Anti-interference method, system and equipment of insulator live detection system in high-voltage electromagnetic environment and medium

Abstract

The invention relates to the technical field of insulator live-line detection, and discloses an anti-interference method, system and device for an insulator live-line detection system in a high-voltage electromagnetic environment and a medium, and the method comprises the steps: obtaining the initial interference signal intensity of the insulator live-line detection system in the high-voltage electromagnetic environment; calculating to obtain a filtered signal according to the initial interference signal intensity and the first evaluation coefficient set; calculating according to the filtered signal and the first characteristic parameter set to obtain an interference characteristic identification result; performing anti-interference processing based on an interference feature recognition result; and checking the detection signal subjected to the anti-interference processing to obtain a characteristic parameter error, and if the characteristic parameter error exceeds a first threshold value, recalculating the filtered signal. The problems of complex interference and high coupling in a high-voltage electromagnetic environment are solved in a targeted manner, the anti-interference precision and effectiveness are remarkably improved, and it is guaranteed that the real state of the insulator is restored to the maximum extent through a detection signal.

Description

Technical Field

[0001] This invention relates to the field of insulator live-line detection technology, and in particular to anti-interference methods, systems, equipment and media for insulator live-line detection systems in high-voltage electromagnetic environments. Background Technology

[0002] In high-voltage transmission systems, insulators, as core components ensuring electrical insulation and mechanical support, directly affect the safety and stability of the power grid. However, traditional insulator live-line detection systems face severe challenges from complex electromagnetic interference (such as power frequency interference, pulse noise, and electromagnetic coupling) when operating in high-voltage electromagnetic environments. This significantly affects the signal acquisition and analysis accuracy of the insulator live-line detection system, leading to misjudgments or missed detections, and ultimately threatening the reliable operation of power grid equipment.

[0003] Currently, existing anti-interference methods suffer from the following shortcomings: Traditional methods often focus on a single dimension such as hardware filtering or software algorithms, lacking a comprehensive consideration of multi-dimensional interference factors such as spatial distribution, environmental coupling, and time-varying characteristics, making them unsuitable for the complex electromagnetic environment requirements of ultra-high voltage power grids. Planar sensor arrays have spatial coverage blind spots, failing to capture electromagnetic interference signals in the high-voltage environment from all angles, resulting in incomplete interference identification. Existing algorithms do not fully incorporate dynamic parameters such as environmental temperature and humidity, and ionospheric disturbances, leading to unstable interference suppression effects, especially with significant performance degradation in severe weather or strong electromagnetic environments. Traditional methods lack dynamic verification and feedback mechanisms for interference processing effects, making it difficult to achieve closed-loop optimization of the anti-interference process and affecting the final reliability of the detected signal.

[0004] With the rapid development of ultra-high voltage power grids, higher requirements are placed on the anti-interference capability of insulator live-line detection systems. There is an urgent need for an anti-interference method that can comprehensively integrate multi-dimensional interference characteristics, dynamically adapt to the high-voltage electromagnetic environment, and have closed-loop verification capability, so as to improve the accuracy and robustness of insulator live-line detection systems and provide more reliable technical support for the safe operation of power grids. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides an anti-interference method, system, equipment, and medium for insulator live-line detection systems in high-voltage electromagnetic environments. The problem it solves is that existing insulator live-line detection technologies suffer from limited anti-interference methods, poor environmental adaptability, and a lack of closed-loop verification mechanisms in high-voltage electromagnetic environments, resulting in insufficient detection accuracy and reliability.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides an anti-interference method for an insulator live-line detection system under high-voltage electromagnetic environment, comprising:

[0009] Obtain the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment;

[0010] The filtered signal is calculated based on the initial interference signal strength and the first set of evaluation coefficients.

[0011] The interference feature identification result is calculated based on the filtered signal and the first feature parameter set;

[0012] Anti-interference processing is performed based on the interference feature identification results;

[0013] The detection signal after the anti-interference processing is verified to obtain the characteristic parameter error. If the characteristic parameter error exceeds the first threshold, the filtered signal is recalculated.

[0014] As a preferred embodiment of the anti-interference method for the insulator live-line detection system of the present invention under high-voltage electromagnetic environment, wherein: the acquisition of the initial interference signal strength includes:

[0015] Electromagnetic signals in a high-voltage electromagnetic environment are collected using an electromagnetic induction sensor array;

[0016] The initial interference signal strength is calculated based on the sensitivity parameters, spatial distribution density parameters, electromagnetic signal spatial attenuation coefficient, estimated distance from the sensor to the interference source, environmental adaptation correction factor, time drift correction factor, electromagnetic compatibility coefficient, and humidity polarization influence coefficient of the electromagnetic induction sensor array.

[0017] As a preferred embodiment of the anti-interference method of the insulator live detection system of the present invention under high voltage electromagnetic environment, wherein: the electromagnetic induction sensor array adopts a three-dimensional staggered distribution;

[0018] The distance adjustment parameters between each sensor are obtained based on the minimum wavelength of the expected detection signal, the distance adjustment coefficient, the spatial anisotropy coefficient, the sensor installation angle, the sensor array structure stability coefficient, the frequency response equalization coefficient, the geomagnetic field influence coefficient, and the plasma environment influence coefficient.

[0019] The beneficial effects of this technical solution are as follows: the electromagnetic induction sensor array adopts a three-dimensional staggered distribution, combined with the precise calculation of distance adjustment parameters, which breaks through the limitations of traditional planar distribution.

[0020] As a preferred embodiment of the anti-interference method of the insulator live detection system of the present invention under high voltage electromagnetic environment, the filtered signal is calculated based on the initial interference signal strength and the first evaluation coefficient set, wherein the first evaluation coefficient set includes the frequency fluctuation coefficient, amplitude distortion coefficient, phase offset coefficient, signal time-varying complexity parameter, environmental temperature and humidity coupling coefficient, and ionospheric disturbance influence coefficient of the detection signal.

[0021] As a preferred embodiment of the anti-interference method of the insulator live detection system of the present invention under high voltage electromagnetic environment, wherein: the interference feature identification result is calculated based on the filtered signal and the first feature parameter set, wherein the first feature parameter set includes a weight parameter matrix, a bias parameter vector, a feature extraction coefficient, a dimension compression factor, a modal coupling coefficient, a noise texture feature coefficient, a fractal dimension feature parameter, a signal higher-order cumulative quantity parameter, and a time-frequency domain cross-entropy parameter.

[0022] As a preferred embodiment of the anti-interference method for the insulator live-line detection system of the present invention under high-voltage electromagnetic environment, wherein: the anti-interference processing is performed based on the interference feature identification result to obtain the anti-interference detection signal X. n The formula is expressed as:

[0023]

[0024] Among them, Y f G is the filtered signal. c F is the gain compensation coefficient. a To adjust the sampling frequency adjustment coefficient of the detection system, f c τ is the center frequency. w χf is the time window offset parameter, Δf is the frequency chirp compensation parameter, and λ is the frequency offset. h γ is the hardware nonlinear distortion compensation coefficient. q For quantum noise suppression parameters, θ s For superconducting quantum interference correction parameters, u t represents the topology reconstruction coefficient of the sensor array.

[0025] The beneficial effects of this preferred technical solution are as follows: the anti-interference processing achieves "customized" interference cancellation by adjusting system parameters such as sampling frequency and gain compensation, combined with compensation for multiple scenarios such as hardware distortion and quantum noise.

[0026] As a preferred embodiment of the anti-interference method for the insulator live-line detection system of the present invention under high-voltage electromagnetic environment, wherein: the error of obtaining the characteristic parameter includes:

[0027] By comparing the standard signal characteristic parameter set {P} of the corresponding type of insulator in the standard signal template library.s1 , P s2 , ..., P sn} and the actual adjustment parameter set {P} of the detection signal after the anti-interference processing r1 , P r2 , ..., P rn The characteristic parameter error E is calculated using the following formula. d :

[0028]

[0029] Among them, w j Let θ be the importance weight of the j-th feature parameter. j Let ρ be the directional deviation angle of the j-th characteristic parameter in space. j Let ι be the historical fluctuation correction coefficient for the j-th characteristic parameter. j Let σ be the environmental sensitivity coefficient of the j-th characteristic parameter. j β is the quantum fluctuation correction coefficient for the characteristic parameter. j is the neighborhood correlation coefficient of the feature parameter.

[0030] Secondly, the present invention provides an anti-interference system for an insulator live-line detection system under a high-voltage electromagnetic environment, comprising:

[0031] The signal strength acquisition module is used to acquire the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment;

[0032] The result calculation module is used to calculate the filtered signal based on the initial interference signal strength and the first evaluation coefficient set; and to calculate the interference feature identification result based on the filtered signal and the first feature parameter set.

[0033] An anti-interference processing module is used to perform anti-interference processing based on the interference feature identification results.

[0034] The error comparison module is used to verify the detection signal after the anti-interference processing to obtain the feature parameter error. If the feature parameter error exceeds the first threshold, the filtered signal is recalculated.

[0035] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of an anti-interference method for an insulator live-line detection system under a high-voltage electromagnetic environment.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of an anti-interference method for an insulator live-line detection system under a high-voltage electromagnetic environment.

[0037] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides an anti-interference method, system, equipment, and medium for insulator live-line detection systems in high-voltage electromagnetic environments. By constructing a complete process of "interference perception - signal purification - feature recognition - active countermeasure - closed-loop verification," it provides comprehensive and high-precision technical support for power grid insulator condition monitoring. At the signal acquisition end, the electromagnetic induction sensor array adopts a three-dimensional staggered distribution, combined with precise calculation of distance adjustment parameters, breaking through the limitations of traditional planar distribution. The multi-dimensional layout and parameterized spacing design can capture high-voltage electromagnetic environment signals from all directions, avoiding spatial blind spots and improving the integrity of interference signal acquisition. At the same time, the spacing is dynamically adjusted according to signal characteristics and environmental factors, allowing the sensors to adapt to complex electromagnetic propagation laws, laying a precise data foundation for subsequent interference analysis, and ensuring the comprehensiveness and accuracy of the signal source from the hardware layer. In the interference feature recognition and anti-interference processing stage, the multi-dimensional feature fusion model and parameterized compensation strategy work together. By utilizing parameters such as weight matrices and modal coupling, interference characteristics are deeply analyzed to accurately identify interference types and modes. Anti-interference processing achieves "customized" interference cancellation by adjusting system parameters such as sampling frequency and gain compensation, combined with compensation for hardware distortion and quantum noise in multiple scenarios. This specifically addresses the complex and highly coupled interference issues in high-voltage electromagnetic environments, significantly improving the accuracy and effectiveness of anti-interference measures and ensuring that the detected signal reproduces the insulator's true state to the greatest extent possible. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the overall flow logic of the anti-interference method of the insulator live detection system under high voltage electromagnetic environment according to an embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Example 1, referring to Figure 1As an embodiment of the present invention, an anti-interference method for an insulator live-line detection system under high-voltage electromagnetic environment is provided, such as... Figure 1 The specific steps shown are as follows:

[0042] S100: Obtain the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment;

[0043] S200: The filtered signal is calculated based on the initial interference signal strength and the first set of evaluation coefficients;

[0044] S300: The interference feature identification result is calculated based on the filtered signal and the first feature parameter set;

[0045] S400: Anti-interference processing based on interference feature recognition results;

[0046] S500: Verify the detection signal after anti-interference processing to obtain the characteristic parameter error. If the characteristic parameter error exceeds the first threshold, recalculate the filtered signal.

[0047] It should be noted that, to address the shortcomings of existing insulator live-line detection technologies in high-voltage electromagnetic environments, such as limited anti-interference methods, poor environmental adaptability, and lack of closed-loop verification mechanisms, steps S100 to S500 construct a complete process of "interference perception - signal purification - feature recognition - active countermeasures - closed-loop verification," providing comprehensive and high-precision technical support for power grid insulator condition monitoring. At the signal acquisition end, the electromagnetic induction sensor array adopts a three-dimensional staggered distribution, combined with precise calculation of distance adjustment parameters, breaking through the limitations of traditional planar distribution. The multi-dimensional layout and parameterized spacing design can capture high-voltage electromagnetic environment signals from all directions, avoiding spatial blind spots and improving the completeness of interference signal acquisition. Simultaneously, the spacing is dynamically adjusted according to signal characteristics and environmental factors, allowing the sensors to adapt to complex electromagnetic propagation laws, laying a precise data foundation for subsequent interference analysis, and ensuring the comprehensiveness and accuracy of the signal source from the hardware level. In the interference feature recognition and anti-interference processing stage, the multi-dimensional feature fusion model and parameterized compensation strategy work synergistically. By utilizing parameters such as weight matrices and modal coupling, interference characteristics are deeply analyzed to accurately identify interference types and modes. Anti-interference processing achieves "customized" interference cancellation by adjusting system parameters such as sampling frequency and gain compensation, combined with compensation for hardware distortion and quantum noise in multiple scenarios. This specifically addresses the complex and highly coupled interference issues in high-voltage electromagnetic environments, significantly improving the accuracy and effectiveness of anti-interference measures and ensuring that the detected signal reproduces the insulator's true state to the greatest extent possible.

[0048] Example 2, based on the previous example, provides a specific implementation method for the anti-interference method of the insulator live detection system under high voltage electromagnetic environment, to illustrate the technical means used in this method.

[0049] In this embodiment of the application, the step S100 of obtaining the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment includes:

[0050] Electromagnetic signals in a high-voltage electromagnetic environment are collected using an electromagnetic induction sensor array;

[0051] Based on the sensitivity parameters, spatial distribution density parameters, electromagnetic signal spatial attenuation coefficient, estimated distance from the sensor to the interference source, environmental adaptation correction factor, time drift correction factor, electromagnetic compatibility coefficient, and humidity polarization influence coefficient of the electromagnetic induction sensor array, the initial interference signal strength I0 is calculated using the following formula:

[0052]

[0053] Among them, K s Let D be the sensitivity parameter of the electromagnetic induction sensor array, and D be the spatial distribution density parameter. s S i Let be the original intensity of the electromagnetic signal collected by the i-th sensor, n be the total number of sensors, ρ be the spatial attenuation coefficient of the electromagnetic signal, and d be the intensity of the electromagnetic signal. i Let ψ be the estimated distance from the i-th sensor to the interference source. i For the environmental adaptation correction factor of the i-th sensor, ω is the time drift correction factor for the i-th sensor. e Electromagnetic compatibility factor, This represents the humidity polarization influence coefficient.

[0054] It should be noted that the sensitivity parameter K s K determines the sensor's ability to detect weak electromagnetic signals. s The higher the value, the better it can detect minute electromagnetic disturbances, ensuring that weak interference components are not missed even under strong interference conditions in high-voltage environments. Spatial distribution density parameter D s Define the sensor density within the detection area to avoid interference signals being missed due to spatial blind spots. For example, around the insulator detection area, press D. s Planning the sensor spacing ensures that electromagnetic interference is collected from different directions and distances, providing a foundation for subsequent multi-source signal fusion calculations. i The original intensity of the electromagnetic signal acquired by the i-th sensor represents the "original electromagnetic disturbance" acquired by a single sensor, reflecting the amplitude of the interference signal at a local location. Sensitivity parameter K. s If the sensor sensitivity is high, then K s The larger the value, the higher the S value collected by the sensor. i It has a higher weight in the total interference, reflecting the impact of hardware performance on signal acquisition. D s(Spatial distribution density): A correction to supplement spatial dimensions; the higher the density (D... s (Larger), the more signals the sensor collects within a unit space, the more it can communicate with K. s ×S i Multiplication enhances the contribution of "spatial coverage" to the total interference, ensuring that signals collected collaboratively by multiple sensors are reasonably accumulated. ρ (electromagnetic signal spatial attenuation coefficient): describes the attenuation law of electromagnetic signals as they propagate with distance, similar to "the rate at which a signal is weakened in space," and is determined by the electromagnetic properties of the environmental medium (such as air, insulator materials, etc.). i (Distance from sensor to interference source): Combined with ρ, through an exponential function Corrects signal attenuation caused by propagation distance. The farther away from the interference source (d i The larger the signal attenuation, the smaller this factor, enabling the simulation of spatial characteristics where "far-field interference contributes weakly and near-field interference contributes strongly," making the calculations more closely resemble the actual electromagnetic propagation laws. i (Environmental Adaptability Correction Factor): Compensates for signal acquisition deviations caused by differences in the sensor's installation environment (such as temperature gradients, distribution of electromagnetic reflectors). For example, if a sensor is near a metal bracket, the electromagnetic signal will be enhanced due to reflection, ψ... i Such deviations can be corrected by pre-setting calibration experiments to ensure that the signals collected by sensors at different locations are "consistent in reference". (Time Drift Correction Factor): Addresses performance drift caused by long-term sensor operation (such as sensitivity changes due to component aging). Obtained through regular calibration. This corrects for signal deviations over time, ensuring comparability of signals acquired at different times and allowing I0 to stably reflect environmental interference rather than sensor drift. ω e (Electromagnetic Compatibility Factor): This factor considers the electromagnetic compatibility characteristics within the detection system, such as electromagnetic coupling interference between the sensor and the insulator detection circuit. If electromagnetic crosstalk exists in the system, ω e The system will be calibrated through experimental testing to correct for interference signal deviations caused by the internal electromagnetic environment, thus avoiding the "mistaken inclusion of internal system interference in environmental interference". (Humidity polarization influence coefficient): In high-voltage environments, humidity will change the air ionization characteristics and the polarization state of the insulator surface, thereby affecting electromagnetic signals. Based on the experimental data of humidity-electromagnetic properties, the modulation effect of humidity on interference signals is corrected to ensure that I0 reflects the comprehensive result of "real environmental interference + humidity influence".

[0055] It should be noted that in the high-voltage electromagnetic environment anti-interference process of the insulator live-line detection system, obtaining the initial interference signal strength I0 is the fundamental and crucial first step. Its working principle revolves around multi-source signal acquisition, multi-dimensional correction, and precise quantization calculation. Through the collaboration of hardware arrays and algorithm models, it achieves accurate perception of complex high-voltage electromagnetic interference. Accurate calculation of I0 is a prerequisite for the anti-interference of the insulator live-line detection system. If the calculation of I0 is inaccurate, the parameters of subsequent filtering algorithms (such as frequency fluctuation coefficient and amplitude distortion coefficient) will lose their basis for accurate correction. Through multi-dimensional correction and accumulation, I0 can truly reflect the interference characteristics of the high-voltage electromagnetic environment (including spatial distribution, environmental influence, and time drift), making subsequent interference feature identification and anti-interference processing more targeted. Ultimately, this ensures the accuracy and reliability of the insulator live-line detection signal, providing high-quality data support for power grid equipment condition assessment.

[0056] In this embodiment, the electromagnetic induction sensor array adopts a three-dimensional staggered distribution; the distance adjustment parameter L between each sensor is... a The formula is obtained based on the expected minimum wavelength of the detected signal, distance adjustment coefficient, spatial anisotropy coefficient, sensor installation angle, sensor array structure stability coefficient, frequency response equalization coefficient, geomagnetic field influence coefficient, and plasma environment influence coefficient.

[0057]

[0058] Where β is the distance adjustment coefficient, λ min Let κ be the minimum wavelength of the expected detection signal, θ be the spatial anisotropy coefficient, and θ be the sensor mounting angle. σ is the stability coefficient of the sensor array structure. f η is the frequency response equalization coefficient of the sensor array. g The influence coefficient of the geomagnetic field, μ p The plasma environment impact coefficient;

[0059] It should be noted that, in this embodiment, to more accurately acquire electromagnetic signals from a high-voltage electromagnetic environment, the electromagnetic induction sensor array adopts a three-dimensional, staggered distribution. This distribution can capture signals from multiple spatial dimensions, improving the comprehensiveness of the acquisition. The distance adjustment parameter L between the sensors... a These are key design parameters, among which the distance adjustment coefficient β is the basic adjustment factor, and the minimum wavelength λ of the expected detection signal is... min The basic scale determining the spatial characteristics of the signal is used to initially define the reference for distance adjustment. The spatial anisotropy coefficient κ and the sensor mounting angle θ, expressed through the term (1+κ×sinθ), reflect the influence of spatial orientation characteristics on the sensor spacing, adapting to signal acquisition requirements under different installation layouts. Sensor array structural stability coefficient... Distance adaptation and frequency response equalization coefficient σ are used to ensure the stability of the array's physical structure. f From the perspective of signal frequency response, it is essential to ensure that the sensor spacing is reasonable when acquiring signals of different frequencies, so that the array can respond well to signals of each frequency. Geomagnetic field influence coefficient η g and plasma environment influence coefficient μ p Taking into account the special factors such as the geomagnetic field and plasma environment, the interference and correction of the sensor spacing in electromagnetic signal acquisition are calculated by integrating multiple parameters to obtain L. a This enables a three-dimensional, staggered sensor array to acquire electromagnetic signals more efficiently and accurately in a high-voltage electromagnetic environment with a suitable spacing, laying the foundation for the accurate acquisition of the initial interference signal strength I0.

[0060] In this embodiment of the application, the above step S200, which calculates the filtered signal based on the initial interference signal strength and the first evaluation coefficient set, includes:

[0061] The filtered signal Y is calculated based on the initial interference signal strength and the first set of evaluation coefficients. f The first set of evaluation coefficients includes the frequency fluctuation coefficient F of the detected signal. v Amplitude distortion coefficient A d Phase offset coefficient Φ0, signal time-varying complexity parameter V t τ, the coupling coefficient of ambient temperature and humidity h ionospheric disturbance influence coefficient ζ i The formula is expressed as:

[0062]

[0063] Where X0 is the original detection signal, α is the adaptive adjustment coefficient, and V max The maximum time-varying complexity is preset.

[0064] This embodiment uses the original detection signal X0 as a basis and introduces an adaptive adjustment coefficient α to construct an "interference cancellation benchmark". Using the initial interference signal strength I0 obtained in step S100, the interference baseline of the high-voltage electromagnetic environment is quantified; combined with the characteristic parameters of the detection signal itself, including the frequency fluctuation coefficient F... v (Describes the degree of fluctuation in signal frequency due to interference), amplitude distortion coefficient A d (Reflects the proportion of signal amplitude distortion caused by interference) and phase offset coefficient Φ0 (indicates the amount of signal phase offset due to interference). From the perspective of the signal's "time-frequency domain characteristics", we can analyze the modulation law of interference on the signal.

[0065] At the same time, the signal time-varying complexity parameter V is incorporated. t(This reflects the complex interference characteristics of the signal changing over time, and the preset maximum time-varying complexity V) max In comparison, the dynamic interference level of the quantized signal and the coupling coefficient τ of the ambient temperature and humidity are compared. h (Weighting of interference from temperature and humidity environment on the signal), ionospheric disturbance influence coefficient ζ i (Characterizing the proportion of the effect of ionospheric interference on the signal), covering the correction dimension of "signal dynamic characteristics + environmental interference factors".

[0066] It should be noted that the environmental interference baseline is first determined by I0, and then by F. v 、A d Φ0 analyzes the disturbance characteristics of the signal itself, and then uses Quantize the dynamic interference of the signal, and finally use τ h ζ i Environmental interference correction is performed. By adaptively adjusting the coefficient α, the combined effects of these interference factors are removed from the original signal X0 in the form of "interference compensation," ultimately reconstructing the filtered signal Y. f This achieves the goal of "removing interference and restoring the true signal," providing a cleaner signal foundation for subsequent interference feature identification and anti-interference processing.

[0067] Specifically, the adaptive adjustment coefficient is calculated as follows:

[0068] α=γ×I0 2 +δ×I0+ε+η×sin(wI0+φ)×σ e ×τ p ×ψ m ×ξ m ×ω v

[0069] Where γ, δ, and ε are fitting coefficients, η is a harmonic correction coefficient, w is an angular frequency parameter, φ is a phase parameter, and σ is a harmonic correction parameter. e τ is the environmental electromagnetic disturbance intensity coefficient. p ψ is the electromagnetic interference pulse shape coefficient. m ξ is the multipath effect correction coefficient. m ω is the electromagnetic shielding coefficient of the metallic material. v This represents the wind speed influence coefficient.

[0070] It should be noted that, with the initial interference signal strength I0 as the core correlation point, a multi-layer correction logic is constructed: first through γ×I0 2+δ×I0+ε, using the fitting coefficients γ, δ, and ε to construct a basic fitting model, adapting to the basic correlation between I0 and α; introducing η×sin(wI0+φ), using the harmonic correction coefficient η, the angular frequency parameter w, and the phase parameter φ, to simulate the modulation of α by the harmonic characteristics of the interference signal, adapting to the periodic interference of the signal; and then superimposing the environmental electromagnetic disturbance intensity coefficient σ. e Electromagnetic interference pulse shape coefficient τ p Multipath effect correction coefficient ψ m Electromagnetic shielding coefficient ξ of metallic materials m Wind speed influence coefficient ω v The algorithm comprehensively corrects α from dimensions such as environmental electromagnetic disturbances, interference pulse morphology, signal propagation multipath, metal shielding characteristics, and wind speed influence. These parameters cover both static (e.g., environmental electromagnetic disturbances, metal shielding) and dynamic (e.g., interference pulses, multipath effects, wind speed) characteristics of the electromagnetic environment, allowing α to dynamically adapt to the complex interference of high-voltage electromagnetic environments and ensuring that the calculated filtered signal Y... f At the same time, the interference compensation amount accurately matches the actual interference scenario, improves the adaptability of the filtering effect to the environment, and provides a more accurate adjustment basis for subsequent anti-interference processes.

[0071] In this embodiment of the application, the above step S300, which calculates the interference feature identification result based on the filtered signal and the first feature parameter set, includes:

[0072] The interference feature identification result R is calculated based on the filtered signal and the first feature parameter set. i The first set of feature parameters includes the weight parameter matrix W. m Bias parameter vector B v Feature extraction coefficient C e Dimensional compression factor Γ d Modal coupling coefficient Ω c Noise texture feature coefficient ξ n fractal dimension characteristic parameter δ f , signal higher-order cumulant parameter χ k and the time-frequency domain cross-entropy parameter θ k The formula is expressed as:

[0073] R i =f(ξ n ×Ω c ×C e ×Γ d ×(W m ×Y f +B v )×δ f ×χ k ×θ x )

[0074] Where f is the activation function, θ x This is the time-frequency domain cross-entropy parameter.

[0075] It should be noted that this embodiment uses the filtered signal Y f As the basis for analysis, a series of feature parameters are integrated to construct a recognition model: weight parameter matrix W m With bias parameter vector B v The "basic mapping relationship" used to construct signal features is similar to assigning "importance weights" to different signal features; feature extraction coefficient C e Define the algorithm rules for feature extraction, and the dimensionality compression factor Γ. d This simplifies signal feature dimensions, reduces computational complexity, and retains key information. A modal coupling coefficient Ω is introduced. c Identify the coupling mode between signal and interference, and the noise texture feature coefficient ξ. n To characterize the texture patterns of interference noise, the fractal dimension characteristic parameter δ f Using fractal theory to describe the complexity of a signal, the higher-order cumulant parameter χ is used. k Extracting nonlinear features of the signal, the time-frequency domain cross-entropy parameter θ k Quantize the difference between the signal and interference in the time-frequency domain.

[0076] It should be noted that the calculation of the interference feature recognition result incorporates multi-dimensional feature parameters such as feature extraction, dimensionality compression, coupling mode, and noise texture in sequence. Finally, a nonlinear transformation is achieved through the activation function f to output the interference feature recognition result R. i By deeply integrating the multi-dimensional features of the filtered signal through mathematical models and intelligent calculations, key characteristics such as the type, intensity, and coupling mode of interference can be accurately identified, making anti-interference strategies more targeted.

[0077] In this embodiment, step S400 above performs anti-interference processing based on the interference feature recognition result to obtain the anti-interference detection signal X. n The formula is expressed as:

[0078]

[0079] Among them, Y f G is the filtered signal. c F is the gain compensation coefficient. a To adjust the sampling frequency adjustment coefficient of the detection system, f c τ is the center frequency. w χf is the time window offset parameter, Δf is the frequency chirp compensation parameter, and λ is the frequency offset. h γ is the hardware nonlinear distortion compensation coefficient. q For quantum noise suppression parameters, θ s For superconducting quantum interference correction parameters, ut represents the topology reconstruction coefficient of the sensor array.

[0080] It should be noted that this embodiment uses the filtered signal Y after interference feature identification. f Based on this, an anti-interference model is constructed by integrating a series of compensation parameters: sampling frequency adjustment coefficient F. a With gain compensation coefficient G c The time window offset parameter τ is used to adjust the "basic acquisition characteristics" of the detection system. w To accommodate interference shifts in the time dimension of the adaptive signal, the frequency chirping compensation parameter χf corrects for nonlinear frequency drift. Simultaneously, a hardware nonlinear distortion compensation coefficient λ is incorporated. h Quantum noise suppression parameter γ q Superconducting quantum interference correction parameter θ s Sensor array topology reconstruction coefficient u t It covers the multi-dimensional interference compensation needs such as "hardware distortion, quantum noise, superconducting interference, and array topology".

[0081] It should be noted that, firstly, let's take G... c With F a Correct the system acquisition gain and sampling frequency, and then... Compensation for time window offset, using Frequency drift is corrected. Subsequently, compensation parameters for hardware distortion, quantum noise, superconducting interference, and array topology are incorporated sequentially for Y. f Multi-dimensional corrections are performed, and the final output is the anti-interference detection signal X. n The interference feature identification result R i This is transformed into specific parameter control commands, and through mathematical models, it accurately compensates for complex interferences in the high-voltage electromagnetic environment (including system acquisition deviations, time and frequency drift, hardware distortion, quantum interference, etc.), achieving a closed-loop anti-interference system of "interference characteristics - parameter compensation - signal reconstruction". This allows the detection signal to restore the true state of the insulator to the greatest extent possible, providing clean data for subsequent state assessment.

[0082] In this embodiment of the application, step S500 verifies the detection signal after anti-interference processing to obtain the characteristic parameter error. If the characteristic parameter error exceeds the first threshold, the filtered signal is recalculated, including the following sub-steps E1 to E2:

[0083] In E1: The detection signal after anti-interference processing is verified, and the characteristic parameter errors are obtained, including:

[0084] By comparing the standard signal characteristic parameter set {P} of the corresponding type of insulator in the standard signal template library. s1 , P s2 , ..., P sn} and the actual adjustment parameter set {P} of the detection signal after anti-interference processing r1 , P r2 , ..., P rn The characteristic parameter error E is calculated using the following formula. d :

[0085]

[0086] Among them, w j Let θ be the importance weight of the j-th feature parameter. j Let ρ be the directional deviation angle of the j-th characteristic parameter in space. j Let ι be the historical fluctuation correction coefficient for the j-th characteristic parameter. j Let σ be the environmental sensitivity coefficient of the j-th characteristic parameter. j β is the quantum fluctuation correction coefficient for the characteristic parameter. j is the neighborhood correlation coefficient of the feature parameter.

[0087] It should be noted that in this embodiment, the standard signal characteristic parameter set {P} of the corresponding type of insulator is retrieved from the standard signal template library. s1 , P s2 , ..., P sn This dataset serves as a "signal characteristic benchmark" for insulators in an ideal clean environment; simultaneously, the interference-resistant detection signal X is extracted. n The actual adjustment parameter set {P r1 , P r2 , ..., P rn}, serving as "actual signal feature samples". Next, multi-dimensional correction parameters are introduced to construct an error model: importance weight w j Assign an "influence weight" to each feature parameter to highlight the contribution of key features to the error; directional deviation angle θ j Historical fluctuation correction coefficient ρ j The differences in characteristic parameters in spatial direction and historical fluctuations are corrected respectively; the environmental sensitivity coefficient ι j Quantum fluctuation correction coefficient σ j Neighborhood correlation coefficient β j It covers the influence of complex factors such as residual environmental interference, quantum-level signal fluctuations, and spatial correlation of characteristic parameters on errors.

[0088] It should be noted that the original difference between the standard and the actual characteristic parameters is first calculated, and then correction terms such as direction, history, environment, quantum, and neighborhood are successively incorporated to perform multi-dimensional compensation and correction on the difference. Finally, the square root is taken to obtain the characteristic parameter error E. d This process precisely quantifies the differences between "standard" and "actual" signal characteristics, combined with the multiple physical factors (spatial orientation, historical fluctuations, environmental interference, etc.) in the insulator testing scenario, allowing E...d It not only reflects the differences in signal values, but also the real deviations of characteristic parameters under complex environments, providing a scientific basis for judging the anti-interference effect (whether re-filtering is triggered).

[0089] In E2: If the characteristic parameter error exceeds the first threshold, the filtered signal is recalculated. The calculation of the first threshold includes:

[0090]

[0091] Where μ is the mean of the characteristic parameter error of historical detection data, σ is the standard deviation, z is the confidence coefficient, ξa is the skewness correction coefficient, and e i Let N be the i-th historical error sample, and N be the number of samples. υ is the time-scale influence coefficient. s w is the sample size weighting coefficient. s γ is the seasonal variation influence coefficient. t Let ξ be the influence coefficient of the temperature gradient. y This is a correction factor for the number of years of testing.

[0092] It should be noted that this embodiment is based on historical detection data. First, the mean μ and standard deviation σ of the feature parameter errors are statistically analyzed. These two indicators reflect the "central tendency" and "dispersion" of historical errors and are the core statistics for threshold calculation. A confidence coefficient z is introduced to adapt to threshold requirements at different confidence levels (e.g., 95% confidence), ensuring the threshold aligns with the reliability requirements of the detection system for error control. Next, multi-dimensional correction parameters are incorporated to compensate for the impact of complex scenarios: a skewness correction coefficient ξa is used to correct the asymmetry of historical error distribution (e.g., adjusting the threshold to match the true distribution when the error is skewed); and a time scale influence coefficient... Sample size weighting coefficient υ s Seasonal variation influence coefficient w s Temperature gradient influence coefficient γ t ξ, the correction factor for the testing period y The study covers the impact of factors such as time span, sample size, seasonal environment, temperature gradient, and equipment aging on the error threshold.

[0093] It should be noted that deeply integrating the statistical patterns of historical errors with the actual scenarios of insulator testing (time, season, temperature, equipment aging, etc.) allows E... dy It not only reflects the statistical characteristics of historical errors but also adapts to the error control requirements in complex environments, providing a dynamic and accurate judgment benchmark for the verification process to determine whether the signal needs to be reprocessed after anti-interference, thus ensuring the reliability and accuracy of the detection system in different scenarios.

[0094] It should be noted that insulator live-line detection systems in high-voltage electromagnetic environments are susceptible to interference from electric and magnetic field coupling. Electromagnetic induction sensors (or equivalent signal acquisition modules) actively collect electromagnetic signals from the environment. After signal conditioning (such as amplification and filtering preprocessing), an algorithm model is used to calculate the initial interference signal strength I0, aiming to quantify the interference base faced by the detection system. When the detection signal is transmitted in a high-voltage electromagnetic environment, it will be distorted due to frequency fluctuations, amplitude distortion, and phase shifts. Simultaneously, environmental temperature and humidity, and ionospheric disturbances will introduce additional interference. Based on I0 from step S100, the frequency fluctuation coefficient F is fused. v (Describes the degree of signal frequency deviation due to interference), amplitude distortion coefficient A d (Reflects the proportion of signal amplitude distortion), phase offset coefficient Φ0 (indicates the amount of phase interference), signal time-varying complexity parameter V t (Reflecting the complex interference characteristics of signals changing over time), environmental temperature and humidity coupling coefficient τ h (The influence of temperature and humidity on the signal), ionospheric disturbance influence coefficient ζ i Parameters such as (quantization of ionospheric interference weights) are used to correct the original detection signal through an adaptive filtering algorithm (such as an optimization model based on minimum mean square error), suppressing interference components and reconstructing a filtered signal Y that is closer to the real signal. f .

[0095] It should be noted that the filtered signal Y f Complex interference may still remain. A weight parameter matrix W is introduced. m (Assigning analysis weights to different signal features), bias parameter vector B v (Baseline deviation of compensated signal feature extraction), feature extraction coefficient C e (Define the algorithm rules for feature extraction), dimensionality compression factor Γ d (Simplifying signal feature dimensions and reducing computational load), mode coupling coefficient Ω c (Identifying the coupling mode between signal and interference), noise texture feature coefficient ξ n (Characterizing the texture patterns of interference noise), fractal dimension characteristic parameter δ f (Using fractal theory to describe signal complexity), signal higher-order cumulant parameter χ k (Extracting nonlinear features of the signal to distinguish between interference and real signals), time-frequency domain cross-entropy parameter θ k (Quantizing the difference between signal and interference in the time-frequency domain), constructing a feature recognition model (such as the convolutional neural network branch in deep learning). For Y f Feature decomposition and pattern matching are performed to accurately identify the type (such as power frequency interference, pulse interference, etc.), intensity, coupling mode, etc. of interference, and output the interference feature identification result R. i Based on step S300, Ri Activate targeted anti-interference strategies. If identified as power frequency interference, the corresponding frequency can be accurately attenuated using a notch filter; if it is pulse interference, pulse suppression algorithms (such as amplitude limiting and pulse width identification and elimination) can be used; if it is complex coupled interference, an adaptive cancellation model can be invoked (dynamically adjusting cancellation parameters based on interference characteristics). Through the collaboration of hardware circuits (such as adjustable filters and interference cancellation modules) and algorithms, the true insulator detection signal is preserved to the greatest extent possible.

[0096] It should be noted that calling the standard signal template library (containing a set of standard signal characteristic parameters for different types of insulators in a clean electromagnetic environment, such as the frequency range, amplitude fluctuation, and phase characteristics of normal signals, etc.) will convert X... n The actual set of adjusted parameters (frequency, amplitude, phase, etc. of the signal after anti-interference) is compared with the standard set, and the characteristic parameter error E is calculated using an error algorithm. d If E d ≤E dy (A preset threshold, set based on insulator detection accuracy requirements and historical error statistics), indicates that the anti-interference is effective and the signal can be used for subsequent insulator condition assessment; if E d >E dy This indicates that the interference was not sufficiently suppressed, triggering closed-loop feedback. The system then returns to step S2 for re-filtering, adjusting the filtering algorithm parameters (e.g., increasing the filtering depth, optimizing the characteristic coefficients) until the signal error meets the requirements. This forms a closed-loop control process of "detection-interference suppression-verification-optimization," ensuring the reliability and accuracy of the detected signal. This invention, through a collaborative process of "interference sensing-signal purification-interference analysis-interference suppression-verification and optimization," enables the insulator live-line detection system to "intelligently identify interference, accurately eliminate interference, and provide closed-loop signal protection" in high-voltage electromagnetic environments, providing high-quality data support for insulator condition monitoring.

[0097] Example 3: This example provides an anti-interference system for an insulator live-line detection system under high-voltage electromagnetic environment, including:

[0098] The signal strength acquisition module is used to acquire the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment;

[0099] The result calculation module is used to calculate the filtered signal based on the initial interference signal strength and the first evaluation coefficient set; and to calculate the interference feature identification result based on the filtered signal and the first feature parameter set.

[0100] An anti-interference processing module is used to perform anti-interference processing based on the results of interference feature identification.

[0101] The error comparison module is used to verify the detection signal after anti-interference processing and obtain the characteristic parameter error. If the characteristic parameter error exceeds the first threshold, the filtered signal is recalculated.

[0102] It should be noted that the technical solution of the anti-interference system of the live insulator detection system under high voltage electromagnetic environment is the same concept as the technical solution of the anti-interference method of the live insulator detection system under high voltage electromagnetic environment described above. For details not described in detail in the technical solution of the anti-interference system of the live insulator detection system under high voltage electromagnetic environment described above, please refer to the description of the technical solution of the anti-interference method of the live insulator detection system under high voltage electromagnetic environment described above.

[0103] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0104] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an anti-interference method for an insulator live-line detection system in a high-voltage electromagnetic environment. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0105] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0106] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0107] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An anti-interference method for an insulator live-line detection system under high-voltage electromagnetic environment, characterized in that, include: Obtain the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment; The filtered signal is calculated based on the initial interference signal strength and the first set of evaluation coefficients. The interference feature identification result is calculated based on the filtered signal and the first feature parameter set; Anti-interference processing is performed based on the interference feature identification results; The detection signal after the anti-interference processing is verified to obtain the characteristic parameter error. If the characteristic parameter error exceeds the first threshold, the filtered signal is recalculated.

2. The anti-interference method of the insulator live-line detection system as described in claim 1 under high-voltage electromagnetic environment, characterized in that, The acquisition of the initial interference signal strength includes: Electromagnetic signals in a high-voltage electromagnetic environment are collected using an electromagnetic induction sensor array; The initial interference signal strength is calculated based on the sensitivity parameters, spatial distribution density parameters, electromagnetic signal spatial attenuation coefficient, estimated distance from the sensor to the interference source, environmental adaptation correction factor, time drift correction factor, electromagnetic compatibility coefficient, and humidity polarization influence coefficient of the electromagnetic induction sensor array.

3. The anti-interference method of the insulator live-line detection system as described in claim 2 under high-voltage electromagnetic environment, characterized in that, The electromagnetic induction sensor array is arranged in a three-dimensional staggered pattern. The distance adjustment parameters between each sensor are obtained based on the minimum wavelength of the expected detection signal, the distance adjustment coefficient, the spatial anisotropy coefficient, the sensor installation angle, the sensor array structure stability coefficient, the frequency response equalization coefficient, the geomagnetic field influence coefficient, and the plasma environment influence coefficient.

4. The anti-interference method of the insulator live-line detection system as described in claim 3 under high-voltage electromagnetic environment, characterized in that, The filtered signal is calculated based on the initial interference signal strength and the first set of evaluation coefficients, wherein the first set of evaluation coefficients includes the frequency fluctuation coefficient, amplitude distortion coefficient, phase offset coefficient, signal time-varying complexity parameter, environmental temperature and humidity coupling coefficient, and ionospheric disturbance influence coefficient of the detected signal.

5. The anti-interference method of the insulator live-line detection system as described in claim 4 under high-voltage electromagnetic environment, characterized in that, The interference feature identification result is calculated based on the filtered signal and the first feature parameter set, wherein the first feature parameter set includes a weight parameter matrix, a bias parameter vector, feature extraction coefficients, a dimension compression factor, a modal coupling coefficient, a noise texture feature coefficient, a fractal dimension feature parameter, a higher-order cumulative quantity parameter of the signal, and a time-frequency domain cross-entropy parameter.

6. The anti-interference method of the insulator live-line detection system as described in claim 5 under high-voltage electromagnetic environment, characterized in that, The interference feature identification result is used to perform anti-interference processing to obtain the anti-interference detection signal X. n The formula is expressed as: Among them, Y f G is the filtered signal. c F is the gain compensation coefficient. a To adjust the sampling frequency adjustment coefficient of the detection system, f c τ is the center frequency. w χf is the time window offset parameter, Δf is the frequency chirp compensation parameter, and λ is the frequency offset. h γ is the hardware nonlinear distortion compensation coefficient. q For quantum noise suppression parameters, θ s For superconducting quantum interference correction parameters, u t represents the topology reconstruction coefficient of the sensor array.

7. The anti-interference method of the insulator live-line detection system as described in claim 6 under high-voltage electromagnetic environment, characterized in that, The error in obtaining the characteristic parameters includes: By comparing the standard signal characteristic parameter set {P} of the corresponding type of insulator in the standard signal template library. s1 , P s2 , ..., P sn } and the actual adjustment parameter set {P} of the detection signal after the anti-interference processing r1 , P r2 , ..., P rn The characteristic parameter error E is calculated using the following formula. d : Among them, w j Let θ be the importance weight of the j-th feature parameter. j Let ρ be the directional deviation angle of the j-th characteristic parameter in space. j Let ι be the historical fluctuation correction coefficient for the j-th characteristic parameter. j Let σ be the environmental sensitivity coefficient of the j-th characteristic parameter. j β is the quantum fluctuation correction coefficient for the characteristic parameter. j is the neighborhood correlation coefficient of the feature parameter.

8. An anti-interference system for a live insulator detection system under high-voltage electromagnetic environment, comprising the anti-interference method for a live insulator detection system under high-voltage electromagnetic environment as described in any one of claims 1 to 7, characterized in that, include: The signal strength acquisition module is used to acquire the initial interference signal strength of the insulator live-line detection system under high-voltage electromagnetic environment; The result calculation module is used to calculate the filtered signal based on the initial interference signal strength and the first evaluation coefficient set. The interference feature identification result is calculated based on the filtered signal and the first feature parameter set; An anti-interference processing module is used to perform anti-interference processing based on the interference feature identification results. The error comparison module is used to verify the detection signal after the anti-interference processing to obtain the feature parameter error. If the feature parameter error exceeds the first threshold, the filtered signal is recalculated.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the anti-interference method of the insulator live detection system according to any one of claims 1 to 7 under high-voltage electromagnetic environment.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the anti-interference method of the insulator live detection system according to any one of claims 1 to 7 under high-voltage electromagnetic environment.

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