Power transmission line dynamic three-dimensional distance measurement method based on microwave interference

Abstract

The invention discloses a power transmission line dynamic three-dimensional distance measurement method based on microwave interference, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a distance measurement task instruction of a target power transmission line; generating a dynamic microwave signal based on the ranging precision grade; transmitting the J microwave signals to a target power transmission line, and receiving J reflection microwave signals of the target power transmission line; based on the J microwave signals and the J reflection microwave signals, outputting J ranging analysis results according to the microwave ranging analysis channel; collecting 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; and integrating J ranging analysis optimization results to generate a target stereo ranging result. According to the invention, the technical problems of low measurement precision and weak anti-interference capability during power transmission line distance measurement in the prior art are solved, and the technical effect of improving the measurement precision and the anti-interference capability of power transmission line distance measurement is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of signal processing, and in particular to a dynamic stereo distance measurement method for transmission lines based on microwave interference. Background Art

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

[0003] The present application provides a dynamic stereoscopic distance measurement method for transmission lines based on microwave interference, which is used to solve the technical problems of low measurement accuracy and weak anti-interference ability in the prior art when performing transmission line distance measurement.

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

[0005] The present application provides a dynamic stereo distance measurement method for transmission lines based on microwave interference, the method comprising: Obtaining a ranging task instruction of a target 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 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; transmitting the J microwave signals to the target transmission line according to a microwave transmitting antenna, and receiving J reflected microwave signals of the target transmission line according to a microwave receiving antenna; outputting J ranging analysis results according to a microwave ranging analysis channel based on the J microwave signals and the J reflected microwave signals; collecting transmission monitoring parameters of the J microwave signals and the J reflected microwave signals to obtain 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; integrating the J ranging analysis optimization results to generate a target stereo ranging result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application obtains a ranging task instruction of a target 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 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 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, according to a microwave ranging analysis channel, J ranging analysis results are output; 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; the J ranging analysis optimization results are integrated to generate a target stereo ranging result. The present invention solves the technical problems of low measurement accuracy and weak anti-interference ability in the prior art when performing transmission line distance measurement. Dynamic microwave signals of multiple frequency bands are generated by determining the distance measurement accuracy level, and the reflected signals of the target transmission line are transmitted and received by the antenna, and multiple distance measurement results are output. After error compensation is performed according to the transmission monitoring data, an accurate target stereo distance measurement result is finally generated, thereby achieving the technical effect of improving the measurement accuracy and anti-interference ability of the transmission line distance measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of a method for dynamic stereoscopic ranging of transmission lines based on microwave interference provided in an embodiment of the present application; Figure 2 A schematic diagram of the process of constructing a microwave ranging analysis channel in the dynamic stereo ranging method for transmission lines based on microwave interference provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application provides a dynamic stereo ranging method for transmission lines based on microwave interference, which is used to solve the technical problems of low measurement accuracy and weak anti-interference ability in the prior art when performing transmission line ranging. Dynamic microwave signals of multiple frequency bands are generated by determining the ranging accuracy level, and the reflected signals of the target transmission line are transmitted and received by the antenna, and multiple ranging results are output. After error compensation is performed according to the transmission monitoring data, an accurate target stereo ranging result is finally generated, thereby achieving the technical effect of improving the measurement accuracy and anti-interference ability of the transmission line ranging.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0012] Examples, such as Figure 1 As shown, the present application provides a dynamic stereo ranging method for transmission lines based on microwave interference, the method comprising: Step S100: Obtaining a distance measurement task instruction of a target transmission line, wherein the distance measurement task instruction includes a distance measurement accuracy level.

[0013] In an embodiment of the present application, the user first inputs the basic information of the ranging task, including the specific location of the target transmission line, the purpose of the ranging, etc., and then automatically generates the ranging task instructions based on the input task information. At this time, the detailed parameters of the 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 accuracy required in the measurement process. For example, for some critical high-voltage transmission lines, a higher ranging accuracy level is required. Finally, the information after the task parameters are set is integrated to generate the ranging task instructions.

[0014] Step S200: 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.

[0015] In an embodiment of the present application, the ranging accuracy level in the ranging task instruction is first parsed, and by calling the preset parameter table, a suitable frequency band range and signal characteristics are selected to meet different measurement requirements. For example, a high-precision ranging task requires a wider frequency band 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 mode. The commonly used method is to perform this through digital signal processing technology. Specifically, an appropriate frequency band range is selected, the transmission power is set to ensure that the signal strength covers the ranging range, and a suitable modulation method, such as frequency modulation or pulse modulation, is selected to optimize the signal transmission characteristics.

[0016] Then, the configured broadband microwave radiation source module is used to generate dynamic microwave signals in multiple frequency bands. The generation of these signals is completed by a frequency synthesizer, which can quickly switch and adjust the signal frequency in multiple frequency bands. To ensure that the time and phase characteristics of the signal achieve the desired effect, the frequency and phase of the signal are dynamically adjusted using a timing control circuit. The generation process includes calling the frequency synthesizer to generate microwave signals in 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 ranging.

[0017] Finally, J frequency band signals are selected according to the ranging accuracy level and environmental conditions. By using filters and 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 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 multi-band characteristics and can effectively complete the ranging task of transmission lines under different environmental conditions.

[0018] Through the above process, a dynamic microwave signal is generated, including J microwave signals corresponding to J frequency bands, where J is a positive integer greater than 1.

[0019] Step S300: transmitting the J microwave signals to the target power transmission line according to the microwave transmitting antenna, and receiving the J reflected microwave signals of the target power transmission line according to the microwave receiving antenna.

[0020] In an embodiment of the present application, first, the microwave signals of J frequency bands generated previously are transmitted to the target power transmission line simultaneously or sequentially through the configured microwave transmitting antenna. When these microwave signals reach the target power transmission line, they are reflected due to the physical properties of the power transmission line, such as 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 such as the distance and position of the power transmission line. These reflected microwave signals are then received by a microwave receiving antenna. The receiving antenna works in conjunction 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.

[0021] Finally, J reflected microwave signals were successfully received, which corresponded to the J frequency bands during the transmission. Each reflected signal contained information about the distance and reflection characteristics of the target transmission line.

[0022] Step S400: Based on the J microwave signals and the J reflected microwave signals, according to the microwave ranging analysis channel, J ranging analysis results are output.

[0023] In the embodiment of the present application, preliminary signal processing is first performed, using a spectrum analysis device to perform spectrum analysis on each microwave signal to extract characteristic information such as the frequency, amplitude and phase of the signal. Next, the J reflected microwave signals are filtered and reconstructed.

[0024] The microwave ranging analysis channel is then used to perform ranging analysis. Based on the difference between the transmitted signal and the reflected signal, the ranging result corresponding to each frequency band is calculated. Specifically, the analysis channel compares the phase difference, time delay, amplitude attenuation and other parameters between each transmitted signal and its corresponding reflected signal, thereby deriving the precise distance between the transmission line and the ranging device. Finally, J ranging analysis results are output, which correspond to the ranging values ​​of J frequency bands.

[0025] Furthermore, in the method provided in the embodiment of the application, based on the J microwave signals and the J reflected microwave signals, according to the microwave ranging analysis channel, J ranging analysis results are output, and the method further includes: Based on the spectrum analysis equipment, spectrum analysis is performed on the J microwave signals respectively to obtain J signal spectrum analysis results; based on the J reflected microwave signals, filtering and reconstruction are performed to obtain J enhanced reflected signals; based on the spectrum analysis equipment, spectrum analysis is performed 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, the J ranging analysis results are obtained according to the microwave ranging analysis channel.

[0026] In the embodiment of the present application, firstly, spectrum analysis is performed on J microwave signals respectively using a spectrum analysis device. The frequency, amplitude, phase and other parameters of the signal are measured by the spectrum analyzer, and these parameters reflect the basic characteristics of the signal. The specific steps include connecting the transmitting antenna to the spectrum analyzer, analyzing the microwave signals of J frequency bands, and recording the spectrum characteristics of each frequency band respectively. The output of this process is the spectrum analysis results of J signals, which contain detailed spectrum information of each microwave signal.

[0027] 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.

[0028] The enhanced reflected signal after filtering and reconstruction is then analyzed again using a spectrum analyzer. This method captures the frequency, amplitude, and phase characteristics of the reflected signal, which are crucial for subsequent ranging analysis. The specific steps include inputting the enhanced reflected signal into the spectrum analyzer, analyzing it frequency band by frequency band, and obtaining J reflected signal spectrum analysis results. These results provide spectral feature information of the enhanced reflected signal to ensure the accuracy of signal analysis.

[0029] Finally, the microwave ranging analysis channel is used to perform ranging calculations. 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 through calculation.

[0030] Furthermore, in the method provided in the embodiment of the application, filtering and reconstructing are performed based on the J reflected microwave signals to obtain J enhanced reflected signals, and the method further includes: White noise is added according to the J reflected microwave signals to obtain a noisy reflected signal set; EMD decomposition is performed according to the noisy reflected signal set to obtain a first signal intrinsic mode function feature sequence set; integration and averaging are performed according to the first signal intrinsic mode function feature sequence set to obtain a second signal intrinsic mode function feature sequence set; denoising and reconstruction are performed according to the second signal intrinsic mode function feature sequence set to output the J enhanced reflected signals.

[0031] In an embodiment of the present application, white noise is first added to the received J reflected microwave signals. Specifically, the J reflected microwave signals are input into a signal processing module, and an appropriate amount of white noise is added to each signal. This process uses a white noise generator that generates uniformly distributed random noise. The white noise is superimposed on each reflected signal to form a noisy reflected signal set.

[0032] Next, the EMD decomposition of the noisy reflection signal set is performed. First, the signals in each noisy reflection signal set are input into the EMD algorithm in turn, and the input noisy reflection signals are analyzed to find all the local maxima and local minima of the signal. By traversing the signal data, all the extreme points of the signal, that is, the peaks and valleys in the signal, are determined. Next, the upper envelope is constructed through all local maximum points using the interpolation method; the lower envelope is constructed through all local minimum points using the same interpolation method. The spline interpolation method is used to interpolate the maximum points and the minimum points respectively to generate the upper envelope and the lower envelope. Then the average value of the upper envelope and the lower envelope is calculated to obtain the current trend component, which is called the "residual". The original signal is subtracted from this residual, and the part obtained is the first IMF component. If the IMF meets the conditions defined by the IMF, that is, zero mean, local symmetry, etc., it is used as the final IMF component; otherwise, this result is used as the signal for the next round of EMD decomposition until the conditions are met. After extracting the first IMF, it is separated from the original signal, and the remaining signal part, that is, the residual part, is used as the new input signal. The above extreme point search, envelope construction and IMF extraction process are repeated until all IMF components are obtained. Finally, after multiple rounds of decomposition, the characteristic sequence set of the first signal intrinsic mode function is obtained.

[0033] Then, the characteristic sequence set of the first signal intrinsic mode function is integrated and averaged. Specifically, the IMF is first weighted averaged, and the weight of each IMF is determined by analyzing the signal-to-noise ratio (SNR) and frequency characteristics. The signal-to-noise ratio and frequency characteristics are combined to determine the weight of each IMF. is the weighting coefficient or weight of the ith IMF, is the signal-to-noise ratio of the ith IMF, It is the frequency characteristic of the i-th IMF, usually the dominant frequency or the characteristic frequency in the spectrum. and These are two weight coefficients used to adjust the signal-to-noise ratio and the influence of frequency characteristics, which are pre-set by technical experts. These weights are then normalized to ensure that the sum of the weights of all IMFs is 1.

[0034] Next, the weighted averaged IMF is integrated and processed using low-pass filters or Kalman filters to smooth the high-frequency components in the signal and remove excess noise. Through this integrated processing step, all filtered IMFs are reintegrated into a comprehensive signal feature set. This process can effectively reduce errors and optimize the expression of signal features. After weighted averaging and integration processing, the second signal intrinsic mode function feature sequence set is generated.

[0035] Finally, denoising and reconstruction are performed based on the characteristic sequence set of the second signal intrinsic mode function. In this step, the processed IMF is reassembled into a complete signal using a denoising algorithm. The residual noise components are removed by a filter, the useful signal within the specified frequency range is retained, and the reconstruction algorithm such as the inverse wavelet transform is used to integrate this information into an enhanced signal. Finally, J enhanced reflection signals are output.

[0036] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the step of constructing the microwave ranging analysis channel also includes: 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; perform supervised learning on K basis 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, and generate K microwave ranging analysis models that meet the ranging analysis accuracy constraint, wherein K is a positive integer greater than 1, and the K basis models are different from each other; use the output data sets of the K microwave ranging analysis models as input information and the sample ranging analysis result set as output information to train a microwave ranging analysis fusion model that meets the ranging analysis accuracy constraint; merge the K microwave ranging analysis models as parallel independent nodes to generate a microwave ranging analysis processing layer; merge the microwave ranging analysis processing layer with the input layer of the microwave ranging analysis fusion model to generate the microwave ranging analysis channel.

[0037] In an embodiment of the present application, firstly, 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 loaded. These data sets are obtained by performing spectrum analysis and reflection analysis on the historical ranging data of the transmission lines, reflecting the transmission and reflection characteristics of signals of different frequencies. These sample data sets are extracted from a database, wherein the sample signal spectrum analysis result set contains the signal spectrum characteristics under different frequency bands, the sample reflection signal spectrum analysis result set records the spectrum characteristics of the corresponding reflection signal, and the sample ranging analysis result set provides the historical ranging results calculated based on these spectrum characteristics.

[0038] Next, supervised learning is performed on the K base models based on the sample data set obtained above. Here, the sample signal spectrum analysis results and the sample reflection signal spectrum analysis results are used as input features, and the sample ranging analysis results are used as output labels. K base models are trained by setting different model architectures, parameters or training methods. Common methods include regression models, such as linear regression and support vector regression, which are used to establish linear or nonlinear relationships between spectral features and ranging results; neural networks, such as multilayer perceptrons or convolutional neural networks, to capture more complex nonlinear relationships; and decision tree models, such as random forests, to process the discretization or nonlinear mapping of spectral features. In the training process of each model, the model parameters are optimized according to the spectral characteristics of the input data and the corresponding ranging results, and finally K different microwave ranging analysis models are generated.

[0039] After generating K base models, the output data sets of K microwave ranging analysis models are used as input information, and the sample ranging analysis result set is used as output information to train the microwave ranging analysis fusion model. In this stage, the fusion learning method is used to comprehensively process the output results of each base model. Common fusion methods include weighted average method, which weights and sums the outputs of each base model. When determining the weight of each model, the performance of each base model on the training set is first evaluated, which is achieved by calculating the mean square error or absolute error of each base model on the training data. Based on these evaluation indicators, the weight of each base model is determined. Specifically, the error of each model is calculated first, and then the inverse of each error is taken as the preliminary weight. Then, the preliminary weights of all models are added together 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 used as input to train a more complex model, such as linear regression or neural network, to further optimize the prediction results. 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.

[0040] 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. Its purpose is to process data from different frequency bands or different features at the same time to improve the system's computing efficiency and ranging accuracy. By configuring each base model as an independent processing node, parallel computing technologies such as multi-threaded 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 system's processing efficiency and ranging accuracy.

[0041] Finally, the microwave ranging analysis processing layer is merged with the input layer of the microwave ranging analysis fusion model 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, which processes these results and outputs 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.

[0042] Step S500: collecting transmission monitoring parameters of the J microwave signals and the J reflected microwave signals to obtain 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.

[0043] In an embodiment of the present application, first, the monitoring parameters of J microwave signals and their corresponding reflected microwave signals during the transmission process are collected by a signal receiver, a sensor and a data recorder, including the transmission time of the signal, signal strength, frequency change, signal attenuation, etc. 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 preliminarily calculated are error compensated, and the influence of these factors on the ranging results is calculated by analyzing the factors such as signal attenuation and interference recorded in the transmission monitoring data set, and these influences are corrected. Through error compensation, J ranging analysis optimization results are generated.

[0044] Furthermore, in the method provided in the embodiment of the application, 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 method further includes: According to the J ranging analysis results, extract the j-th ranging analysis result, wherein j is a positive integer and j belongs to J; based on the j-th ranging analysis result, perform feature extraction on the ranging signal transmission monitoring data set to determine the j-th signal transmission monitoring data and the j-th reflection signal transmission monitoring data; perform error compensation on the j-th ranging analysis result based on the j-th signal transmission monitoring data to obtain the j-th ranging analysis correction result; perform error compensation on the j-th ranging analysis result based on the j-th reflection signal transmission monitoring data to obtain the j-th ranging analysis correction result; fuse the j-th ranging analysis correction result and the j-th ranging analysis correction result, output the j-th ranging analysis optimization result, and add the j-th ranging analysis optimization result to the J ranging analysis optimization results.

[0045] In an embodiment of the present application, the jth ranging analysis result is first extracted from the J ranging analysis results that have been calculated, where j is a positive integer between 1 and J, representing the specific ranging analysis result currently being processed. Next, the jth ranging analysis result is analyzed, and 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. The signal transmission characteristics related to the jth ranging result are extracted therefrom to form the jth signal transmission monitoring data; at the same time, the reflection signal transmission characteristics related to the jth ranging result are extracted to form the jth reflection signal transmission monitoring data. To ensure the analyzability of these data, feature extraction algorithms such as Fourier transform or wavelet transform are used to convert these monitoring data into frequency domain or time domain features that are easy to analyze.

[0046] After obtaining the characteristic data, the jth ranging analysis result is subjected to error compensation based on the signal transmission monitoring data. In this stage, the jth signal transmission monitoring data is analyzed to identify the error factors that may be introduced during the signal transmission process, such as signal attenuation, delay, interference, etc.

[0047] The preliminary j-th ranging analysis result is corrected according to the characteristics of the monitoring data. Through this step, the j-th ranging analysis correction result is generated. At the same time, based on the monitoring data transmitted by the j-th reflection signal, the j-th ranging analysis result is further error compensated. The compensation process is similar to the process of error compensation for the ranging analysis result, and the j-th ranging analysis correction result is obtained through the same process.

[0048] Then, the jth range resolution correction result is fused with the jth range resolution correction result to generate the final jth range resolution optimization result. In this fusion process, the two correction results are combined by weighted averaging to ensure that the final result combines the correction advantages of signal transmission and reflection paths. When performing weighted averaging, the jth range resolution correction result and the jth range resolution correction result have the same weight. Through the weighted averaging process, the jth range resolution correction result and the jth range resolution correction result are combined to generate a comprehensive optimization result, that is, the generated jth range resolution optimization result, and the jth range resolution optimization result is added to the J range resolution optimization results.

[0049] Finally, repeat the above steps to obtain J ranging analysis optimization results.

[0050] Furthermore, in the method provided in the embodiment of the application, error compensation is performed on the j-th ranging analysis result based on the j-th signal transmission monitoring data to obtain the j-th ranging analysis correction result, and further includes: Perform feature identification based on the j-th signal transmission monitoring data to determine the j-th signal transmission obstacle feature data and the j-th signal transmission environment feature data; perform attenuation error correction on the j-th ranging analysis result based on the j-th signal transmission obstacle feature data to determine the j-th attenuation feature error correction data; perform interference error correction on the j-th ranging analysis result based on the j-th signal transmission environment feature data to determine the j-th interference feature error correction data; perform compensation optimization on the j-th ranging analysis result based on the j-th attenuation feature error correction data and the j-th interference feature error correction data, and output the j-th ranging analysis correction result.

[0051] In an embodiment of the present application, feature recognition is first performed from the j-th signal transmission monitoring data. This step uses a pattern recognition algorithm, such as a support vector machine, a neural network, or a decision tree model. These models are pre-trained. During the training process, historical signal transmission data are used as a training set. These data have been labeled with features such as obstacle types and environmental interference conditions. Through training, the model learns to recognize similar features in new data. After obtaining the j-th signal transmission monitoring data, the trained model is used to extract the j-th signal transmission obstacle feature data (such as the distance, material, and size of the obstacle) and the j-th signal transmission environment feature data (such as the intensity and location of electromagnetic interference and weather conditions).

[0052] Next, based on the identified characteristic data of the jth signal transmission obstacle, the jth ranging analysis result is corrected for attenuation errors. Signal attenuation models are used, which are also trained. During the training process, a large amount of obstacle data of different materials and sizes is used, and the model parameters are adjusted to enable it to accurately predict the degree of attenuation of the signal when passing through different obstacles. The trained attenuation model can calculate the attenuation value of the signal based on the characteristic data of the jth signal transmission obstacle. Then, this attenuation value is used to adjust the jth ranging analysis result to generate the jth attenuation characteristic error correction data, which corrects the attenuation error caused by the obstacle.

[0053] At the same time, based on the j-th signal transmission environment characteristic data, the j-th ranging analysis result is corrected for interference errors. The interference correction model used here has also undergone a similar training process. The model is trained using historical data containing various electromagnetic interference and weather conditions so that it can identify and correct environmental interference to the signal. The trained interference correction model can calculate the interference impact on the signal during transmission based on the j-th signal transmission environment characteristic data. Use these calculation results to correct the j-th ranging analysis result and generate the j-th interference characteristic error correction data.

[0054] 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 optimization process combines the attenuation error correction data and the interference error correction data through a weighted fusion method. In this process, the weights of the two correction data are the same, and the fused result is used as the final jth ranging analysis correction result, and the jth ranging analysis correction result is output.

[0055] Furthermore, in the method provided in the embodiment of the application, based on the j-th signal transmission obstacle characteristic data, the j-th ranging analysis result is corrected for attenuation error to determine the j-th attenuation characteristic error correction data, and further includes: Perform attenuation influence depth evaluation according to the j-th signal transmission obstacle characteristic data to obtain the j-th signal attenuation influence coefficient; determine whether the j-th signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold; if the j-th signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold, activate the obstacle characteristic attenuation error correction model; based on the j-th signal transmission obstacle characteristic data and the j-th ranging analysis result, output the j-th attenuation characteristic error correction data according to the obstacle characteristic attenuation error correction model.

[0056] In the embodiment of the present application, the attenuation impact depth is first evaluated based on the characteristic data of the jth signal transmission obstacle. In this step, the attenuation of the signal is directly calculated based on the existing electromagnetic wave propagation theory and empirical formula. Usually, the attenuation of the signal is estimated by the free space path loss formula or based on the physical properties of the obstacle (such as material, thickness and distance). For example, the following basic formula is used to calculate the attenuation: ; Where L is the attenuation, d is the signal propagation distance, and λ is the wavelength of the signal. is the attenuation coefficient related to the obstacle material, and h is the obstacle thickness. Next, a quantitative result is obtained through calculation, that is, the jth signal attenuation influence coefficient.

[0057] Then, it is determined whether the j-th signal attenuation influence coefficient exceeds a preset signal attenuation influence threshold. This threshold is set by technical experts through previous actual measurements to distinguish the severity of signal attenuation. The calculated attenuation influence coefficient is compared with the preset threshold. If the attenuation influence coefficient is lower than the threshold, it means that the obstacle has little impact on the signal and this impact can be ignored; but if the attenuation influence coefficient is greater than or equal to the threshold, it means that the obstacle has a significant impact on the signal and error correction is required.

[0058] If the j-th 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, a historical data set containing different obstacle features, such as material, thickness, distance, etc. and their corresponding signal attenuation data, is first collected from the historical database. Then, these data are preprocessed, including feature extraction, normalization, and denoising, to ensure the quality and consistency of the data. Then, a suitable machine learning algorithm, such as multivariate regression, random forest, or neural network, is selected to train the model using the preprocessed data so that it can learn the relationship between obstacle features and signal attenuation. During the training process, cross-validation and other techniques are used to optimize the model parameters to prevent overfitting and improve the generalization ability of the model. After training, the model is evaluated using an independent test set to verify its prediction accuracy and stability. Finally, the trained and verified obstacle feature attenuation error correction model can accurately predict and correct the attenuation error in signal transmission based on the obstacle feature data collected in real time.

[0059] Next, the j-th signal transmission obstacle feature data and the j-th 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 preliminary ranging data, which may have been affected by the obstacle. The correction model uses these input data to correct the error caused by the obstacle and outputs the j-th attenuation feature error correction data.

[0060] Furthermore, in the method provided in the embodiment of the application, interference error correction is performed on the j-th ranging analysis result based on the j-th signal transmission environment characteristic data to determine the j-th interference characteristic error correction data, and further includes: Anomaly detection is performed according to the j-th signal transmission environment characteristic data to obtain the j-th signal transmission abnormal environment data; ranging interference evaluation is performed based on the j-th signal transmission abnormal environment data to obtain the j-th environment ranging interference coefficient; it is determined whether the j-th environment ranging interference coefficient is greater than / equal to the predetermined environment ranging interference coefficient; if the j-th environment ranging interference coefficient is greater than / equal to the predetermined environment ranging interference coefficient, interference error correction is performed on the j-th ranging analysis result according to the j-th signal transmission abnormal environment data to generate the j-th interference characteristic error correction data.

[0061] In the embodiment of the present application, firstly, anomaly detection is performed according to the j-th signal transmission environment characteristic data. In this step, an anomaly detection method based on statistical analysis is used to identify abnormal conditions in the environment characteristic data during signal transmission. This method identifies abnormal data points that are significantly deviated from normal values ​​by analyzing the basic statistical characteristics of the signal, such as mean, standard deviation, rate of change, etc. Abnormal conditions include electromagnetic interference, extreme weather conditions, etc. When performing anomaly detection, environmental characteristic data are first obtained from the signal transmission monitoring data, which include signal strength, frequency change, noise level, etc. collected in real time. Then, the collected environmental characteristic data are statistically analyzed to calculate basic statistical indicators such as the mean and standard deviation of the data. By comparing the statistical characteristics of real-time data with historical data, abnormal data points are identified. For example, if the fluctuation range of the current signal strength exceeds the normal range, such as exceeding three times the standard deviation of the mean, the signal strength may be affected by abnormal environmental factors. Once an abnormality is detected, it is recorded as the j-th signal transmission abnormal environment data.

[0062] Next, the ranging interference evaluation is performed based on the j-th signal transmission abnormal environment data to quantify the impact of the environmental anomaly on the ranging result. In this application, the impact of the interference is calculated by the following formula: ; in, is the jth environment ranging interference coefficient, is the electromagnetic interference intensity, is the signal frequency offset, is the noise level, a, b, c are coefficients pre-set by technical experts. The calculated j-th environmental ranging interference coefficient is used to indicate the degree of interference of environmental anomalies on ranging results. The higher the value, the greater the impact of interference on ranging results.

[0063] Next, the jth environment ranging interference coefficient is compared with the predetermined environment ranging interference coefficient, where the predetermined environment 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, it is considered that the interference has little effect on the ranging result and can be ignored; if the coefficient is greater than or equal to the threshold, further interference error correction is required.

[0064] When the j-th environment ranging interference coefficient is greater than / equal to a predetermined threshold, the j-th ranging analysis result is corrected for interference errors according to the j-th signal transmission abnormal environment data. Specifically, filtering technology or signal reconstruction algorithm is used to correct errors in ranging results. First, the interference error correction method is activated, such as using a Kalman filter to smooth the interfered signal, or using a signal reconstruction algorithm to reconstruct the original signal. Then, the interference factors in the abnormal environment data are used as input, the specific interference influence is calculated, and it is eliminated or compensated from the preliminary ranging results. Specific methods include adjusting the propagation time of the signal, correcting the strength of the signal, etc. Finally, the corrected data is generated, i.e., the j-th interference feature error correction data.

[0065] Similar to the above process of obtaining the j-th distance measurement analysis correction result, the j-th distance measurement analysis correction result is obtained as follows: 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 enable it 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 environment 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, which is based on electromagnetic wave propagation theory and empirical formulas, such as the free space path loss formula. This formula is used to calculate the attenuation value of the signal when passing through the obstacle 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 interference error of the jth ranging analysis result is corrected based on the jth signal transmission environment feature data. In this step, anomaly detection is first performed. Using a statistical analysis-based method, anomalies in the environment, such as electromagnetic interference or extreme weather, are identified by calculating the mean and standard deviation of signal strength, frequency variation, and noise level. Once an anomaly is identified, it is recorded as the jth signal transmission abnormal environment data. Then, the degree of interference of these environmental anomalies on the signal is quantified, and the jth environmental ranging interference coefficient is calculated. This coefficient is compared with the predetermined interference threshold. If the coefficient exceeds the threshold, it indicates that the interference is significant. A low-pass filter is used to smooth the signal to reduce the impact of noise, and finally the jth interference feature error correction data is generated.

[0066] Finally, the jth attenuation characteristic error correction data and the jth interference characteristic 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 weighted process, the attenuation correction and interference correction have the same weight, which ensures that the contribution of the two correction factors in the final result is balanced.

[0067] Step S600: Integrate the J distance measurement analysis optimization results to generate a target stereo distance measurement result.

[0068] In the embodiment of the present application, 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 together, and the sum is divided by the number of J to calculate an average value, which is the final target stereo ranging result.

[0069] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application obtains a ranging task instruction of a target 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 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 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, according to a microwave ranging analysis channel, J ranging analysis results are output; 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; the J ranging analysis optimization results are integrated to generate a target stereo ranging result. The present invention solves the technical problems of low measurement accuracy and weak anti-interference ability in the prior art when performing transmission line distance measurement. Dynamic microwave signals of multiple frequency bands are generated by determining the distance measurement accuracy level, and the reflected signals of the target transmission line are transmitted and received by the antenna, and multiple distance measurement results are output. After error compensation is performed according to the transmission monitoring data, an accurate target stereo distance measurement result is finally generated, thereby achieving the technical effect of improving the measurement accuracy and anti-interference ability of the transmission line distance measurement.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0072] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A dynamic stereo distance measurement method for transmission lines based on microwave interferometry, characterized in that: The method comprises: Obtaining a ranging task instruction of a target 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 the broadband microwave radiation source module, wherein the dynamic microwave signal includes J microwave signals corresponding to J frequency bands, where J is a positive integer greater than 1; Transmitting the J microwave signals to the target transmission line according to the microwave transmitting antenna, and receiving the J reflected microwave signals of the target transmission line according to the 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 to obtain 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; The J distance measurement analysis optimization results are integrated to generate a target stereo distance measurement result.

2. The method according to claim 1, characterized in that Based on the J microwave signals and the J reflected microwave signals, according to the microwave ranging analysis channel, J ranging analysis results are output, including: Based on the spectrum analysis equipment, spectrum analysis is performed on the J microwave signals respectively to obtain spectrum analysis results of J signals; Perform filtering and reconstruction based on the J reflected microwave signals to obtain J enhanced reflected signals; Based on the spectrum analysis device, spectrum analysis is performed on the J enhanced reflected signals respectively to obtain spectrum analysis results of J reflected signals; Based on the J signal spectrum analysis results and the J reflected signal spectrum analysis results, the J ranging analysis results are obtained according to the microwave ranging analysis channel.

3. The method according to claim 2, characterized in that Performing filtering and reconstruction based on the J reflected microwave signals to obtain J enhanced reflected signals includes: Adding white noise according to the J reflected microwave signals to obtain a noisy reflected signal set; Perform EMD decomposition according to the noisy reflected signal set to obtain a first signal intrinsic mode function feature sequence set; Integrate and average the first signal intrinsic mode function feature sequence set to obtain a second signal intrinsic mode function feature sequence set; De-noising and reconstruction are performed according to the characteristic sequence set of the eigenmode function of the second signal, and the J enhanced reflection signals are output.

4. The method according to claim 1, characterized in that The steps of constructing the microwave ranging analysis channel include: Loading 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; According to the sample signal spectrum analysis result set, the sample reflection signal spectrum analysis result set and the sample ranging analysis result set, supervised learning is performed on K basis models to generate K microwave ranging analysis models that meet the ranging analysis accuracy constraint, wherein K is a positive integer greater than 1, and the K basis models are different from each other; Taking the output data sets of the K microwave ranging analysis models as input information and the sample ranging analysis result set as output information, training 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 is merged with the input layer of the microwave ranging analysis fusion model to generate the microwave ranging analysis channel.

5. The method according to claim 1, characterized in that 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, including: Extracting the j-th ranging analysis result according to the J ranging analysis results, wherein j is a positive integer and j belongs to J; Based on the j-th ranging analysis result, feature extraction is performed on the ranging signal transmission monitoring data set to determine the j-th signal transmission monitoring data and the j-th reflection signal transmission monitoring data; Performing error compensation on the j-th ranging analysis result based on the j-th signal transmission monitoring data to obtain a j-th ranging analysis correction result; Performing error compensation on the j-th ranging analysis result based on the j-th reflection signal transmission monitoring data to obtain the j-th ranging analysis correction result; The j-th ranging analysis correction result and the j-th ranging analysis correction result are integrated to output the j-th ranging analysis optimization result, and the j-th ranging analysis optimization result is added to the J ranging analysis optimization results.

6. The method according to claim 5, characterized in that Performing error compensation on the j-th ranging analysis result based on the j-th signal transmission monitoring data to obtain the j-th ranging analysis correction result includes: Performing feature recognition according to the j-th signal transmission monitoring data to determine the j-th signal transmission obstacle feature data and the j-th signal transmission environment feature data; Performing attenuation error correction on the j-th ranging analysis result based on the j-th signal transmission obstacle characteristic data to determine the j-th attenuation characteristic error correction data; Perform interference error correction on the j-th ranging analysis result based on the j-th signal transmission environment characteristic data to determine the j-th interference characteristic error correction data; The jth ranging analysis result is compensated and optimized based on the jth attenuation characteristic error correction data and the jth interference characteristic error correction data, and the jth ranging analysis correction result is output.

7. The method according to claim 6, characterized in that Performing attenuation error correction on the j-th ranging analysis result based on the j-th signal transmission obstacle characteristic data to determine the j-th attenuation characteristic error correction data includes: Performing attenuation influence depth evaluation according to the j-th signal transmission obstacle characteristic data to obtain the j-th signal attenuation influence coefficient; Determine whether the j-th signal attenuation influence coefficient is greater than / equal to a signal attenuation influence threshold; If the j-th signal attenuation influence coefficient is greater than / equal to the signal attenuation influence threshold, activating the obstacle characteristic attenuation error correction model; Based on the j-th signal transmission obstacle characteristic data and the j-th ranging analysis result, the j-th attenuation characteristic error correction data is output according to the obstacle characteristic attenuation error correction model.

8. The method according to claim 6, characterized in that Performing interference error correction on the j-th ranging analysis result based on the j-th signal transmission environment characteristic data to determine the j-th interference characteristic error correction data includes: Perform anomaly detection according to the j-th signal transmission environment characteristic data to obtain j-th signal transmission abnormal environment data; Performing ranging interference evaluation based on the j-th signal transmission abnormal environment data to obtain a j-th environment ranging interference coefficient; Determining whether the j-th environmental ranging interference coefficient is greater than / equal to a predetermined environmental 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 to generate the jth interference feature error correction data.

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