Dynamic three-dimensional ranging method for power transmission lines based on microwave interference

Abstract

The application discloses a power transmission line dynamic stereoscopic ranging method based on microwave interference and relates to the technical field of signal processing, which comprises the following steps: obtaining a ranging task instruction of a target power transmission line; generating dynamic microwave signals based on ranging accuracy levels; transmitting J microwave signals to the target power transmission line and receiving J reflected microwave signals of the target power transmission line; outputting J ranging analysis results according to microwave ranging analysis channels based on the J microwave signals and the J reflected microwave signals; collecting ranging signal transmission monitoring data sets and performing error compensation on the J ranging analysis results according to the ranging signal transmission monitoring data sets to obtain J ranging analysis optimization results; and integrating the J ranging analysis optimization results to generate a target stereoscopic ranging result. The application solves the technical problems of low measurement accuracy and weak anti-interference capability of the prior art in power transmission line ranging, and achieves the technical effect of improving the measurement accuracy and anti-interference capability of power transmission line ranging.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a dynamic three-dimensional ranging method for power transmission lines based on microwave interference. BACKGROUND

[0002] With the continuous expansion and complication of power transmission networks, the safety monitoring and maintenance of power transmission lines become increasingly important. During the operation of high-voltage transmission lines, accurately measuring the distance between the power transmission line and external damage sources is crucial for preventing power accidents and ensuring stable operation of the power grid. However, with the diversification of power transmission line arrangements and the complication of operating environments, traditional power transmission line ranging methods face the problems of insufficient accuracy and weak anti-interference ability, making it difficult to meet the needs of modern power systems for efficient, accurate, and safe monitoring. SUMMARY

[0003] The present application provides a dynamic three-dimensional ranging method for power transmission lines based on microwave interference, which solves the technical problems of low measurement accuracy and weak anti-interference ability in the prior art when performing power transmission line ranging.

[0004] In view of the above problems, the present application provides a dynamic three-dimensional ranging method for power transmission lines based on microwave interference.

[0005] The present application provides a dynamic three-dimensional ranging method for power transmission lines based on microwave interference, which includes:

[0006] Obtaining a ranging task instruction for a target power transmission line, wherein the ranging task instruction includes a ranging accuracy level; based on the ranging accuracy level, generating a dynamic microwave signal according to a wideband microwave radiation source module, wherein the dynamic microwave signal includes J microwave signals corresponding to J frequency bands, J is a positive integer greater than 1; transmitting the J microwave signals to the target power transmission line according to a microwave transmitting antenna, and receiving J reflected microwave signals of the target power transmission line according to a microwave receiving antenna; based on the J microwave signals and the J reflected microwave signals, outputting J ranging analysis results according to a microwave ranging analysis channel; collecting transmission monitoring parameters of the J microwave signals and the J reflected microwave signals, obtaining a ranging signal transmission monitoring data set, and performing error compensation on the J ranging analysis results according to the ranging signal transmission monitoring data set, obtaining J ranging analysis optimization results; integrating the J ranging analysis optimization results to generate a target three-dimensional ranging result.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] The application obtains a ranging task instruction of the target power transmission line, wherein the ranging task instruction includes a ranging accuracy level; based on the ranging accuracy level, a dynamic microwave signal is generated according to a wideband microwave radiation source module, wherein the dynamic microwave signal includes J microwave signals corresponding to J frequency bands, and J is a positive integer greater than 1; J microwave signals are transmitted to the target power transmission line according to a microwave transmitting antenna, and J reflected microwave signals of the target power transmission line are received according to a microwave receiving antenna; based on the J microwave signals and the J reflected microwave signals, J ranging analysis results are output according to a microwave ranging analysis channel; transmission monitoring parameters of the J microwave signals and the J reflected microwave signals are collected to obtain a ranging signal transmission monitoring data set, and error compensation is performed on the J ranging analysis results according to the ranging signal transmission monitoring data set to obtain J ranging analysis optimization results; and the J ranging analysis optimization results are integrated to generate a target three-dimensional ranging result. The application solves the technical problems of low measurement accuracy and weak anti-interference capability of the prior art in power transmission line ranging, generates dynamic microwave signals of multiple frequency bands by determining the ranging accuracy level, transmits and receives reflected signals of the target power transmission line by using an antenna, outputs multiple ranging results, and finally generates an accurate target three-dimensional ranging result after error compensation according to transmission monitoring data, thereby achieving the technical effects of improving the measurement accuracy and anti-interference capability of power transmission line ranging. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0010] Figure 1 A flowchart of a microwave interference-based dynamic three-dimensional ranging method for power transmission lines is provided for the embodiments of the present application.

[0011] Figure 2 A flowchart of constructing a microwave ranging analysis channel in the microwave interference-based dynamic three-dimensional ranging method for power transmission lines is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0012] The present application provides a microwave interference-based dynamic three-dimensional ranging method for power transmission lines, which is used to solve the technical problems of low measurement accuracy and weak anti-interference capability of the prior art in power transmission line ranging. The dynamic microwave signals of multiple frequency bands are generated by determining the ranging accuracy level, the reflected signals of the target power transmission line are transmitted and received by using an antenna, multiple ranging results are output, and finally an accurate target three-dimensional ranging result is generated after error compensation according to the transmission monitoring data, thereby achieving the technical effects of improving the measurement accuracy and anti-interference capability of power transmission line ranging.

[0013] The technical solutions in the embodiments of the present application will be clearly and completely described in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0014] It should be noted that any variation of the terms "comprise" and "have" is intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to those clearly listed steps or units, but can include other steps or modules that are not clearly listed or inherent to the process, method, product or device.

[0015] As shown in the embodiments, the present application provides a dynamic three-dimensional ranging method for a power transmission line based on microwave interference, which comprises the following steps: Figure 1 As shown in the embodiments, the present application provides a dynamic three-dimensional ranging method for a power transmission line based on microwave interference, which comprises the following steps:

[0016] Step S100: obtaining a ranging task instruction of a target power transmission line, wherein the ranging task instruction comprises a ranging accuracy level.

[0017] In the embodiments of the present application, first, the user inputs the basic information of the ranging task, including the specific location of the target power transmission line, the purpose of ranging, etc., and then generates the ranging task instruction according to the input task information. At this time, the detailed parameters of ranging are set in combination with the pre-set ranging accuracy level. The ranging accuracy level is pre-set according to different application scenarios and requirements, and determines the required accuracy in the measurement process. For example, for some key high-voltage power transmission lines, a higher ranging accuracy level is required. Finally, the information after setting the task parameters is integrated to generate the ranging task instruction.

[0018] Step S200: generating a dynamic microwave signal based on the wideband microwave radiation source module according to the ranging accuracy level, wherein the dynamic microwave signal comprises J microwave signals corresponding to J frequency bands, and J is a positive integer greater than 1.

[0019] In the embodiments of the present application, the ranging accuracy level in the ranging task instruction is first analyzed, and by calling a preset parameter table, the appropriate frequency band range and signal characteristics are selected to meet different measurement requirements, such as high-precision ranging tasks requiring wider frequency bands and higher signal strength. Then, according to the selected ranging accuracy level, the broadband microwave radiation source module is dynamically configured. This step includes adjusting parameters such as frequency band selection, signal transmission power, and modulation method, and the commonly used method is to use digital signal processing technology. Specifically, an appropriate frequency band range is selected, the transmission power is set to ensure signal strength coverage of the ranging range, and the appropriate modulation method, such as frequency modulation or pulse modulation, is selected to optimize signal transmission characteristics.

[0020] Subsequently, the broadband microwave radiation source module configured generates dynamic microwave signals of multiple frequency bands. The generation of these signals is completed by a frequency synthesizer, which can quickly switch and adjust the signal frequency within multiple frequency bands. To ensure that the time and phase characteristics of the signal achieve the desired effect, a timing control circuit is used to dynamically adjust the frequency and phase of the signal. The generation process includes calling the frequency synthesizer to generate microwave signals of J different frequency bands, and using the timing control circuit to dynamically adjust the frequency and phase of these signals to meet the accuracy requirements of the ranging.

[0021] Finally, J frequency band signals are selected according to the ranging accuracy level and environmental conditions. By using filters and frequency dividers, the broadband signal is decomposed into multiple independent frequency band signals. This process includes selecting J frequency bands from a preset frequency band table, using frequency dividers to divide the broadband signal into independent frequency band signals, and using filters to ensure the purity and stability of each frequency band signal. Finally, the generated dynamic microwave signal has multiple frequency band characteristics and can effectively complete the ranging task of the transmission line under different environmental conditions.

[0022] Through the above process, dynamic microwave signals are generated, including J microwave signals corresponding to J frequency bands, J being a positive integer greater than 1.

[0023] Step S300: According to the microwave transmitting antenna, the J microwave signals are transmitted to the target transmission line, and according to the microwave receiving antenna, the J reflected microwave signals of the target transmission line are received.

[0024] In the embodiments of the present application, first, the previously generated microwave signals of J frequency bands are simultaneously or sequentially transmitted to the target power transmission line through the configured microwave transmitting antenna. When these microwave signals reach the target power transmission line, they are reflected due to the physical characteristics of the power transmission line, such as the surface material, shape, and position. The signals of each frequency band will produce different reflection paths and reflection intensities according to the characteristics of the power transmission line. These reflected signals carry key information about the distance and position of the power transmission line. Then, the reflected microwave signals are received by the microwave receiving antenna. The receiving antenna works cooperatively with the transmitting antenna to accurately capture the reflected signals corresponding to the J frequency bands, ensuring that the signals of each frequency band can be effectively received.

[0025] Finally, J reflected microwave signals are successfully received, which correspond to the J frequency bands at the time of transmission. Each reflected signal contains distance and reflection characteristic information related to the target power transmission line.

[0026] Step S400: Based on the J microwave signals and the J reflected microwave signals, the microwave ranging analysis channel is used to output J ranging analysis results.

[0027] In the embodiments of the present application, first, preliminary signal processing is performed, and the frequency spectrum analysis device is used to perform frequency spectrum analysis on each microwave signal to extract characteristic information such as frequency, amplitude, and phase of the signal. Next, the J reflected microwave signals are filtered and reconstructed.

[0028] Then, the ranging analysis is performed by the microwave ranging analysis channel, and the ranging results corresponding to each frequency band are calculated according to the differences between the transmitted signals and the reflected signals. Specifically, the analysis channel compares the phase difference, time delay, and amplitude attenuation parameters between each transmitted signal and its corresponding reflected signal, thereby deducing the accurate distance between the power transmission line and the ranging device. Finally, J ranging analysis results are output, which correspond to the ranging values of the J frequency bands respectively.

[0029] Further, in the method provided by the embodiments of the present application, based on the J microwave signals and the J reflected microwave signals, the J ranging analysis results are output according to the microwave ranging analysis channel, which further includes:

[0030] Based on the frequency spectrum analysis device, the J microwave signals are respectively subjected to frequency spectrum analysis to obtain J signal frequency spectrum analysis results; the J reflected microwave signals are subjected to filtering and reconstruction to obtain J enhanced reflected signals; the J enhanced reflected signals are respectively subjected to frequency spectrum analysis based on the frequency spectrum analysis device to obtain J reflected signal frequency spectrum analysis results; and based on the J signal frequency spectrum analysis results and the J reflected signal frequency spectrum analysis results, the J ranging analysis results are obtained according to the microwave ranging analysis channel.

[0031] In the embodiments of the present application, first, the spectrum analysis device is used to analyze the J microwave signals respectively. The frequency, amplitude, phase and other parameters of the signal are measured by the spectrum analyzer, which reflect the basic characteristics of the signal. The specific steps include connecting the transmitting antenna to the spectrum analyzer, analyzing the J-band microwave signals, and recording the spectrum characteristics of each frequency band respectively. The output of this process is the J signal spectrum analysis results, which contain detailed spectrum information of each microwave signal.

[0032] Next, the received J reflected microwave signals are processed. Since the reflected microwave signals may be affected by environmental noise and other interference, in order to improve the effectiveness of the signal, a signal reconstruction algorithm is used to process the signal to obtain J enhanced reflected signals.

[0033] Subsequently, the enhanced reflected signals after filtering and reconstruction are analyzed again using the spectrum analyzer. In this way, the frequency, amplitude and phase characteristics of the reflected signal are captured, which are crucial for subsequent range analysis. The specific steps include inputting the enhanced reflected signals into the spectrum analyzer, analyzing each frequency band, and obtaining J reflected signal spectrum analysis results. These results provide the spectrum characteristic information of the enhanced reflected signals, ensuring the accuracy of signal analysis.

[0034] Finally, the microwave ranging analysis channel is used for ranging calculation. The J signal spectrum analysis results and the J reflected signal spectrum analysis results are input into the microwave ranging analysis channel, and J ranging analysis results are obtained by calculation.

[0035] Further, the method provided by the embodiments of the application further comprises:

[0036] According to the J reflected microwave signals, white noise is added to obtain a set of noisy reflected signals; EMD decomposition is performed on the set of noisy reflected signals to obtain a first set of signal intrinsic mode function feature sequences; integration and averaging are performed on the first set of signal intrinsic mode function feature sequences to obtain a second set of signal intrinsic mode function feature sequences; and denoising reconstruction is performed on the second set of signal intrinsic mode function feature sequences to output the J enhanced reflected signals.

[0037] In the embodiments of the present application, first, white noise is added to the received J reflected microwave signals. Specifically, the J reflected microwave signals are input into the signal processing module, and an appropriate amount of white noise is added to each signal. A white noise generator is used in this process, which generates uniformly distributed random noise. The white noise is superimposed on each reflected signal to form a set of noisy reflected signals.

[0038] Next, the EMD decomposition is performed on the set of noisy reflection signals. First, each signal in the set of noisy reflection signals is input into the EMD algorithm one by one. The input noisy reflection signal is analyzed to find all local maxima and local minima of the signal. By traversing the signal data, all extreme points of the signal, i.e., the peaks and valleys in the signal, are determined. Next, an upper envelope line is constructed using an interpolation method through all local maxima points, and a lower envelope line is constructed using the same interpolation method through all local minima points. The spline interpolation method is used to interpolate the maxima points and minima points, respectively, to generate the upper envelope line and the lower envelope line. Then, the average of the upper envelope line and the lower envelope line is calculated to obtain the current trend component, referred to as the "residual". The original signal is subtracted by this residual, and the resulting part is the first IMF component. If the IMF satisfies the conditions defined by the IMF, i.e., zero mean, local symmetry, etc., it is taken as the final IMF component; otherwise, this result is taken as the signal for the next round of EMD decomposition until the conditions are met. After the first IMF is extracted, it is separated from the original signal, and the remaining signal part, i.e., the residual part, is taken as the new input signal. The above processes of extreme point finding, envelope line construction, and IMF extraction are repeated until all IMF components are obtained. Finally, after multiple rounds of decomposition, the first signal intrinsic mode function feature sequence set is obtained.

[0039] The first signal intrinsic mode function feature sequence set is then integrated and averaged. Specifically, the IMFs are first weighted and averaged, and the weight of each IMF is determined by analyzing the signal-to-noise ratio (SNR) and frequency characteristics of each IMF. The weight of each IMF is determined by combining the signal-to-noise ratio and the frequency characteristics through the formula wherein is the weighting coefficient or weight of the i-th IMF, is the signal-to-noise ratio of the i-th IMF, is the frequency characteristic of the i-th IMF, which is usually the dominant frequency or the characteristic frequency in the frequency spectrum. and are two weight coefficients for adjusting the influence degree of the signal-to-noise ratio and the frequency characteristics, which are pre-set by technical experts. Subsequently, the weights are normalized to ensure that the sum of the weights of all IMFs is 1.

[0040] Next, the integrated and averaged IMFs are integrated. Low-pass filters or Kalman filters are used to process the weighted and averaged IMFs to smooth the high-frequency components in the signal and remove redundant noise. Through this integration step, all filtered IMFs are re-integrated into a comprehensive signal feature set, which can effectively reduce errors and optimize the expression of signal features. After the weighted averaging and integration, the second signal intrinsic mode function feature sequence set is generated.

[0041] Finally, denoising reconstruction is performed according to the second signal intrinsic mode function feature sequence set. In this step, the processed IMF is recombined into a complete signal using a denoising algorithm. The residual noise components are removed by a filter, the useful signal in the specified frequency range is retained, and these information is integrated into an enhanced signal using a reconstruction algorithm such as inverse wavelet transform. Finally, J enhanced reflection signals are output.

[0042] Further, as shown in Figure 2 The method provided by the application embodiment further includes the following steps in the step of constructing the microwave ranging analysis channel:

[0043] Load a sample signal spectrum analysis result set, a sample reflection signal spectrum analysis result set, and a sample ranging analysis result set corresponding to a plurality of sample transmission lines. Supervised learning is performed on K base models according to the sample signal spectrum analysis result set, the sample reflection signal spectrum analysis result set, and the sample ranging analysis result set to generate K microwave ranging analysis models that meet a ranging analysis accuracy constraint, where K is a positive integer greater than 1, and the K base models are different from each other. The output data set of the K microwave ranging analysis models is used as input information, and the sample ranging analysis result set is used as output information to train a microwave ranging analysis fusion model that meets the ranging analysis accuracy constraint. The K microwave ranging analysis models are merged as parallel independent nodes to generate a microwave ranging analysis processing layer. The microwave ranging analysis processing layer and an input layer of the microwave ranging analysis fusion model are merged to generate the microwave ranging analysis channel.

[0044] In the application embodiment, a sample signal spectrum analysis result set, a sample reflection signal spectrum analysis result set, and a sample ranging analysis result set corresponding to a plurality of sample transmission lines are first loaded. These data sets are obtained by performing spectrum analysis and reflection analysis on historical ranging data of the transmission lines and reflect the transmission and reflection characteristics of signals at different frequencies. These sample data sets are extracted from a database, where the sample signal spectrum analysis result set contains signal spectrum features at different frequency bands, the sample reflection signal spectrum analysis result set records spectrum features of corresponding reflection signals, and the sample ranging analysis result set provides historical ranging results calculated based on these spectrum features.

[0045] Next, the K base models are supervised learned according to the aforementioned acquired sample dataset. Here, the sample signal spectrum analysis result and the sample reflection signal spectrum analysis result are taken as input features, and the sample ranging analysis result is taken as output label. The K base models are trained by setting different model architectures, parameters or training methods. Common methods include regression models such as linear regression, support vector regression, used to establish linear or nonlinear relationships between spectrum features and ranging results; through neural networks such as multilayer perceptron or convolutional neural network to capture more complex nonlinear relationships; and through decision tree models such as random forest to handle the discretization or nonlinear mapping of spectrum features. In the training process of each model, according to the spectrum features of the input data and the corresponding ranging results, the model parameters are optimized, and finally K different microwave ranging analysis models are generated.

[0046] After generating the K base models, the output dataset of the K microwave ranging analysis models is taken as input information, and the sample ranging analysis result set is taken as output information to train the microwave ranging analysis fusion model. In this stage, the fusion learning method is adopted to comprehensively process the output results of each base model. Common fusion methods include weighted average method, which sums the outputs of each base model by weighting. When determining the weight of each model, first evaluate the performance of each base model on the training set, and realize it by calculating the mean square error or absolute error of each base model on the training data. Based on these evaluation indexes, the weight of each base model is determined. Specifically, first calculate the error of each model, then take the reciprocal of each error as the preliminary weight, then add the preliminary weights of all models to calculate the total weight. Finally, the final weight of each model is determined by dividing the preliminary weight of the model by the total weight. Next, the output of the base model is taken as input to train a more complex model such as linear regression or neural network to further optimize the prediction result. Through multiple rounds of training, the optimal parameters of the fusion model are determined to ensure the accuracy requirements of the overall ranging analysis, and finally a microwave ranging analysis fusion model is generated.

[0047] Subsequently, the K microwave ranging analysis models are merged as parallel independent nodes to generate a microwave ranging analysis processing layer. This processing layer is a parallel computing structure composed of multiple base models, and the purpose is to process data from different frequency bands or different features at the same time to improve the computing efficiency and ranging accuracy of the system. By configuring each base model as an independent processing node, parallel computing techniques such as multithreading processing or GPU acceleration are used to improve processing speed and efficiency. The processing layer receives input signal spectrum data, calls each base model for independent ranging prediction, and outputs the analysis results of each model. This processing layer significantly improves the processing efficiency and ranging accuracy of the system.

[0048] Finally, the microwave ranging analysis processing layer and the input layer of the microwave ranging analysis fusion model are merged to generate a complete microwave ranging analysis channel. Specifically, the output results of the microwave ranging analysis processing layer are directly input into the microwave ranging analysis fusion model, and the microwave ranging analysis fusion model processes these results to output the final ranging analysis results. By seamlessly integrating the processing layer and the fusion model, the input signal data is processed in real time, and accurate ranging results are output through the microwave ranging analysis channel.

[0049] Step S500: Collecting transmission monitoring parameters of the J microwave signals and the J reflected microwave signals, obtaining a ranging signal transmission monitoring data set, and performing error compensation on the J ranging analysis results according to the ranging signal transmission monitoring data set to obtain J ranging analysis optimization results.

[0050] In the embodiments of the present application, first, the signal receiver, sensor and data recorder are used to collect the monitoring parameters of the J microwave signals and their corresponding reflected microwave signals in the transmission process, including the transmission time, signal strength, frequency change, signal attenuation, etc. of the signal. Next, these collected transmission monitoring parameters are summarized to form a complete ranging signal transmission monitoring data set. Based on this ranging signal transmission monitoring data set, the J ranging analysis results calculated initially are error compensated, the signal attenuation and interference factors recorded in the transmission monitoring data set are analyzed, the influence of these factors on the ranging results is calculated, and these influences are corrected. Through error compensation, J ranging analysis optimization results are generated.

[0051] Further, in the method provided by the embodiments of the present application, the error compensation on the J ranging analysis results according to the ranging signal transmission monitoring data set to obtain J ranging analysis optimization results further includes:

[0052] According to the J ranging analysis results, the jth ranging analysis result is extracted, where j is a positive integer and j belongs to J; based on the jth ranging analysis result, the ranging signal transmission monitoring data set is feature extracted to determine the jth signal transmission monitoring data and the jth reflected signal transmission monitoring data; based on the jth signal transmission monitoring data, the jth ranging analysis result is error compensated to obtain a jth ranging analysis correction result; based on the jth reflected signal transmission monitoring data, the jth ranging analysis result is error compensated to obtain a jth ranging analysis correction result; the jth ranging analysis correction result and the jth ranging analysis correction result are fused to output a jth ranging analysis optimization result, and the jth ranging analysis optimization result is added to the J ranging analysis optimization results.

[0053] In the embodiments of the present application, first, the jth ranging analysis result is extracted from the J ranging analysis results that have been calculated, where j is a positive integer between 1 and J, representing a specific ranging analysis result currently processed. Next, the jth ranging analysis result is analyzed, and the feature data related to the jth ranging analysis result is extracted from the ranging signal transmission monitoring data set through signal processing technology. These feature data include signal transmission time, signal strength, frequency offset, and phase change, etc. The signal transmission features related to the jth ranging result are extracted to form the jth signal transmission monitoring data; at the same time, the reflected signal transmission features related to the jth ranging result are extracted to form the jth reflected signal transmission monitoring data. In order to ensure the analyzability of these data, Fourier transform or wavelet transform and other feature extraction algorithms are used to convert these monitoring data into frequency domain or time domain features that are easy to analyze.

[0054] After obtaining the feature data, the jth ranging analysis result is compensated for errors based on the signal transmission monitoring data. In this stage, the jth signal transmission monitoring data is analyzed to identify error factors that may be introduced in the signal transmission process, such as signal attenuation, delay, interference, etc.

[0055] And the preliminary jth ranging analysis result is corrected according to the features of the monitoring data. Through this step, the jth ranging analysis correction result is generated. At the same time, the jth ranging analysis result is further compensated for errors based on the jth reflected signal transmission monitoring data. The compensation process is similar to the process of compensating for errors in the ranging analysis result, and the jth ranging analysis correction result is obtained through the same process.

[0056] After that, the jth ranging analysis correction result and the jth ranging analysis correction result are fused to generate the final jth ranging analysis optimization result. In this fusion process, the two correction results are combined by weighted average to ensure that the final result integrates the correction advantages of the signal transmission and reflection path. When performing weighted average, the jth ranging analysis correction result and the jth ranging analysis correction result have the same weight, and through the weighted average process, the jth ranging analysis correction result and the jth ranging analysis correction result are combined together to generate a comprehensive optimization result, that is, the jth ranging analysis optimization result is generated, and the jth ranging analysis optimization result is added to the J ranging analysis optimization results.

[0057] Finally, the above steps are repeated to obtain J ranging analysis optimization results.

[0058] Further, in the method provided by the embodiments of the present application, the jth ranging analysis result is compensated for errors based on the jth signal transmission monitoring data to obtain the jth ranging analysis correction result, and the method further comprises:

[0059] According to the feature recognition of the jth signal transmission monitoring data, jth signal transmission obstacle feature data and jth signal transmission environment feature data are determined; based on the jth signal transmission obstacle feature data, attenuation error correction is performed on the jth ranging analysis result to determine jth attenuation feature error correction data; based on the jth signal transmission environment feature data, interference error correction is performed on the jth ranging analysis result to determine jth interference feature error correction data; based on the jth attenuation feature error correction data and the jth interference feature error correction data, compensation optimization is performed on the jth ranging analysis result, and the jth ranging analysis correction result is output.

[0060] In the embodiments of the present application, feature recognition is first performed from the jth signal transmission monitoring data, and a pattern recognition algorithm such as a support vector machine, a neural network or a decision tree model is used in this step. These models are pre-trained. In the training process, historical signal transmission data is used as the training set, and these data have been labeled with features such as obstacle type and environmental interference. Through training, the model learns to identify similar features in new data. After obtaining the jth signal transmission monitoring data, the trained model is used to extract the jth signal transmission obstacle feature data (such as the distance, material and size of the obstacle) and the jth signal transmission environment feature data (such as the strength and position of electromagnetic interference and weather conditions).

[0061] Next, based on the identified jth signal transmission obstacle feature data, attenuation error correction is performed on the jth ranging analysis result. Signal attenuation models are used, and these models have also been trained. In the training process, a large amount of obstacle data of different materials and sizes is used, and by adjusting the model parameters, the model can accurately predict the degree of signal attenuation when passing through different obstacles. The trained attenuation model can calculate the attenuation value of the signal according to the jth signal transmission obstacle feature data. Then, this attenuation value is used to adjust the jth ranging analysis result to generate jth attenuation feature error correction data, which corrects the attenuation error caused by the obstacle.

[0062] At the same time, based on the jth signal transmission environment feature data, interference error correction is performed on the jth ranging analysis result. The interference correction model used here has also undergone a similar training process. Historical data containing various electromagnetic interferences and weather conditions are used to train the model, so that it can identify and correct the interference of the environment on the signal. The trained interference correction model can calculate the interference influence on the signal in the transmission process according to the jth signal transmission environment feature data. These calculation results are used to modify the jth ranging analysis result to generate jth interference feature error correction data.

[0063] Finally, the jth attenuation feature error correction data and the jth interference feature error correction data are combined to compensate and optimize the jth ranging analysis result. The compensation and optimization process is performed by a weighted fusion method to integrate the attenuation error correction data and the interference error correction data. In this process, the weights of the two correction data are the same, and the fused result is taken as the final jth ranging analysis correction result, and the jth ranging analysis correction result is output.

[0064] Further, the method provided by the application embodiment further comprises the following steps:

[0065] According to the jth signal transmission obstacle feature data, an attenuation influence depth evaluation is performed to obtain a jth signal attenuation influence coefficient; it is determined whether the jth signal attenuation influence coefficient is greater than / equal to a signal attenuation influence threshold value; if the jth signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold value, an obstacle feature attenuation error correction model is activated; based on the jth signal transmission obstacle feature data and the jth ranging analysis result, the jth attenuation feature error correction data is output according to the obstacle feature attenuation error correction model.

[0066] In the application embodiment, first, the jth signal transmission obstacle feature data is used to perform an attenuation influence depth evaluation. In this step, the signal attenuation is directly calculated based on the existing electromagnetic wave propagation theory and empirical formula. Generally, the signal attenuation is estimated by a free space path loss formula or according to the physical properties (such as material, thickness and distance) of the obstacle. For example, the following basic formula is used to calculate the attenuation:

[0067] ;

[0068] wherein L is the attenuation amount, d is the signal propagation distance, λ is the wavelength of the signal, is the attenuation coefficient related to the material of the obstacle, and h is the thickness of the obstacle. Next, a quantitative result, i.e., the jth signal attenuation influence coefficient, is obtained by calculation.

[0069] Then, it is determined whether the jth signal attenuation influence coefficient exceeds a preset signal attenuation influence threshold value. The threshold value is set by a technical expert through previous actual measurement, and is used to distinguish the severity of signal attenuation. By comparing the calculated attenuation influence coefficient with the preset threshold value, if the attenuation influence coefficient is lower than the threshold value, it indicates that the influence of the obstacle on the signal is small and can be ignored; but if the attenuation influence coefficient is greater than or equal to the threshold value, it indicates that the influence of the obstacle on the signal is significant, and error correction is needed.

[0070] If the jth signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold, the obstacle feature attenuation error correction model is activated. The obstacle feature attenuation error correction model is pre-trained. During training, first, historical data sets containing different obstacle features, such as material, thickness, distance, etc., and their corresponding signal attenuation data are collected from a historical database. Then, the data is preprocessed, including feature extraction, normalization, and denoising, to ensure data quality and consistency. Then, a suitable machine learning algorithm, such as multiple regression, random forest, or neural network, is selected to train the model using the preprocessed data, enabling it to learn the relationship between obstacle features and signal attenuation. During training, techniques such as cross-validation are used to optimize model parameters, preventing overfitting and improving model generalization. After training, the model is evaluated using an independent test set to verify its prediction accuracy and stability. Finally, the trained and validated obstacle feature attenuation error correction model can accurately predict and correct attenuation errors in signal transmission based on real-time collected obstacle feature data.

[0071] Next, the jth signal transmission obstacle feature data and the jth ranging analysis result are input into the activated obstacle feature attenuation error correction model. The obstacle feature data includes key information such as the material, thickness, and distance of the obstacle, while the ranging analysis result is the preliminary ranging data that may have been affected by the obstacle. The correction model uses these input data to correct errors caused by obstacles, outputting the jth attenuation feature error correction data.

[0072] Furthermore, the method provided by the application embodiment further includes:

[0073] According to the jth signal transmission environment feature data, abnormality detection is performed to obtain jth signal transmission abnormal environment data; based on the jth signal transmission abnormal environment data, ranging interference evaluation is performed to obtain a jth environment ranging interference coefficient; it is judged whether the jth environment ranging interference coefficient is greater than / equal to a predetermined environment ranging interference coefficient; if the jth environment ranging interference coefficient is greater than / equal to the predetermined environment ranging interference coefficient, interference error correction is performed on the jth ranging analysis result according to the jth signal transmission abnormal environment data, and the jth interference feature error correction data is generated.

[0074] In the embodiments of the present application, first, abnormality detection is performed according to the jth signal transmission environment characteristic data. In this step, a statistical analysis-based abnormality detection method is used to identify abnormal situations in the environment characteristic data in the signal transmission process. This method identifies abnormal data points that deviate significantly from the normal value by analyzing the basic statistical characteristics of the signal, such as mean, standard deviation, rate of change, etc. Abnormal situations include electromagnetic interference, extreme weather conditions, etc. When performing abnormality detection, first, the environment characteristic data is obtained from the signal transmission monitoring data, which includes real-time collected signal strength, frequency change, noise level, etc. Then, statistical analysis is performed on the collected environment characteristic data to calculate the basic statistical indicators such as mean, standard deviation, etc. By comparing the statistical characteristics of real-time data with historical data, abnormal data points are identified. For example, if the fluctuation amplitude of the current signal strength exceeds the normal range, such as more than three times the standard deviation of the mean, the signal strength may be affected by abnormal environmental factors. Once the abnormality is detected, it is recorded as the jth signal transmission abnormal environment data.

[0075] Next, ranging interference evaluation is performed based on the jth signal transmission abnormal environment data to quantify the influence of environmental abnormalities on the ranging result. In the present application, the degree of interference is calculated by the following formula:

[0076] ;

[0077] wherein, is the jth environmental ranging interference coefficient, is the electromagnetic interference intensity, is the signal frequency offset, is the noise level, and a, b, c are coefficients preset by technical experts. The jth environmental ranging interference coefficient calculated is used to represent the degree of interference of environmental abnormalities on the ranging result, and the higher the value, the greater the influence of interference on the ranging result.

[0078] Next, the jth environmental ranging interference coefficient is compared with the predetermined environmental ranging interference coefficient, wherein the predetermined environmental ranging interference coefficient is set by technical experts based on a large amount of historical data and experimental results. If the coefficient is less than the threshold value, it is considered that the influence of interference on the ranging result is small and can be ignored; if the coefficient is greater than or equal to the threshold value, further interference error correction is needed.

[0079] When the jth environmental ranging interference coefficient is greater than / equal to a predetermined threshold, the jth ranging analysis result is corrected for interference error according to the jth signal transmission abnormal environment data. Specifically, a filtering technique or signal reconstruction algorithm is used to correct the error in the ranging result. First, activate the interference error correction method, such as using a Kalman filter to smooth the disturbed signal, or using a signal reconstruction algorithm to reconstruct the original signal. Then, take the interference factors in the abnormal environment data as input, calculate the specific interference influence, and eliminate or compensate it from the preliminary ranging result. The specific methods include adjusting the signal propagation time, correcting the signal strength, etc. Finally, the corrected data, i.e., the jth interference feature error correction data, is generated.

[0080] Similar to the aforementioned process of obtaining the jth ranging analysis correction result, the jth ranging analysis correction result is obtained as follows:

[0081] First, feature recognition is performed from the jth signal transmission monitoring data. This process uses a simple pattern recognition algorithm, such as a decision tree model, to identify key features in the data. By analyzing historical signal transmission data, the decision tree model is trained to be able to identify and classify data related to obstacle features and environmental features. When the jth signal transmission monitoring data is obtained, the trained decision tree model is used to extract the jth signal transmission obstacle feature data and the jth signal transmission environmental feature data. Next, the jth ranging analysis result is corrected for attenuation error based on the identified jth signal transmission obstacle feature data. This process uses a simple signal attenuation model based on electromagnetic wave propagation theory and empirical formulas, such as the free space path loss formula. Using this formula, the attenuation value of the signal when passing through the obstacle is calculated based on the material, thickness, and distance of the obstacle. Then, the calculated attenuation value is applied to the preliminary ranging result to generate the jth attenuation feature error correction data. At the same time, the jth ranging analysis result is corrected for interference error based on the jth signal transmission environmental feature data. In this step, first, perform anomaly detection using a statistical analysis-based method by calculating the mean and standard deviation of signal strength, frequency change, and noise level to identify anomalies in the environment, such as electromagnetic interference or extreme weather. Once the anomaly is identified, it is recorded as the jth signal transmission abnormal environment data. Then, quantify the degree of interference of these environmental anomalies on the signal, and calculate the jth environmental ranging interference coefficient. Compare this coefficient with the predetermined interference threshold. If the coefficient exceeds the threshold, it indicates significant interference, and a low-pass filter is used to smooth the signal to reduce the impact of noise, and finally generate the jth interference feature error correction data.

[0082] Finally, the jth attenuation feature error correction data and the jth interference feature error correction data are combined to compensate and optimize the jth ranging analysis result. In this process, the weighted average method is used to fuse the attenuation correction and interference correction data to obtain the final jth ranging analysis correction result. In the weighting process, the weights of attenuation correction and interference correction are the same, which ensures that the contributions of the two correction factors in the final result are balanced.

[0083] Step S600: integrate the J ranging analysis optimization results to generate a target stereo ranging result.

[0084] In the embodiments of the present application, the J ranging analysis optimization results are integrated, each result is based on ranging data of different frequency bands or different paths, they are directly added, and the sum is divided by the number of J to calculate an average value, which is the final target stereo ranging result.

[0085] In the embodiments of the present application, as described above, the embodiments of the present application have at least the following technical effects:

[0086] The ranging task instruction of the target transmission line is obtained, wherein the ranging task instruction includes a ranging accuracy level; based on the ranging accuracy level, a dynamic microwave signal is generated according to a broadband microwave radiation source module, wherein the dynamic microwave signal includes J microwave signals corresponding to J frequency bands, and J is a positive integer greater than 1; the J microwave signals are transmitted to the target transmission line according to a microwave transmitting antenna, and the J reflected microwave signals of the target transmission line are received according to a microwave receiving antenna; based on the J microwave signals and the J reflected microwave signals, J ranging analysis results are output according to a microwave ranging analysis channel; transmission monitoring parameters of the J microwave signals and the J reflected microwave signals are collected to obtain a ranging signal transmission monitoring data set, and error compensation is performed on the J ranging analysis results according to the ranging signal transmission monitoring data set to obtain J ranging analysis optimization results; and the J ranging analysis optimization results are integrated to generate a target stereo ranging result. The present application solves the technical problems of low measurement accuracy and weak anti-interference ability in the prior art for transmission line ranging, generates dynamic microwave signals of multiple frequency bands by determining the ranging accuracy level, transmits and receives reflected signals of the target transmission line by using an antenna, outputs multiple ranging results, and finally generates an accurate target stereo ranging result after error compensation according to the transmission monitoring data, thereby achieving the technical effects of improving the measurement accuracy and anti-interference ability for transmission line ranging.

[0087] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0088] The above description is merely exemplary of the application, one skilled in the art will readily devise many variations and modifications of the application without departing from the scope of the application as defined by the following claims. Accordingly, all such variations and modifications are intended to be included within the scope of the application.

[0089] The description and drawings are merely illustrative of the application and do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the method and apparatus without departing from the scope or spirit of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for dynamic stereoscopic ranging of power transmission lines based on microwave interferometry, characterized in that, The method comprises: obtaining a ranging task instruction of a target power transmission line, wherein the ranging task instruction comprises a ranging accuracy level; based on the ranging accuracy level, generating a dynamic microwave signal according to a wideband microwave radiation source module, wherein the dynamic microwave signal comprises J microwave signals corresponding to J frequency bands, J is a positive integer greater than 1; transmitting the J microwave signals to the target power transmission line according to a microwave transmitting antenna, and receiving J reflected microwave signals of the target power transmission line according to a microwave receiving antenna; based on the J microwave signals and the J reflected microwave signals, outputting J ranging analysis results according to a microwave ranging analysis channel; collecting transmission monitoring parameters of the J microwave signals and the J reflected microwave signals, obtaining ranging signal transmission monitoring data sets, and performing error compensation on the J ranging analysis results according to the ranging signal transmission monitoring data sets, to obtain J ranging analysis optimization results; integrating the J ranging analysis optimization results to generate a target three-dimensional ranging result; the construction step of the microwave ranging analysis channel comprises: loading a sample signal spectrum analysis result set, a sample reflected signal spectrum analysis result set and a sample ranging analysis result set corresponding to a plurality of sample power transmission lines; based on the sample signal spectrum analysis result set, the sample reflected signal spectrum analysis result set and the sample ranging analysis result set, performing supervised learning on K base models to generate K microwave ranging analysis models meeting ranging analysis accuracy constraints, wherein K is a positive integer greater than 1, and the K base models are different from each other; taking the output data set of the K microwave ranging analysis models as input information and taking the sample ranging analysis result set as output information, training a microwave ranging analysis fusion model meeting the ranging analysis accuracy constraints; merging the K microwave ranging analysis models as parallel independent nodes to generate a microwave ranging analysis processing layer; merging the microwave ranging analysis processing layer and the input layer of the microwave ranging analysis fusion model to generate the microwave ranging analysis channel.

2. The method of claim 1, wherein, based on the J microwave signals and the J reflected microwave signals, outputting J ranging analysis results according to a microwave ranging analysis channel, comprising: based on a spectrum analysis device, performing spectrum analysis on the J microwave signals respectively to obtain J signal spectrum analysis results; based on the J reflected microwave signals, performing filter reconstruction to obtain J enhanced reflected signals; based on a spectrum analysis device, performing spectrum analysis on the J enhanced reflected signals respectively to obtain J reflected signal spectrum analysis results; based on the J signal spectrum analysis results and the J reflected signal spectrum analysis results, obtaining the J ranging analysis results according to the microwave ranging analysis channel.

3. The method of claim 2, wherein, based on the J reflected microwave signals, performing filter reconstruction to obtain J enhanced reflected signals, comprising: performing white noise addition according to the J reflected microwave signals to obtain a set of noise-added reflected signals; performing EMD decomposition according to the set of noise-added reflected signals to obtain a set of first signal intrinsic mode function features; According to the first signal eigenmode function feature sequence set, integration and averaging are performed to obtain a second signal eigenmode function feature sequence set; According to the second signal eigenmode function feature sequence set, denoising reconstruction is performed, and the J enhanced reflection signals are output.

4. The method of claim 1, wherein, According to the ranging signal transmission monitoring data set, error compensation is performed on the J ranging analysis results to obtain J ranging analysis optimization results, including: According to the J ranging analysis results, the jth ranging analysis result is extracted, where j is a positive integer and j belongs to J; Based on the jth ranging analysis result, feature extraction is performed on the ranging signal transmission monitoring data set to determine the jth signal transmission monitoring data and the jth reflection signal transmission monitoring data; Based on the jth signal transmission monitoring data, error compensation is performed on the jth ranging analysis result to obtain a jth ranging analysis correction result; Based on the jth reflection signal transmission monitoring data, error compensation is performed on the jth ranging analysis result to obtain a jth ranging analysis correction result; The jth ranging analysis correction result and the jth ranging analysis correction result are fused to output a jth ranging analysis optimization result, and the jth ranging analysis optimization result is added to the J ranging analysis optimization results.

5. The method of claim 4, wherein, Based on the jth signal transmission monitoring data, error compensation is performed on the jth ranging analysis result to obtain a jth ranging analysis correction result, including: According to the jth signal transmission monitoring data, feature recognition is performed to determine the jth signal transmission obstacle feature data and the jth signal transmission environment feature data; Based on the jth signal transmission obstacle feature data, attenuation error correction is performed on the jth ranging analysis result to determine the jth attenuation feature error correction data; Based on the jth signal transmission environment feature data, interference error correction is performed on the jth ranging analysis result to determine the jth interference feature error correction data; Based on the jth attenuation feature error correction data and the jth interference feature error correction data, compensation optimization is performed on the jth ranging analysis result to output the jth ranging analysis correction result.

6. The method of claim 5, wherein, Based on the jth signal transmission obstacle feature data, attenuation error correction is performed on the jth ranging analysis result to determine the jth attenuation feature error correction data, including: According to the jth signal transmission obstacle feature data, attenuation influence depth evaluation is performed to obtain a jth signal attenuation influence coefficient; It is judged whether the jth signal attenuation influence coefficient is greater than / equal to a signal attenuation influence threshold value; If the jth signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold value, the obstacle feature attenuation error correction model is activated; Based on the jth signal transmission obstacle feature data and the jth ranging analysis result, according to the obstacle feature attenuation error correction model, the jth attenuation feature error correction data is output.

7. The method of claim 5, wherein, Based on the jth signal transmission environment feature data, interference error correction is performed on the jth ranging analysis result to determine the jth interference feature error correction data, including: According to the jth signal transmission environment feature data, anomaly detection is performed to obtain jth signal transmission abnormal environment data; perform ranging interference evaluation based on the jth signal transmission abnormal environment data, to obtain a jth environment ranging interference coefficient; determine whether the jth environment ranging interference coefficient is greater than / equal to a predetermined environment ranging interference coefficient; if the jth environment ranging interference coefficient is greater than / equal to the predetermined environment ranging interference coefficient, perform interference error correction on the jth ranging analysis result according to the jth signal transmission abnormal environment data, to generate jth interference feature error correction data.

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