Contact network on-line monitoring and early warning system based on operating environment

Abstract

The invention discloses a contact network on-line monitoring and early warning system based on an operating environment, and relates to the technical field of contact network on-line monitoring. A coupling correction model is constructed based on historical monitoring data of a contact network, an environment field is constructed by collecting environment parameters, the environment field and contact network structure parameters are subjected to modelling processing and then input into the model, and theoretical monitoring values, theoretical offset and adjustment factors of the monitoring parameters are output. According to the influence of the environmental parameters on the monitoring parameters, the adjustment direction of narrowing or widening of the threshold is matched, then the parameter threshold is adjusted based on the adjustment factor, and finally online early warning is achieved based on comparison of a theoretical monitoring value and the parameter threshold. According to the method, the original monitoring parameters are not tampered, the original corresponding relation between the original monitoring parameters and the environment parameters is completely reserved, on one hand, real and reliable original data support is provided for fault recovery, and operation and maintenance personnel can conveniently trace the environment background and the parameter state when an exception occurs;

Description

Technical Field

[0001] This invention belongs to the field of online monitoring technology for overhead contact lines, specifically an online monitoring and early warning system for overhead contact lines based on the operating environment. Background Technology

[0002] In the field of online monitoring technology for overhead contact lines, existing monitoring and early warning systems mostly collect monitoring parameters such as conductor height, pull-out value, and contact force of the overhead contact line by deploying sensors. The collected measured parameters are then compared with preset fixed standard thresholds to determine whether there are any abnormalities in the operation of the overhead contact line and to issue an early warning.

[0003] When existing technologies provide early warnings, directly correcting the original monitoring parameters can disrupt the original correspondence between the original monitoring data and environmental parameters. This makes it impossible to trace the true environmental background and parameter status when the anomaly occurred during fault review, making it difficult for maintenance personnel to accurately locate the root cause of the fault.

[0004] This invention provides an online monitoring and early warning system for overhead contact lines based on the operating environment, in order to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an online monitoring and early warning system for overhead contact lines based on the operating environment.

[0006] To achieve the above objectives, a first aspect of the present invention provides an online monitoring and early warning system for overhead contact lines based on the operating environment, comprising: Online early warning module: used to extract structural parameters and environmental field of the overhead contact system; inputting the structural parameters and environmental field into a coupled correction model to predict and obtain early warning parameters for the overhead contact system; wherein, the environmental field is constructed based on environmental parameters, and the early warning parameters include theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters; and, It is used to adjust the standard threshold according to the adjustment factor and obtain the parameter threshold; and to provide online early warning for the catenary based on the theoretical monitoring value and the parameter threshold.

[0007] In one possible implementation, the environmental field of the overhead contact line is constructed, including: Environmental parameters of the overhead contact system are extracted. These parameters are collected by distributed sensors along the line or obtained through a meteorological forecasting platform. The types of environmental parameters include temperature, wind speed, humidity, and icing thickness. After interpolating the environmental parameters, they are mapped onto the entire three-dimensional mesh of the overhead contact line to obtain the environmental field; the entire three-dimensional mesh is constructed based on the overhead contact line.

[0008] In one possible implementation, the predictive acquisition of early warning parameters for the overhead contact system includes: The coupling correction model is invoked; the coupling correction model includes a feature extraction layer, a coupling calculation layer, and an output optimization layer. After adapting the structural parameters and environmental field of the overhead contact system to a model, the data are input into a coupled correction model to obtain the early warning parameters of the overhead contact system. The early warning parameters include the theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters.

[0009] In one possible implementation, the coupling correction model is built upon an artificial intelligence model, including: A basic correction model is constructed based on an artificial intelligence model; the artificial intelligence model includes a BP neural network model. Historical monitoring data of the overhead contact system is extracted, and a standard training set is constructed based on the historical monitoring data. The basic correction model is trained using the standard training set to obtain the coupled correction model.

[0010] In one possible implementation, the adjustment factor corresponds to a single environmental variable or multiple environmental variables; wherein the adjustment factor corresponding to multiple environmental variables is obtained by weighted summation of the adjustment factors corresponding to the single environmental variable.

[0011] In one possible implementation, the standard threshold is adjusted according to an adjustment factor, including: The adjustment direction is matched according to the impact of environmental parameters on monitoring parameters; among which, the adjustment direction includes threshold narrowing or threshold widening; The standard threshold is adjusted based on the adjustment direction and adjustment factor to obtain the parameter threshold; where the standard threshold is the preset threshold corresponding to the monitoring parameter.

[0012] In one possible implementation, online early warning of the overhead contact system is based on theoretical monitoring values ​​and parameter thresholds, including: Extract the theoretical monitoring values ​​of the monitoring parameters from the early warning parameters; Determine whether the theoretical monitoring value exceeds the corresponding parameter threshold; if yes, determine that the data is abnormal and perform attribution analysis on the abnormal data based on the warning parameters; if no, determine that the data is normal.

[0013] In one possible implementation, attribution analysis of abnormal data is performed based on warning parameters, including: Extract the theoretical monitoring values ​​corresponding to the data anomalies; The theoretical monitoring values ​​were validated for environmental fit and historical health baseline verification, and the causes of data anomalies were determined based on the verification results.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a coupled correction model based on historical monitoring data of the overhead contact system. It builds an environmental field by collecting environmental parameters, and then adapts the environmental field and overhead contact system structural parameters to the model before inputting them into the model. The output includes theoretical monitoring values, theoretical offsets, and adjustment factors for the monitoring parameters. Based on the influence of environmental parameters on the monitoring parameters, it matches the adjustment direction of threshold narrowing or widening, and then adjusts the parameter thresholds based on the adjustment factors. Finally, it achieves online early warning based on the comparison between theoretical monitoring values ​​and parameter thresholds. This invention does not tamper with the original monitoring parameters, fully preserving the original correspondence between the original monitoring parameters and environmental parameters. On the one hand, it provides reliable original data support for fault review, facilitating maintenance personnel to trace the environmental background and parameter status at the time of anomalies. On the other hand, it provides high-quality samples for the self-updating of the coupled correction model, avoiding model fitting bias caused by data tampering, ensuring accurate learning of the overhead contact system's operating rules during model iteration, and sustainably improving the adaptability and accuracy of the early warning system in the long run. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method for online monitoring and early warning of overhead contact lines based on the operating environment in this invention; Figure 2 This is a schematic diagram of the method for adjusting the standard threshold according to the adjustment factor in this invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figures 1-2The first aspect of the present invention provides an online monitoring and early warning system for overhead contact lines based on the operating environment, including an online early warning module: for extracting structural parameters and environmental fields of the overhead contact lines; inputting the structural parameters and environmental fields into a coupled correction model to predict and obtain early warning parameters of the overhead contact lines; wherein the environmental field is constructed based on environmental parameters, and the early warning parameters include theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters; and for adjusting standard thresholds according to the adjustment factors to obtain parameter thresholds; and for providing online early warnings for the overhead contact lines based on the theoretical monitoring values ​​and parameter thresholds.

[0019] In a preferred embodiment, constructing the environmental field of the overhead contact line includes: extracting environmental parameters of the overhead contact line; wherein the environmental parameters are collected by distributed sensors along the line or obtained through a meteorological forecasting platform, and the types of environmental parameters include temperature, wind speed, humidity, and icing thickness; after interpolating the environmental parameters, mapping them to the full-line three-dimensional mesh of the overhead contact line to obtain the environmental field; wherein the full-line three-dimensional mesh is constructed based on the overhead contact line.

[0020] When constructing the environmental field, the environmental parameters corresponding to the catenary are interpolated using the Kriging space interpolation method. The interpolated environmental parameters are then mapped to the full-line 3D mesh of the catenary according to their data types, thus obtaining the environmental field of the catenary. The environmental field corresponds to environmental parameters, including temperature field, wind speed field, humidity field, icing field, etc.

[0021] After the environmental field is constructed, environmental parameters need to be collected regularly to dynamically update the field. Simultaneously, meteorological forecast data for the area where the overhead contact line is located can be collected through a meteorological forecasting platform. This data can be used to proactively update the environmental field, allowing for the determination of whether extreme environments exist and the prediction of monitoring parameters for the overhead contact line under such conditions. This enables early warning of potential faults in the overhead contact line.

[0022] In one example, taking a 30km overhead contact line section as an example, 60 integrated sensors are deployed along the contact line at 500-meter intervals. Each integrated sensor is bound to GPS positioning and synchronously collects four environmental parameters: temperature, wind speed, humidity, and icing thickness. These environmental parameters are preprocessed by spatiotemporal alignment, spatial calibration, and anomaly removal using the 3σ criterion to obtain an environmental dataset.

[0023] Using the catenary extension direction as the X-axis, a grid cell is divided every 10 meters; the direction perpendicular to the catenary is the Y-axis, with a grid cell every 5 meters; and the catenary guide height direction is the Z-axis, with a grid cell every 0.5 meters. This generates a full-line 3D grid for the aforementioned catenary section. Each grid cell in the full-line 3D grid is assigned a unique ID and associated with its corresponding spatial coordinates. Based on the Kriging spatial interpolation method, various types of data in the environmental parameters are interpolated, and the interpolation results are mapped to the full-line 3D grid of the catenary to obtain the environmental field of the catenary.

[0024] It should be noted that the environmental field needs to be dynamically updated. This means that environmental parameters are collected by integrated sensors at a set frequency, processed according to the above-described workflow, and then incrementally updated. Simultaneously, meteorological forecast data obtained from a meteorological forecasting platform can be used to proactively update the environmental field, generating an environmental field under predicted operating conditions, providing data support for the advance adjustment of parameter thresholds.

[0025] In a preferred embodiment, predicting and obtaining early warning parameters for the overhead contact system includes: calling a coupled correction model; wherein the coupled correction model includes a feature extraction layer, a coupled calculation layer, and an output optimization layer; after adapting the structural parameters of the overhead contact system and the environmental field to a model, the data are input into the coupled correction model to obtain the early warning parameters of the overhead contact system; wherein the early warning parameters include the theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters.

[0026] After extracting the structural parameters and environmental field of the overhead contact system, these parameters and field are modeled to generate model input data. This model input data is then fed into a pre-constructed coupled correction model to obtain the early warning parameters for the overhead contact system. These early warning parameters mainly include the theoretical monitoring values, theoretical offsets, and adjustment factors of the corresponding monitoring parameters of the overhead contact system. These parameters form the data foundation for online early warning of the overhead contact system.

[0027] It should be noted that the modeling of the overhead contact line's structural parameters and the environmental field refers to processing these data according to the methods used for processing input data during model training, including normalization and outlier handling, so that the coupled correction model can output the warning parameters corresponding to the model input data based on the internal nonlinear mapping relationship.

[0028] In a preferred embodiment, the coupling correction model is constructed based on an artificial intelligence model, including: constructing a basic correction model based on the artificial intelligence model; wherein the artificial intelligence model includes a BP neural network model; extracting historical monitoring data of the overhead contact line, constructing a standard training set based on the historical monitoring data; and training the basic correction model using the standard training set to obtain the coupling correction model.

[0029] The coupled correction model is built upon an artificial intelligence model. Its input variables include basic catenary parameters and environmental parameters, and its output variables are the predicted early warning parameters of the catenary. The early warning parameters include the theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters.

[0030] When training the coupled correction model, a standard training set is first prepared. Historical monitoring data of the overhead contact line with no faults and environmental parameters covering all operating conditions are collected, and a standard training set is constructed based on this historical monitoring data.

[0031] The purpose of ensuring that the overhead contact line is fault-free is to eliminate the interference of equipment deterioration / damage on monitoring parameters, allowing the model to learn the inherent coupling relationship between environmental parameters and monitoring parameters under normal operating conditions of the overhead contact line, avoiding model fitting deviations caused by fault data interference, and ensuring that the model output data conforms to engineering reality.

[0032] Comprehensive environmental condition coverage not only encompasses single-variable and multi-variable coupled operating conditions, but also ensures sufficient sample data are collected for each environmental variable interval. For example, each 5°C temperature change should be considered a variable interval, with at least 500 sets of sample data collected. Historical monitoring data should be complete, including environmental fields, catenary structural parameters, and measured monitoring parameters.

[0033] It should be noted that if the historical monitoring data of the overhead contact system cannot meet the above requirements, a digital twin model of the overhead contact system can be generated through digital twin technology. By changing the structural and environmental parameters, the monitoring parameters of the overhead contact system can be simulated to make up for the lack or incompleteness of the historical monitoring data.

[0034] Historical monitoring data includes structural parameters of the overhead contact system, distributed environmental parameters along the line, and corresponding monitoring parameters. After spatiotemporal alignment of each data item in the historical monitoring data, outliers are removed to obtain a standard dataset. Monitoring parameters include parameters required for overhead contact system fault diagnosis such as conductor height, pull-out value, contact force, contact wire temperature, and insulator leakage current; environmental parameters include parameters that affect the accuracy of overhead contact system early warning, such as temperature, wind speed, humidity, and icing thickness.

[0035] After collecting historical monitoring data, data cleaning is performed. A dual mechanism of "3σ criterion + operating condition filtering" can be used to remove abnormal data: 1) Remove out-of-range data caused by sensor failure and abnormal data transmission according to the 3σ criterion, such as wind speed suddenly rising to 100m / s; 2) Filter data from contact network maintenance, temporary construction and extreme interference periods, and retain samples under normal equipment operation conditions; 3) Directly delete erroneous data that has been manually verified to ensure that the sample validity rate is ≥90%.

[0036] After data cleaning, historical monitoring data undergoes standardization processing. Normalization can be used to map data of different magnitudes to a unified range: 1) For environmental parameters, temperature is mapped to the range [-1, 1], and wind speed, humidity, and icing thickness are mapped to the range [0, 1]; 2) Structural parameters (such as the elastic modulus of the contact wire and the stiffness of the support post) are normalized according to design standards, ensemble parameters in the monitoring parameters are normalized according to standard thresholds, and contact force, insulator leakage current, etc., are normalized according to full scale. If there are missing data in the sample data, linear interpolation is used to supplement the missing data when the missing rate is ≤5%, and sample data with a missing rate >5% are removed to ensure data integrity.

[0037] The standardized sample data is divided into training, validation and test sets in a 7:2:1 ratio. The basic correction model is trained using the training, validation and test sets. The trained basic correction model is then used as the coupled correction model.

[0038] The structural parameters of the overhead contact system are derived from its design drawings, maintenance records, and periodic inspection data. Their core function is to provide engineering boundary constraints and mechanical / geometric characteristic benchmarks for coupled calculations, preventing pure algorithm fitting from deviating from actual equipment characteristics and ensuring that the obtained early warning parameters conform to the operating rules of the overhead contact system. The structural parameters mainly include static structural parameters such as contact wire parameters (e.g., model, material properties, cross-sectional area, linear density), support and cantilever parameters (e.g., support model, support spacing, support stiffness, cantilever length, cantilever installation angle, cantilever material and stiffness), suspension structure parameters (e.g., suspension type, dropper spacing, dropper length, catenary parameters, weight of the weight and standard tension value of the weight), and anchor section parameters (e.g., anchoring method, type of compensation device, and compensation stroke), as well as dynamic structural parameters such as contact wire tension value, cantilever adjustment deviation value, and insulator parameters (e.g., insulator type, installation position).

[0039] When adapting structural parameters to a model, numerical parameters can be handled using normalization methods, while non-numerical data can be handled using one-hot encoding. Of course, if existing solutions disclose other, better processing methods, these can also be referenced; therefore, no specific processing method is specified here.

[0040] The basic correction model consists of a feature extraction layer, a coupled computation layer, and an output optimization layer.

[0041] The feature extraction layer is used to preprocess and extract features from multi-source input variables to eliminate dimensional differences. This layer is responsible for normalizing environmental field parameters and overhead contact line structural parameters using one-hot encoding, removing out-of-range abnormal data caused by sensor malfunctions, and outputting standardized feature vectors to lay the data foundation for the coupled computation layer.

[0042] The coupled computation layer is built based on a BP neural network model. This layer is responsible for introducing constraints based on the characteristics of the overhead contact system structure to prevent the algorithm from becoming disconnected from actual operating conditions; constructing a nonlinear coupling relationship between environmental parameters and monitoring parameters through the hidden layers of the neural network model; and outputting the theoretical monitoring values ​​of each monitoring parameter as well as the contribution weights of the variables.

[0043] The output optimization layer is used to calibrate and optimize the calculation results. This layer is responsible for calculating the theoretical offset corresponding to the theoretical monitoring values ​​based on standard operating conditions; and combining the theoretical offset with the contribution weights of environmental variables to calculate the adjustment factors for each monitoring parameter.

[0044] The output optimization layer can also calibrate the theoretical monitoring values ​​of the monitoring parameters through finite element analysis. Specifically, it uses finite element simulation tools (such as ANSYS) to simulate the mechanical and geometric responses of the contact network under corresponding environmental conditions, generating accurate simulation calibration values. These values ​​are then used as a benchmark to correct the initial theoretical monitoring values, eliminating the pure algorithm fitting bias and ensuring that the fitting error of the final output theoretical monitoring values ​​is controlled within ±0.2mm (geometric parameters) and ±3% (electrical / mechanical parameters). This provides high-precision data support for subsequent initial calculation of adjustment factors, dynamic adjustment of thresholds, and attribution verification.

[0045] In a preferred embodiment, the adjustment factor corresponds to a single environmental variable or multiple environmental variables; wherein, the adjustment factor corresponding to multiple environmental variables is obtained by weighted summation of the adjustment factors corresponding to a single environmental variable.

[0046] The output optimization layer combines the theoretical offset with the contribution weights of environmental variables to calculate the adjustment factor for each monitoring parameter. Specifically, using standard operating conditions (such as 25℃, no wind, and 50% humidity) as a benchmark, the theoretical monitoring value of the monitoring parameter is subtracted from the theoretical monitoring value under the standard operating conditions to obtain the theoretical offset; then, the preset theoretical offset under the standard operating conditions is extracted, which is the benchmark offset corresponding to the monitoring parameter when a single environmental variable does not change by a fixed range under the standard operating conditions.

[0047] Calculate the adjustment factor for a single environmental variable: the ratio of the theoretical offset obtained by subtraction to the theoretical offset under standard operating conditions is used as the adjustment factor for the corresponding monitoring parameter.

[0048] Calculate the adjustment factor for multiple environmental variables: The adjustment factor for a single environmental variable is weighted and summed with the contribution weight of the environmental variable to obtain the adjustment factor for the corresponding monitoring parameter.

[0049] In one example, the standard operating conditions are: temperature 25℃, wind speed 0m / s, humidity 50%, and icing thickness 0mm. Under these conditions, the adjustment factor for each monitoring parameter is 0. Under these conditions, the guide height offset is 2mm for every 10℃ change in temperature, 3mm for every 10m / s change in wind speed, and 0.8mm for every 25% change in humidity. Taking guide height as an example, the adjustment factor corresponding to a single environmental variable is calculated. Assuming the temperature is 35℃, deviating from the standard operating condition by +10℃, and other environmental parameters are consistent with the standard operating condition (or have negligible changes), it is determined to be a single temperature effect. The theoretical offset of guide height output by the coupled correction model is 1.8mm. In this case, the adjustment factor of guide height is: theoretical offset / standard operating condition offset = 1.8mm / 2mm = 0.9.

[0050] Taking the output value as an example, the adjustment factors corresponding to multiple environmental variables are calculated. Assuming a wind speed of 18 m / s, deviating from the standard operating condition by +18 m / s, the theoretical offset component of the output value is 4.8 mm. Humidity of 75%, deviating from the standard operating condition by +25%, corresponds to a theoretical offset component of 0.6 mm. The adjustment factor for wind speed is 4.8 mm / (18 m / s ÷ 10 m / s) × 3 mm = 4.8 mm / 5.4 mm ≈ 0.89, and the adjustment factor for humidity is 0.6 mm / 0.8 mm = 0.75. If the contribution weights of wind speed and humidity are 0.7 and 0.3 respectively, the adjustment factors corresponding to multiple environmental variables are obtained by weighted summation of the adjustment factors of each individual environmental variable and their contribution weights.

[0051] It should be noted that the adjustment factor can range from [0,1]. Constraints can also be designed for the adjustment factor of a single environmental variable to avoid excessive adjustment of parameter thresholds. For example, if the design constraint is that the adjustment factor ≤ 0.8, and the adjustment factor for wind speed is 0.89, then the adjustment factor for wind speed can be set to 0.8.

[0052] Whether a monitoring parameter is specifically affected by a single environmental variable or by the coupling effect of multiple environmental variables can be determined using the following methods: Method 1: Based on the environmental field judgment, identify the state of environmental parameters. If only one environmental parameter deviates from the standard operating condition, it is determined to be the influence of a single environmental variable; if two or more environmental variables deviate at the same time, it is determined to be the influence of multivariate coupling.

[0053] Method 2: Based on the contribution weight of environmental variables, the multivariate coupling correction model outputs the contribution weight of each environmental variable corresponding to the monitoring parameter. If only one environmental variable has a contribution weight ≥ 0.8 and the contribution weights of other environmental variables are ≤ 0.2, it is considered to be the influence of a single environmental variable. In this case, weak coupling is also classified as the influence of a single environmental variable. If the contribution weights of at least two environmental variables are ≥ 0.3, it is determined to be the influence of multivariate coupling.

[0054] Before training, the structural parameters of the basic correction model are preset as follows: 1) Feature extraction layer: One input node layer is set, with the number of input nodes corresponding to the dimension of the standardized feature vector. In this invention, the number of input nodes corresponds to 4 environmental parameters and 3 structural parameters, for a total of 7 input nodes. The feature vector is output after PCA dimensionality reduction, and the number of output nodes is set to 5, corresponding to the core features after PCA dimensionality reduction; 2) Coupled calculation layer: Two hidden layers are set, with the number of nodes in the first hidden layer set to 12 and the number of nodes in the second hidden layer set to 8. The ReLU function is used to avoid gradient vanishing. The number of nodes in the output layer is consistent with the type of monitoring parameters; 3) Output optimization layer: The initial values ​​of the finite element simulation calibration coefficients are preset, and the fitting error thresholds are set to ±0.2mm (geometric parameters) and ±3% (electrical / mechanical parameters).

[0055] It is worth noting that in the structural design of the basic correction model, the number of nodes in each layer does not correspond, because the number of input nodes and output nodes in each layer is determined by their functions.

[0056] Taking the pre-defined structural parameters of this invention as an example: For the feature extraction layer, its purpose is dimensionality reduction and purification. The original 7-dimensional features contain some highly correlated redundant information. After removing redundancy using the PCA algorithm, the 5-dimensional features with the highest contribution are retained, reducing the complexity of subsequent coupling calculations and avoiding redundant data interfering with model accuracy. For the coupling calculation layer, its purpose is to achieve nonlinear mapping. Through gradient adjustment of the number of hidden layer nodes, combined with the weight iteration of the BP neural network model, the 5-dimensional features are transformed into intermediate features that reflect the coupling relationship of multiple variables, ultimately mapping to the theoretical monitoring values ​​of 6 monitoring parameters. The node number design balances fitting ability and computational efficiency. For the output optimization layer, its purpose is accuracy calibration and result output. It does not need to correspond to the 6 output nodes of the coupling calculation layer; it only needs to receive the theoretical monitoring values ​​of the 6 monitoring parameters, calculate the theoretical offset of the monitoring parameters and the preliminary adjustment factor, and output these data to adjust the parameter thresholds corresponding to the monitoring parameters.

[0057] It should be noted that this invention constructs a basic correction model based on a BP neural network model. However, it can also construct a basic correction model based on an improved BP neural network model or other types of artificial intelligence models. This invention does not limit the artificial intelligence model used to construct the basic correction model. For example, an improved BP neural network algorithm can be used to address the problems of slow convergence and susceptibility to local optima in traditional BP algorithms. A momentum factor (set to 0.9) and an adaptive learning rate (initial learning rate 0.01) are introduced to improve training efficiency and fitting accuracy.

[0058] In a preferred embodiment, adjusting the standard threshold according to the adjustment factor includes: matching the adjustment direction according to the influence of environmental parameters on the monitoring parameters; wherein the adjustment direction includes threshold narrowing or threshold widening; adjusting the standard threshold based on the adjustment direction and the adjustment factor to obtain the parameter threshold; wherein the standard threshold is a preset threshold corresponding to the monitoring parameter.

[0059] In a preferred embodiment, online early warning of the overhead contact line based on theoretical monitoring values ​​and parameter thresholds includes: extracting the theoretical monitoring values ​​of the monitoring parameters from the early warning parameters; determining whether the theoretical monitoring values ​​exceed the corresponding parameter thresholds; if yes, determining that the data is abnormal, and performing attribution analysis on the abnormal data according to the early warning parameters; if no, determining that the data is normal.

[0060] Adjustment factors are extracted from the early warning parameters output by the coupled correction model. Using a preset standard threshold as a benchmark, and combining the adjustment factors with differentiated calculations based on the monitoring parameter type, the adjusted parameter threshold is obtained. The adjustment formula for the parameter threshold is: Parameter threshold = Standard threshold × (1 / 2) * ... Adjustment factor). The standard threshold is the preset threshold for each monitoring parameter.

[0061] The adjustment direction for the parameter thresholds corresponding to the monitoring parameters needs to be determined based on the influence of environmental parameters. For geometric parameters, such as conductor height and pull-out value, the environment can cause slight deviations in these geometric parameters. If the parameter threshold is too wide, it is easy to overlook hidden dangers such as abnormal contact wire sag and slight deformation of the cantilever arm. For electrical parameters, such as insulator leakage current, the environment can easily accelerate insulation degradation. If the parameter threshold is too wide, it will be impossible to capture hidden dangers of insulation performance degradation. Therefore, for geometric and electrical parameters, the parameter threshold can be adjusted by narrowing, that is, parameter threshold = standard threshold × (1 Adjustment factor).

[0062] For mechanical parameters, such as contact wire tension, the fluctuation range is large due to environmental influences and is mostly reversible. Narrowing the threshold will lead to a large number of false alarms. Therefore, for mechanical parameters, the threshold can be adjusted by widening it, i.e., parameter threshold = standard threshold × (1 Adjustment factor).

[0063] After adjusting the parameter thresholds, the theoretical monitoring values ​​of the monitored parameters are compared with the adjusted thresholds. If the values ​​exceed the adjusted thresholds, it indicates data anomalies, requiring online alerts. Simultaneously, attribution analysis is performed on the alert parameters output by the coupled correction model to identify possible causes of the data anomalies, and these causes are then sent to the operations and maintenance personnel.

[0064] In a preferred embodiment, attribution analysis of abnormal data is performed based on early warning parameters, including: extracting theoretical monitoring values ​​corresponding to the data anomalies; performing environmental fitting verification and historical health baseline verification on the theoretical monitoring values; and determining the cause of the data anomalies based on the verification results.

[0065] When deviations occur in the basic network monitoring parameters, the causes can be categorized into two types: reversible deviations caused by environmental parameters and irreversible deviations caused by equipment degradation. This invention links environmental fitting verification and historical health baseline verification to sequentially analyze whether the deviations are due to normal environmental influences, the cumulative effects of extreme environments, or the effects of equipment degradation. This avoids misattribution caused by a single verification, while preserving the original monitoring parameters and completing the traceability chain.

[0066] Environmental fitting verification is mainly used to determine whether the deviation of monitoring parameters is caused by normal environmental fluctuations. It compares the real-time collected catenary monitoring parameters with the theoretical monitoring value range output by the coupled correction model. If the range is within the theoretical range, the parameter deviation is determined to be a normal, reversible effect caused by the operating environment. If the range is exceeded, the deviation exceeds the scope of influence from the normal operating environment, requiring further investigation to determine the underlying cause. The theoretical monitoring value range is obtained during the training of the coupled correction model, representing the fluctuation range of the monitoring parameters under historical monitoring data.

[0067] Equipment baseline verification is primarily used to distinguish whether parameter deviations are caused by the superposition of extreme environmental factors or by deterioration of the overhead contact system itself. When monitored parameters exceed theoretical monitoring values, the cause is determined by combining the historical health baseline of the corresponding section with the adjusted parameter thresholds. If the monitored parameters exceed the adjusted parameter thresholds and the deviation from the historical health baseline exceeds the preset baseline deviation threshold, it indicates that the parameter deviation is not primarily caused by the operating environment, but rather by irreversible damage to the overhead contact system itself. If the monitored parameters exceed the adjusted parameter thresholds and the deviation from the historical health baseline does not exceed the preset baseline deviation threshold, it indicates that the parameter deviation is caused by the superposition of extreme environmental factors, and the overhead contact system itself is not faulty.

[0068] Historical health baselines can be constructed by statistical analysis of fault-free historical data, segmentation, or combination with the equipment lifecycle. The specific construction process will not be detailed here.

[0069] To address the balance between algorithm fitting accuracy and adaptability to actual working conditions, this invention calibrates the adjustment factor before adjusting the parameter threshold, thus avoiding the failure of parameter threshold adjustment due to the disconnect between theory and practice.

[0070] The adjustment factor is calculated based on the theoretical offset and variable contribution, but it still has inherent biases that affect the accuracy of parameter threshold adjustment. First, although the BP neural network can guarantee a low theoretical fitting error, there are still deviations from algorithm idealization and complex field conditions, resulting in slight deviations between the adjustment factor and the actual working conditions. Second, the adjustment factor is calculated only based on the theoretical offset / standard working condition offset, without considering the structural differences between different sections of the overhead contact line, which may lead to over- or under-adjustment of parameter thresholds.

[0071] Calibrate adjustment factors for a single environmental variable: After the coupled correction model outputs the theoretical offset of the monitoring parameters and the preliminary calculated adjustment factor, the measured parameter values ​​of the monitoring parameters under the same operating conditions (same environmental parameters, same section, same time period, etc.) are extracted. The measured offset is calculated based on the measured parameter values, and the benchmark factor is calculated based on the measured offset. If the numerical deviation between the adjustment factor and the benchmark factor is within the preset factor deviation threshold, no calibration is required, and the adjustment factor is used. If the data deviation is not within the preset factor deviation threshold, the benchmark factor is used to replace the adjustment factor.

[0072] Adjustment factor for multivariable coupling calibration: After the adjustment factor output by the coupled correction model is calibrated for a single environmental variable, the calibrated adjustment factor is obtained. This calibrated adjustment factor is then combined with a weighted summation to obtain the benchmark factor for multiple environmental variables. The benchmark factor is compared with the adjustment factors for multiple environmental variables. If the numerical deviation between the adjusted factor and the benchmark factor is within a preset factor deviation threshold, no calibration is required, and the adjusted factor is used. If the data deviation is outside the preset factor deviation threshold, the benchmark factor is used to replace the adjusted factor.

[0073] The factor deviation threshold can be set empirically, such as 0.02.

[0074] When eliminating the influence of the operating environment on the accuracy of online early warning, this invention does not directly use the environmental parameters of the operating environment to correct the original monitoring parameters. Instead, it calibrates the parameter thresholds corresponding to each monitoring parameter. This solves the inherent defects of directly calibrating monitoring parameters in online early warning of overhead contact lines, as detailed below: 1. Maintaining the integrity of original monitoring parameters: Directly correcting data involves tampering with the original monitoring parameters, resulting in the loss of the native correspondence between the original monitoring parameters and environmental parameters. This loss of native correspondence affects catenary maintenance scenarios that rely on real original data, such as fault debriefing, model iteration optimization, and pantograph-catenary dynamic characteristic analysis. This invention retains the original monitoring parameters by adjusting parameter thresholds, ensuring the integrity and reliability of the online monitoring data chain. It also provides high-quality sample support for the self-updating of multivariate coupled correction models, avoiding analytical bias and model accuracy degradation caused by data tampering.

[0075] 2. Adapting to the dynamic reversibility of the environment to avoid overcorrection and adaptation lag: Parameter deviations in the overhead contact system caused by the operating environment are mostly dynamically reversible. That is, when monitoring parameters deviate under non-standard operating conditions, they return to the normal range when the operating environment returns to standard conditions. Directly calibrating the monitoring parameters would solidify this reversible drift into the corrected data. On the one hand, this could lead to overcorrection by misjudging normal deviations as abnormalities; on the other hand, it would fail to match the dynamic fluctuations of environmental parameters in real time, resulting in adaptation lag. This invention adjusts the parameter threshold by adjusting a factor. This parameter threshold can adapt in real time to changes in environmental parameters, accurately matching dynamic environmental changes while avoiding overcorrection of reversible deviations, ensuring a balance between the accuracy and reliability of early warning.

[0076] 3. Ensuring attribution verification and achieving accurate definition of impact types: Direct calibration of monitoring parameters corrects both environmental parameters and parameter offsets caused by equipment anomalies, failing to distinguish the boundaries between the two. For example, permanent geometric deformation of the overhead contact line due to aging and temporary tension increases caused by low temperatures may appear as normal data after direct calibration, easily leading to missed equipment fault reports. In this invention, the coupled correction model generates adjustment factors while outputting the theoretical offset of monitoring parameters under the corresponding operating conditions. This theoretical offset can be directly used as the core basis for attribution verification. By comparing actual monitoring parameters with theoretical offsets, it can be determined whether the impact is a normal influence of the operating environment; furthermore, by combining historical data with baselines, it can be determined whether the equipment itself is damaged. Through the original monitoring parameters and theoretical offsets, scenarios of normal reversible environmental impacts, superimposed impacts of extreme environments, and irreversible equipment damage can be clearly distinguished. Direct calibration of monitoring parameters, due to the loss of original difference information, cannot achieve attribution verification and fails to meet the needs of accurate operation and maintenance of the overhead contact line.

[0077] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments.

[0078] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any other combination thereof. When implemented using a software program, it can be implemented entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0079] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An online monitoring and early warning system for overhead contact lines based on the operating environment, characterized in that, include: Online early warning module: used to extract structural parameters and environmental field of the overhead contact line; Structural parameters and environmental fields are input into a coupled correction model to predict and obtain early warning parameters for the overhead contact system. The environmental field is constructed based on the environmental parameters, and the early warning parameters include the theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters. as well as, Used to adjust the standard threshold according to the adjustment factor to obtain the parameter threshold; and to provide online early warning for the overhead contact line based on the theoretical monitoring value and the parameter threshold.

2. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, Constructing the environmental field for the overhead contact system includes: Environmental parameters of the overhead contact system are extracted. These parameters are collected by distributed sensors along the line or obtained through a meteorological forecasting platform. The types of environmental parameters include temperature, wind speed, humidity, and icing thickness. The environmental parameters are interpolated and then mapped onto the entire three-dimensional mesh of the overhead contact line to obtain the environmental field; wherein the entire three-dimensional mesh is constructed based on the overhead contact line.

3. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, Predicting and obtaining early warning parameters for the overhead contact system, including: The coupling correction model is invoked; the coupling correction model includes a feature extraction layer, a coupling calculation layer, and an output optimization layer. After adapting the structural parameters and environmental field of the overhead contact system to a model, the data are input into the coupled correction model to obtain the early warning parameters of the overhead contact system. The early warning parameters include the theoretical monitoring values, theoretical offsets, and adjustment factors of the monitoring parameters.

4. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, The coupling correction model is built based on an artificial intelligence model and includes: A basic correction model is constructed based on an artificial intelligence model; the artificial intelligence model includes a BP neural network model. Historical monitoring data of the overhead contact system is extracted, and a standard training set is constructed based on the historical monitoring data. The basic correction model is trained using the standard training set to obtain the coupled correction model.

5. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, The adjustment factor corresponds to a single environmental variable or multiple environmental variables; wherein, the adjustment factor corresponding to multiple environmental variables is obtained by weighted summation of the adjustment factors corresponding to a single environmental variable.

6. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, Adjusting the standard threshold according to the adjustment factor includes: The adjustment direction is matched according to the impact of environmental parameters on monitoring parameters; among which, the adjustment direction includes threshold narrowing or threshold widening; The standard threshold is adjusted based on the adjustment direction and the adjustment factor to obtain the parameter threshold; wherein, the standard threshold is a preset threshold corresponding to the monitoring parameter.

7. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 1, characterized in that, Online early warning of the overhead contact system based on the theoretical monitoring values ​​and the parameter thresholds includes: Extract the theoretical monitoring values ​​of the monitoring parameters from the early warning parameters; Determine whether the theoretical monitoring value exceeds the corresponding parameter threshold; if yes, determine that the data is abnormal and perform attribution analysis on the abnormal data based on the warning parameters; if no, determine that the data is normal.

8. The online monitoring and early warning system for overhead contact lines based on the operating environment according to claim 7, characterized in that, Attribution analysis of abnormal data is performed based on early warning parameters, including: Extract the theoretical monitoring values ​​corresponding to the data anomalies; The theoretical monitoring values ​​are subjected to environmental fitting verification and historical health baseline verification, and the cause of data anomalies is determined based on the verification results.

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