation considered the proportional relationship of the change magnitude and the consistency of the change direction, with a value ranging from 0 to 1, where 0 represents no correlation and 1 represents complete correlation.

[0031] When constructing the capacitance change correlation matrix, the rows and columns of the matrix correspond to the various measuring points on the crossarm, and the matrix element values ​​are the capacitance change correlation degrees between corresponding two measuring points. The matrix is ​​a symmetric matrix, with the main diagonal elements having a value of 1, indicating that the measuring point is completely correlated with itself. To improve the stability of the matrix, a time averaging method is used to smooth the correlation degree values. The time window is set to 10 minutes, and the average value of the correlation degree values ​​within the window is taken as the final result.

[0032] Based on the constructed capacitance change correlation matrix, the capacitance change patterns among measuring points are analyzed. Cluster analysis is used to group measuring points with similar correlation into the same category. Hierarchical clustering with a distance threshold of 0.2 is employed to progressively merge groups of measuring points with high correlation. For the clustering results, the degree of mutual influence between measuring points is extracted, defined as a comprehensive index of correlation and distance. The closer two measuring points are, the higher their correlation, and the greater their mutual influence. The degree of influence is divided into three levels: high, medium, and low, with corresponding thresholds of 0.7 and 0.4, respectively.

[0033] Based on the degree of mutual influence between measuring points, a verification criterion for measuring point capacitance data is established. This criterion includes a reasonable range of variation in the capacitance values ​​of the measuring points and a requirement for consistency in capacitance changes between measuring points. For measuring point pairs with a high degree of influence, their capacitance changes should be highly consistent, with an allowable deviation range of 10%; for measuring point pairs with a moderate degree of influence, the allowable deviation range is 20%; and for measuring point pairs with a low degree of influence, the allowable deviation range is 30%. The verification criteria are stored in matrix form for easy and rapid subsequent retrieval and application.

[0034] The multi-point mutual verification mechanism is constructed based on verification criteria and includes a point selection strategy, anomaly detection rules, and data correction methods. The point selection strategy determines the set of points participating in the mutual verification; the anomaly detection rules identify points with abnormal capacitance data; and the data correction methods correct the capacitance values ​​of abnormal points. The mutual verification mechanism uses a majority voting method; when the capacitance value of a point deviates from the expected value of most relevant points beyond the range specified in the verification criteria, the data at that point is deemed abnormal.

[0035] Multiple measuring points meeting stress transfer requirements were selected on the crossarm based on a multi-point cross-verification mechanism. The selection of measuring points followed two principles: covering key stress-bearing areas of the crossarm and ensuring cross-verification capability between measuring points. Key stress-bearing areas of the crossarm included the center point, quarter points, and support points; stress changes at these locations reflected the overall stress state of the crossarm. Nine measuring points were set, distributed at typical locations on the crossarm surface. The coordinates of each measuring point were determined with the center of the crossarm as the origin, and the coordinate unit was centimeters. The measuring points were distributed in a grid pattern, covering the main areas of the crossarm to ensure the overall stress distribution of the crossarm could be captured.

[0036] As a load-bearing component, the stress distribution within the crossarm follows the principles of elasticity. A mechanical model is established based on the crossarm's geometry and support method. The crossarm is primarily subjected to bending and torsion, and the corresponding stress distribution can be transformed into the spatial distribution law of capacitance changes. The mechanical equilibrium of the crossarm requires the balance of the resultant force and moment of stress in all parts; this condition is translated into a constraint relationship between the capacitance values ​​at each measuring point. This constraint relationship considers factors such as the measuring point location, crossarm stiffness, and stress transfer efficiency, forming a linear combination equation for the capacitance values ​​at the measuring points.

[0037] The established constraints form a system of constraint equations, with the number of equations roughly matching the number of measurement points, ensuring a definite solution. The constraint equations are solved using an iterative method, with the initial values ​​set to the actual measured capacitance values. During the iteration process, the capacitance values ​​at each measurement point are continuously adjusted according to the constraints until all constraints are met, the number of iterations reaches a preset upper limit of 100, or the difference between two consecutive iterations is less than 0.01 pF. The capacitance values ​​obtained at each measurement point are the theoretical capacitance values, serving as the benchmark values ​​for subsequent data anomaly detection and correction.

[0038] When the deviation between the measured capacitance value and the theoretical capacitance value at a certain measuring point exceeds a preset threshold, the data at that measuring point is determined to be abnormal. The threshold is set at 15% of the theoretical capacitance value and can be adjusted according to actual needs. For measuring points determined to be abnormal, the theoretical capacitance value is used to replace the abnormal value, or a weighted correction is performed based on the theoretical capacitance value and historical data, with weighting coefficients of 0.7 and 0.3. The corrected data is then re-submitted to the multi-measuring-point mutual verification mechanism to ensure that the correction result meets the mechanical equilibrium conditions of the crossarm.

[0039] This invention significantly improves the reliability and accuracy of capacitance data by establishing a cross-checking mechanism among multiple measuring points on the crossarm. The multi-measuring-point cross-checking mechanism fully utilizes the mechanical characteristics of the crossarm and the spatial distribution of the measuring points, achieving self-calibration and self-correction of the capacitance data, thus enhancing robustness and adaptability. By comparing and analyzing theoretical capacitance values ​​with measured values, it is possible not only to monitor data anomalies but also to reflect changes in the stress state of the crossarm structure, providing more reliable data support for crossarm condition assessment and early warning.

[0040] The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction factor to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence including: Obtain the actual capacitance value at each measuring point, calculate the difference sequence between the actual capacitance value and the theoretical capacitance value, and generate a measuring point capacitance deviation sequence based on the difference sequence. Based on the stress characteristics of the crossarm, the stress transmission path is determined. The variation characteristics of the capacitance deviation sequence of the measuring points are analyzed along the stress transmission path. The deviation change and transmission law between adjacent measuring points are extracted, and the measuring point compensation parameters are constructed. Spatial distribution correction of deviation variation is performed using measurement point compensation parameters, and dynamic compensation coefficients are generated based on the transmission law. The dynamic compensation coefficient is used to correct the actual capacitance measurement value of each measuring point according to the stress transmission path sequence, so as to obtain the capacitance data sequence after mutual verification.

[0041] The actual capacitance values ​​at each measuring point are obtained in real time using capacitance sensors arranged on the crossarm. The sampling frequency of the capacitance sensors is set to 20Hz, and the sampling accuracy is 0.01pF. After temperature compensation and baseline calibration, the collected capacitance data forms a sequence of actual capacitance values. The theoretical capacitance values ​​are calculated using the constraint equations constructed based on the mechanical equilibrium conditions of the crossarm. These theoretical capacitance values ​​represent the capacitance values ​​that the crossarm should have at each measuring point under normal stress. The actual capacitance values ​​are compared with the theoretical capacitance values ​​to obtain a difference sequence, which records the capacitance deviation at each measuring point at different time points.

[0042] The difference sequence is calculated using a point-by-point comparison method, subtracting the actual capacitance value from the corresponding theoretical value at each time point to form a time series data. The difference sequence is then smoothed using a sliding window with a window length of 15 sampling points. The smoothed data serves as the capacitance deviation sequence for each measurement point. The capacitance deviation sequence reflects the degree of deviation of the capacitance value at each measurement point from the theoretical value. The positive or negative sign of the deviation sequence indicates whether the capacitance value is too high or too low, and the absolute value of the deviation sequence indicates the degree of deviation. The deviation sequence is also filtered using a threshold to remove minor fluctuations. The threshold is set to 0.05 pF; deviations below this threshold are considered normal fluctuations and are ignored.

[0043] As a key load-bearing component in transmission lines, the crossarm exhibits a clear stress transmission pattern. Based on the crossarm's material properties and geometry, and combined with finite element analysis results, the main stress transmission paths on the crossarm are determined. These stress transmission paths typically begin at the stress point and spread along the crossarm structure towards both ends, forming a tree-like or network-like transmission network. The stress relationships between adjacent nodes along the stress transmission paths show a clear correlation, which can be used to guide the correction of capacitance deviations. The stress transmission paths are classified into primary and secondary paths based on their intensity of influence. The primary path covers the most significantly stressed parts of the crossarm, while the secondary path covers the less significantly stressed parts.

[0044] When analyzing the variation characteristics of the capacitance deviation sequence along the stress transmission path, the main focus is on the amplitude variation, propagation delay, and attenuation law of the deviation sequence. Amplitude variation reflects the difference in deviation magnitude between adjacent measuring points, propagation delay reflects the time required for the stress wave to propagate from one measuring point to another, and attenuation law reflects the degree of stress weakening during transmission. By comparing and analyzing the deviation sequences of adjacent measuring points, the deviation change is extracted and expressed as the difference in capacitance deviation between adjacent measuring points. The deviation change, combined with the spatial distance and time delay between measuring points, forms the characteristics of deviation transmission between measuring points.

[0045] The propagation law of deviation between measuring points includes a spatial attenuation coefficient and a time delay parameter. The spatial attenuation coefficient describes the degree of stress reduction as it propagates from one measuring point to an adjacent measuring point, and is usually related to the distance between the measuring points and the properties of the crossarm material. The time delay parameter describes the time required for the stress wave to propagate between measuring points, and is usually related to the distance between the measuring points and the elastic wave velocity of the crossarm material. Based on the extracted deviation change and propagation law, measuring point compensation parameters are constructed. These parameters include a spatial correction factor and a time calibration factor, used to correct the capacitance deviation at the measuring points.

[0046] When using measuring point compensation parameters to correct the spatial distribution of deviation changes, spatial interpolation is employed to fill in the deviation variations between measuring points. For any position between two adjacent measuring points, the deviation variation can be calculated using a spatial correction factor and the deviation variations at both ends of the measuring point. Spatial distribution correction considers the non-uniform spatial distribution of measuring points, employing different interpolation strategies for sparse and dense regions. Linear interpolation is used in sparse regions, while weighted average interpolation is used in dense regions, with the weights inversely proportional to the distance between measuring points. After spatial distribution correction, the deviation variation forms a continuously distributed spatial field, more accurately reflecting the stress distribution on the crossarm.

[0047] The dynamic compensation coefficient adjusts according to changes in the crossarm stress, adapting to deviation correction requirements under different working conditions. The calculation of the dynamic compensation coefficient is based on a time calibration factor and historical measurement data, employing an exponential weighted average method to fuse information from multiple time scales. The dynamic compensation coefficient is divided into steady-state and transient coefficients; the steady-state coefficient is used for long-term, slowly changing deviation correction, while the transient coefficient is used for deviation correction due to sudden changes. The update frequency of the dynamic compensation coefficient is consistent with the capacitance sampling frequency, ensuring timely compensation that follows changes in the crossarm stress.

[0048] When calculating the dynamic compensation coefficient to correct the actual capacitance measurements at each measuring point according to the stress transmission path, it is necessary to determine the correction starting point and direction. The correction starting point is selected from the most reliable measuring point on the crossarm, usually the point with the smallest deviation confirmed through multiple verifications. Starting from the correction starting point, the correction is performed point by point along the stress transmission path in topological order. The correction calculation for each measuring point considers the correction results of the upstream measuring point and the dynamic compensation coefficient of the current measuring point. The correction formula is the actual capacitance value minus the corrected deviation. The correction calculation adopts a progressive process, where the correction result of the previous measuring point affects the correction calculation of subsequent measuring points, forming a cascading effect.

[0049] When multiple stress transfer paths exist on a crossarm, the correction calculation needs to consider the special treatment of path intersections. Path intersections receive influence from multiple upstream measuring points, and their correction amounts need to be synthesized from the correction results of multiple paths. The multi-path synthesis adopts a weighted average method, with the weights proportional to the path reliability and influence intensity. Path reliability is verified and evaluated using historical data, and the influence intensity is determined through stress analysis. For potential correction conflicts, a conflict resolution mechanism is set up, prioritizing the correction results of paths with higher reliability.

[0050] The corrected capacitance data sequence obtained through the above calculations serves as the basis for subsequent crossarm condition assessment. To ensure the reliability of the correction results, a correction validity verification mechanism is introduced. The validity of the correction is assessed by comparing the consistency and physical rationality of the data before and after the correction. The consistency assessment uses correlation coefficient and standard deviation index, while the physical rationality assessment is based on the crossarm's stress principle and structural characteristics. The crossarm's stress principle considers the stress distribution characteristics of the crossarm under various working conditions, including the complex stress state caused by factors such as the vertical force generated by the suspended conductor, horizontal tension, and wind load. If the correction validity verification fails, an alarm is triggered and the system reverts to the original data to avoid misjudgments caused by incorrect corrections.

[0051] This invention analyzes the variation characteristics of the capacitance deviation sequence at measurement points, extracts the deviation transmission law between adjacent measurement points, and establishes a compensation mechanism adapted to dynamic stress changes in the crossarm. The strategy of point-by-point correction along the stress transmission path fully utilizes the mechanical properties of the crossarm structure, giving the correction process physical meaning. The introduction of a dynamic compensation coefficient enhances the adaptability of the correction, enabling it to cope with deviation changes under different operating conditions. The cross-validated capacitance data sequence exhibits higher accuracy and stability, providing a reliable data foundation for crossarm condition monitoring and fault diagnosis, effectively improving the safety and reliability of transmission line operation.

[0052] The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. This preliminary stress data sequence is then converted back into capacitance data to obtain the reconstructed capacitance data sequence, which includes: Analyze the capacitance change trend of the measurement points in the capacitance data sequence after mutual verification, calculate the capacitance change amplitude between adjacent measurement points, identify the abrupt change position based on the capacitance change amplitude, and determine the data segmentation interval. Extract the stress-deformation characteristics of the measuring points within the data segment intervals, analyze the correspondence between the stress-deformation characteristics and capacitance changes, generate the capacitance-stress conversion coefficient for each data segment interval, and construct a segmented mapping relationship from capacitance to stress. The segmented mapping relationship is used to transform and calculate the cross-validated capacitance data sequence, and the preliminary stress data sequence is obtained by smoothing the numerical connection at the segment boundaries. The capacitance-stress conversion coefficients are reversed to establish a reverse calculation relationship between stress and capacitance. The preliminary stress data sequence is then reversed to obtain a reconstructed capacitance data sequence.

[0053] The cross-validated capacitance data sequence is high-reliability capacitance measurement data obtained through prior processing, containing capacitance variation information at each measuring point on the crossarm under different operating conditions. Analyzing the variation trend of this capacitance data sequence can identify the law of capacitance change with stress. The capacitance variation trend analysis uses the difference method to calculate the difference in capacitance values ​​at adjacent time points, forming a capacitance change rate sequence. The capacitance change amplitude between adjacent measuring points is obtained by comparing the capacitance change rate sequences of different measuring points, and is expressed as the difference in capacitance change rate between measuring points.

[0054] The capacitance variation amplitude sequence reflects the spatial variation characteristics of stress distribution on the crossarm. By setting a capacitance variation amplitude threshold, locations where the amplitude exceeds the threshold can be identified; these locations are defined as capacitance abrupt change locations. These abrupt change locations typically correspond to areas of significant change in the crossarm's stress state, such as stress concentration zones or load transition zones. The capacitance variation amplitude threshold is set based on historical data statistics and engineering experience, generally taking twice the average capacitance variation amplitude, but can be adjusted according to the actual application scenario. Based on the identified abrupt change locations, the capacitance data sequence is divided into several segmented intervals, with relatively consistent capacitance variation characteristics within each segmented interval.

[0055] After determining the data segmentation intervals, the stress-deformation characteristics of the measuring points are extracted within each interval. These characteristics include key information such as the linear, saturation, and transition intervals of capacitance change, reflecting the mechanical response characteristics of the crossarm under different load levels. By analyzing the distribution characteristics of capacitance data within each segmented interval, the central tendency and dispersion of the data are determined, and outlier data points are identified and filtered. Outlier data is defined as data points deviating from the interval mean by more than three standard deviations. The filtered data more accurately reflects the stress-deformation characteristics of the crossarm.

[0056] Data matching analysis was used to establish the correspondence between stress-deformation characteristics and capacitance changes. For each segmented interval, capacitance measurements under known load conditions were collected, and stress measurements at the corresponding locations were recorded, forming capacitance-stress data pairs. These pairs were then processed through regression analysis to establish a mapping relationship between capacitance and stress values. The regression analysis employed a piecewise fitting strategy, using different fitting curves for different load ranges to accommodate the nonlinear variation of the capacitance-stress relationship under different load levels.

[0057] Based on the regression analysis results, capacitance-stress conversion coefficients are generated for each data segment interval. These coefficients consist of a scaling factor and an offset, corresponding to the slope and intercept of the capacitance-stress mapping, respectively. For the linear interval, the scaling factor remains constant; for the nonlinear interval, it is dynamically adjusted according to the capacitance value. The accuracy of the capacitance-stress conversion coefficients directly affects the accuracy of stress calculations. Multiple calibration and verification procedures are employed during coefficient generation to ensure the reliability of the conversion coefficients.

[0058] After the segmented mapping relationship between capacitance and stress is established, this mapping relationship is used to convert and calculate the cross-validated capacitance data sequence. During the conversion calculation, for the capacitance value of each measuring point, the corresponding capacitance-stress conversion coefficient is selected according to the segment interval in which it is located, and the corresponding stress value is calculated. When the capacitance value is near the segment boundary, a smooth transition strategy is adopted to handle the segment boundary in order to avoid abrupt changes in the calculation results. The smooth transition adopts a weighted average method, and the calculation results of the segments on both sides of the boundary are weighted and averaged according to their distance from the boundary, with the weight proportional to the distance of the measuring point from the boundary.

[0059] Numerical smoothing at the segment boundaries ensures the continuity of stress data. However, at the junctions of adjacent segments, there is a risk of discontinuity in the calculation results, potentially leading to unnatural jumps in stress distribution. To address this issue, a transition region is established at the boundaries, typically with a width of 5% to 10% of the segment interval length. Within this transition region, stress values ​​are smoothly transitioned using linear interpolation, ensuring the continuity and smoothness of stress data throughout the entire interval. The stress data obtained through this process is called the preliminary stress data sequence, which reflects the stress distribution of the crossarm under the current load conditions.

[0060] To verify the accuracy of the preliminary stress data sequence, the capacitance-stress conversion coefficients need to be reversed to establish a reverse calculation relationship from stress to capacitance. During the reverse configuration, the independent and dependent variables in the capacitance-stress mapping relationship are swapped to form a stress-capacitance mapping relationship. The establishment of the reverse calculation relationship also adopts a piecewise strategy, setting different conversion parameters for different stress level intervals. The accuracy of the reverse calculation relationship matches that of the forward conversion coefficients, ensuring the reversibility of the conversion.

[0061] By utilizing the inverse calculation relationship between stress and capacitance, the initial stress data sequence is reverse-converted to obtain a reconstructed capacitance data sequence. The calculation process for the inverse conversion is similar to the forward conversion; appropriate conversion parameters are selected based on the interval containing the stress value, and the corresponding capacitance value is calculated. The inverse conversion result is compared with the original cross-validated capacitance data sequence, and the deviation between the two is calculated. The magnitude of the deviation reflects the accuracy loss during the conversion process. The deviation threshold is set at 1% of the original capacitance value; measurement points exceeding this threshold require readjustment of the conversion parameters and recalculation.

[0062] The consistency verification between the reconstructed capacitance data sequence and the original cross-validated capacitance data sequence is crucial for evaluating the effectiveness of the entire conversion process. Consistency verification employs a comprehensive evaluation using multiple indicators, including statistical measures such as correlation coefficient and root mean square error, as well as the degree of agreement on capacitance change trends. The successfully verified reconstructed capacitance data sequence can serve as a reliable basis for assessing the stress state of the crossarms, providing data support for subsequent structural safety analysis.

[0063] This invention establishes a bidirectional mapping mechanism between capacitance measurements and crossarm stress states by segmenting the cross-validated capacitance data sequence into stress values ​​and performing reverse verification, thus achieving accurate conversion from the capacitance domain to the stress domain. The segmented processing strategy effectively addresses the nonlinear variation of the capacitance-stress relationship under different load levels, improving the adaptability and accuracy of the conversion. Smooth connections at segment boundaries ensure the continuity of stress distribution, avoiding misjudgments caused by unnatural jumps. The reverse conversion mechanism provides a self-verification method for the conversion process, significantly enhancing the reliability of stress calculation results.

[0064] like Figure 2 As shown, the residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point location is extracted, abrupt measurement points are identified, and the transformation relationship between adjacent measurement points is used to correct the abrupt measurement points, resulting in the optimized crossarm stress data sequence, including: Calculate the numerical difference between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence at the measurement point location, generate measurement point residual data, and calculate the residual change between measurement points based on the measurement point residual data; A residual distribution function is constructed using the residual data and residual change at the measurement points. The spatial directional derivative of the residual distribution function at the measurement point is calculated to obtain the spatial gradient value of the residual. Analyze the locations of measurement points where the residual space gradient value exceeds the preset gradient range to determine abrupt measurement points, extract the capacitance changes of measurement points on both sides of the abrupt measurement point, and generate a measurement point transformation function; The measurement point conversion function is applied to the stress data at the abrupt measurement point for correction and compensation, resulting in an optimized crossarm stress data sequence.

[0065] When calculating the residuals between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence, a detailed comparison of the two sets of sequences is required. The reconstructed capacitance data sequence is the capacitance estimate obtained by inversely transforming the stress data, while the cross-validated capacitance data sequence is the measured capacitance value after preprocessing. Ideally, the two sets of sequences should be highly consistent, but in practice, differences may occur due to limitations in the accuracy of the conversion model, noise interference, and other factors. The measurement point residual data is obtained by comparing the difference between the reconstructed capacitance value and the measured capacitance value. The measurement point residual data reflects the degree of error in the capacitance-stress conversion process; the smaller the absolute value, the more accurate the conversion; the larger the error, the more likely there is a problem with the conversion relationship at that point, requiring close attention.

[0066] The residual variation between measuring points refers to the magnitude of change in residual data between adjacent measuring points. The residual variation reflects the spatial variation characteristics of the residual distribution; locations with larger variations typically correspond to abrupt changes in the residual distribution. For measuring points uniformly distributed on a crossarm, the residual variation between adjacent measuring points should theoretically remain relatively stable. When the residual variation between measuring points exceeds three standard deviations of the statistical mean, an anomaly can be preliminarily identified in that area. The calculation of residual variation needs to consider the spatial distribution characteristics of the measuring points. For measuring points with uneven spacing, normalization can be used to convert the residual variation into a rate of change per unit distance.

[0067] When constructing the residual distribution function using the residual data and residual changes at measurement points, interpolation methods are used to extend the discrete residual data into a continuous spatial distribution function. Commonly used interpolation methods include linear interpolation and spline interpolation, among which cubic spline interpolation can ensure the smoothness of the curve. During interpolation, the spatial location of the measurement points is used as the independent variable, and the residual data at the measurement points is used as the dependent variable to generate a residual distribution function covering the entire crossarm region. The construction of the residual distribution function requires consideration of boundary conditions; generally, natural boundary conditions are used to ensure a smooth transition in the residual distribution.

[0068] After the residual distribution function is constructed, the spatial directional derivative of the function at the measurement point is calculated to obtain the spatial gradient value of the residual. The spatial directional derivative represents the rate of change of the residual in space and can be calculated using the finite difference approximation method. Central difference has high accuracy and is suitable for gradient calculation at most measurement point locations; while for boundary measurement points, forward difference or backward difference can be used. The unit of the spatial gradient value of the residual is related to the distance between measurement points. To ensure comparability between different regions, the gradient value is usually normalized.

[0069] When analyzing residual spatial gradient values ​​to identify abrupt change points, a preset gradient range needs to be set as a criterion. This preset gradient range is usually determined based on historical data statistics and professional knowledge, and can be taken as the statistical average of residual spatial gradient values ​​under normal operating conditions, fluctuating by a certain percentage above and below. When the residual spatial gradient value of a measurement point exceeds the preset gradient range, that measurement point is marked as an abrupt change point. The determination of abrupt change points also needs to consider the gradient distribution characteristics of the surrounding area to avoid misjudgments due to local noise. Generally, a valid abrupt change is only confirmed if the gradient value of the abrupt change point exceeds the preset range for at least three consecutive sampling points.

[0070] After identifying the abrupt change measurement point, the capacitance changes at the measurement points on both sides of the abrupt change point are extracted, and their variation patterns are analyzed. The capacitance change refers to the magnitude of the change in capacitance value at the measurement point relative to the reference state, reflecting the stress variation of the crossarm at that location. Typically, three to five measurement points are selected on both sides of the abrupt change measurement point as a reference interval, and the capacitance change sequences at these measurement points under different load levels are extracted. By analyzing the distribution characteristics of the capacitance changes within the reference interval, the cause of the abrupt change measurement point anomaly can be determined.

[0071] The measurement point transformation function is constructed based on the capacitance-stress correspondence between normal measurement points on both sides of the abrupt change measurement point, and a weighted interpolation method is used to fuse the transformation characteristics of the two measurement points. The weighting coefficient is inversely proportional to the distance from the measurement point to the abrupt change location; the closer the measurement point, the greater its contribution to the transformation function. The construction strategy of the measurement point transformation function differs for different types of abrupt change causes.

[0072] The stress data at abrupt change points is corrected and compensated using a measurement point transformation function to obtain an optimized crossarm stress data sequence. The correction process is based on the original stress data, calculating the correction value according to the measurement point transformation function. To ensure a smooth transition, a transition region is set near the abrupt change points. The correction amount within the transition region gradually decreases with increasing distance from the abrupt change point until it seamlessly integrates with the original stress data. The corrected and compensated stress data needs to be validated for effectiveness. Validation methods include recalculating the residual distribution to check for smoothness and comparing it with historical data to analyze trend consistency.

[0073] This invention accurately identifies data anomalies through residual spatial gradient analysis and uses the transformation relationship between adjacent measuring points for targeted correction. This avoids potential global error accumulation, maintains accurate representation of local features, and effectively improves the accuracy and reliability of crossarm stress monitoring. The optimization process requires no additional sensor deployment, fully utilizing existing measurement data for self-calibration, thus reducing implementation costs. The residual distribution function construction, spatial gradient calculation, and abrupt change measuring point correction strategy form a complete data optimization closed loop, enabling crossarm stress monitoring to maintain stable and reliable performance under complex operating conditions, providing more precise technical support for the safe operation of transmission lines.

[0074] The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated, including: Analyze the stress distribution characteristics of the measuring points in the optimized crossarm stress data sequence, extract the stress change trend of the measuring points, and construct the crossarm stress distribution map. Calculate the stress concentration in each region of the crossarm based on the stress distribution map of the crossarm, evaluate the stress state of the crossarm, and generate the crossarm bearing state evaluation result. Based on the crossarm bearing condition assessment results, stress over-limit areas are identified, the stress concentration degree within the stress over-limit areas is calculated, and stress risk assessment indicators are constructed. The stress risk assessment indicators are combined with the degree of stress concentration for graded judgment, generating early warning information that includes risk level and regional location.

[0075] When analyzing the stress distribution characteristics of measuring points in the optimized crossarm stress data sequence, spatial correlation analysis of the stress values ​​at each measuring point is required. Crossarm measuring points are typically arranged along the length of the crossarm, with a spacing generally between 1 / 10 and 1 / 20 of the crossarm length to ensure sufficient spatial resolution. Stress distribution characteristics include parameters such as maximum stress value, minimum stress value, average stress level, and stress gradient. Extracting the stress change trend at the measuring points requires time-domain analysis of the stress data sequence, which can be achieved using the sliding window method to calculate the rate of change of stress values ​​at each measuring point. The sliding window size is recommended to be set to 5 to 30 minutes, with a window overlap rate of 50% to 80% to balance computational efficiency and trend sensitivity. Stress change trends can be categorized into three basic types: rising, falling, and stable. A valid trend change is defined as the rate of change of stress exceeding a preset threshold (recommended value: twice the standard deviation) within three consecutive windows.

[0076] Constructing a stress distribution map of a crossarm is the process of transforming stress data from discrete measuring points into a continuous spatial distribution representation. The stress distribution map is represented in a two-dimensional plane, with the horizontal axis representing the spatial coordinates of the crossarm and the vertical axis representing the stress value. For regions between measuring points, interpolation algorithms are used to generate continuous stress distribution curves. Commonly used interpolation methods include linear interpolation and spline interpolation. Higher-order spline interpolation is suitable for sparse measuring points and can generate smooth stress distribution curves; while piecewise linear interpolation is suitable for densely populated measuring point regions and has higher computational efficiency. The map update frequency should match the data acquisition frequency, generally updating every 5 to 10 minutes.

[0077] When calculating the stress concentration in each region of a crossarm based on its stress distribution map, a method for calculating stress concentration needs to be defined. Stress concentration describes the deviation of the stress level in a local area from the overall average stress level, and can be expressed as the ratio of the local average stress to the overall average stress. The crossarm can be divided into several sub-regions for stress concentration calculation. The principle for sub-region division is to ensure that each sub-region contains at least two measuring points and its length does not exceed 1 / 5 of the total length of the crossarm. Sub-region division can be equidistant or based on functional zones according to the crossarm's structural characteristics. When the stress concentration in a region exceeds 1.5, it indicates the presence of stress concentration in that region; when the stress concentration exceeds 2.0, it is considered significant stress concentration and requires close monitoring.

[0078] Assessing the stress state of a crossarm is a comprehensive judgment process based on stress concentration, historical data, and theoretical models. The stress state of a crossarm can be classified into four levels: normal, slightly abnormal, moderately abnormal, and severely abnormal. A normal state refers to a stress concentration of no more than 1.5 in all areas, with a typical bimodal or unimodal stress distribution. Slightly abnormal refers to areas with stress concentrations between 1.5 and 2.0, but the overall stress distribution remains relatively stable. Moderately abnormal refers to areas with stress concentrations exceeding 2.0, or significant changes in the stress distribution pattern. Severely abnormal refers to areas with stress concentrations exceeding 3.0, or the interconnection of multiple high-stress areas. The crossarm load-bearing state assessment results should include key information such as the stress state level, location markers of abnormal areas, and the percentage of stress exceeding limits.

[0079] When identifying stress-exceeding regions based on the crossarm load-bearing state assessment results, it is necessary to establish stress-exceeding judgment criteria. These criteria should consider the strength characteristics and safety margin requirements of the crossarm material, generally using 70% to 80% of the material's yield strength as the stress-exceeding threshold. For composite material crossarms, the anisotropic characteristics of the material must also be considered, setting separate stress-exceeding thresholds for longitudinal and transverse directions. The identification of stress-exceeding regions uses a threshold screening method, marking areas in the stress distribution spectrum that exceed the threshold as stress-exceeding regions. The boundary of the stress-exceeding region is determined using a gradient tracking method, expanding outwards from the exceeding point until the stress gradient change rate is lower than a preset value (a recommended value is 20% of the average gradient value).

[0080] Calculating the stress concentration degree within the stress over-limit area is a quantitative assessment of the severity of the over-limit situation. The stress concentration degree considers three factors: the spatial extent of the over-limit area, the magnitude of the over-limit, and the duration. The spatial extent factor refers to the proportion of the over-limit area to the total length of the crossarm; a larger extent indicates a higher concentration. The magnitude of the over-limit factor refers to the percentage of the average stress value within the over-limit area that exceeds the threshold; more stress exceeding the threshold indicates a higher concentration. The duration of the stress over-limit is the length of time the stress remains over-limit; a longer duration indicates a higher concentration. The three factors are calculated using a weighted average method to obtain the final stress concentration degree. A recommended weighting ratio is spatial extent: magnitude of over-limit: duration = 3:5:2. The stress concentration degree value ranges from 0 to 100; a higher value indicates a higher risk.

[0081] When constructing stress risk assessment indicators, the degree of stress accumulation is considered in conjunction with the historical load-bearing capacity of the crossarm and environmental factors. The historical load-bearing capacity of the crossarm reflects its fatigue level and cumulative damage status, and can be calculated by analyzing the number and amplitude of historical stress cycles. Environmental factors mainly include temperature, humidity, and wind speed; extreme environmental conditions can reduce the actual load-bearing capacity of the crossarm. The stress risk assessment indicators are calculated using a multi-factor comprehensive scoring method, with the weight of each factor determined based on practical engineering experience. The assessment indicators range from 0 to 100, and 60 is generally used as the trigger threshold for risk warning.

[0082] When combining stress risk assessment indicators with stress concentration levels for grading, a four-level risk classification standard is established: Normal, Caution, Warning, and Danger. The Normal level corresponds to a risk assessment indicator of less than 60 and a stress concentration level of less than 40; the Caution level corresponds to a risk assessment indicator between 60 and 75 or a stress concentration level between 40 and 60; the Warning level corresponds to a risk assessment indicator between 75 and 90 or a stress concentration level between 60 and 80; and the Danger level corresponds to a risk assessment indicator greater than 90 or a stress concentration level greater than 80. The generated warning information includes the risk level, the coordinates of the out-of-limit area, and the expected development trend. Warning information can be pushed to maintenance personnel via SMS, email, etc., and is also displayed prominently on the monitoring interface.

[0083] This invention achieves accurate assessment and early warning of the crossarm's load-bearing status through multi-dimensional analysis of its stress distribution characteristics. Based on the optimized crossarm stress data sequence, a complete stress analysis and processing workflow is constructed, from stress distribution map construction to stress concentration calculation, and then to risk level judgment and early warning information generation. The stress concentration calculation method and risk classification standard adopted effectively balance sensitivity and reliability, avoiding false alarms and missed alarms. The regional positioning function of the early warning information provides precise guidance for subsequent maintenance, saving maintenance resources.

[0084] This invention provides a crossarm capacitive stress data accuracy optimization system, the system comprising: The data acquisition and temperature compensation unit is used to acquire the capacitance change signal of the crossarm through a capacitive sensor to obtain the original capacitance data sequence, and to perform temperature compensation processing on the original capacitance data sequence to obtain the temperature-compensated capacitance data sequence. The multi-point mutual verification unit is used to establish a multi-point mutual verification mechanism based on the temperature-compensated capacitance data sequence. It selects multiple measurement points on the crossarm, constructs the constraint relationship between the capacitance data of each measurement point according to the mechanical equilibrium condition of the crossarm, and calculates the theoretical capacitance value of each measurement point. The data compensation unit is used to compensate for the deviation between the actual capacitance value and the theoretical capacitance value as a correction amount to obtain the capacitance data sequence after mutual verification. The data conversion unit is used to segment the cross-validated capacitance data sequence into stress values ​​to obtain a preliminary stress data sequence, and then convert the preliminary stress data sequence back into capacitance data to obtain a reconstructed capacitance data sequence. The abrupt change measurement point correction unit is used to calculate the residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence, extract the spatial gradient of the residual along the measurement point position, identify abrupt change measurement points, and correct the abrupt change measurement points using the transformation relationship of adjacent measurement points to obtain the optimized crossarm stress data sequence. The early warning unit is used to assess the load-bearing status of the crossarm based on the optimized crossarm stress data, identify areas of abnormal stress, and generate early warning information.

[0085] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for optimizing the accuracy of crossarm capacitive stress data, characterized in that, Includes the following steps: The capacitance change signal of the crossarm is collected by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence. A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is then calculated. The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction amount to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence. The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. The preliminary stress data sequence is then converted back into capacitance data to obtain a reconstructed capacitance data sequence. The residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point position is extracted, abrupt measurement points are identified, and the abrupt measurement points are corrected by the transformation relationship between adjacent measurement points to obtain the optimized crossarm stress data sequence. The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated.

2. The method according to claim 1, characterized in that, The capacitance change signal of the crossarm is acquired by a capacitive sensor to obtain the original capacitance data sequence. The original capacitance data sequence is then subjected to temperature compensation processing to obtain the temperature-compensated capacitance data sequence, which includes: The capacitance change signal at a preset measuring point on the crossarm surface is collected by a capacitive sensor, and the temperature data at the preset measuring point is collected simultaneously to form an original capacitance data sequence containing capacitance change signal and temperature data. Calculate the temperature distribution difference between preset measuring points based on temperature data, and determine the temperature gradient change of preset measuring points based on the temperature distribution difference. A temperature compensation coefficient is generated based on the mapping relationship between the temperature gradient change and the capacitance change signal, and the temperature compensation coefficient is used to construct a temperature compensation correction matrix according to the positional relationship of the preset measurement points. Calculate the temperature change trends of the preset measuring points in the horizontal and vertical directions respectively, and combine the temperature change trends with the temperature compensation correction matrix to construct a two-way compensation factor; The capacitance change signal in the original capacitance data sequence is compensated in layers according to the bidirectional compensation factor to obtain the temperature-compensated capacitance data sequence.

3. The method according to claim 1, characterized in that, A multi-point mutual verification mechanism is established based on the temperature-compensated capacitance data sequence. Multiple measurement points on the crossarm are selected, and the constraint relationship between the capacitance data of each measurement point is constructed according to the mechanical equilibrium condition of the crossarm. The theoretical capacitance value of each measurement point is calculated, including: Trend analysis is performed on the temperature-compensated capacitance data sequence to calculate the capacitance value change trend between adjacent measuring points on the crossarm, extract the capacitance change amplitude and direction between measuring points, generate the capacitance change correlation value between measuring points, and construct the capacitance change correlation matrix. Based on the capacitance change correlation matrix analysis, the capacitance change pattern among the measurement points is analyzed, the mutual influence between the measurement points is extracted, the measurement point capacitance data verification criteria are established, and a multi-measurement point mutual verification mechanism is formed. Based on the multi-point mutual verification mechanism, multiple measuring points that meet the stress transfer requirements are selected on the crossarm, and the distribution position of each measuring point is determined. Based on the mechanical equilibrium condition of the crossarm and the distribution of each measuring point, the stress transfer relationship between multiple measuring points is calculated, and the constraint relationship between the capacitance data of multiple measuring points is constructed. The distribution locations of multiple measuring points are substituted into the constraint relationship between the capacitance data of multiple measuring points to construct a set of constraint equations. The set of constraint equations is then solved to calculate the theoretical capacitance value of each measuring point.

4. The method according to claim 1, characterized in that, The deviation between the actual capacitance value and the theoretical capacitance value is used as a correction factor to compensate for the data at each measurement point, resulting in a cross-validated capacitance data sequence including: Obtain the actual capacitance value at each measuring point, calculate the difference sequence between the actual capacitance value and the theoretical capacitance value, and generate a measuring point capacitance deviation sequence based on the difference sequence. Based on the stress characteristics of the crossarm, the stress transmission path is determined. The variation characteristics of the capacitance deviation sequence of the measuring points are analyzed along the stress transmission path. The deviation change and transmission law between adjacent measuring points are extracted, and the measuring point compensation parameters are constructed. Spatial distribution correction of deviation variation is performed using measurement point compensation parameters, and dynamic compensation coefficients are generated based on the transmission law. The dynamic compensation coefficient is used to correct the actual capacitance measurement value of each measuring point according to the stress transmission path sequence, so as to obtain the capacitance data sequence after mutual verification.

5. The method according to claim 1, characterized in that, The cross-validated capacitance data sequence is segmented and converted into stress values ​​to obtain a preliminary stress data sequence. This preliminary stress data sequence is then converted back into capacitance data to obtain the reconstructed capacitance data sequence, which includes: Analyze the capacitance change trend of the measurement points in the capacitance data sequence after mutual verification, calculate the capacitance change amplitude between adjacent measurement points, identify the abrupt change position based on the capacitance change amplitude, and determine the data segmentation interval. Extract the stress-deformation characteristics of the measuring points within the data segment intervals, analyze the correspondence between the stress-deformation characteristics and capacitance changes, generate the capacitance-stress conversion coefficient for each data segment interval, and construct a segmented mapping relationship from capacitance to stress. The segmented mapping relationship is used to transform and calculate the cross-validated capacitance data sequence, and the preliminary stress data sequence is obtained by smoothing the numerical connection at the segment boundaries. The capacitance-stress conversion coefficients are reversed to establish a reverse calculation relationship between stress and capacitance. The preliminary stress data sequence is then reversed to obtain a reconstructed capacitance data sequence.

6. The method according to claim 1, characterized in that, The residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence is calculated. The spatial gradient of the residual along the measurement point location is extracted, abrupt measurement points are identified, and the abrupt measurement points are corrected using the transformation relationship between adjacent measurement points. The optimized crossarm stress data sequence includes: Calculate the numerical difference between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence at the measurement point location, generate measurement point residual data, and calculate the residual change between measurement points based on the measurement point residual data; A residual distribution function is constructed using the residual data and residual change at the measurement points. The spatial directional derivative of the residual distribution function at the measurement point is calculated to obtain the spatial gradient value of the residual. Analyze the locations of measurement points where the residual space gradient value exceeds the preset gradient range to determine abrupt measurement points, extract the capacitance changes of measurement points on both sides of the abrupt measurement point, and generate a measurement point transformation function; The measurement point conversion function is applied to the stress data at the abrupt measurement point for correction and compensation, resulting in an optimized crossarm stress data sequence.

7. The method according to claim 1, characterized in that, The load-bearing status of the crossarm is assessed based on the optimized crossarm stress data, and areas of abnormal stress are identified and early warning information is generated, including: Analyze the stress distribution characteristics of the measuring points in the optimized crossarm stress data sequence, extract the stress change trend of the measuring points, and construct the crossarm stress distribution map. Calculate the stress concentration in each region of the crossarm based on the stress distribution map of the crossarm, evaluate the stress state of the crossarm, and generate the crossarm bearing state evaluation result. Based on the crossarm bearing condition assessment results, stress over-limit areas are identified, the stress concentration degree within the stress over-limit areas is calculated, and stress risk assessment indicators are constructed. The stress risk assessment indicators are combined with the degree of stress concentration for graded judgment, generating early warning information that includes risk level and regional location.

8. A cross-arm capacitive stress data accuracy optimization system, used to implement the method described in any one of claims 1-7, characterized in that, The system includes: The data acquisition and temperature compensation unit is used to acquire the capacitance change signal of the crossarm through a capacitive sensor to obtain the original capacitance data sequence, and to perform temperature compensation processing on the original capacitance data sequence to obtain the temperature-compensated capacitance data sequence. The multi-point mutual verification unit is used to establish a multi-point mutual verification mechanism based on the temperature-compensated capacitance data sequence. It selects multiple measurement points on the crossarm, constructs the constraint relationship between the capacitance data of each measurement point according to the mechanical equilibrium condition of the crossarm, and calculates the theoretical capacitance value of each measurement point. The data compensation unit is used to compensate for the deviation between the actual capacitance value and the theoretical capacitance value as a correction amount to obtain the capacitance data sequence after mutual verification. The data conversion unit is used to segment the cross-validated capacitance data sequence into stress values ​​to obtain a preliminary stress data sequence, and then convert the preliminary stress data sequence back into capacitance data to obtain a reconstructed capacitance data sequence. The abrupt change measurement point correction unit is used to calculate the residual between the reconstructed capacitance data sequence and the cross-validated capacitance data sequence, extract the spatial gradient of the residual along the measurement point position, identify abrupt change measurement points, and correct the abrupt change measurement points using the transformation relationship of adjacent measurement points to obtain the optimized crossarm stress data sequence. The early warning unit is used to assess the load-bearing status of the crossarm based on the optimized crossarm stress data, identify areas of abnormal stress, and generate early warning information.

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