Electromagnetic source positioning method and system based on generalized cross-correlation

Abstract

The invention relates to the technical field of signal processing, in particular to an electromagnetic source positioning method and system based on generalized cross-correlation. According to the invention, four electromagnetic probes are uniformly arranged on the unmanned aerial vehicle to collect electromagnetic signals of target equipment, and a generalized cross-correlation function is used to calculate the signal arrival time difference between the probes. And performing abnormal value elimination and optimization processing on the time difference by adopting a statistical analysis method, and calculating an azimuth angle and an elevation angle of the electromagnetic wave in combination with a plane wave hypothesis. The included angle between the orientation and the magnetic north direction is measured through a magnetometer of the unmanned aerial vehicle, and the azimuth angle is converted into an absolute direction reference. By using a two-point positioning method, data are collected at different positions and equations are established to solve three-dimensional space coordinates of the electromagnetic source. According to the method, the multi-source data acquisition and fusion technology is combined with the generalized cross-correlation and abnormal value processing algorithm, the multi-path effect and the measurement error in the complex electromagnetic environment are effectively overcome, and an efficient and reliable positioning scheme is provided for power equipment fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to an electromagnetic source positioning method and system based on generalized cross-correlation. Background Art

[0002] The operating status of power equipment is directly related to the safety and stability of the power grid. As the equipment runs for a long time, problems such as insulation aging, poor contact or partial discharge may cause the equipment to generate abnormal pulse electromagnetic signals. These pulse signals are one of the important characteristics of equipment failure. By detecting these signals, early judgment of equipment failure can be achieved, providing a basis for timely maintenance and repair. However, traditional electromagnetic signal detection methods mainly rely on fixed detection stations or ground manual detection, which have many problems in practical applications. Fixed monitoring stations are limited by the deployment location and number, making it difficult to achieve real-time monitoring of a large range of equipment. Manual detection requires checking equipment one by one, which is time-consuming and labor-intensive, especially in complex environments such as high-voltage transmission lines and substations, where the detection efficiency is lower.

[0003] In order to solve the above problems, drone technology has been introduced into the inspection of power equipment. Due to its high maneuverability and flexibility, drones can quickly cover a large area, and by carrying an electromagnetic sensor array, they can detect abnormal electromagnetic signals. Once an abnormal signal is detected, the drone inspector can preliminarily determine that there may be a fault in nearby equipment. However, this method cannot directly determine the specific source location of the abnormal signal. It is necessary to use a sensor array and judge it by the time difference between the signals collected by each probe in the array. The metal structure around the power facilities will cause reflection and diffraction of electromagnetic signals, presenting a multipath effect, making the time difference and direction calculation of the signal inaccurate. At the same time, the traditional method relying on single-point measurement cannot estimate the distance information of the signal source, and it is difficult to achieve accurate positioning in three-dimensional space. Therefore, it is necessary to improve the arrival time difference algorithm and combine the multi-point measurement capability of the drone to achieve high-precision three-dimensional positioning of the electromagnetic signal source. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to solve the problem that as the equipment runs for a long time, problems such as insulation aging, poor contact or partial discharge may cause the equipment to generate abnormal pulse electromagnetic signals. Fixed monitoring stations are limited by the deployment location and number, making it difficult to achieve real-time monitoring of a large range of equipment. Manual detection requires checking equipment one by one, which is time-consuming and labor-intensive, especially in complex environments such as high-voltage transmission lines and substations, and the detection efficiency is lower.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an electromagnetic source positioning method based on generalized cross-correlation, which includes collecting electromagnetic signals of a target device using four electromagnetic probes carried by a drone, wherein the four electromagnetic probes are evenly distributed on a circle, and the distance between adjacent probes is L, wherein probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise;

[0008] The signal arrival time difference between probe No. 1 and probe No. 3, and probe No. 2 and probe No. 4 is calculated using the generalized cross-correlation function;

[0009] The azimuth and elevation of the electromagnetic wave are calculated according to the time difference, and the angle between the direction of the drone and the magnetic north direction is measured by the magnetometer carried by the drone, so as to convert the azimuth into an angle based on the magnetic north direction;

[0010] The two-point positioning method is used to collect data at different positions, and the three-dimensional spatial coordinates of the electromagnetic source are solved by simultaneous equations.

[0011] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the step of calculating the signal arrival time difference using the generalized cross-correlation function specifically includes: calculating the generalized cross-correlation function between probe No. 1 and probe No. 3, and between probe No. 2 and probe No. 4; obtaining the time difference corresponding to the peak point of the generalized cross-correlation function to obtain τ13n and τ24n; and performing outlier elimination and optimization processing on the time difference to obtain optimized time differences τ13f and τ24f.

[0012] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the steps of outlier removal and optimization processing specifically include: calculating the mean μ and standard deviation σ of the time difference; removing outliers that exceed the range of plus or minus two times the standard deviation of the mean; taking the average of the time difference set after removing the outliers to obtain the optimized time difference.

[0013] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the step of calculating the azimuth and elevation of the electromagnetic wave according to the time difference is based on the plane wave assumption, specifically including: calculating the azimuth according to the optimized time difference τ13f; calculating the elevation according to the optimized time difference τ24f; and calculating the angle in combination with the propagation speed of the electromagnetic wave in the air.

[0014] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the step of collecting data using the two-point positioning method specifically includes: recording the GPS coordinates (x1, y1, z1) of the first position of the drone and the corresponding azimuth and elevation; the drone moves to the second position, recording the GPS coordinates (x2, y2, z2) of the second position and the corresponding azimuth and elevation; establishing a group of equations with the distances between the drone and the signal source being d1 and d2 as unknown quantities.

[0015] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the signal source position expression (x s1 ,y s1 ,z s1 );According to the second position of the UAV, establish the signal source position expression (x s2 ,y s2 ,z s2 ); Solve the three component equations of the two positions to obtain d1 and d2; Substitute the distances into the position expression to determine the final spatial coordinates of the electromagnetic source (x s ,y s ,z s ).

[0016] As a preferred solution of the electromagnetic source positioning method based on generalized cross-correlation described in the present invention, the determining of the spatial coordinates of the electromagnetic source also includes: when the distance between the electromagnetic source and the probe array is much greater than the probe spacing, using the plane wave assumption; using the GPS module of the drone to record the position coordinates of the drone in real time; combining the azimuth, elevation and distance information to calculate the final spatial position of the electromagnetic source.

[0017] In a second aspect, an embodiment of the present invention provides an electromagnetic source positioning system based on generalized cross-correlation, which includes a signal acquisition module, which uses four electromagnetic probes carried by a drone to collect electromagnetic signals of a target device, wherein the four electromagnetic probes are evenly distributed on a circle, and the distance between adjacent probes is L, wherein probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise;

[0018] The time difference calculation module calculates the signal arrival time difference between probe No. 1 and probe No. 3, and probe No. 2 and probe No. 4 using the generalized cross-correlation function;

[0019] The angle output module calculates the azimuth and elevation of the electromagnetic wave according to the time difference, measures the angle between the drone's direction and the magnetic north direction through the drone's built-in magnetometer, and converts the azimuth into an angle based on the magnetic north direction;

[0020] The data positioning module uses the two-point positioning method to collect data at different locations and solves the three-dimensional spatial coordinates of the electromagnetic source through simultaneous equations.

[0021] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the electromagnetic source positioning method based on generalized cross-correlation as described in the first aspect of the present invention are implemented.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the electromagnetic source positioning method based on generalized cross-correlation as described in the first aspect of the present invention are implemented.

[0023] The beneficial effects of the present invention are as follows: by evenly arranging four electromagnetic probes on the drone and adopting a standardized numbering method, all-round acquisition of electromagnetic signals is achieved, and the symmetrical layout of the probes simplifies the signal processing algorithm. By using the generalized cross-correlation function to calculate the signal delay between the probe pairs, combined with the outlier removal and optimization processing mechanism, the multipath effect and the influence of environmental noise are effectively overcome, and the accuracy of the delay estimation is significantly improved. By combining the drone magnetometer to measure the heading angle, the relative azimuth is converted into an absolute azimuth, and the two-point positioning method is innovatively adopted. Only relying on algorithm innovation, the three-dimensional coordinates of the signal source are accurately positioned, avoiding the hardware cost of adding additional ranging equipment. In addition, the system also has a plane wave approximate condition judgment and compensation mechanism, which can adaptively adjust the algorithm parameters according to the actual measurement conditions, ensuring the consistency of the far-field and near-field measurement accuracy. The organic combination of these technical features not only overcomes the problems of limited coverage of traditional fixed monitoring stations, time-consuming and inefficient manual investigation, but also maintains a high positioning accuracy in a complex electromagnetic environment, providing an efficient and reliable solution for power equipment fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of 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.

[0025] Figure 1 It is a flow chart of the electromagnetic source localization method based on generalized cross-correlation;

[0026] Figure 2 Figure 1 is a computer device diagram of an electromagnetic source localization method based on generalized cross-correlation. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0029] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0030] Example 1

[0031] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides an electromagnetic source positioning method based on generalized cross-correlation, comprising:

[0032] S100: Collect electromagnetic signals of the target device using four electromagnetic probes carried by the drone. The four electromagnetic probes are evenly distributed on a circle. The distance between adjacent probes is L. Probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise.

[0033] Specifically, the four electromagnetic probes may include: a main reference probe (probe No. 1) for collecting reference electromagnetic signals, a diagonal ranging probe (probe No. 3) for calculating azimuth in conjunction with probe No. 1, and a lateral ranging probe (probe No. 2 and probe No. 4) for calculating elevation. The four probes are evenly distributed on a circle with a diameter of 40 cm and fixed with a high-precision positioning bracket to ensure that the installation accuracy error is less than 0.1 mm.

[0034] In an optional embodiment, the combination of four electromagnetic probes can be adjusted according to different application scenarios. For example, in an open area, the probe spacing can be increased to 60 cm to improve spatial resolution, and in a complex electromagnetic environment, it can be reduced to 20 cm to enhance anti-interference ability. When detecting UHF signals, the four probes can also be arranged on a smaller circumference to reduce measurement errors.

[0035] In an optional embodiment, in addition to the standard four-probe configuration, the probe configuration can be increased or adjusted according to actual needs. For example, a central probe (i.e., the fifth probe) can be added for signal strength calibration, or a "3+1" configuration (three probes evenly distributed on the circumference and one probe in the center) can be used for measurement of specific scenarios. In addition, orthogonal polarization antennas can be added to the original probes to obtain the polarization information of the signal and further improve the positioning accuracy.

[0036] In an optional embodiment, if the detection target is a low-frequency signal source, the lateral ranging probe can be omitted, and only probes 1 and 3 are used for two-dimensional positioning. However, in this application, in order to ensure the accuracy of three-dimensional positioning, a complete four-probe configuration is still used.

[0037] It should be noted that the above-mentioned probe configuration scheme can fully cover electromagnetic signals of different frequency bands, including but not limited to industrial frequency signals, radio frequency signals, pulse signals, etc., so as to ensure the accuracy and comprehensiveness of the measurement results. At the same time, by introducing a variety of probe combinations, such as orthogonal configuration, differential configuration, etc., the advantages of various measurement methods can be fully utilized, complement each other, and improve the efficiency and accuracy of positioning. In addition, according to the needs of different application scenarios, the installation parameters and position relationship of the probe can be flexibly adjusted to meet the needs of actual measurement work. This flexible and adjustable probe configuration strategy not only improves the adaptability and robustness of the system, but also provides a reliable data basis for subsequent signal processing.

[0038] S200: calculating the signal arrival time difference between probe No. 1 and probe No. 3, and between probe No. 2 and probe No. 4 using a generalized cross-correlation function;

[0039] Specifically, the generalized cross-correlation function processing includes: frequency domain cross-correlation calculation for extracting signal delay information, signal preprocessing for removing background noise and interference, peak detection for determining the precise time difference, and data optimization for improving the reliability of delay estimation. The signal sampling rate between the probe pairs (No. 1-3 and No. 2-4) is set to 10MHz, the sample length is 1024 points, and the processing time of each set of data does not exceed 10ms.

[0040] In an optional embodiment, the processing method of the generalized cross-correlation function can be adjusted according to different signal characteristics. For example, when processing narrowband signals, the phase correlation method can be used to improve accuracy; when processing wideband signals, the sub-band decomposition method can be used to improve performance; when the signal-to-noise ratio is low, the adaptive cross-correlation algorithm can be used to enhance the anti-noise capability.

[0041] In an optional embodiment, in addition to the standard generalized cross-correlation processing, other signal processing methods can be added according to actual needs. For example, wavelet transform preprocessing can be added to suppress multipath effects, and the empirical mode decomposition method can be used to extract effective signal components. In addition, in order to cope with the influence of complex electromagnetic environments, an adaptive filtering algorithm can be introduced to dynamically process the collected signals to ensure that accurate delay estimation can be obtained under various interference conditions.

[0042] In an optional embodiment, if the signal strength is strong and the environmental interference is small, it can be simplified to a common cross-correlation calculation. However, in order to ensure the positioning accuracy in a complex environment, the complete generalized cross-correlation processing solution is still used in this application.

[0043] It should be noted that the above-mentioned signal processing scheme can effectively process various electromagnetic signal characteristics, including but not limited to continuous wave signals, pulse signals, modulated signals, etc., so as to ensure the accuracy of delay estimation. At the same time, by introducing a variety of signal processing methods, such as frequency domain processing, time domain processing, etc., the advantages of different algorithms can be fully utilized, complement each other, and improve the reliability of delay estimation. In addition, according to the needs of different application scenarios, the processing parameters and algorithm configuration can be flexibly adjusted to meet the requirements of actual measurement work. This flexible signal processing strategy not only improves the adaptability of the system, but also provides accurate delay data for subsequent spatial positioning.

[0044] S201: The step of using the generalized cross-correlation function to calculate the signal arrival time difference specifically includes: calculating the generalized cross-correlation function between probe No. 1 and probe No. 3, and between probe No. 2 and probe No. 4; obtaining the time difference corresponding to the peak point of the generalized cross-correlation function to obtain τ13n and τ24n; and performing outlier elimination and optimization processing on the time difference to obtain optimized time differences τ13f and τ24f.

[0045] S202: The steps of outlier removal and optimization processing specifically include: calculating the mean μ and standard deviation σ of the time difference; removing outliers that are beyond the range of plus or minus two times the standard deviation of the mean; averaging the time difference set after removing the outliers to obtain the optimized time difference.

[0046] S300: Calculating the azimuth and elevation of the electromagnetic wave according to the time difference, measuring the angle between the direction of the drone and the magnetic north direction by using a magnetometer carried by the drone, and converting the azimuth into an angle based on the magnetic north direction;

[0047] Specifically, outlier processing includes: statistical analysis to calculate the mean and standard deviation of time differences, threshold judgment to identify outliers, data screening to retain valid data, and optimization processing to improve the reliability of results. The system sets ±2σ (standard deviation) as the normal value range, and data outside this range will be marked as outliers.

[0048] In an optional embodiment, the method of removing outliers can be adjusted according to different application scenarios. For example, for the measurement of stable signal sources, the 3σ criterion can be used to improve data utilization; for environments with strong interference, the box plot method can be used to enhance the screening effect; when the amount of sampled data is large, the cluster analysis method can be used to identify outliers.

[0049] In an optional embodiment, in addition to the standard statistical analysis method, other anomaly detection algorithms can be introduced. For example, an entropy-based anomaly detection algorithm can be used, or a deep learning algorithm can be used to automatically identify abnormal patterns. In addition, considering the dynamic change characteristics of the electromagnetic environment, an adaptive threshold algorithm can be introduced to dynamically adjust the abnormal value judgment criteria to ensure that accurate screening results can be obtained under different working conditions.

[0050] It should be noted that the above-mentioned outlier processing scheme can effectively identify and handle various abnormal situations, including but not limited to sudden interference, multipath effects, equipment failures, etc., so as to ensure data quality. At the same time, by introducing a variety of anomaly detection methods, such as statistical analysis, machine learning, etc., we can make full use of the characteristics of various algorithms, verify each other, and improve the accuracy of anomaly detection. This diversified anomaly handling strategy not only improves the reliability of the system, but also provides a high-quality data foundation for subsequent direction estimation.

[0051] S301: The step of calculating the azimuth and elevation of the electromagnetic wave according to the time difference is performed based on the plane wave assumption, and specifically includes: calculating the azimuth according to the optimized time difference τ13f; calculating the elevation according to the optimized time difference τ24f; and calculating the angle in combination with the propagation speed of the electromagnetic wave in the air.

[0052] S400: Collect data at different positions using the two-point positioning method, and solve the three-dimensional spatial coordinates of the electromagnetic source through simultaneous equations.

[0053] S401: The steps of collecting data using the two-point positioning method specifically include: recording the GPS coordinates (x1, y1, z1) of the first position of the drone and the corresponding azimuth and elevation; moving the drone to a second position, recording the GPS coordinates (x2, y2, z2) of the second position and the corresponding azimuth and elevation; establishing a set of equations with the distances between the drone and the signal source being d1 and d2 as unknowns.

[0054] S402: Establish the signal source position expression (x s1 ,y s1 ,z s1 );According to the second position of the UAV, establish the signal source position expression (x s2 ,y s2 ,z s2 ); Solve the three component equations of the two positions to obtain d1 and d2; Substitute the distances into the position expression to determine the final spatial coordinates of the electromagnetic source (x s ,y s ,z s ).

[0055] S403: The determining of the spatial coordinates of the electromagnetic source also includes: when the distance between the electromagnetic source and the probe array is much greater than the probe spacing, adopting the plane wave assumption; using the GPS module of the drone to record the position coordinates of the drone in real time; and combining the azimuth, elevation and distance information to calculate the final spatial position of the electromagnetic source.

[0056] Specifically, direction estimation includes: azimuth calculation based on the time difference of probes 1-3, elevation calculation based on the time difference of probes 2-4, beamforming for improving angle resolution, and spatial filtering for suppressing sidelobe interference. The system uses 32-bit floating-point operations, with an angle resolution of up to 0.1 degrees and a real-time calculation delay of less than 5ms.

[0057] In an optional embodiment, the processing method of direction estimation can be adjusted according to different application requirements. For example, in high-precision scenarios, the phase comparison method can be used to improve the angle accuracy; when measuring wide-beam signal sources, the multi-beam synthesis technology can be used to improve the angle resolution; when there are multiple signal sources, the high-resolution DOA algorithm can be used to achieve signal separation.

[0058] In an optional embodiment, in addition to the direction estimation based on the time difference, other estimation methods can be added according to actual needs. For example, signal strength measurement can be added for auxiliary positioning, and array manifold matching method can be used to improve positioning accuracy. In addition, in order to improve the anti-interference ability of the system, an adaptive beamforming algorithm can be introduced to perform spatial filtering on the received signal to ensure that accurate direction information can be obtained in a complex electromagnetic environment.

[0059] In an optional embodiment, if the measurement environment is ideal and the accuracy requirement is not high, it can be simplified to a single time difference estimation. However, in order to ensure the accuracy of three-dimensional spatial positioning in this application, a complete dual time difference estimation solution is still used.

[0060] It should be noted that the above-mentioned direction estimation scheme can accurately handle the calculation of various arrival angles, including but not limited to parallel incidence, oblique incidence, multipath propagation, etc., so as to ensure the accuracy of angle measurement. At the same time, by introducing a variety of direction estimation methods, such as time difference method, phase method, etc., the advantages of different measurement principles can be fully utilized, complement each other, and improve the reliability of direction estimation. In addition, according to the needs of different working environments, the processing parameters and algorithm configuration can be flexibly adjusted to meet the requirements of actual positioning work. This flexible processing strategy not only improves the adaptability of the system, but also provides accurate angle information for subsequent spatial positioning.

[0061] The angle between the drone's orientation and the magnetic field direction is measured using the drone's built-in magnetometer;

[0062] Specifically, magnetic field measurement includes: three-axis magnetic induction intensity acquisition, geomagnetic field vector decomposition, heading angle calculation, and attitude compensation. The system uses a high-precision magnetometer with a sampling rate of 100Hz, a measurement range of ±8Gauss, and a resolution of 0.1 degrees.

[0063] In an optional embodiment, the magnetic field measurement method can be adjusted according to different interference environments. For example, when there is interference from ferromagnetic materials, a differential measurement method can be used to eliminate hard magnetic interference; in areas with strong electromagnetic interference, dynamic calibration technology can be used to improve measurement accuracy; when the drone makes large maneuvers, a gyroscope can be used to assist in measurement to maintain directional stability.

[0064] In an optional embodiment, in addition to standard magnetic field measurement, other orientation methods can be added according to actual needs. For example, a GPS compass can be added as a backup system, or an inertial navigation unit can be used to provide auxiliary orientation information. In addition, considering the influence of complex environments, a multi-source information fusion algorithm can be introduced to comprehensively utilize various sensor data to ensure that accurate orientation information can be obtained under various working conditions.

[0065] In an optional embodiment, if the magnetic field of the working environment is relatively stable and the interference is small, it can be simplified to a single magnetometer measurement. However, in order to ensure the reliability of the direction measurement in this application, a complete multi-source fusion solution is still used.

[0066] It should be noted that the above magnetic field measurement scheme can effectively handle various interference situations, including but not limited to hard magnetic interference, soft magnetic interference, environmental magnetic field fluctuations, etc., so as to ensure the accuracy of direction measurement. At the same time, by introducing a variety of measurement methods, such as magnetic field measurement, inertial navigation, etc., we can make full use of the advantages of various sensors, complement each other, and improve the reliability of direction determination. This multi-source fusion measurement strategy not only improves the stability of the system, but also provides an accurate reference direction for spatial coordinate conversion.

[0067] Furthermore, this embodiment also provides an electromagnetic source positioning system based on generalized cross-correlation, comprising:

[0068] The signal acquisition module uses four electromagnetic probes carried by the drone to collect electromagnetic signals of the target device. The four electromagnetic probes are evenly distributed on the circumference, and the distance between adjacent probes is L. Probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise;

[0069] The time difference calculation module calculates the signal arrival time difference between probe No. 1 and probe No. 3, and probe No. 2 and probe No. 4 using the generalized cross-correlation function;

[0070] The angle output module calculates the azimuth and elevation of the electromagnetic wave according to the time difference, measures the angle between the drone's direction and the magnetic north direction through the drone's built-in magnetometer, and converts the azimuth into an angle based on the magnetic north direction;

[0071] The data positioning module uses the two-point positioning method to collect data at different locations and solves the three-dimensional spatial coordinates of the electromagnetic source through simultaneous equations.

[0072] In summary, by evenly arranging four electromagnetic probes on the drone and adopting the standardized layout scheme of "probe No. 1 is directly in front, and the others are numbered clockwise", we can achieve all-round collection of electromagnetic signals and ensure the geometric symmetry of the probe array. This arrangement not only simplifies the subsequent signal processing algorithm, but also improves the system's anti-interference ability, ultimately achieving the effect of improving positioning accuracy.

[0073] By using the generalized cross-correlation function to calculate the signal delay between the probe pairs, the problem of performance degradation of the traditional cross-correlation algorithm in a multipath environment is effectively solved. This method performs weighted processing in the frequency domain, which can suppress the influence of noise and interference, improve the accuracy of delay estimation, and lay the foundation for subsequent direction estimation.

[0074] By eliminating outliers and optimizing the time difference, the automatic screening and optimization of the measurement data is realized. Based on the principle of statistical analysis, this method can effectively identify and eliminate outliers caused by factors such as multipath effects and environmental interference, thereby improving the reliability of the system in complex electromagnetic environments.

[0075] By combining the magnetometer on the drone to measure the heading angle, the relative azimuth is converted into the absolute azimuth, solving the problem of low coordinate conversion accuracy in traditional positioning systems. This method avoids cumulative errors and improves positioning stability during long-term work.

[0076] By innovatively adopting the two-point positioning method, the directional information collected by the drone at different locations is used to solve the problem, overcoming the limitation that single-point measurement cannot obtain distance information. This method does not require the addition of additional distance measuring equipment, and can achieve accurate positioning of the three-dimensional coordinates of the signal source through algorithm innovation alone.

[0077] By adopting the plane wave approximation condition judgment and compensation mechanism, the problem of inconsistent measurement accuracy in the far and near fields is effectively solved. The system can adaptively adjust the algorithm parameters according to the actual measurement conditions to ensure the consistency of measurement accuracy under different distance conditions.

[0078] Example 2

[0079] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, provides an electromagnetic source positioning method based on generalized cross-correlation.

[0080] In order to simulate the partial discharge fault of power equipment, the present invention uses two metal balls with a diameter of 50 mm, and applies a high voltage power supply between the balls to generate spark discharge, thereby simulating the actual partial discharge phenomenon. Under the premise of ensuring the discharge safety of the equipment, eight fixed measurement points are set, and at each point, the drone is made to perform fixed-point flight at five different heights.

[0081] Step 1: Multi-channel electromagnetic signal acquisition

[0082] The electromagnetic signals of the target device are collected using four electromagnetic probes carried by the drone. The probes are evenly distributed on the circumference, and the distance between adjacent probes is L. Probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise.

[0083] The electromagnetic sensor array carried by the drone consists of four probes, which are evenly distributed on a circle with a diameter of 40 cm. Probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise. The drone collects 60 seconds of electromagnetic signal data at each measurement point, and through multi-channel synchronous sampling, it provides rich information for subsequent signal processing.

[0084] Step 2: Signal preprocessing

[0085] The received four-channel electromagnetic signal may be affected by the multipath effect, and the received 4-channel electromagnetic signal is cut into multiple groups of sub-waveforms of different lengths, and the waveform length is Ln (n = 1, 2, ..., N). Fourier transform is performed on each sub-wavelength to obtain a frequency domain signal.

[0086] Each group of multi-channel signals collected is cut into 20 groups of sub-waveforms of different lengths, and a 30% window overlap is introduced during cutting to further improve the signal resolution. Each sub-wavelength is Fourier transformed to obtain a frequency domain signal.

[0087] Step 3: Generalized cross-correlation of segmented signals

[0088] Calculate the generalized cross-correlation function of the sub-waveforms between probe No. 1 and probe No. 3, and between probe No. 2 and probe No. 4.

[0089]

[0090] Where X(f) is the frequency domain representation of the sub-waveform, * represents the complex conjugate, and τ is the signal arrival time difference. When the cross-correlation function reaches its peak, the corresponding τ is the time difference of the signal arriving at the probe.

[0091] τ=arg max R(τ)

[0092] Where μ and σ are the distances between the two channels τ n After removing outliers, the retained time difference set {τ m} Take the average value and get the signal arrival time difference τ between probe 1 and probe 3, and probe 2 and probe 4 respectively 13f and τ 24f .

[0093] Step 4: Outlier Removal

[0094] Due to multipath effects and other interference, some time differences may deviate from the true value. In order to improve the accuracy of the results, outlier removal and optimization are required. Statistical analysis methods are used to remove outliers that exceed the following ranges:

[0095] τ n ∈[μ-2σ,μ+2σ]

[0096] Where μ and σ are the mean and standard deviation of τn between the two channels. After removing the outliers, the average value of the retained time difference set {τm} is taken to obtain the signal arrival time differences τ13f and τ24f between probe 1 and probe 3, and probe 2 and probe 4, respectively.

[0097] Step 5: Estimation of the spatial direction of the signal source

[0098] When the distance between the electromagnetic source and the probe array is much greater than the probe spacing, the electromagnetic wave can be regarded as a plane wave. According to the optimized time differences τ13f and τ24f, the azimuth angle α and elevation angle β of the wave are calculated.

[0099]

[0100] Among them, c is the propagation speed of electromagnetic waves in the air, and the reference of the azimuth angle is the direction from the center of the drone to the midpoint of probes 1 and 2. The angle between the drone's direction and the magnetic north direction is measured by the drone's built-in magnetometer, and then the azimuth angle is converted into an angle based on the magnetic north direction.

[0101] α'=α+θ+45°

[0102] Step 6: Signal source spatial position representation

[0103] Use GPS to record the position (x, y, z) of the drone, and combine the direction angle and elevation angle to get the expression of the signal source position.

[0104] (x s ,y s ,z s )=(x+d cosβcosα',y+d cosβsinα',z+d sinβ)

[0105] Where d represents the distance between the drone and the signal source.

[0106] Step 7: Two-point positioning method

[0107] The drone is at the first position (x1, y1, z1) and gets the position of the signal source (x s1 ,y s1 ,z s1 ), d1 is an unknown number. The drone moves to (x2, y2, z2), and repeats steps 1-6 to get the location of the signal source (x s2 ,y s2 ,z s2 ), d2 is an unknown number. Since the spatial coordinates of the signal source are fixed, d1 and d2 are obtained by combining the three component equations.

[0108] Measurements are taken at eight points to obtain eight sets of expressions for azimuth, elevation and signal source position. The spatial coordinates of the electromagnetic source are obtained by combining the expressions between the two.

[0109]

[0110] Substitute the distance d into the signal source position expression to finally determine the spatial coordinates of the electromagnetic source (x s ,y s ,z s ).

[0111] Since there are errors in the whole process, we take the midpoint of the closest line segment between the two expressions as the estimated value of the spatial coordinates of the electromagnetic source.

[0112] The experimental results show that in 40 measurements at 8 measurement points, the measured azimuth error is less than 2° in 40%, 2°-3° in 27.5%, 3°-4° in 17.5%, and more than 4° in 15%. The measured elevation error is less than 1° in 42.5%, 1°-2° in 32.5%, 2°-3° in 15%, and more than 4° in 10%. Randomly select two measurement results to form 100 sets of data, and the calculated deviations of the distance between the signal source position and the actual position and the distance between the drone position and the actual position of the signal source are less than 3% in 42%, 3%-4% in 28%, 4%-5% in 19%, and more than 5% in 11%.

[0113] Example 3

[0114] This embodiment also provides a computer device, which is applicable to a method for locating an electromagnetic source based on generalized cross-correlation, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0115] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0116] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0117] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0119] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0120] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for locating electromagnetic sources based on generalized cross-correlation, characterized by: It includes collecting electromagnetic signals of the target device using four electromagnetic probes carried by the drone, wherein the four electromagnetic probes are evenly distributed on a circle, and the distance between adjacent probes is L, wherein probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise; The signal arrival time difference between probe No. 1 and probe No. 3, and probe No. 2 and probe No. 4 is calculated using the generalized cross-correlation function; The azimuth and elevation of the electromagnetic wave are calculated according to the time difference, and the angle between the direction of the drone and the magnetic north direction is measured by the magnetometer carried by the drone, so as to convert the azimuth into an angle based on the magnetic north direction; The two-point positioning method is used to collect data at different positions, and the three-dimensional spatial coordinates of the electromagnetic source are solved by simultaneous equations.

2. The electromagnetic source positioning method based on generalized cross-correlation according to claim 1, characterized in that: The step of using the generalized cross-correlation function to calculate the signal arrival time difference specifically includes: calculating the generalized cross-correlation function between probe No. 1 and probe No. 3, and between probe No. 2 and probe No. 4; obtaining the time difference corresponding to the peak point of the generalized cross-correlation function to obtain τ13n and τ24n; and eliminating outliers and optimizing the time difference to obtain optimized time differences τ13f and τ24f.

3. The electromagnetic source positioning method based on generalized cross-correlation as claimed in claim 2, characterized in that: The steps of outlier removal and optimization processing specifically include: calculating the mean μ and standard deviation σ of the time difference; removing outliers that are beyond the range of plus or minus two times the standard deviation of the mean; averaging the time difference set after removing the outliers to obtain the optimized time difference.

4. The electromagnetic source positioning method based on generalized cross-correlation as claimed in claim 3, characterized in that: The step of calculating the azimuth and elevation of the electromagnetic wave according to the time difference is based on the plane wave assumption, and specifically includes: calculating the azimuth according to the optimized time difference τ13f; calculating the elevation according to the optimized time difference τ24f; and calculating the angle in combination with the propagation speed of the electromagnetic wave in the air.

5. The electromagnetic source positioning method based on generalized cross-correlation as claimed in claim 4, characterized in that: The steps of collecting data using the two-point positioning method specifically include: recording the GPS coordinates (x1, y1, z1) of the first position of the drone and the corresponding azimuth and elevation; moving the drone to a second position, recording the GPS coordinates (x2, y2, z2) of the second position and the corresponding azimuth and elevation; establishing a set of equations with the distances between the drone and the signal source being d1 and d2 as unknown quantities.

6. The electromagnetic source positioning method based on generalized cross-correlation as claimed in claim 5, characterized in that: The step of solving the three-dimensional spatial coordinates of the electromagnetic source by using simultaneous equations specifically includes: establishing a signal source position expression (x s1 ,y s1 ,z s1 );According to the second position of the UAV, establish the signal source position expression (x s2 ,y s2 ,z s2 ); Solve the three component equations of the two positions to obtain d1 and d2; Substitute the distances into the position expression to determine the final spatial coordinates of the electromagnetic source (x s ,y s ,z s) .

7. The electromagnetic source positioning method based on generalized cross-correlation according to claim 6, characterized in that: The method of determining the spatial coordinates of the electromagnetic source also includes: when the distance between the electromagnetic source and the probe array is much greater than the probe spacing, using a plane wave assumption; using the GPS module of the drone to record the position coordinates of the drone in real time; and combining the azimuth, elevation and distance information to calculate the final spatial position of the electromagnetic source.

8. An electromagnetic source positioning system based on generalized cross-correlation, based on the electromagnetic source positioning method based on generalized cross-correlation according to any one of claims 1 to 7, characterized in that: It also includes a signal acquisition module, which uses four electromagnetic probes carried by the drone to collect electromagnetic signals of the target device, wherein the four electromagnetic probes are evenly distributed on the circumference, and the distance between adjacent probes is L, wherein probe No. 1 is located directly in front of the drone, and the remaining probes are numbered clockwise; The time difference calculation module calculates the signal arrival time difference between probe No. 1 and probe No. 3, and probe No. 2 and probe No. 4 using the generalized cross-correlation function; The angle output module calculates the azimuth and elevation of the electromagnetic wave according to the time difference, measures the angle between the drone's direction and the magnetic north direction through the drone's built-in magnetometer, and converts the azimuth into an angle based on the magnetic north direction; The data positioning module uses the two-point positioning method to collect data at different locations and solves the three-dimensional spatial coordinates of the electromagnetic source through simultaneous equations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electromagnetic source positioning method based on generalized cross-correlation according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electromagnetic source positioning method based on generalized cross-correlation according to any one of claims 1 to 7 are implemented.

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