A method for suppressing interference of ultra-high frequency signals based on joint spatial and temporal domain discrimination

Abstract

In order to solve the problem that the existing technology has low accuracy in distinguishing interference signals that have different transmission paths from real ultra-high frequency signals but similar time-frequency characteristics, resulting in poor interference suppression effect, the present application relates to an ultra-high frequency signal interference suppression method based on joint space-time domain distinction, including: building a distributed receiving network based on a number of signal receiving nodes and their spatial position relationship, and synchronously receiving ultra-high frequency signals within a set time window; clustering the ultra-high frequency signals to form associated signal groups and signal feature groups based on the spatial domain, time domain, and spectral characteristics of the ultra-high frequency signals; calculating the positions of signal sources corresponding to all signal groups in combination with the spatial position relationship of the signal receiving nodes; modeling the transmission path and scoring the path rationality based on the position of the signal source, and then constructing a joint scoring function to distinguish the real signal source from the signal source to be suppressed, and performing interference suppression on the signal source to be suppressed.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information transmission, and in particular to a method for suppressing ultra-high frequency signal interference based on space-time domain joint discrimination. Background Art

[0002] In the context of partial discharge monitoring for power equipment, true ultra-high frequency (UHF) signals, typically generated by microscopic discharges within the insulation materials of power equipment, serve as a key early warning indicator of early insulation degradation. Detection of these signals enables online diagnosis of latent insulation defects, effectively preventing power equipment failures. However, in strong electromagnetic environments such as substations, which house power equipment, numerous co-frequency interference signals can easily overwhelm the true UHF signals. Therefore, interference suppression is crucial to ensuring detection accuracy.

[0003] Both known interference signals and real UHF signals are UHF signals. Traditional methods typically achieve interference suppression by acquiring UHF signals at a single point and analyzing their time-domain waveform or frequency-domain characteristics to distinguish interference signals from real UHF signals. However, these methods only analyze the time dimension and fail to incorporate the spatial propagation characteristics of the signal. Consequently, the accuracy of distinguishing interference signals with similar time-frequency characteristics to real UHF signals, even though they have different transmission paths, is low, resulting in poor interference suppression.

[0004] In view of this, there is an urgent need to provide a UHF signal interference suppression method based on joint space-time domain discrimination, which can accurately discriminate the real UHF signal and the interference signal based on the space domain and time domain, so as to achieve adaptive interference suppression in complex electromagnetic environments. Summary of the Invention

[0005] Based on the above analysis, the main purpose of the present invention is to provide a UHF interference suppression method based on joint space-time domain discrimination, so as to solve the problem that the existing technology has low accuracy in discriminating interference signals with different transmission paths from real UHF signals but similar time-frequency characteristics, resulting in poor interference suppression effect on UHF signals.

[0006] In this regard, the present invention provides a method for suppressing ultra-high frequency interference based on joint space-time domain discrimination, comprising the following steps:

[0007] a. Constructing a distributed receiving network based on a number of signal receiving nodes and their spatial position relationship, synchronously receiving UHF signals based on the number of signal receiving nodes within a set time window;

[0008] b. Based on the spatial domain characteristics, time domain characteristics and spectral characteristics of all UHF signals, all UHF signals are clustered to form associated signal groups and signal feature groups; the positions of the signal sources corresponding to all signal groups are calculated based on the spatial position relationship of all signal feature groups and the plurality of signal receiving nodes;

[0009] c. Modeling the transmission paths of all UHF signals in the corresponding signal group based on the location of the signal source, scoring the path rationality of all transmission paths based on line-of-sight propagation conditions, constructing a joint scoring function based on the results of the path rationality scoring and the corresponding signal feature group, and distinguishing the signal source as a real signal source or a signal source to be suppressed based on the results of the joint scoring;

[0010] d. Implement interference suppression on the signal source to be suppressed.

[0011] Preferably, step a includes: the several signal receiving nodes include a main signal receiving node and several auxiliary signal receiving nodes, and a distributed receiving network is constructed based on the main signal receiving node and the several auxiliary signal receiving nodes and the set spatial position relationship; wherein the spatial position relationship includes: the several auxiliary signal receiving nodes are coplanar to form a regular polygon array, and the main signal receiving node is located on a vertical line of the plane where the regular polygon array is located.

[0012] Preferably, in step b: the spatial domain characteristics of the UHF signal include: a signal propagation direction vector; the time domain characteristics of the UHF signal include: a signal arrival timestamp, a signal peak amplitude, a signal polarity identifier, and the time window; the spectral characteristics of the UHF signal include: a spectral feature vector; and the signal group is used to accommodate the UHF signal, and the signal feature group associated with the signal group is used to accommodate the spatial domain characteristics, time domain characteristics, and frequency domain characteristics of the corresponding UHF signal.

[0013] As a further preferred embodiment, the step b specifically includes: b1. within the set time window, pre-assigning all UHF signals into signal groups to be clustered based on the signal propagation direction vector; b2. performing vector inner product operation based on the spectral feature vectors corresponding to all UHF signals, calculating the cosine similarity of the UHF signals in all signal groups to be clustered, and converting the cosine similarity into spectral distance; b3. determining the dynamic time difference of each signal group to be clustered based on the signal peak amplitude corresponding to the UHF signal in each signal group to be clustered; b4. formulating clustering labels for the UHF signals in all signal groups to be clustered based on the spectral distance and the dynamic time difference, and determining the initial clustering center through density peak detection, and forming the signal group and the signal feature group associated with the initial clustering center according to the clustering label.

[0014] As a further preferred embodiment, the step b2 specifically includes: grouping all ultra-high frequency signals in the signal group to be clustered into signal pairs, and matching the spectral feature vectors corresponding to all signal pairs into vector pairs; calculating the inner product value of each vector pair one by one, and calculating the module-length product of the spectral feature vector corresponding to each vector pair respectively; defining the cosine similarity according to the inner product value and the module-length product; performing a linear transformation on the cosine similarity to generate the corresponding spectral distance with a value interval of [0,1], and the cosine similarity is negatively correlated with the spectral distance.

[0015] As a further preferred embodiment, the step b3 specifically includes: determining the maximum signal peak amplitude and the minimum signal peak amplitude of each signal group to be clustered based on the signal peak amplitude of the ultra-high frequency signal in each signal group to be clustered, and obtaining the attenuation amount based on the maximum signal peak amplitude and the minimum signal peak amplitude; presetting a basic time tolerance and an adjustment factor, and taking the product of the attenuation amount and the adjustment factor and the sum of the basic time tolerance as the dynamic time difference of the corresponding signal group to be clustered.

[0016] As a further preferred embodiment, step b also includes: calculating the signal arrival time difference of the UHF signal based on the signal arrival timestamp corresponding to the UHF signal in the same signal group; constructing a nonlinear equation group based on the signal arrival time difference with the position of the signal source as an unknown variable; combining the spatial position relationship of the several signal receiving nodes, using an iterative optimization algorithm to solve the nonlinear equation group, and obtaining the positions of all signal sources in three-dimensional space.

[0017] As a further preferred embodiment, step c includes: using the finite time-domain difference method to simulate the line-of-sight transmission path of the signal from the position of the signal source to each signal receiving node to generate a theoretical propagation time; calculating the average absolute deviation between the arrival timestamps of all ultra-high frequency signals and the theoretical propagation time, and normalizing the average absolute deviation based on the width of the time window to obtain the path rationality score reflecting the degree of fit between the arrival timestamp and the theoretical propagation time, and the path rationality score is proportional to the degree of fit.

[0018] As a further preferred embodiment, step c also includes: calculating the confidence based on the position of the signal source to obtain the spatial positioning accuracy of the signal source; calculating the time polarity consistency of the UHF signal based on the signal polarity identifier corresponding to all UHF signals; constructing a joint scoring function based on linear weighting of the spatial positioning accuracy, the time polarity consistency and the path rationality score and setting a dynamic threshold, and judging based on the result of the joint scoring and the dynamic threshold: the signal source whose result of the joint scoring is not lower than the dynamic threshold is the true signal source, and the signal source whose joint score is lower than the dynamic threshold is the signal source to be suppressed.

[0019] Preferably, a transmission path similarity comparison is performed between all signal sources to be suppressed and all real signal sources. If the transmission path similarity is not lower than an adaptive strength threshold, feature matching and dynamic shielding are performed on the corresponding signal sources to be suppressed. If the transmission path similarity is lower than a set threshold, spatial shielding cone angles are set at several signal receiving nodes based on the positions of the corresponding signal sources to be suppressed, so as to implement interference suppression on the signal sources to be suppressed.

[0020] The UHF signal interference suppression method based on space-time domain joint discrimination of the present invention has the following beneficial effects:

[0021] First, unlike traditional methods that only rely on single-point acquisition and analysis of time domain waveforms or frequency domain features, the present invention constructs a distributed receiving network based on several signal receiving nodes and their spatial position relationships, and synchronously receives ultra-high frequency signals based on each signal receiving node within a set time window; it breaks through the limitations of single-point acquisition and analysis, and the distributed receiving network composed of several signal receiving nodes can provide spatial and temporal perception when receiving ultra-high frequency signals, which is convenient for subsequent joint identification of the signal transmission path based on the spatial domain and time domain, so as to trace the position of the signal source, and distinguish the signal source to be suppressed and the real signal source, thereby realizing effective identification of the signal source to be suppressed that is different from the real ultra-high frequency signal transmission path.

[0022] Second, unlike traditional methods that are limited to static rules of template matching, the present invention extracts the spatial domain features, time domain features and spectral features of all UHF signals, and combines the dynamic similarity algorithm with the adaptive time difference constraint mechanism to cluster the UHF signals, thereby effectively distinguishing interference signals with time-frequency characteristics similar to those of real UHF signals; combining the spatial position relationship between the signal feature group and the signal receiving node to trace the position of the signal source, thereby achieving effective discrimination of the signal source to be suppressed that has a different transmission path from the real UHF signal but has similar time-frequency characteristics.

[0023] Third, the present invention models the transmission paths of all UHF signals in the corresponding signal group based on the positions of the signal sources, and scores the rationality of all transmission path models based on the line-of-sight propagation conditions, and then performs joint scoring. By introducing a rationality constraint mechanism for the physical transmission path and line-of-sight conditions, the present invention accurately identifies interference signals whose time-frequency characteristics are highly similar to those of real discharge signals but whose transmission paths do not conform to the actual physical propagation laws. In a complex electromagnetic environment, the present invention significantly improves the discrimination of signal source types. Finally, by implementing interference suppression on the signal source to be suppressed, accurate and effective interference suppression is achieved, and the interference suppression effect is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for suppressing UHF signal interference based on joint spatial and temporal domain discrimination according to an embodiment of the present invention;

[0025] Figure 2 FIG. 1 is a schematic diagram of a distributed receiving network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be described in more detail below with reference to the accompanying drawings. It should be noted that the following description of the present invention with reference to the accompanying drawings is only illustrative and not restrictive.

[0027] Where possible, the various embodiments described below may be recombined with each other to form other embodiments not shown in the following description; the various technical features described below may also be recombined with each other to form other embodiments not shown in the following description.

[0028] Please refer to the attached Figure 1 and attached Figure 2 .

[0029] Example 1:

[0030] In the context of partial discharge monitoring in power equipment, true UHF signals, often originating from microscopic discharge processes within the insulation materials of the equipment, are key physical quantities that characterize early insulation degradation. Furthermore, online diagnosis based on true UHF signals enables nondestructive detection of latent insulation defects, thereby preventing equipment breakdown. However, the operating environment of power equipment often contains a large amount of broadband electromagnetic interference in the same frequency band as the true UHF signals. The power spectral density of this broadband electromagnetic interference can overwhelm the true UHF signals, degrading the signal-to-interference ratio (SIR).

[0031] Traditional interference suppression methods typically use single-point signal acquisition and interference identification through time-domain waveform analysis or frequency-domain feature extraction. For example, interference identification is performed based on the UHF signal's pulse rising edge, pulse width, or spectral envelope. While this method can achieve a certain interference suppression effect, it fails to incorporate the signal's spatial propagation characteristics, such as the UHF signal's direction of arrival and multipath delay. This results in limited suppression effectiveness against interference sources that have different transmission paths but similar time-frequency characteristics to the UHF signal, such as corona discharge and switching transients.

[0032] Therefore, in order to solve the problem that the existing technology has low accuracy in distinguishing interference signals with different transmission paths but similar time-frequency characteristics to the real UHF signal, resulting in poor interference suppression effect, please refer to the attached Figure 1 This embodiment provides a method for suppressing interference of ultra-high frequency signals based on joint spatial and temporal domain discrimination, which mainly includes the following steps:

[0033] a. A distributed receiving network is constructed based on several signal receiving nodes and their spatial positional relationships. Each signal receiving node synchronously receives UHF signals within a set time window. High-precision clocks are used to synchronize the UHF signal acquisition of each signal receiving node. The spatial correlation of multiple signal receiving nodes can be exploited to capture the same UHF signal within the same time window, preventing missed signals and laying the foundation for subsequent tracing of the signal source.

[0034] b. Based on the spatial domain characteristics, time domain characteristics and spectral characteristics of all UHF signals, the UHF signals are clustered to form associated signal groups and signal feature groups. At this time, the signal groups accurately classify the UHF signals so that the UHF signals from the same signal source are placed in the same signal group. The positions of the signal sources corresponding to the associated signal groups are calculated based on the signal feature groups containing the spatial domain characteristics of the UHF signals, so as to effectively grasp the signal sources of all UHF signals received by each signal receiving node within the time window.

[0035] c. Model the transmission paths of all UHF signals in the corresponding signal group based on the position of the signal source, and perform rationality scoring on all transmission path models based on the line-of-sight propagation condition, wherein the rationality scoring can effectively determine the source of the corresponding UHF signal, and the rationality scoring is used to map whether the corresponding UHF signal meets the line-of-sight propagation condition. When the UHF signal meets the line-of-sight propagation condition, it is likely to be a real UHF signal. When the UHF signal does not meet the line-of-sight propagation condition, it may be an external interference signal or a real signal reflected multiple times. Combine the results of the rationality scoring with the signal feature group to construct a joint scoring function, and according to the obtained joint scoring result, the signal source is judged as a real signal source or a signal source to be suppressed, wherein the joint scoring function can reflect the comprehensive judgment results of the UHF signal in dimensions such as space, time, and characteristics, so it can comprehensively judge the category of the signal source and effectively prevent misjudgment.

[0036] d. Implement interference suppression on the signal source to be suppressed to effectively prevent it from interfering with the real signal source and achieve accurate monitoring of the real UHF signal.

[0037] This embodiment is based on a joint discrimination mechanism in the spatial and temporal domains. By quantifying the propagation delay differences at the acquisition points, it synchronously integrates the spatial propagation characteristics and temporal dimension features of the signal to distinguish between real UHF signals and interference signals at the physical transmission layer, thereby achieving adaptive interference suppression in complex electromagnetic environments.

[0038] To enhance the spatial perception of UHF signals by a distributed receiving network, a preferred embodiment includes constructing a distributed receiving network based on primary and auxiliary signal receiving nodes and their spatial relationship. The primary signal receiving nodes provide primary UHF signal information, while the auxiliary signal receiving nodes, while receiving the UHF signal information, cooperate with the primary signal receiving nodes to provide a spatially distributed architecture for obtaining the location of the corresponding UHF signal source. In practical applications, the perception capabilities of the distributed receiving network can be enhanced by designing the positional relationship between the primary and auxiliary signal receiving nodes.

[0039] In order to further enhance the spatial perception of the distributed receiving network to the UHF signal, in a preferred embodiment, it also includes: constructing a distributed receiving network based on the main signal receiving node and several auxiliary signal receiving nodes and their spatial position relationship, and the spatial position relationship between the main signal receiving node and several auxiliary signal receiving nodes includes: several auxiliary signal receiving nodes are coplanar to form a regular polygon array, and the main signal receiving node is located on the vertical line of the regular polygon. It should be noted that the main signal receiving node can be coplanar or non-coplanar with the regular polygon array. When the main signal node is non-coplanar with the regular polygon array, the perception of the UHF signal in the direction of the vertical line of the regular polygon array is more accurate and clear. When the main signal node is coplanar with the regular polygon array, the entire distributed receiving network can be made more three-dimensional, which is convenient for perceiving the direction of the signal source. Figure 2 In the rectangular coordinate system shown in FIG, the circle formed by the dotted line represents the main signal receiving node, and all the main signal receiving nodes shown in the figure only represent the specific locations where they can be set; the circle formed by the solid line represents the auxiliary signal receiving node, and the diamond S represents the location of a signal source corresponding to the UHF signal. Specifically, in the attached Figure 2 The distributed receiving network shown in the figure utilizes a primary signal receiving node and three auxiliary signal receiving nodes. The three auxiliary signal receiving nodes form an equilateral triangle array, with the primary signal receiving node located on a perpendicular line of the triangle, forming a pyramidal or flat equilateral triangle sensing network. This enables distributed, synchronous reception of ultra-high frequency signals. Furthermore, the auxiliary signal receiving nodes are designed as a regular polygonal array, reducing computational complexity for subsequent rapid calculation of the signal source's location. This regular polygonal array design facilitates the determination of the location of each auxiliary signal receiving node, improving system accuracy.

[0040] In order to enhance the feasibility of the solution and associate it with actual application scenarios, in a further preferred embodiment, GIS (geographic information system) is combined with actual application scenarios, and sensing equipment is deployed as signal receiving nodes. At the same time, high-precision clock control is performed on the sensing equipment to achieve synchronous reception of ultra-high frequency signals.

[0041] To more comprehensively understand the relevant characteristics of UHF signals, in a preferred embodiment, the spatial domain characteristics of the UHF signal include: a signal propagation direction vector; the time domain characteristics of the UHF signal include: a signal arrival timestamp, a signal peak amplitude, a signal polarity identifier, and a time window; and the spectral characteristics of the UHF signal include: a spectral feature vector. Furthermore, the signal group formed in step b is used to accommodate the UHF signal, while the signal feature group associated with the signal group is used to accommodate the aforementioned spatial domain characteristics, time domain characteristics, and frequency domain characteristics. By obtaining the aforementioned characteristics of the UHF signal and clustering them into signal groups and signal feature groups, a data foundation can be provided for more accurate and stable subsequent identification of partial discharge signals, thereby improving the accuracy of spatiotemporal joint analysis and source location, and enhancing the ability to distinguish interference signals.

[0042] In order to accurately cluster the UHF signals collected by different signal receiving nodes in the same time window, in a further preferred embodiment, the following processing steps are designed: first, within the set time window, the UHF signals received by each signal receiving node are pre-grouped according to the signal propagation direction corresponding to the signal, and the UHF signals that may come from the same signal source are preliminarily divided to obtain the signal groups to be clustered; secondly, based on the spectral feature vectors corresponding to the UHF signals, the cosine similarity of the UHF signals in each signal group to be clustered is calculated by vector inner product operation, and the cosine similarity is converted into spectral distance, where the spectral distance is used to measure the UHF The differences of UHF signals in the frequency domain are used to enhance the ability to distinguish different types of UHF signals; secondly, the dynamic time difference of each signal group to be clustered is determined based on the signal peak amplitude corresponding to the UHF signal in each signal group to be clustered. The dynamic time difference obtained in this way can adapt to the propagation characteristics of signals with different strengths; finally, cluster labels are formulated for the UHF signals in all signal groups to be clustered based on the spectrum distance and dynamic time difference, and the initial clustering center is automatically identified through density peak detection, and then the associated signal groups and signal feature groups are expanded to achieve accurate clustering and effective separation of UHF signals according to different signal sources.

[0043] In order to perform more refined clustering processing on the UHF signals collected by different signal receiving nodes in the same time window, in a further preferred embodiment, all UHF signals in the signal group to be clustered are grouped into signal pairs. , match the two spectral feature vectors corresponding to each signal pair into vector pairs , in order to achieve pair-by-pair analysis of the spectral similarity between UHF signals.

[0044] Computes the inner product of a single vector pair , and calculate the modulus length of the two spectral feature vectors corresponding to a single vector pair and , and then get the module length product ; The ratio of the inner product value to the modulus length product is defined as the cosine similarity, that is:

[0045]

[0046] in, ,and The closer it is to 1, the closer the spectra of the two UHF signals in the signal pair are.

[0047] The cosine similarity is linearly transformed to generate a normalized spectral distance with a value range of [0,1]. In order to convert the similarity into a "distance" indicator that is convenient for clustering, the following linear transformation method is used to map the cosine similarity to the [0,1] interval, and ensure that the larger the value, the greater the difference:

[0048] ,Right now: ;

[0049] The spectral distance defined in this way has the following properties:

[0050] ;

[0051] The spectrum distance is negatively correlated with the cosine similarity. The closer the spectrum distance is to 1, the closer the cosine similarity is to -1. The closer the spectrum distance is to 0, the closer the cosine similarity is to 1.

[0052] In this embodiment, by converting the similarity between spectral feature vectors into a distance metric at the same scale, the spectral differences between different signals are easier to quantify and compare, thereby improving the accuracy and robustness of the clustering results. The normalized spectral distance can effectively distinguish true discharge signals from interference signals in complex electromagnetic environments, especially in the presence of multipath propagation or multi-source aliasing, helping to preserve the characteristics of the target signal.

[0053] In order to further optimize the signal clustering process in the space-time domain joint discrimination method, in a further preferred embodiment, a dynamic time difference threshold mechanism based on the difference in signal peak amplitude is introduced, and the specific execution steps include: determining the maximum peak amplitude in a single signal group to be clustered, and obtaining the signal peak amplitudes of all UHF signals in the signal group to be clustered. Assuming that a single signal group to be clustered includes UHF signal, and the corresponding signal peak amplitude is recorded as ; The maximum signal peak amplitude in the signal group to be clustered is , the minimum signal peak amplitude in the signal group to be clustered is ;

[0054] Based on the maximum signal peak amplitude and minimum signal peak amplitude Calculate attenuation A:

[0055] ;

[0056] in A can be used to describe the "attenuation degree" of the weakest signal relative to the strongest signal, that is, the attenuation amount. Preset basic time tolerance and regulatory factors , add the product of the characteristic amplitude difference and the adjustment factor to the basic time tolerance, and the result is used as the dynamic time difference allowed for the signal pair :

[0057] ;

[0058] Among them, the basic time tolerance It can ensure that there is a certain time difference tolerance range even between strong signals; adjustment factor Used to convert amplitude differences into time tolerances; Used to determine whether to classify a signal pair into the same signal group.

[0059] This embodiment incorporates signal strength information into the temporal consistency judgment criteria, enabling the algorithm to more accurately identify time offsets caused by multipath, partial discharge attenuation, and other factors, effectively suppressing misclustering and false signal interference. Weak signals are often more susceptible to noise or non-line-of-sight propagation. Using a dynamic time difference threshold related to signal strength allows these signals to be included within a reasonable range and not misidentified as interference sources, thereby improving the recognition rate of true signals.

[0060] In order to accurately cluster and effectively separate the UHF signals, in a further preferred embodiment, the step of forming the associated signal group and the signal feature group includes:

[0061] For each signal group to be clustered signals, determine their spectral distance Dynamic time difference , and calculate the local density based on the above two parameters:

[0062]

[0063] in is the average spectral distance within the signal group to be clustered, not the fixed threshold in the prior art. Therefore, after calculating the local density, the double exponential term can automatically balance the spectral distance and the dynamic time difference.

[0064] Next, calculate the minimum distance:

[0065]

[0066] Identify cluster centers and calculate the product of local density and minimum distance: , and later Sort in descending order and select the signal before the obvious inflection point as the initial cluster center .

[0067] The non-central signal Assign to the initial cluster center that minimizes the joint cost , calculate the joint cost of the non-central signal and the initial cluster center:

[0068]

[0069] Signal groups and signal feature groups are formed, where each signal group includes UHF signals belonging to the same cluster, and each signal feature group contains features such as the signal propagation direction vector, signal arrival timestamp, signal peak amplitude, signal polarity identifier and time window, and spectrum feature vector of the UHF signal in the corresponding signal group.

[0070] This embodiment improves the logical consistency of the clustering algorithm in judging the spectrum distance and time difference by fusing the signal pair-level spectrum distance and the signal group-level dynamic time difference parameter. A dynamic time difference threshold based on amplitude difference is adopted, and combined with the normalization term, adaptive tolerance adjustment of the signal pair level is realized, which effectively accommodates the time offset of weak signals and avoids misjudgment. The second-order derivative extreme value detection method is introduced in the cluster center identification to replace the subjective threshold setting, thereby realizing adaptive clustering without threshold dependence. The three types of feature groups constructed, namely space, time and spectrum, have the advantages of clear physical meaning, strong anti-noise ability and prominent main energy band. The overall solution shows good robustness and separation ability in complex electromagnetic environments, especially in multipath propagation scenarios, it can still accurately identify the real signal source and suppress interference.

[0071] In order to further optimize and enhance the accuracy of signal source positioning in the joint discrimination of space and time domains, in another preferred embodiment, a nonlinear equation modeling and iterative optimization positioning method based on TDOA (time difference positioning technology) is introduced, including: assuming that a single signal group contains UHF signals, based on the signal arrival timestamps corresponding to all UHF signals in the same signal group , taking the first signal as the reference signal, calculate the arrival time difference of the remaining UHF signals in the signal group received by each signal receiving node:

[0072] ,

[0073] Based on the signal arrival time difference, the signal source Coordinates are unknown variables , according to the propagation speed of electromagnetic waves in free space (about 3×108m / s), combined with The spatial coordinates of the signal receiving nodes , the following nonlinear equations can be established:

[0074] , , expand to get:

[0075]

[0076] An iterative optimization algorithm is used to solve the nonlinear equations and obtain the corresponding signal source position in three-dimensional space. The objective function is defined as:

[0077] ;

[0078] By iteratively optimizing and minimizing the objective function, the optimal signal source position can be obtained:

[0079] ;

[0080] This embodiment utilizes the time difference between receiving UHF signals at different signal receiving nodes, combined with a nonlinear optimization method, to achieve high-precision signal source localization in three-dimensional space, significantly outperforming simple geometric intersection or plane projection methods. This iterative optimization mechanism effectively addresses non-ideal propagation conditions caused by multipath propagation and irregular sensor layout, improving robustness in practical engineering applications. The resulting signal source location can be used for subsequent path simulation verification and path score calculation, thereby enabling closed-loop feedback control in the joint spatial and temporal domain judgment logic.

[0081] In order to further enhance the ability to judge the authenticity of the signal transmission path in the joint space-time domain discrimination method, in another preferred embodiment, a path rationality scoring mechanism based on FDTD simulation is introduced, and its operation steps include:

[0082] Assume the position of the signal source is , No. The spatial coordinates of the signal receiving node are ;

[0083] The finite time-domain difference method is used to simulate the line-of-sight transmission path of electromagnetic waves from the signal source to each receiving point, and the theoretical propagation time is obtained as follows: :

[0084] ;

[0085] in, Represents the relative dielectric constant of the medium; Represents the relative magnetic permeability of the medium;

[0086] Set up the first The arrival timestamp of the UHF signal is , then the absolute deviation between it and the theoretical propagation time is:

[0087]

[0088] Average the deviations of all signals in the group:

[0089]

[0090] The average deviation is calculated according to the width of the time window After normalization, we can get the dimensionless time consistency index:

[0091]

[0092] The path rationality score is defined as:

[0093]

[0094] in, ; When the actual arrival timestamp is exactly the same as the theoretical propagation time, , indicating that the path is highly reasonable; if the deviation reaches more than half of the time window width, the score is less than 0.5, indicating that the path is non-line-of-sight propagation or there is an interference signal; if the deviation exceeds the time window width, the score may be negative and the lower limit needs to be set to 0.

[0095] Through simulation modeling, this embodiment can effectively distinguish line-of-sight propagation signals from non-ideal propagation signals such as multipath, reflection, and diffraction, thereby improving the accuracy of identifying true discharge signals. For false signals caused by electromagnetic interference, equipment noise, etc., their arrival time often cannot match the theoretical propagation time. The path rationality score can effectively identify and eliminate them. This score can be used in subsequent joint scoring functions as one of the key weight factors, and is used in conjunction with indicators such as spectrum similarity, polarity consistency, and spatial positioning residuals to achieve more comprehensive signal classification and judgment. By setting different dielectric parameters (such as dielectric constant and magnetic permeability), FDTD simulation can simulate the internal structures of various typical power equipment such as GIS equipment, transformer oil tanks, and cable channels, and has good engineering applicability.

[0096] In order to systematically optimize the signal source classification and judgment module in the space-time domain joint discrimination method, in another preferred embodiment, it includes: calculating the confidence based on the position of the signal source to obtain the spatial positioning accuracy; assuming that the estimated position of the signal source corresponding to a certain signal group is: S , its spatial positioning error can be measured by the minimum value of the TDOA positioning objective function:

[0097]

[0098] Define spatial positioning accuracy as:

[0099]

[0100] in: , is the scale factor used for normalization; , the closer the value is to 1, the more accurate the spatial positioning is.

[0101] Calculate the temporal polarity consistency based on the signal polarity identifiers corresponding to all UHF signals; assume that the polarity of all signals in the signal group is: , define temporal consistency as:

[0102]

[0103] That is, the more signals with the same polarity there are, the closer the value is to 1; if the polarity is completely opposite, the value is 0.

[0104] It can be further mapped into confidence form:

[0105]

[0106] in, represents the strong consistency threshold, Represents the weak consistency threshold.

[0107] The spatial positioning accuracy Time polarity consistency and path rationality score Perform linear weighted fusion to form a joint scoring function, set a dynamic threshold, and determine that the signal source with a joint score not lower than the dynamic threshold is the real signal source, and the signal source with a joint score lower than the dynamic threshold is the signal source to be suppressed:

[0108]

[0109] in: , represents the weight coefficient, satisfying , which can be dynamically adjusted through historical data training or online learning.

[0110] Set a dynamic scoring threshold , which can be statistically set based on historical score distribution, for example: ,in: Represents the average value of the joint score in the current period; Represents the standard deviation of the joint score in the current period, The coefficient representing the control sensitivity usually ranges from 1 to 3 and is used to control the strictness of the judgment threshold. The larger the value, the stricter the threshold.

[0111] The judgment rules are as follows:

[0112] like , then it is determined to be a real signal source. If , it is determined to be the signal source to be suppressed.

[0113] This embodiment utilizes multi-dimensional features such as spatial positioning residuals, polarity consistency, and path simulation scoring to improve the accuracy of identifying true discharge signals and reduce false positives and missed detections. This multi-dimensional scoring mechanism effectively resists the influence of factors such as noise, multipath propagation, and local interference, maintaining high recognition stability even in complex electromagnetic environments.

[0114] In order to further improve the interference suppression effect in the space-time domain joint discrimination method, in a preferred embodiment, a dual-mode interference suppression mechanism based on transmission path similarity is introduced, including: formulating the following interference suppression rules based on the determined real signal source and the signal source to be suppressed:

[0115] Assume that the position of a signal source to be suppressed is , the position of a real signal source that has been determined is , use FDTD (finite difference time domain method) to simulate the theoretical propagation time of the two to each signal receiving node:

[0116] For the first signal receiving nodes, the propagation time corresponding to the real signal source is , the propagation time corresponding to the signal source to be suppressed is , define the transmission path similarity between the two as:

[0117]

[0118] in, is the number of signal receiving nodes, , is the time window width, used for normalization, The closer the value is to 1, the more similar the path is. Set a transmission path similarity threshold ,like: , it is considered that the signal source to be suppressed has a highly similar transmission path to a real signal source.

[0119] If the above conditions are met, we further determine whether the signal to be suppressed is also similar to the real signal in terms of spectrum. The spectrum similarity is defined as follows:

[0120]

[0121] In this embodiment, for example, the adaptive strength threshold is set Related to the amplitude of the strongest signal, for example:

[0122]

[0123] like: , it is considered that the signal to be suppressed is similar to the real signal spectrum, meets the feature matching conditions, and should be shielded; In addition, the constant 65 is the set reference amplitude value (unit is dB V), which is used to map the actual signal amplitude to the appropriate input range of the tanh function.

[0124] For example, if the transmission path similarity of all real signal sources and the signal source to be suppressed is less than the set threshold ,Right now:

[0125]

[0126] The dynamic spatial shielding cone angle mechanism is enabled for the signal source to be suppressed, and the shielding cone angle is defined as:

[0127]

[0128] in, The peak amplitude of the signal to be suppressed. The larger the amplitude, the wider the shielding cone angle, which prevents strong interference signals from escaping. The shielding direction is based on the direction from the signal source to each sensor. The real signal is retained because it does not meet the shielding direction condition.

[0129] This embodiment employs different shielding strategies based on the degree of match between the transmission path and spectral characteristics of the signal to be suppressed and the actual signal, preventing accidental damage to the actual signal while effectively identifying and shielding interfering signals. Signals whose transmission paths are highly similar to the actual signal but whose spectra are not synchronized can be identified as non-authentic based on spectral distance. Interfering signals with completely different transmission paths are isolated through spatial shielding, enhancing the system's anti-aliasing capabilities. Shielding rules automatically adjust the shielding range (e.g., cone angle and spectral threshold) based on signal strength, making the system more environmentally adaptable and robust, making it particularly suitable for partial discharge detection tasks in complex electromagnetic environments.

[0130] It should be understood that the embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the appended claims of the application.

Claims

1. A method for suppressing interference of ultra-high frequency signals based on joint spatial and temporal domain discrimination, characterized in that: include: Step a. constructing a distributed receiving network based on a number of signal receiving nodes and their spatial position relationship, synchronously receiving UHF signals based on the number of signal receiving nodes within a set time window; Step b based on the spatial domain characteristics of all UHF signals, time domain characteristics and spectral characteristics, all UHF signals are clustered to form associated signal groups and signal feature groups; Calculate the positions of signal sources corresponding to all signal groups based on the spatial position relationship between all signal feature groups and the plurality of signal receiving nodes; Step c. Modeling the transmission paths of all UHF signals in the corresponding signal group based on the location of the signal source, scoring the path rationality of all transmission paths based on the line-of-sight propagation condition, constructing a joint scoring function based on the results of the path rationality scoring and the corresponding signal feature group, and distinguishing the signal source as a real signal source or a signal source to be suppressed based on the result of the joint scoring; Step d. performing interference suppression on the signal source to be suppressed.

2. The method for suppressing UHF signal interference according to claim 1, wherein: The step a comprises: The plurality of signal receiving nodes include a primary signal receiving node and a plurality of auxiliary signal receiving nodes, and a distributed receiving network is constructed based on the primary signal receiving node and the plurality of auxiliary signal receiving nodes and a set spatial position relationship; The spatial position relationship includes: the plurality of auxiliary signal receiving nodes are coplanar to form a regular polygon array, and the main signal receiving node is located on a vertical line of the plane where the regular polygon array is located.

3. The method for suppressing UHF signal interference according to claim 1, wherein: In the step b: The spatial domain characteristics of the UHF signal include: a signal propagation direction vector; The time domain features of the UHF signal include: signal arrival timestamp, signal peak amplitude, signal polarity identifier and the time window in step a; The spectrum characteristics of the UHF signal include: a spectrum characteristic vector; and The signal group is used to accommodate the ultra-high frequency signal, and the signal feature group associated with the signal group is used to accommodate the spatial domain features, time domain features and frequency domain features of the corresponding ultra-high frequency signal.

4. The method for suppressing UHF signal interference according to claim 3, wherein: The step b specifically includes: Step b1. within the set time window, pre-assigning all UHF signals into signal groups to be clustered based on the signal propagation direction vector; Step b2. Performing a vector inner product operation based on the spectral feature vectors corresponding to all UHF signals, calculating the cosine similarity of the UHF signals in all signal groups to be clustered, and converting the cosine similarity into a spectral distance; Step b3. Determine the dynamic time difference of each of the signal groups to be clustered based on the signal peak amplitude corresponding to the UHF signal in each of the signal groups to be clustered; Step b4. Based on the spectral distance and the dynamic time difference, cluster labels are respectively formulated for the UHF signals in all signal groups to be clustered, and the initial cluster center is determined by density peak detection, and the signal group and the signal feature group are associated with the initial cluster center according to the cluster label.

5. The method for suppressing UHF signal interference according to claim 4, wherein: The step b2 specifically includes: All UHF signals in the signal group to be clustered are grouped into signal pairs, and the spectrum feature vectors corresponding to all signal pairs are matched into vector pairs; Calculating the inner product value of each vector pair one by one, and respectively calculating the module-length product of the spectrum feature vector corresponding to each vector pair; defining the cosine similarity according to the inner product value and the module-length product; The cosine similarity is linearly transformed to generate the spectrum distance with a value interval of [0, 1], and the cosine similarity is negatively correlated with the spectrum distance.

6. The method for suppressing UHF signal interference according to claim 4, wherein: The step b3 specifically includes: Determining a maximum signal peak amplitude and a minimum signal peak amplitude of each of the signal groups to be clustered based on the signal peak amplitudes of the UHF signals in each of the signal groups to be clustered, and obtaining an attenuation amount based on the maximum signal peak amplitude and the minimum signal peak amplitude; A basic time tolerance and an adjustment factor are preset, and the sum of the product of the attenuation and the adjustment factor and the basic time tolerance is used as the dynamic time difference of the corresponding signal group to be clustered.

7. The method for suppressing UHF signal interference according to claim 4, wherein: The step b further comprises: Calculating the signal arrival time differences of the UHF signals based on signal arrival timestamps corresponding to the UHF signals in the same signal group, and constructing a nonlinear equation system based on the signal arrival time differences with the positions of the signal sources as unknown variables; In combination with the spatial position relationship of the plurality of signal receiving nodes, an iterative optimization algorithm is used to solve the nonlinear equation group, and the positions of all signal sources are obtained in three-dimensional space.

8. The method for suppressing UHF signal interference according to any one of claims 3 to 7, characterized in that: The step c comprises: simulating a line-of-sight transmission path of a signal from the location of the signal source to the plurality of signal receiving nodes using a finite difference time domain method to generate a theoretical propagation time; The mean absolute deviation between the arrival timestamps of all UHF signals and the theoretical propagation time is calculated, and the mean absolute deviation is normalized based on the width of the time window to obtain the path rationality score reflecting the degree of fit between the arrival timestamps and the theoretical propagation time, and the path rationality score is proportional to the degree of fit.

9. The method for suppressing UHF signal interference according to claim 8, wherein: The step c also includes: Calculating a confidence level based on the position of the signal source to obtain a spatial positioning accuracy of the signal source; Calculating the temporal polarity consistency of the UHF signal based on the signal polarity identifiers corresponding to all UHF signals; Based on the spatial positioning accuracy, the temporal polarity consistency and the path rationality score, linear weighting is performed to construct a joint scoring function and a dynamic threshold is set. Based on the result of the joint scoring and the dynamic threshold, it is determined that: the signal source whose result of the joint scoring is not lower than the dynamic threshold is the true signal source, and the signal source whose joint score is lower than the dynamic threshold is the signal source to be suppressed.

10. The method for suppressing UHF signal interference according to claim 9, wherein: The step d specifically includes: The transmission path similarity comparison is performed on all signal sources to be suppressed and all real signal sources. If the transmission path similarity is not lower than the adaptive strength threshold, feature matching and dynamic shielding are implemented on the corresponding signal sources to be suppressed. If the transmission path similarity is lower than the set threshold, spatial shielding cone angles are set at several signal receiving nodes based on the position of the corresponding signal source to be suppressed to implement interference suppression on the signal source to be suppressed.

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