Signal-to-noise evaluation method in HPLC + HRF dual-mode communication system

Abstract

The invention relates to the technical field of power system unmanned aerial vehicle inspection. The invention relates to a signal-to-noise evaluation method in an HPLC (High Performance Liquid Chromatography) + HRF (High Radio Frequency) dual-mode communication system. The method comprises the following steps: acquiring an HPLC signal sampling sequence and an HRF signal sampling sequence of each unmanned aerial vehicle node, and forming a space-time signal matrix according to the nodes; performing sparse reconstruction on the space-time signal matrix to generate a sparse reconstruction signal and a residual matrix; and carrying out time-frequency transformation on the residual matrix to generate a time-frequency residual image, carrying out two-dimensional adaptive filtering based on an inter-node element difference value in the residual matrix and the sparse reconstruction signal, and outputting a filtering residual error. According to the scheme, the space-time correlation matrix is constructed by jointly counting the signal characteristics of time and space dimensions, so that the propagation path and the distribution mode of noise can be represented more accurately, the robustness of signal-noise evaluation is improved, a basis is provided for dynamically adjusting filtering parameters and an anomaly detection threshold value, and the reliability of signal-noise evaluation is improved. And finally, the accuracy and the real-time performance of a signal-to-noise evaluation result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle inspection of power systems, in particular to a signal-to-noise evaluation method in a HPLC+HRF dual-mode communication system. BACKGROUND

[0002] In the operation and maintenance process of modern power systems, real-time monitoring of transmission lines is of great significance to ensure the safe and stable operation of the power grid. With the development of unmanned aerial vehicle inspection technology, cooperative monitoring based on unmanned aerial vehicle groups has gradually become the mainstream way of transmission line operation and maintenance. Unmanned aerial vehicles usually rely on two types of communication methods during inspection: one is power line communication (HPLC) using transmission lines as transmission media, and the other is high-speed radio frequency communication (HRF) to realize wireless interconnection between unmanned aerial vehicles and the ground or between unmanned aerial vehicles. HPLC communication has the advantage of long-distance transmission using existing power lines, and HRF communication shows flexibility in dynamic networking and real-time control. Therefore, in the transmission line inspection scenario, the dual-mode fusion communication system of HPLC and HRF becomes an important basis for realizing real-time cooperation and task scheduling of unmanned aerial vehicle groups.

[0003] However, in the process of implementing the technical solutions of the embodiments of the present application, it is found that the above-mentioned technology at least has the following technical problems: In the existing HPLC and HRF dual-mode communication system, the evaluation of communication link quality mainly relies on traditional signal-to-noise ratio measurement methods. However, due to the existence of strong spatial correlation and temporal dynamic noise near the transmission line, the above methods are difficult to capture the variation law in the space-time dimension, resulting in signal-to-noise estimation lag or distortion. SUMMARY

[0004] The purpose of the present application is to provide a signal-to-noise evaluation method in a HPLC+HRF dual-mode communication system to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, a signal-to-noise evaluation method in a HPLC+HRF dual-mode communication system is provided, comprising the following steps: S1, obtaining the HPLC signal sampling sequence and the HRF signal sampling sequence of each unmanned aerial vehicle node, and then forming a space-time signal matrix according to the nodes; S2, performing sparse reconstruction on the space-time signal matrix to generate a sparse reconstruction signal and a residual matrix; S3, performing time-frequency transformation on the residual matrix to generate a time-frequency residual map, and performing two-dimensional adaptive filtering based on the element difference between the nodes in the residual matrix and the sparse reconstruction signal, and outputting the filtered residual; S4, constructing a space-time fusion matrix based on the filtered residual and the sparse reconstruction signal, performing space-time anomaly detection, and generating a dynamic anomaly score matrix; S5, outputting a multi-dimensional weighted power matrix by using the sparse reconstruction signal, the filtered residual and the dynamic anomaly score matrix; S6, calculating a node local signal-to-noise evaluation result according to the multi-dimensional weighted power matrix and the dynamic anomaly score matrix, and performing weighted aggregation on all node local signal-to-noise evaluation results to obtain a signal-to-noise evaluation result.

[0006] As a further improvement of the technical solution, the S1 further comprises calculating inter-node space-time correlation after forming the space-time signal matrix by the node, and specifically comprises: statistically combining the HPLC signal sampling sequence and the HRF signal sampling sequence in the time dimension and the space dimension to obtain an inter-node space-time correlation matrix.

[0007] As a further improvement of the technical solution, the S2 performing sparse reconstruction on the space-time signal matrix to generate a sparse reconstruction signal and a residual matrix specifically comprises: performing sparse reconstruction on the space-time signal matrix to generate a sparse reconstruction signal and a residual matrix; further generating a local noise sensitivity matrix according to the residual matrix.

[0008] As a further improvement of the technical solution, the S2 further generating a local noise sensitivity matrix according to the residual matrix comprises the following steps: ; wherein, denotes a local noise sensitivity value of the node i at the time t, denotes a residual value of the node i at the time t, denotes a time mean value of the residual of the node i, denotes a time standard deviation of the residual of the node i, and ε denotes a small constant for preventing division by zero.

[0009] As a further improvement of the technical solution, the S3 two-dimensional adaptive filtering specifically comprises: performing short-time Fourier transform on the residual matrix to generate a time-frequency graph; dynamically adjusting a filtering window and a filtering coefficient based on the difference between the elements in the residual matrix and the sparse reconstruction signal, combining the local noise sensitivity matrix, and outputting a filtered residual.

[0010] As a further improvement of the technical solution, the S4 performing space-time anomaly detection specifically comprises: constructing a space-time fusion matrix based on the filtered residual and the sparse reconstruction signal; outputting a dynamic anomaly score matrix by twice weighting the space-time fusion matrix with the sparse reconstruction signal and the local noise sensitivity matrix.

[0011] As a further improvement of the technical solution, the step of outputting the dynamic anomaly score matrix S4 is as follows: ; Wherein, S i (t) represents the dynamic anomaly score of node i at time t, represents the space-time fusion signal value of node i at time t, represents the sparse reconstruction signal value of node i at time t, and α represents the anomaly score scaling coefficient, and β represents the noise sensitivity weighting coefficient.

[0012] As a further improvement of the technical solution, the S5 outputs the multi-dimensional weighted power matrix using the sparse reconstruction signal, the filtered residual and the dynamic anomaly score matrix, which specifically comprises: The multi-dimensional weighted power matrix is output by performing multi-factor superposition calculation on the sparse reconstruction signal, the filtered residual, the local noise sensitivity matrix and the dynamic anomaly score matrix.

[0013] As a further improvement of the technical solution, the S6 performs weighted aggregation on the local signal-to-noise evaluation results of all nodes to obtain the signal-to-noise evaluation result, which specifically comprises: performing weighted aggregation on the local signal-to-noise evaluation results of all nodes; introducing the inter-node space-time correlation matrix in the aggregation process to obtain the signal-to-noise evaluation result.

[0014] Compared with the prior art, the application has the following advantages: 1. The present application can more accurately represent the propagation path and distribution mode of noise by constructing a space-time correlation matrix by jointly statistical signal characteristics in time and space dimensions, thereby improving the robustness of signal-to-noise evaluation, providing a basis for dynamically adjusting the filtering parameters and the anomaly detection threshold, and ultimately improving the accuracy and real-time performance of the signal-to-noise evaluation result.

[0015] 2. The present application can accurately capture the noise intensity difference of different spatial positions and time periods by constructing a local noise sensitivity matrix, thereby providing a dynamic adjustment basis for subsequent adaptive filtering and improving the effectiveness of subsequent filtering and the accuracy of signal-to-noise evaluation.

[0016] 3. The present application can effectively distinguish between independent noise events and systematic interference by introducing a space-time correlation matrix to dynamically adjust the weight, so that the final signal-to-noise evaluation result can accurately reflect the real communication quality of the unmanned aerial vehicle group in the transmission line monitoring, thereby providing a reliable basis for the cooperative control of unmanned aerial vehicles and improving the robustness of the evaluation result. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The present application is a whole flow chart. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to 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 of the present application. 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.

[0019] In the prior art, power line monitoring relies on cooperative operation of a drone swarm, a communication system of which adopts HPLC and HRF dual-mode fusion technology, and a traditional signal-to-noise evaluation method is based on static signal analysis and is difficult to cope with a complex electromagnetic environment around a power line. Due to the coupling effect of spatial correlation noise and time dynamic interference, a conventional measurement method cannot accurately separate effective signals and background noise, resulting in that a signal-to-noise ratio calculation result deviates from an actual link quality.

[0020] In order to solve the above problems, it is necessary to establish an analysis model capable of synchronously capturing signal space-time characteristics. Through analysis, it is found that noise presents non-uniform distribution characteristics in space-time dimensions, and a single-dimensional filtering method cannot eliminate cross interference. Therefore, it is proposed to construct node signals into a multi-dimensional matrix, separate signal and noise components through sparse reconstruction, further consider time-frequency characteristics of a residual matrix, adopt a dynamically adjusted filtering strategy, and combine an abnormality detection mechanism to realize dynamic correction of signal-to-noise evaluation.

[0021] Please refer to Figure 1 The embodiment is aimed to provide a signal-to-noise evaluation method in an HPLC+HRF dual-mode communication system, applied to a drone swarm for power line monitoring, and including the following steps. S1, obtaining HPLC signal sampling sequences and HRF signal sampling sequences of each drone node, and then forming a space-time signal matrix according to the nodes; The application further proposes that after the space-time signal matrix is formed according to the nodes, it further includes calculating space-time correlation between the nodes, specifically including: jointly statistically processing the HPLC signal sampling sequences and the HRF signal sampling sequences in time dimensions and space dimensions to obtain a space-time correlation matrix between the nodes.

[0022] The space-time correlation matrix between the nodes refers to a mathematical representation constructed by quantifying the signal correlation degree of different drone nodes in time and space dimensions, and specifically can be realized by using a covariance matrix or cross-correlation analysis method. The joint statistics in the time dimensions and the space dimensions refer to synchronous analysis of fluctuation characteristics of the signal sequences on a time axis and spatial distribution characteristics between the nodes, and specifically can be realized by using a multi-dimensional statistical quantity calculation or tensor decomposition method.

[0023] Specifically, in the power line monitoring scenario, the HPLC signals and HRF signals collected by the UAV nodes are simultaneously affected by power line noise and spatial electromagnetic interference, and the noise has spatio-temporal correlation. By jointly statistically processing the sampling sequences of the two types of signals in the time dimension and the spatial distribution information between the nodes, a spatio-temporal correlation matrix reflecting the correlation strength of the signals between the nodes can be constructed. For example, for the signal sequence of each node, the autocorrelation function of the node in a time sliding window can be calculated, and the cross-correlation value of the signals of adjacent nodes is combined to form a multi-dimensional statistical quantity. The matrix can quantify the signal similarity of different nodes in a specific time period, providing data support for subsequent noise suppression and signal-to-noise evaluation.

[0024] The present scheme can more accurately represent the propagation path and distribution mode of the noise by jointly statistically processing the signal characteristics in the time and space dimensions to construct a spatio-temporal correlation matrix, thereby improving the robustness of signal-to-noise evaluation. Through the above technical scheme, the present application solves the signal-to-noise evaluation distortion problem caused by ignoring the spatio-temporal correlation of noise in the prior art. By constructing the spatio-temporal correlation matrix between the nodes, the spatio-temporal propagation characteristics of the noise in the UAV swarm can be reflected in real time, providing a basis for dynamically adjusting the filtering parameters and the abnormal detection threshold, and ultimately improving the accuracy and real-time performance of the signal-to-noise evaluation result.

[0025] S2, sparsely reconstructing the spatio-temporal signal matrix to generate a sparsely reconstructed signal and a residual matrix; The present application further proposes that the sparsely reconstructing the spatio-temporal signal matrix to generate a sparsely reconstructed signal and a residual matrix specifically includes: The sparsely reconstructing the spatio-temporal signal matrix to generate a sparsely reconstructed signal and a residual matrix; Further, a local noise sensitivity matrix is generated according to the residual matrix, and the specific calculation formula is as follows: ; Wherein, The local noise sensitivity value of node i at time t is denoted as The residual value of node i at time t is denoted as The time mean value of the residual of node i is denoted as The time standard deviation of the residual of node i is denoted as ε, and ε represents a small constant to prevent division by zero.

[0026] Wherein, the residual matrix refers to the difference matrix between the original spatio-temporal signal matrix and the sparsely reconstructed signal, which can be realized by matrix subtraction operation; The local noise sensitivity matrix refers to a two-dimensional matrix generated based on the statistical characteristics of the residual matrix, which can be realized by calculating the variance distribution of the residual matrix using a sliding window.

[0027] Specifically, in the power line monitoring scenario, the HRF signals collected by the UAV group are affected by line electromagnetic interference, and the noise thereof has spatial correlation and temporal non-stationarity. After the sparse reconstruction signal is separated through sparse reconstruction, the residual matrix is used to construct a local noise sensitive matrix. The local noise sensitive matrix reflects the noise sensitivity degree by analyzing the noise intensity difference of different nodes in the residual matrix and combining the sliding window statistical method.

[0028] The application can accurately capture the noise intensity difference of different spatial positions and time periods by constructing the local noise sensitive matrix, thereby providing a dynamic adjustment basis for subsequent adaptive filtering. Through the above technical solution, the application effectively solves the problem of insufficient adaptability of the traditional signal-to-noise evaluation method in the time-space dynamic noise scene. The introduction of the local noise sensitive matrix enables the system to distinguish the noise sensitivity degree of different sections of the line, for example, automatically increasing the noise suppression intensity near the insulator damage point, thereby improving the effectiveness of subsequent filtering processing and the accuracy of signal-to-noise evaluation.

[0029] S3, performing time-frequency transformation on the residual matrix to generate a time-frequency residual map, and performing two-dimensional adaptive filtering based on the element difference between nodes in the residual matrix and the sparse reconstruction signal to output a filtered residual; The application further proposes that the two-dimensional adaptive filtering specifically includes: performing short-time Fourier transformation on the residual matrix to generate a time-frequency map; based on the element difference between nodes in the residual matrix and the sparse reconstruction signal, combining the local noise sensitive matrix, dynamically adjusting the filtering window and the filtering coefficient, and outputting a filtered residual.

[0030] The short-time Fourier transformation refers to a method of converting a time-domain signal into a time-frequency domain representation, and can be specifically implemented by performing Fourier transformation on a signal divided by a sliding window; The element difference between nodes refers to the difference amount of signals corresponding to different UAV nodes in the residual matrix, and can be specifically implemented by calculating the Euclidean distance of a row vector or a column vector of the matrix; The local noise sensitive matrix refers to a two-dimensional data distribution reflecting the noise sensitive area in the residual matrix, and can be specifically generated by gradient calculation or variance statistics of the residual matrix; The dynamic adjustment of the filtering window and the filtering coefficient refers to the adaptive selection of filtering parameters according to the time-frequency map characteristics and the noise sensitive area, and can be specifically implemented by a preset window size set and a coefficient mapping table.

[0031] Specifically, in the power line monitoring scenario, the HRF signals collected by the UAV group are affected by line electromagnetic interference and spatial radio frequency noise, and the noise in the residual matrix presents time-varying and space-varying characteristics. After the residual matrix is converted into a time-frequency diagram through short-time Fourier transform, the time persistence characteristics of the noise in a specific frequency band can be identified. Combined with the spatial noise difference calculated by the difference between the elements of the nodes and the baseline reference provided by the sparse reconstructed signal, the signal-related components and random noise can be distinguished. The local noise-sensitive matrix further demarcates the areas with high noise energy in the residual matrix, providing a basis for the selection of the filter window size. For example, a smaller filter window can be used in the noise-sensitive area to preserve the detailed features, and a larger window can be used in the smooth area to improve the noise reduction effect.

[0032] The scheme realizes adaptive adjustment of the filtering parameters through joint judgment of time-frequency analysis and spatial difference, and solves the contradiction between transient noise suppression and signal detail preservation of the fixed filter. Through the above technical solution, the application can realize adaptive filtering processing of the residual signal according to the spatio-temporal dynamic noise characteristics in the power line monitoring scenario. By fusing time-frequency analysis and spatial difference information, the signal components and noise components can be effectively distinguished, avoiding the problems of insufficient noise suppression or excessive signal smoothing caused by fixed filter parameters in traditional methods, and providing a more accurate residual data basis for subsequent signal-to-noise evaluation.

[0033] S4, constructing a space-time fusion matrix based on the filtered residual and the sparse reconstructed signal, performing space-time anomaly detection, and generating a dynamic anomaly score matrix; The application further proposes that the space-time anomaly detection specifically includes: constructing a space-time fusion matrix based on the filtered residual and the sparse reconstructed signal; For the space-time fusion matrix, through secondary weighting with the sparse reconstructed signal and the local noise-sensitive matrix, a dynamic anomaly score matrix is outputted, and the specific calculation formula is as follows: ; wherein, S i (t) represents the dynamic anomaly score of node i at time t, represents the space-time fusion signal value of node i at time t, represents the sparse reconstructed signal value of node i at time t, and a represents an anomaly score scaling coefficient, and β represents a noise-sensitive weighting coefficient.

[0034] wherein, the secondary weighting refers to the operation of adjusting the weight twice for the space-time fusion matrix. Specifically, dynamic weight distribution can be performed based on the energy distribution of the sparse reconstructed signal and the noise intensity of the local noise-sensitive matrix. The first layer weight suppresses noise interference, and the second layer weight strengthens the abnormal features.

[0035] Specifically, when constructing the spatiotemporal fusion matrix, the time-frequency anomaly features extracted from the filter residuals are fused with the spatial features in the sparse reconstructed signal in multiple dimensions to form a joint matrix containing spatiotemporal correlation information. Subsequently, the first weighting coefficient is determined based on the energy distribution of each node in the sparse reconstructed signal, and the second weighting coefficient is generated by combining the noise sensitivity parameters recorded in the local noise sensitivity matrix. The fusion matrix is ​​dynamically adjusted through two layers of weighting operations. For example, in the case of sudden noise caused by electromagnetic interference in the transmission line monitoring scenario, the energy distribution of the sparse reconstructed signal can reflect the effective signal strength, while the local noise sensitivity matrix can quantify the noise resistance of different nodes. Through two weighting operations, the distortion of the real signal and transient noise interference can be effectively distinguished.

[0036] This solution achieves joint analysis of multi-dimensional features through a spatiotemporal fusion matrix. Combined with a secondary weighting mechanism to dynamically balance noise suppression and anomaly enhancement, it can more accurately identify spatiotemporally correlated composite interference in power transmission line monitoring scenarios. Through the above technical solution, this application effectively solves the problem of anomaly misjudgment caused by the spatiotemporal coupling characteristics of electromagnetic noise in power transmission line monitoring, significantly improves the accuracy of UAV swarm communication link quality assessment, and can accurately capture real signal anomalies caused by equipment failures or line defects in strong noise backgrounds through spatiotemporal fusion and dynamic weighting mechanism, providing a reliable signal-to-noise assessment basis for UAV collaborative monitoring.

[0037] S5. Utilize the sparse reconstructed signal, filtered residuals, and dynamic anomaly scoring matrix to output a multidimensional weighted power matrix; This application further proposes to utilize sparse reconstructed signals, filtered residuals, and dynamic anomaly scoring matrices to output a multidimensional weighted power matrix, specifically including: Multi-factor superposition calculation is performed using sparse reconstructed signal, filter residual, local noise sensitivity matrix and dynamic anomaly scoring matrix to output multi-dimensional weighted power matrix.

[0038] Among them, multi-factor superposition calculation refers to the nonlinear combination of the energy distribution of the sparse reconstructed signal, the noise component of the filter residual, the noise sensitivity of the local noise sensitivity matrix, and the abnormal weight of the dynamic anomaly scoring matrix. Specifically, it can be achieved by weighted summation or matrix element-wise multiplication. The multidimensional weighted power matrix refers to a multidimensional data matrix formed by fusing signal power, noise interference, and anomaly weights. Specifically, it can be generated by weighted projection of different factors in the spatiotemporal dimension. This matrix can dynamically adjust the evaluation weights of signal quality at each node.

[0039] Specifically, the sparse reconstruction signal reflects the main component of the signal, the filtered residual represents the noise residue, the local noise sensitivity matrix quantifies the sensitivity of the node to noise, and the dynamic anomaly score matrix provides the spatial and temporal distribution weight of the abnormal event. By superimposing the four types of factors, for example, multiplying the power normalized sparse reconstruction signal with the energy spectrum of the filtered residual, combining the sensitivity coefficient of the local noise sensitivity matrix for weighting, and finally adjusting adaptively through the abnormal weight of the dynamic anomaly score matrix, a multi-dimensional weighted power matrix containing space-time-frequency multi-dimensional information is generated.

[0040] The scheme jointly models the signal reconstruction quality, noise suppression effect, node noise sensitivity, and abnormal event weight through a multi-factor superposition mechanism, so that the generated power matrix can more comprehensively reflect the real signal quality distribution under a complex noise environment. Through the above technical solution, the application can effectively overcome the defects of traditional methods in evaluating distortion under dynamic noise environment, significantly improve the accuracy of signal-to-noise evaluation through multi-dimensional feature fusion, especially in the presence of sudden electromagnetic interference or local device anomalies, the multi-dimensional weighted power matrix can adaptively adjust the evaluation weight to avoid excessive influence of abnormal events on the overall evaluation result.

[0041] S6, according to the multi-dimensional weighted power matrix and the dynamic anomaly score matrix, calculate the node local signal-to-noise evaluation result, weight and aggregate all node local signal-to-noise evaluation results to obtain the signal-to-noise evaluation result.

[0042] The application further proposes to weight and aggregate all node local signal-to-noise evaluation results to obtain the signal-to-noise evaluation result, which specifically includes: Weight and aggregate all node local signal-to-noise evaluation results; Introduce the inter-node space-time correlation matrix in the aggregation process to obtain the signal-to-noise evaluation result.

[0043] Wherein, the weighted aggregation refers to assigning a dynamic weight to each node according to the difference in signal contribution degree of the node in the space-time dimension, and specifically can use entropy weight method or principal component analysis method to quantify the importance of the node; The inter-node space-time correlation matrix refers to a matrix generated by statistical signal correlation of different nodes in the time dimension and the space dimension, which can be realized by mutual information calculation or covariance matrix decomposition method.

[0044] Specifically, in the power line monitoring scenario, the signal interference between UAV nodes has spatial correlation and temporal dynamics. By taking the space-time correlation matrix as the weight adjustment basis, nodes in high interference areas are assigned lower weights and nodes in low interference areas are assigned higher weights in the aggregation process. For example, when the space-time correlation matrix of a node and its adjacent nodes shows that the signal of the node is greatly affected by the power line power frequency noise, the local evaluation result of the node will be appropriately suppressed in aggregation, thereby reducing the pollution of noise on the final signal-to-noise evaluation result.

[0045] The space-time signal matrix refers to a two-dimensional data set formed by arranging the HPLC and HRF signals collected by different nodes in time sequence and spatial position. Specifically, the signal sequence of each node can be stacked as a matrix row vector to achieve this. The sparse reconstruction refers to the process of extracting the main signal components from the space-time signal matrix through the compressive sensing algorithm. Specifically, the orthogonal matching pursuit algorithm can be used to achieve this. The two-dimensional adaptive filtering refers to the process of dynamically adjusting the filtering parameters by combining the spatial gradient information of the time-frequency residual map and the amplitude characteristics of the sparse reconstruction signal. Specifically, a convolution kernel with a variable window size can be designed to achieve this. The space-time fusion matrix refers to a composite data structure formed by tensor concatenating the time-frequency characteristics of the filtered residual and the spatial distribution of the sparse reconstruction signal. Specifically, channel stacking of multi-dimensional arrays can be used to achieve this. The multi-dimensional weighted power matrix refers to the calculation result of normalizing and weighting the energy distribution of the sparse reconstruction signal, the noise intensity of the filtered residual, and the dynamic anomaly score. Specifically, Hadamard product can be used for multi-factor fusion.

[0046] Specifically, first, the HRF signals are synchronously collected by the UAV group, and a space-time signal matrix containing time sequence and spatial position is constructed according to node number. After sparse reconstruction of the space-time signal matrix, the sparse reconstruction signal representing the main communication signal and the residual matrix containing noise components are obtained. The short-time Fourier transform is performed on the residual matrix to generate a time-frequency residual map. The filtering parameters are dynamically adjusted according to the signal differences between nodes, such as selecting the filtering window size according to the correlation of the residual amplitudes of adjacent nodes, thereby outputting the optimized filtered residual. Subsequently, the filtered residual and the sparse reconstruction signal are space-time fused. The dynamic anomaly score is generated by analyzing the distribution pattern of abnormal points in the fusion matrix, such as using the local outlier factor algorithm to detect signal points deviating from the normal range. Finally, the local signal-to-noise evaluation value of each node is calculated based on the multi-dimensional weighted power matrix, and the global evaluation result is obtained by spatial correlation weight aggregation.

[0047] Compared with the prior art, the traditional method only calculates the signal-to-noise ratio of the time domain signal of a single communication mode, while the scheme realizes joint analysis of the dual-mode signal through space-time matrix construction and sparse reconstruction, the prior art uses a filter with fixed parameters to process noise, and the scheme dynamically adjusts the filter coefficients through the difference between the nodes of the residual matrix, effectively suppresses the spatially correlated noise, and through the space-time fusion matrix and the dynamic scoring mechanism, it can timely identify and correct the evaluation deviation caused by burst interference; through the above technical scheme, the application solves the problem of space-time dynamic interference of the signal-to-noise evaluation of the dual-mode communication system in the power transmission environment, and through the construction and sparse decomposition of the space-time signal matrix, the effective signal and noise are accurately separated; based on the two-dimensional adaptive filtering of the residual matrix, the influence of the spatially correlated noise on the evaluation result is eliminated; combined with the dynamic abnormal scoring mechanism, the robustness of the signal-to-noise evaluation in the complex electromagnetic environment is improved.

[0048] The scheme can effectively distinguish between independent noise events and systematic interference by introducing a space-time correlation matrix to dynamically adjust the weight, thereby improving the robustness of the evaluation result; through the above technical scheme, the application solves the problem of distorted aggregation result of the traditional signal-to-noise evaluation method in the space-time dynamic noise environment, and through the dynamic adjustment of the node weight by the space-time correlation matrix, the final signal-to-noise evaluation result can accurately reflect the real communication quality of the UAV group in the power transmission line monitoring, thereby providing a reliable basis for the cooperative control of the UAV.

[0049] The above shows and describes the basic principles, main features and advantages of the application. Those skilled in the art should understand that the application is not limited by the above examples, the above examples and descriptions in the specification are only preferred examples of the application and are not intended to limit the application, various changes and improvements can be made to the application without departing from the spirit and scope of the application, and these changes and improvements all fall within the scope of the claimed application. The scope of protection of the application is defined by the appended claims and their equivalents.

Claims

1. A signal-to-noise assessment method in an HPLC+HRF dual-mode communication system, applied to a swarm of unmanned aerial vehicles (UAVs) for power transmission line monitoring, characterized in that... Includes the following steps: S1. Obtain the HPLC signal sampling sequence and HRF signal sampling sequence of each UAV node, and then form a space-time signal matrix according to the nodes; S2. Perform sparse reconstruction on the space-time signal matrix to generate a sparse reconstructed signal and a residual matrix; S3. Perform time-frequency transformation on the residual matrix to generate a time-frequency residual map, and perform two-dimensional adaptive filtering based on the element differences between nodes in the residual matrix and the sparse reconstructed signal to output the filtered residual. S4. Construct a space-time fusion matrix based on the filter residual and sparse reconstructed signal, perform space-time anomaly detection, and generate a dynamic anomaly scoring matrix; S5. Utilize the sparse reconstructed signal, filtered residuals, and dynamic anomaly scoring matrix to output a multidimensional weighted power matrix; S6. Based on the multidimensional weighted power matrix and the dynamic anomaly scoring matrix, calculate the local signal-to-noise assessment results of the nodes, and perform weighted aggregation on all the local signal-to-noise assessment results of the nodes to obtain the signal-to-noise assessment results.

2. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: After S1 forms a space-time signal matrix according to the nodes, it also includes calculating the space-time correlation between the nodes, specifically including: Joint statistics were performed on the HPLC signal sampling sequences and HRF signal sampling sequences in both time and spatial dimensions to obtain the spatiotemporal correlation matrix between nodes.

3. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The S2 process for sparse reconstruction of the space-time signal matrix, generating a sparse reconstructed signal and a residual matrix, specifically includes: Sparse reconstruction is performed on the space-time signal matrix to generate a sparse reconstructed signal and a residual matrix; A local noise-sensitive matrix is ​​then generated based on the residual matrix.

4. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The step S2, which further generates a local noise sensitivity matrix based on the residual matrix, is as follows: ； in, This represents the local noise sensitivity value of node i at time t. This represents the residual value of node i at time t. This represents the time mean of the residual at node i. Let ε represent the time standard deviation of the residual at node i, and let ε represent a small constant to prevent division by zero.

5. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The S3 two-dimensional adaptive filtering specifically includes: The time-frequency plot is generated by performing a short-time Fourier transform on the residual matrix. Based on the element differences between nodes in the residual matrix and the sparse reconstructed signal, combined with the local noise-sensitive matrix, the filtering window and filtering coefficients are dynamically adjusted to output the filtered residual.

6. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The S4 process for spacetime anomaly detection specifically includes: A space-time fusion matrix is ​​constructed based on the filtered residuals and sparse reconstructed signals; The spatiotemporal fusion matrix is ​​weighted twice with the sparse reconstructed signal and the local noise sensitivity matrix to output a dynamic anomaly scoring matrix.

7. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The steps for S4 to output the dynamic anomaly scoring matrix are as follows: ； Among them, S i (t) represents the dynamic anomaly score of node i at time t. This represents the space-time fusion signal value of node i at time t. Let represent the sparse reconstructed signal value of node i at time t, α represent the anomaly scoring scaling factor, and β represent the noise sensitivity weighting factor.

8. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: The S5 method utilizes the sparse reconstructed signal, filtered residuals, and dynamic anomaly scoring matrix to output a multidimensional weighted power matrix, specifically including: Multi-factor superposition calculation is performed using sparse reconstructed signal, filter residual, local noise sensitivity matrix and dynamic anomaly scoring matrix to output multi-dimensional weighted power matrix.

9. The signal-to-noise assessment method in an HPLC+HRF dual-mode communication system according to claim 1, characterized in that: S6 performs a weighted aggregation of the local signal-to-noise assessment results for all nodes to obtain the signal-to-noise assessment results, which specifically include: The local signal-to-noise ratio assessment results of all nodes are weighted and aggregated. By introducing the inter-node spatiotemporal correlation matrix during the aggregation process, the signal-to-noise ratio evaluation results are obtained.

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