A method for adaptive electromagnetic interference suppression of a patrol device and related equipment

Abstract

The application discloses a kind of patrol equipment electromagnetic interference adaptive suppression method and related equipment, it is related to electromagnetic interference suppression technical field, the electromagnetic interference signal of the power facility area where patrol equipment is located is modeled in real time, and its frequency band distribution and intensity information are dynamically acquired, to build a double-target optimization objective function;Subsequently, the ant colony optimization algorithm is introduced, and the optimization process of filter feedback coefficient is converted into the intelligent search process of ant colony, and finally the global optimal feedback coefficient is converged;Finally, this coefficient is applied to filter, realizes the real-time dynamic suppression of electromagnetic interference, and outputs the effective signal of high-fidelity patrol equipment.The application can evolve the suppression strategy independently with the transient evolution of electromagnetic environment, thereby greatly improving the operation robustness and data accuracy of patrol equipment under strong electromagnetic impact, to ensure that the patrol task in complex power scene is stably and reliably executed.

Description

Technical Field

[0001] This application relates to the field of electromagnetic interference suppression technology, and more specifically, to an adaptive electromagnetic interference suppression method and related equipment for inspection equipment. Background Technology

[0002] In power facility areas, especially substations, converter stations, and areas with dense high-voltage equipment, there exists a complex electromagnetic interference environment generated by corona discharge, switching operations, and high-frequency switching of power electronic devices. Inspection equipment, as a key mobile terminal ensuring the safe operation of the power system, relies on wireless communication, high-precision positioning, and real-time control signals to complete autonomous navigation and data acquisition. However, high-density, wide-band, and dynamically changing electromagnetic interference can easily couple into the sensors and communication links of the inspection equipment, causing signal distortion, positioning drift, and in severe cases, even leading to erroneous control command triggering or equipment shutdown, directly threatening the safety and data reliability of the inspection task. The severity of this problem is particularly prominent in high-voltage, high-radiation power hub areas.

[0003] Therefore, there is an urgent need for a method that can sense and adaptively suppress complex electromagnetic interference in real time to ensure the reliable operation of inspection equipment in strong electromagnetic environments. Summary of the Invention

[0004] This application provides an adaptive electromagnetic interference suppression method and related equipment for inspection equipment, which can autonomously evolve the suppression strategy with the transient evolution of the electromagnetic environment, thereby greatly improving the operational robustness and data accuracy of the inspection equipment under strong electromagnetic shocks, and ensuring the stable and reliable execution of inspection tasks in complex power scenarios.

[0005] An adaptive suppression method for electromagnetic interference of inspection equipment includes: Model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and obtain the frequency band distribution and intensity information of the electromagnetic interference signal in real time; Based on the frequency band distribution and intensity information of the electromagnetic interference signal, an optimization objective function is constructed with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony optimization algorithm is used to adaptively optimize the feedback coefficients of the filter to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant based on the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to perform real-time dynamic suppression of the electromagnetic interference signal, outputting the effective signal of the inspection equipment after interference suppression.

[0006] Optionally, the step of modeling the electromagnetic interference signal in the power facility area where the inspection equipment is located, and acquiring the frequency band distribution and intensity information of the electromagnetic interference signal in real time, includes: The time-frequency joint transformation of the time-domain electromagnetic signal collected by the receiving end of the inspection equipment is performed to obtain the time-frequency joint distribution result of the electromagnetic signal; Based on the time-frequency joint distribution results, multiple electromagnetic interference sources within the power facility area are identified, and the time intervals and candidate frequency band ranges corresponding to each electromagnetic interference source are divided. For each electromagnetic interference source in each time interval, a time-frequency joint model of a single electromagnetic interference signal is constructed by combining the interference observation sampling parameters, amplitude parameters, intensity factors, and propagation distance from the interference source to the receiver. Based on the time-frequency joint model, the interference intensity and phase characteristics in each candidate frequency band are calculated, and the frequency band distribution and intensity information of electromagnetic interference signals in the entire frequency band range are statistically obtained.

[0007] Optionally, in the time-frequency joint model of the single-channel electromagnetic interference signal, the expression of the single-channel electromagnetic interference signal within the time interval is:

[0008] in, This is a single-channel electromagnetic interference signal; The frequency response of the electromagnetic signal; The intensity factor of the electromagnetic interference signal; The sampling period for electromagnetic interference observations; The amplitude of the electromagnetic interference observation; d This is the distance from the electromagnetic interference source to the receiving antenna; It is an exponential factor used to adjust the signal diffusion and attenuation characteristics.

[0009] Optionally, the expression for the optimization objective function is:

[0010] in, W The objective function value; , These are the lower and upper limits of the interference signal frequency band, respectively; For the filter to filter single-channel electromagnetic interference signals The adaptive correction feedback coefficient characterizes the filter's response strength to electromagnetic interference signals; F This refers to the bandwidth of the frequency band.

[0011] Optionally, the step of adaptively optimizing the feedback coefficients of the filter using an ant colony optimization algorithm to determine the globally optimal feedback coefficients includes: Initialize the ant colony population and map the position of each ant in the solution space to a set of candidate filter feedback coefficients; The optimization objective function is used as the fitness evaluation function. The fitness value of the candidate feedback coefficient for each ant is calculated. Based on the fitness value, the probability that each candidate solution is selected as the guiding benchmark for this round of local search is determined. Each ant performs a local search based on the guiding benchmark selected according to the probability in the current iteration, updates its own position in the solution space according to the step size update rule, and obtains new candidate filter feedback coefficients. After completing a single iteration, a global pheromone update is performed, synchronously updating the current global optimal feedback coefficient; Determine whether the preset convergence condition is met. If it is met, terminate the iteration and output the global optimal feedback coefficient. If it is not met, return to the fitness calculation step and continue the iteration.

[0012] Optionally, the expression for step size update in the ant colony optimization algorithm is:

[0013] in, For the adaptive correction feedback coefficient during ant search process The step size update amount; This is the optimal solution for the current iteration; This is the optimal solution from the previous iteration; This is the step size adjustment factor; This is a random factor used to maintain the diversity of the solution space.

[0014] Optionally, the filter parameter update formula is:

[0015] in, The response strength of the filter after parameter update; This represents the initial response of the filter; The amount of feedback coefficient update obtained by optimizing the ant colony.

[0016] An adaptive electromagnetic interference suppression device for inspection equipment, comprising: The interference signal modeling unit is used to model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and to acquire the frequency band distribution and intensity information of the electromagnetic interference signal in real time. The objective function construction unit is used to construct an optimized objective function based on the frequency band distribution and intensity information of the electromagnetic interference signal, with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony adaptive optimization unit is used to adaptively optimize the feedback coefficients of the filter using the ant colony optimization algorithm to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant using the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The filter execution output unit is used to apply the globally optimal feedback coefficient to the filter, and use the updated filter to perform real-time dynamic suppression of the electromagnetic interference signal, and output the effective signal of the inspection equipment after interference suppression.

[0017] An adaptive electromagnetic interference suppression device for inspection equipment includes a memory and a processor; The memory is used to store programs; The processor is used to execute the program to implement the various steps of the adaptive suppression method for electromagnetic interference of inspection equipment as described in any of the above claims.

[0018] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the adaptive suppression method for electromagnetic interference of inspection equipment as described in any of the preceding claims.

[0019] As can be seen from the above technical solutions, the adaptive electromagnetic interference suppression method and related equipment for inspection equipment provided in this application firstly models the electromagnetic interference signal in the power facility area where the inspection equipment is located in real time, and dynamically acquires the frequency band distribution and intensity information of the electromagnetic interference signal; then, based on the frequency band distribution and intensity information, an optimization objective function is constructed with the goal of minimizing the response intensity of the filter in the interference frequency band and controlling the effective signal loss; next, an ant colony optimization algorithm is used to adaptively optimize the feedback coefficient of the filter, and the fitness of each individual ant is calculated using the optimization objective function, and local search and global pheromone update are performed based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficient; finally, the globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to dynamically suppress the electromagnetic interference signal in real time, outputting the effective signal of the inspection equipment after interference suppression.

[0020] This scheme continuously senses the electromagnetic environment of the power facility area through real-time modeling, allowing the frequency band distribution and intensity information of interference signals to be updated in real time as the environment changes. This drives the adjustment of filter parameters to always be precisely synchronized with the current interference state. This dynamic tracking mechanism gives the method the ability to adapt to the time-varying characteristics of the environment in real time. No matter how the interference frequency band drifts or how new interference sources appear, the filtering strategy can complete autonomous matching in a very short time, ensuring that the suppression effect does not degrade due to environmental changes. In constructing the optimization objective function, the scheme unifies the two objectives of minimizing the interference frequency band response intensity and controlling effective signal loss into a quantitative comprehensive performance index. The two are mathematically mutually constrained and work together to optimize. This means that in the entire process of searching for filter feedback coefficients, any set of candidate coefficients will be subject to dual evaluation of interference suppression depth and signal fidelity. Thus, the final filtering operation does not simply pursue the maximum attenuation of interference components, but achieves a fine balance between filtering out interference and retaining the signal, fundamentally ensuring the waveform integrity and information availability of the useful signal. The introduction of the ant colony optimization algorithm transforms the optimization process of feedback coefficients into the intelligent search behavior of an ant colony. Multiple individual ants explore in parallel within the feedback coefficient solution space. Each movement combines local search based on current optimal experience with directional guidance based on global pheromone concentration. Paths with high fitness accumulate more pheromone, creating a positive feedback effect that gradually converges the search towards high-fitness regions. Simultaneously, local random search maintains necessary exploration diversity. This allows the algorithm to effectively overcome multiple local extrema that may exist in the objective function under complex electromagnetic interference environments, reliably converging to the globally optimal feedback coefficient. Compared to optimization methods relying solely on single gradient information, this swarm intelligence-based parallel search and pheromone positive feedback mechanism significantly improves the probability and efficiency of finding the optimal solution in a non-convex, multi-peak solution space. When the globally optimal feedback coefficient is loaded into the filter in real time, the filter acquires transmission characteristics that precisely match the current electromagnetic interference features. It can form a deep notch in the frequency band where the interference occurs, while exhibiting a flat amplitude-frequency response and a linear phase-frequency response in the frequency band where the effective signal is located. Thus, while achieving dynamic suppression of strong interference, it preserves the original characteristics of the communication, positioning, and control signals of the inspection equipment to the greatest extent, outputting a high-fidelity, high signal-to-noise ratio effective signal.

[0021] In summary, the entire technical solution forms a closed-loop chain from environmental perception and intelligent decision-making to real-time filtering. It does not rely on any manually preset fixed parameters and can autonomously evolve its suppression strategy in response to the transient changes in the electromagnetic environment. This significantly improves the operational robustness and data accuracy of the inspection equipment under strong electromagnetic shocks, ensuring the stable and reliable execution of inspection tasks in complex power scenarios. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart of an adaptive suppression method for electromagnetic interference of inspection equipment disclosed in an embodiment of this application; Figure 2 This is a schematic diagram of an adaptive electromagnetic interference suppression device for inspection equipment disclosed in an embodiment of this application; Figure 3 This is a hardware structure block diagram of an electromagnetic interference adaptive suppression device for inspection equipment disclosed in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] This application can be used in a wide variety of general-purpose or special-purpose computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc.

[0026] The following section introduces the solution proposed in this application. The technical solution is as follows, and details are provided below.

[0027] Figure 1 This is a flowchart of an adaptive suppression method for electromagnetic interference of inspection equipment disclosed in an embodiment of this application.

[0028] like Figure 1 As shown, the method may include: Step S1: Model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and obtain the frequency band distribution and intensity information of the electromagnetic interference signal in real time.

[0029] Specifically, the inspection equipment is equipped with a broadband electromagnetic sensing front-end, which continuously collects spatial electromagnetic signals from the operating environment of the inspection equipment. The collected signals are a mixture of communication signals, navigation and positioning signals, control command signals required for the normal operation of the inspection equipment, and environmental electromagnetic interference signals. This step first performs time-frequency domain joint analysis on the mixed signal collected by the sensing front-end, and obtains the power spectral density distribution of the mixed signal through spectrum estimation technology, presenting the energy strength of each frequency component in the frequency domain. Based on the prior knowledge that electromagnetic interference signals in power facility areas usually exhibit abnormal energy concentration in specific frequency bands in the spectrum, this step identifies frequency bands with energy significantly higher than the background noise floor and that persist as the frequency bands occupied by interference signals. At the same time, components with energy close to or slightly higher than the noise floor and conforming to the known frequency characteristics of the inspection equipment's communication protocol are determined as valid signal frequency bands. For each identified interference frequency band, this step further calculates its in-band average power or peak power as the intensity information of the interference frequency band. The start and end frequency boundaries, center frequency position, and corresponding intensity values ​​of the above-mentioned interference frequency bands together constitute a complete model of the electromagnetic interference signal at the current moment. The complete process of signal acquisition, spectrum estimation, frequency band identification, and intensity calculation described above is not executed only once, but is continuously and cyclically executed at preset time intervals or based on triggering conditions of electromagnetic environment changes. Whenever the distribution of electromagnetic interference in the environment changes, such as a frequency drift of an interference source causing an overall shift in the interference band on the spectrum, or a sudden increase or decrease in the interference energy of a certain frequency band due to a switch in the operating state of power equipment, or even the emergence of a completely new interference band that has never appeared before, this change will be captured in real time by the subsequent modeling cycle and reflected in the updated interference model. This real-time rolling update modeling mechanism ensures that the system maintains a high degree of sensitivity to changes in the electromagnetic environment, providing accurate and timely input for the dynamic response of subsequent steps.

[0030] Step S2: Based on the frequency band distribution and intensity information of the electromagnetic interference signal, construct an optimization objective function with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss.

[0031] Specifically, step S2 constructs an optimization objective function based on the electromagnetic interference signal frequency band distribution and intensity information acquired in real time in step S1. This objective function will serve as the evaluation criterion in the subsequent intelligent optimization process. The design of the optimization objective function revolves around two mutually restrictive technical requirements. The first requirement is to maximize the suppression depth, which requires the filter to form a sufficiently deep attenuation dip for each interference frequency band identified in step S1 in the amplitude-frequency response of its transmission characteristics. Specifically, for the residual interference frequency band energy in the filter output signal, the objective function normalizes it and incorporates it into the evaluation index. The lower the residual energy, the stronger the suppression effect of the filter on the interference frequency band, and the higher the score of this index. The second requirement is the strict control of effective signal loss, which requires the filter to retain the signal components in the effective signal frequency band to the greatest extent possible without damage while implementing deep attenuation of the interference frequency band. In practical implementation, this requirement manifests as constraints on the passband flatness and insertion loss of the filter in the effective signal frequency band: the objective function measures the amplitude attenuation introduced by the filter in the effective signal frequency band; the smaller the attenuation, the less energy loss the effective signal suffers when passing through the filter. Simultaneously, the objective function can also evaluate the phase distortion introduced by the filter, preventing distortion of the effective signal waveform due to the filter's nonlinear phase characteristics. These two requirements are organically integrated into a unified scalar index in the optimization objective function. Through weighting or constraints, the interference suppression effect and signal fidelity effect are numerically directly comparable and jointly determine the final evaluation result. The structural design of this objective function ensures that the filtering performance corresponding to any set of filter feedback coefficients will be comprehensively evaluated from two dimensions: interference suppression depth and signal preservation degree. Parameter combinations that excel in only one dimension while neglecting the other will not achieve the optimal score. Only coefficient combinations that achieve an optimal balance between the two objectives can be determined as the globally optimal solution during the optimization process.

[0032] The expression for the optimization objective function is:

[0033] in, W The objective function value; , These are the lower and upper limits of the interference signal frequency band, respectively; For the filter to filter single-channel electromagnetic interference signals The adaptive correction feedback coefficient characterizes the filter's response strength to electromagnetic interference signals; F This refers to the bandwidth of the frequency band.

[0034] Step S3: Adaptively optimize the feedback coefficients of the filter using an ant colony optimization algorithm to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant using the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients.

[0035] Specifically, an ant colony optimization algorithm is used to adaptively and globally optimize the feedback coefficients of the filter to determine the globally optimal feedback coefficients that achieve the optimal value of the objective function constructed in step S2. In the algorithm initialization phase, the dimension of the variable to be optimized is first determined. This dimension corresponds to the number of feedback coefficients in the filter. Each feedback coefficient can continuously take values ​​within its allowed range, and all feedback coefficients together constitute a multi-dimensional continuous solution space. Subsequently, several ant individuals are randomly scattered within this solution space. The spatial position vector of each ant individual corresponds to a specific combination of filter feedback coefficients, representing a candidate filter configuration scheme. After the algorithm enters the iterative process, each iteration includes three core stages: fitness calculation, local search, and pheromone update. In the fitness calculation stage, for each ant individual, the system actually loads the feedback coefficient combination it represents into the filter model, simulates or measures the complete frequency response characteristics of the filter under this set of coefficients, and then calculates the fitness value of the ant individual according to the optimization objective function defined in step S2. The magnitude of the fitness value directly quantifies the comprehensive performance of this set of coefficients in both interference suppression and signal fidelity. In the local search phase, each ant randomly explores its neighborhood, generating several candidate solutions and calculating their fitness. If a solution with better fitness than the current position is found, the ant moves to that better position, thus achieving fine-grained exploration of the local area. In the pheromone update phase, the algorithm distributes pheromone increments along the paths traversed by each ant based on its fitness value. Ants with higher fitness leave larger pheromone increments on their paths. Simultaneously, pheromones already present on all paths in the solution space are globally evaporated in a certain proportion to prevent the algorithm from prematurely converging to local extrema. The pheromone distribution thus exhibits dynamic evolutionary characteristics: high-fitness regions continuously receive positive feedback pheromone accumulation, attracting more ants to gather in these regions for intensive searching in subsequent iterations; while pheromones in low-fitness regions gradually dissipate over time, reducing the probability of these regions being explored in subsequent searches. Through multiple iterations, the ant colony's search behavior gradually converges from the initial wide-area random exploration to the vicinity of the optimal region in the solution space. The guiding role of global pheromones enables the colony to form a search consensus, while the local search mechanism ensures the solution accuracy within the optimal region. When the convergence condition is met, the combination of feedback coefficients corresponding to the current globally optimal ant position is output as the globally optimal feedback coefficient.

[0036] Step S4: Apply the global optimal feedback coefficient to the filter, and use the updated filter to perform real-time dynamic suppression of the electromagnetic interference signal, and output the effective signal of the inspection equipment after interference suppression.

[0037] Specifically, the optimized globally optimal feedback coefficients are applied to the filter, and the updated filter is used to perform real-time dynamic suppression of the mixed signals received by the inspection equipment, outputting an effective signal after interference suppression. In practice, the filter can be implemented using a digital filter structure, which internally contains several feedforward and feedback branches. The weighting coefficients on each branch constitute the set of feedback coefficients for the filter. When the globally optimal feedback coefficients of the converged output are written into the filter's coefficient register, the filter's transfer function is fundamentally reshaped in the frequency domain. Specifically, the updated filter automatically forms stopband dips with sufficient attenuation depth at the frequency positions corresponding to the identified interference signal frequency bands on its amplitude-frequency response curve. The center frequency, bandwidth, and attenuation of these stopbands are precisely controlled by optimized coefficients, so that interference components falling within the stopband range are significantly weakened or even eliminated when passing through the filter. At the same time, within the frequency band occupied by the effective signal, the filter's amplitude-frequency response remains flat, the insertion loss is controlled at an extremely low level, and the phase-frequency response remains linear or nearly linear throughout the entire effective signal bandwidth, ensuring that the frequency components of the effective signal do not produce distortion in amplitude and phase relationships. As the inspection equipment continuously moves within the power facility area or the environmental electromagnetic conditions dynamically evolve, the modeling results of the electromagnetic interference signal in step S1 will be updated in real time. Once a change in the distribution or intensity of the interference frequency band exceeding a preset threshold is detected, the system will trigger steps S2 and S3 to re-execute the optimization process, calculate the updated global optimal feedback coefficients that match the new electromagnetic environment, and immediately write them into the filter through step S4 to complete the online adaptive switching of filter parameters. Thus, the real-time perception in step S1, the target quantization in step S2, the intelligent optimization in step S3, and the filtering execution in step S4 constitute a closed adaptive loop. The filter parameters autonomously follow and adjust with the fluctuations of the electromagnetic environment, enabling the inspection equipment to obtain interference suppression effects that are precisely adapted to the current environment at any time and under any electromagnetic conditions. Finally, a high signal-to-noise ratio and high fidelity effective signal of the inspection equipment is stably obtained from the filter output, providing a reliable data foundation for subsequent signal demodulation, positioning calculation, and control command parsing.

[0038] The filter parameter update formula is as follows:

[0039] in, The response strength of the filter after parameter update; This represents the initial response of the filter; The amount of feedback coefficient update obtained by optimizing the ant colony.

[0040] As can be seen from the above technical solutions, the adaptive electromagnetic interference suppression method and related equipment for inspection equipment provided in this application firstly models the electromagnetic interference signal in the power facility area where the inspection equipment is located in real time, and dynamically acquires the frequency band distribution and intensity information of the electromagnetic interference signal; then, based on the frequency band distribution and intensity information, an optimization objective function is constructed with the goal of minimizing the response intensity of the filter in the interference frequency band and controlling the effective signal loss; next, an ant colony optimization algorithm is used to adaptively optimize the feedback coefficient of the filter, and the fitness of each individual ant is calculated using the optimization objective function, and local search and global pheromone update are performed based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficient; finally, the globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to dynamically suppress the electromagnetic interference signal in real time, outputting the effective signal of the inspection equipment after interference suppression.

[0041] This scheme continuously senses the electromagnetic environment of the power facility area through real-time modeling, allowing the frequency band distribution and intensity information of interference signals to be updated in real time as the environment changes. This drives the adjustment of filter parameters to always be precisely synchronized with the current interference state. This dynamic tracking mechanism gives the method the ability to adapt to the time-varying characteristics of the environment in real time. No matter how the interference frequency band drifts or how new interference sources appear, the filtering strategy can complete autonomous matching in a very short time, ensuring that the suppression effect does not degrade due to environmental changes. In constructing the optimization objective function, the scheme unifies the two objectives of minimizing the interference frequency band response intensity and controlling effective signal loss into a quantitative comprehensive performance index. The two are mathematically mutually constrained and work together to optimize. This means that in the entire process of searching for filter feedback coefficients, any set of candidate coefficients will be subject to dual evaluation of interference suppression depth and signal fidelity. Thus, the final filtering operation does not simply pursue the maximum attenuation of interference components, but achieves a fine balance between filtering out interference and retaining the signal, fundamentally ensuring the waveform integrity and information availability of the useful signal. The introduction of the ant colony optimization algorithm transforms the optimization process of feedback coefficients into the intelligent search behavior of an ant colony. Multiple individual ants explore in parallel within the feedback coefficient solution space. Each movement combines local search based on current optimal experience with directional guidance based on global pheromone concentration. Paths with high fitness accumulate more pheromone, creating a positive feedback effect that gradually converges the search towards high-fitness regions. Simultaneously, local random search maintains necessary exploration diversity. This allows the algorithm to effectively overcome multiple local extrema that may exist in the objective function under complex electromagnetic interference environments, reliably converging to the globally optimal feedback coefficient. Compared to optimization methods relying solely on single gradient information, this swarm intelligence-based parallel search and pheromone positive feedback mechanism significantly improves the probability and efficiency of finding the optimal solution in a non-convex, multi-peak solution space. When the globally optimal feedback coefficient is loaded into the filter in real time, the filter acquires transmission characteristics that precisely match the current electromagnetic interference features. It can form a deep notch in the frequency band where the interference occurs, while exhibiting a flat amplitude-frequency response and a linear phase-frequency response in the frequency band where the effective signal is located. Thus, while achieving dynamic suppression of strong interference, it preserves the original characteristics of the communication, positioning, and control signals of the inspection equipment to the greatest extent, outputting a high-fidelity, high signal-to-noise ratio effective signal.

[0042] In summary, the entire technical solution forms a closed-loop chain from environmental perception and intelligent decision-making to real-time filtering. It does not rely on any manually preset fixed parameters and can autonomously evolve its suppression strategy in response to the transient changes in the electromagnetic environment. This significantly improves the operational robustness and data accuracy of the inspection equipment under strong electromagnetic shocks, ensuring the stable and reliable execution of inspection tasks in complex power scenarios.

[0043] In some embodiments of this application, the process of step S1, which involves modeling the electromagnetic interference signal in the power facility area where the inspection equipment is located and acquiring the frequency band distribution and intensity information of the electromagnetic interference signal in real time, is described. Specifically, it may include: Step S11: Perform time-frequency joint transformation on the time-domain electromagnetic signal collected by the receiving end of the inspection equipment to obtain the time-frequency joint distribution result of the electromagnetic signal; Step S12: Based on the time-frequency joint distribution results, identify multiple electromagnetic interference sources in the power facility area and divide the time interval and candidate frequency band range corresponding to each electromagnetic interference source. Step S13: For each electromagnetic interference source in each time interval, construct a time-frequency joint model of a single electromagnetic interference signal by combining the interference observation sampling parameters, amplitude parameters, intensity factors and the propagation distance from the interference source to the receiver. Step S14: Calculate the interference intensity and phase characteristics in each candidate frequency band based on the time-frequency joint model, and statistically obtain the frequency band distribution and intensity information of electromagnetic interference signals in the entire frequency band range.

[0044] Specifically, step S11 performs a time-frequency joint transformation on the time-domain electromagnetic signal collected by the inspection equipment receiver to obtain the time-frequency joint distribution result of the electromagnetic signal. The original signal collected by the inspection equipment receiver is a voltage or current waveform that varies with time. The communication and control signals of the inspection equipment itself, along with various electromagnetic interference signals from the environment, are superimposed in the time domain, making it difficult to extract interference features completely from a single time waveform or spectrum. This step uses time-frequency joint transformation technology to process this time-domain signal, mapping the one-dimensional time signal to a two-dimensional time-frequency plane. On the plane spanned by the time axis and frequency axis, the signal intensity information at different times and frequencies is displayed in the form of energy density distribution. After this transformation, the time-varying frequency components of the signal and their energy variation patterns are simultaneously presented. Transient interference that was originally indistinguishable in the time domain and intermittent interference that was originally unlocatable in the frequency domain can be clearly located and identified in the time-frequency joint distribution result. This result provides an analytical basis with both time and frequency resolution for subsequent interference source identification, time interval division, and frequency band range definition.

[0045] Step S12 identifies multiple electromagnetic interference sources within the power facility area based on the time-frequency joint distribution results obtained in step S11, and divides the time intervals and candidate frequency bands corresponding to each electromagnetic interference source. In the actual electromagnetic environment of the power facility area, multiple electromagnetic interference sources with different sources often coexist, such as corona discharge from high-voltage equipment at different locations, transient switching operations at different time points, and continuously operating power electronic converters. These interference sources manifest as independent energy accumulation regions with different time duration characteristics and frequency distribution features on the time-frequency plane. This step, through energy threshold detection and connected component analysis on the time-frequency joint distribution results, marks regions on the time-frequency plane with energy significantly higher than background noise and a certain degree of spatiotemporal continuity as independent interference sources. For each identified interference source, its start and end times on the time axis are extracted as the activity time interval of the interference source; simultaneously, its start and end frequency boundaries on the frequency axis are extracted as the candidate frequency band range of the interference source's energy distribution. Thus, the activity range and frequency domain occupancy of multiple interference sources in the time-frequency plane are distinguished and calibrated one by one, providing clear spatiotemporal-frequency boundary conditions for the subsequent establishment of refined models for each interference source.

[0046] Step S13 constructs a time-frequency joint model of a single electromagnetic interference signal for each electromagnetic interference source within each time interval, combining interference observation sampling parameters, amplitude parameters, intensity factors, and the propagation distance from the interference source to the receiver. In practical scenarios, the electromagnetic interference signal received by the inspection equipment receiver is the result of the interference source's transmitted signal undergoing spatial propagation attenuation, multipath reflection, and the receiver's own sampling characteristics. To accurately describe the characteristics of the interference signal formed by each interference source at the receiver, this step introduces several key modeling parameters: interference observation sampling parameters characterize the time sampling interval and sampling window length of the interference signal at the receiver, determining the resolution of the interference signal in the time dimension; amplitude parameters characterize the initial amplitude level of the interference source's transmitted signal; intensity factors reflect the concentration of radiated energy or modulation characteristics of the interference source; and the propagation distance from the interference source to the receiver determines the path attenuation caused by the signal propagating in space. This step uses the above parameters as variables and, based on the electromagnetic wave propagation law and the receiver's sampling mechanism, constructs a time-frequency joint model describing the signal morphology of a single interference source from transmission through space to sampling at the receiver. This model can comprehensively reflect the pulse characteristics, transient duration, spectral broadening, and center frequency position of the interference source signal in the time domain.

[0047] Step S14 calculates the interference intensity and phase characteristics of each candidate frequency band based on the time-frequency joint model established in step S13, and statistically obtains the frequency distribution and intensity information of electromagnetic interference signals across the entire frequency band. For each candidate frequency band identified in step S12, this step applies the model established in step S13 to the corresponding interference source, integrates or averages the energy contribution of the interference signal within the frequency band along the time axis, and obtains the interference intensity value within the candidate frequency band. Simultaneously, based on the phase term structure of the model, the phase offset or phase distribution range of the interference signal within the frequency band is extracted as the interference phase characteristic within the candidate frequency band. The above calculations are performed one by one on all candidate frequency bands of all interference sources identified in step S12, and the interference intensity and phase characteristics of each frequency band are summarized and uniformly statistically organized from a full-band perspective. The final output frequency band distribution information clarifies which frequency ranges in the current electromagnetic environment are significantly occupied by interference signals and the start and end frequency boundaries of each interference frequency band. The intensity information quantitatively describes the energy level within each interference frequency band, and the phase information provides a basis for further coherent processing or cancellation strategies. The statistical results constitute the complete content of the electromagnetic interference signal model output in step S1, and directly provide accurate input conditions for the subsequent construction of the objective function and the optimization of filter parameters.

[0048] In the time-frequency joint model of the single-channel electromagnetic interference signal, the expression of the single-channel electromagnetic interference signal within the time interval is:

[0049] in, This is a single-channel electromagnetic interference signal; The frequency response of the electromagnetic signal; The intensity factor of the electromagnetic interference signal; The sampling period for electromagnetic interference observations; The amplitude of the electromagnetic interference observation; d This is the distance from the electromagnetic interference source to the receiving antenna; It is an exponential factor used to adjust the signal diffusion and attenuation characteristics.

[0050] In some embodiments of this application, the process of step S3, which involves adaptively optimizing the feedback coefficients of the filter using an ant colony optimization algorithm to determine the globally optimal feedback coefficients, is described. Specifically, it may include: Step S31: Initialize the ant colony population and map the position of each ant in the solution space to a set of candidate filter feedback coefficients. Step S32: Use the optimization objective function as the fitness evaluation function, calculate the fitness value of the candidate feedback coefficient corresponding to each ant, and determine the probability that each candidate solution is selected as the guiding benchmark for this round of local search based on the fitness value; Step S33: Each ant performs a local search based on the guiding benchmark selected according to the probability in the current iteration, updates its own position in the solution space according to the step size update rule, and obtains new candidate filter feedback coefficients. Step S34: After completing a single iteration, perform a global pheromone update and synchronously update the current global optimal feedback coefficient. Step S35: Determine whether the preset convergence condition is met. If it is met, terminate the iteration and output the global optimal feedback coefficient. If it is not met, return to the fitness calculation step to continue the iteration.

[0051] Specifically, step S31 initializes the ant colony and maps the position of each ant in the solution space to a set of candidate filter feedback coefficients. During the algorithm startup phase, the number of feedback coefficients to be optimized is first determined based on the filter structure. This number determines the dimension of the solution space targeted by the ant colony search. Each dimension of the solution space corresponds to a filter feedback coefficient, and the coordinate range of each dimension is pre-set according to filter stability constraints or the actual hardware implementation range. All dimensions together span a bounded multidimensional continuous solution space. Subsequently, a preset number of ant individuals are randomly generated within this solution space. The spatial position of each ant individual is represented by a multidimensional vector, and the components of this vector in each dimension correspond to a specific set of filter feedback coefficient values. At this point, the position of each ant represents a specific combination scheme of filter feedback coefficients, and all ant individuals in the ant colony together constitute the candidate solution set for the current iteration. This initialization process establishes a starting point for subsequent fitness evaluation and iterative search. The initial distribution of individual ants in the solution space adopts a uniform random distribution strategy to ensure that the search range fully covers the solution space and avoids missing potential optimal solution regions due to initial distribution deviations.

[0052] Step S32 uses the optimization objective function as the fitness evaluation function, calculates the fitness value of the candidate feedback coefficients for each ant, and determines the probability that each candidate solution will be selected as the guiding benchmark for the current round of local search based on the fitness value. In each iteration, for each individual ant in the colony, the system extracts a set of candidate filter feedback coefficients represented by its position vector, substitutes these coefficients into the filter model to obtain the corresponding frequency response characteristics, and then calculates the fitness value of the individual ant based on the optimization objective function constructed in step S2. This fitness value is a scalar index that comprehensively reflects the joint performance of the set of feedback coefficients in two dimensions: interference band suppression depth and effective signal fidelity. The better the fitness value, the closer the set of coefficients is to the ideal solution under the dual-objective optimization requirement. After obtaining the fitness values ​​of all individual ants, this step normalizes the fitness values ​​of each candidate solution and uses the normalized relative fitness level as the basis for probability allocation to determine the probability that the candidate solution represented by each ant will be selected as the guiding benchmark for the current round of local search. Candidate solutions with higher fitness values ​​are assigned a higher selection probability, giving them a greater chance of continuing to receive finer results in subsequent iterations; candidate solutions with lower fitness values ​​are assigned a lower selection probability, but are not completely eliminated, thus preserving the diversity of solution space exploration.

[0053] In step S33, each ant performs a local search based on the guiding benchmark selected according to the probability in the current iteration, updating its position in the solution space according to the step size update rule to obtain new candidate filter feedback coefficients. During the local search phase, each ant first randomly selects a corresponding guiding benchmark based on the selection probability output in step S32. The ant uses this selected candidate solution as a search reference benchmark and probes within its neighborhood. This neighborhood is defined by a preset search step size, which can be dynamically adjusted with the iteration process. A larger step size is used in the early stages of iteration to support wide-area exploration of the solution space, while the step size is gradually reduced in the later stages to improve the convergence accuracy to the optimal solution region. Within the neighborhood, each ant generates a new candidate position according to the update rule, which combines the tendency to approach the selected guiding benchmark with a certain degree of random perturbation, ensuring that the ant retains the probability of escaping the local extreme value region while moving closer to the guiding benchmark. The filter feedback coefficient combination corresponding to the newly generated candidate position is temporarily recorded as the updated candidate solution obtained by the ant after local search in the current iteration, providing input for the fitness evaluation and global pheromone update in the next round.

[0054] Step S34 performs a global pheromone update after completing a single iteration, and simultaneously updates the current global optimal feedback coefficient. After all ant individuals complete local search and fitness evaluation in a single iteration, this step summarizes the optimal candidate solution information generated in this iteration. On one hand, it compares this information with the historical global optimal solution. If a candidate solution with a fitness value better than the current global optimal solution appears in this iteration, the feedback coefficient combination corresponding to that candidate solution is updated to a new global optimal feedback coefficient; otherwise, the original global optimal feedback coefficient remains unchanged. On the other hand, this step updates the pheromone concentration on all paths in the solution space: pheromone increments are applied to regions with high fitness values ​​in the solution space according to their fitness level, enhancing the pheromone concentration in high-quality solution regions; simultaneously, pheromone in all regions of the solution space is attenuated according to a preset evaporation coefficient to weaken the excessive influence of historical search traces on the current search. After the pheromone update, the pheromone distribution in the solution space is readjusted. Regions with consistently excellent fitness gain higher pheromone accumulation, while regions with poor fitness gradually lose their attractiveness due to pheromone evaporation.

[0055] Step S35 determines whether the preset convergence condition is met. If it is, the iteration terminates and the global optimal feedback coefficient is output. If not, the iteration returns to the fitness calculation step to continue. After the global pheromone update is completed in each iteration, this step performs a convergence check. The convergence condition can include one or a combination of the following: the change in the current global optimal fitness value in several consecutive iterations is less than a preset threshold, indicating that the search has entered a stable state; the current iteration number has reached the preset maximum number of iterations to prevent the algorithm from running indefinitely; the dispersion of the ant individual position distribution in the solution space is lower than a preset threshold, indicating that the group search has become highly clustered. If any of the above convergence conditions are met, the algorithm is determined to have converged, the iteration process is terminated, and the currently recorded global optimal feedback coefficient is output as the final result. If all convergence conditions are not met, the process returns to step S32, and the fitness calculation and search process for the next iteration is continued based on the updated pheromone distribution and ant positions until the convergence condition is met.

[0056] The expression for step size update in the ant colony optimization algorithm is:

[0057] in, For the adaptive correction feedback coefficient during ant search process The step size update amount; This is the optimal solution for the current iteration; This is the optimal solution from the previous iteration; This is the step size adjustment factor; This is a random factor used to maintain the diversity of the solution space.

[0058] The following describes an adaptive electromagnetic interference suppression device for inspection equipment provided in an embodiment of this application. The adaptive electromagnetic interference suppression device for inspection equipment described below can be referred to in correspondence with the adaptive electromagnetic interference suppression method for inspection equipment described above.

[0059] See Figure 2 , Figure 2 This is a schematic diagram of an electromagnetic interference adaptive suppression device for inspection equipment disclosed in an embodiment of this application.

[0060] like Figure 2 As shown, the electromagnetic interference adaptive suppression device for inspection equipment may include: The interference signal modeling unit 110 is used to model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and to acquire the frequency band distribution and intensity information of the electromagnetic interference signal in real time. The objective function construction unit 120 is used to construct an optimized objective function based on the frequency band distribution and intensity information of the electromagnetic interference signal, with the goal of minimizing the response intensity of the filter in the frequency band of the interference signal and controlling the effective signal loss. Ant colony adaptive optimization unit 130 is used to adaptively optimize the feedback coefficients of the filter using the ant colony optimization algorithm to determine the global optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant based on the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the global optimal feedback coefficients. The filter execution output unit 140 is used to apply the global optimal feedback coefficient to the filter, use the updated filter to perform real-time dynamic suppression of the electromagnetic interference signal, and output the effective signal of the inspection equipment after interference suppression.

[0061] As can be seen from the above technical solutions, the adaptive electromagnetic interference suppression method and related equipment for inspection equipment provided in this application firstly models the electromagnetic interference signal in the power facility area where the inspection equipment is located in real time, and dynamically acquires the frequency band distribution and intensity information of the electromagnetic interference signal; then, based on the frequency band distribution and intensity information, an optimization objective function is constructed with the goal of minimizing the response intensity of the filter in the interference frequency band and controlling the effective signal loss; next, an ant colony optimization algorithm is used to adaptively optimize the feedback coefficient of the filter, and the fitness of each individual ant is calculated using the optimization objective function, and local search and global pheromone update are performed based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficient; finally, the globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to dynamically suppress the electromagnetic interference signal in real time, outputting the effective signal of the inspection equipment after interference suppression.

[0062] This scheme continuously senses the electromagnetic environment of the power facility area through real-time modeling, allowing the frequency band distribution and intensity information of interference signals to be updated in real time as the environment changes. This drives the adjustment of filter parameters to always be precisely synchronized with the current interference state. This dynamic tracking mechanism gives the method the ability to adapt to the time-varying characteristics of the environment in real time. No matter how the interference frequency band drifts or how new interference sources appear, the filtering strategy can complete autonomous matching in a very short time, ensuring that the suppression effect does not degrade due to environmental changes. In constructing the optimization objective function, the scheme unifies the two objectives of minimizing the interference frequency band response intensity and controlling effective signal loss into a quantitative comprehensive performance index. The two are mathematically mutually constrained and work together to optimize. This means that in the entire process of searching for filter feedback coefficients, any set of candidate coefficients will be subject to dual evaluation of interference suppression depth and signal fidelity. Thus, the final filtering operation does not simply pursue the maximum attenuation of interference components, but achieves a fine balance between filtering out interference and retaining the signal, fundamentally ensuring the waveform integrity and information availability of the useful signal. The introduction of the ant colony optimization algorithm transforms the optimization process of feedback coefficients into the intelligent search behavior of an ant colony. Multiple individual ants explore in parallel within the feedback coefficient solution space. Each movement combines local search based on current optimal experience with directional guidance based on global pheromone concentration. Paths with high fitness accumulate more pheromone, creating a positive feedback effect that gradually converges the search towards high-fitness regions. Simultaneously, local random search maintains necessary exploration diversity. This allows the algorithm to effectively overcome multiple local extrema that may exist in the objective function under complex electromagnetic interference environments, reliably converging to the globally optimal feedback coefficient. Compared to optimization methods relying solely on single gradient information, this swarm intelligence-based parallel search and pheromone positive feedback mechanism significantly improves the probability and efficiency of finding the optimal solution in a non-convex, multi-peak solution space. When the globally optimal feedback coefficient is loaded into the filter in real time, the filter acquires transmission characteristics that precisely match the current electromagnetic interference features. It can form a deep notch in the frequency band where the interference occurs, while exhibiting a flat amplitude-frequency response and a linear phase-frequency response in the frequency band where the effective signal is located. Thus, while achieving dynamic suppression of strong interference, it preserves the original characteristics of the communication, positioning, and control signals of the inspection equipment to the greatest extent, outputting a high-fidelity, high signal-to-noise ratio effective signal.

[0063] In summary, the entire technical solution forms a closed-loop chain from environmental perception and intelligent decision-making to real-time filtering. It does not rely on any manually preset fixed parameters and can autonomously evolve its suppression strategy in response to the transient changes in the electromagnetic environment. This significantly improves the operational robustness and data accuracy of the inspection equipment under strong electromagnetic shocks, ensuring the stable and reliable execution of inspection tasks in complex power scenarios.

[0064] Optionally, the step of modeling the electromagnetic interference signal in the power facility area where the inspection equipment is located, and acquiring the frequency band distribution and intensity information of the electromagnetic interference signal in real time, includes: The time-frequency joint transformation of the time-domain electromagnetic signal collected by the receiving end of the inspection equipment is performed to obtain the time-frequency joint distribution result of the electromagnetic signal; Based on the time-frequency joint distribution results, multiple electromagnetic interference sources within the power facility area are identified, and the time intervals and candidate frequency band ranges corresponding to each electromagnetic interference source are divided. For each electromagnetic interference source in each time interval, a time-frequency joint model of a single electromagnetic interference signal is constructed by combining the interference observation sampling parameters, amplitude parameters, intensity factors, and propagation distance from the interference source to the receiver. Based on the time-frequency joint model, the interference intensity and phase characteristics in each candidate frequency band are calculated, and the frequency band distribution and intensity information of electromagnetic interference signals in the entire frequency band range are statistically obtained.

[0065] Optionally, in the time-frequency joint model of the single-channel electromagnetic interference signal, the expression of the single-channel electromagnetic interference signal within the time interval is:

[0066] in, This is a single-channel electromagnetic interference signal; The frequency response of the electromagnetic signal; The intensity factor of the electromagnetic interference signal; The sampling period for electromagnetic interference observations; The amplitude of the electromagnetic interference observation; d This is the distance from the electromagnetic interference source to the receiving antenna; It is an exponential factor used to adjust the signal diffusion and attenuation characteristics.

[0067] Optionally, the expression for the optimization objective function is:

[0068] in, W The objective function value; , These are the lower and upper limits of the interference signal frequency band, respectively; For the filter to filter single-channel electromagnetic interference signals The adaptive correction feedback coefficient characterizes the filter's response strength to electromagnetic interference signals; F This refers to the bandwidth of the frequency band.

[0069] Optionally, the step of adaptively optimizing the feedback coefficients of the filter using an ant colony optimization algorithm to determine the globally optimal feedback coefficients includes: Initialize the ant colony population and map the position of each ant in the solution space to a set of candidate filter feedback coefficients; The optimization objective function is used as the fitness evaluation function. The fitness value of the candidate feedback coefficient for each ant is calculated. Based on the fitness value, the probability that each candidate solution is selected as the guiding benchmark for this round of local search is determined. Each ant performs a local search based on the guiding benchmark selected according to the probability in the current iteration, updates its own position in the solution space according to the step size update rule, and obtains new candidate filter feedback coefficients. After completing a single iteration, a global pheromone update is performed, synchronously updating the current global optimal feedback coefficient; Determine whether the preset convergence condition is met. If it is met, terminate the iteration and output the global optimal feedback coefficient. If it is not met, return to the fitness calculation step and continue the iteration.

[0070] Optionally, the expression for step size update in the ant colony optimization algorithm is:

[0071] in, For the adaptive correction feedback coefficient during ant search process The step size update amount; This is the optimal solution for the current iteration; This is the optimal solution from the previous iteration; This is the step size adjustment factor; This is a random factor used to maintain the diversity of the solution space.

[0072] Optionally, the filter parameter update formula is:

[0073] in, The response strength of the filter after parameter update; This represents the initial response of the filter; The amount of feedback coefficient update obtained by optimizing the ant colony.

[0074] The electromagnetic interference adaptive suppression device for inspection equipment provided in this application embodiment can be applied to electromagnetic interference adaptive suppression devices for inspection equipment. Figure 3 The hardware structure block diagram of the electromagnetic interference adaptive suppression device for inspection equipment is shown. (Refer to...) Figure 3 The hardware structure of the electromagnetic interference adaptive suppression device for inspection equipment may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4; In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4; Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device; The memory stores a program, which the processor can call. The program is used for: Model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and obtain the frequency band distribution and intensity information of the electromagnetic interference signal in real time; Based on the frequency band distribution and intensity information of the electromagnetic interference signal, an optimization objective function is constructed with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony optimization algorithm is used to adaptively optimize the feedback coefficients of the filter to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant based on the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to perform real-time dynamic suppression of the electromagnetic interference signal, outputting the effective signal of the inspection equipment after interference suppression.

[0075] Optionally, the refined and extended functions of the program can be referred to the above description.

[0076] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for: Model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and obtain the frequency band distribution and intensity information of the electromagnetic interference signal in real time; Based on the frequency band distribution and intensity information of the electromagnetic interference signal, an optimization objective function is constructed with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony optimization algorithm is used to adaptively optimize the feedback coefficients of the filter to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant based on the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to perform real-time dynamic suppression of the electromagnetic interference signal, outputting the effective signal of the inspection equipment after interference suppression.

[0077] Optionally, the refined and extended functions of the program can be referred to the above description.

[0078] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive suppression method for electromagnetic interference in inspection equipment, characterized in that, include: Model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and obtain the frequency band distribution and intensity information of the electromagnetic interference signal in real time; Based on the frequency band distribution and intensity information of the electromagnetic interference signal, an optimization objective function is constructed with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony optimization algorithm is used to adaptively optimize the feedback coefficients of the filter to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant based on the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The globally optimal feedback coefficient is applied to the filter, and the filter with updated parameters is used to perform real-time dynamic suppression of the electromagnetic interference signal, outputting the effective signal of the inspection equipment after interference suppression.

2. The method according to claim 1, characterized in that, The process of modeling electromagnetic interference signals in the power facility area where the inspection equipment is located, and acquiring the frequency band distribution and intensity information of the electromagnetic interference signals in real time, includes: The time-frequency joint transformation of the time-domain electromagnetic signal collected by the receiving end of the inspection equipment is performed to obtain the time-frequency joint distribution result of the electromagnetic signal; Based on the time-frequency joint distribution results, multiple electromagnetic interference sources within the power facility area are identified, and the time intervals and candidate frequency band ranges corresponding to each electromagnetic interference source are divided. For each electromagnetic interference source in each time interval, a time-frequency joint model of a single electromagnetic interference signal is constructed by combining the interference observation sampling parameters, amplitude parameters, intensity factors, and propagation distance from the interference source to the receiver. Based on the time-frequency joint model, the interference intensity and phase characteristics in each candidate frequency band are calculated, and the frequency band distribution and intensity information of electromagnetic interference signals in the entire frequency band range are statistically obtained.

3. The method according to claim 2, characterized in that, In the time-frequency joint model of the single-channel electromagnetic interference signal, the expression of the single-channel electromagnetic interference signal within the time interval is: in, This is a single-channel electromagnetic interference signal; The frequency response of the electromagnetic signal; The intensity factor of the electromagnetic interference signal; The sampling period for electromagnetic interference observations; The amplitude of the electromagnetic interference observation; d This is the distance from the electromagnetic interference source to the receiving antenna; It is an exponential factor used to adjust the signal diffusion and attenuation characteristics.

4. The method according to claim 1, characterized in that, The expression for the optimization objective function is: in, W The objective function value; , These are the lower and upper limits of the interference signal frequency band, respectively; For the filter to filter single-channel electromagnetic interference signals The adaptive correction feedback coefficient characterizes the filter's response strength to electromagnetic interference signals; F This refers to the bandwidth of the frequency band.

5. The method according to claim 1, characterized in that, The step of using an ant colony optimization algorithm to adaptively optimize the feedback coefficients of the filter and determine the globally optimal feedback coefficients includes: Initialize the ant colony population and map the position of each ant in the solution space to a set of candidate filter feedback coefficients; The optimization objective function is used as the fitness evaluation function. The fitness value of the candidate feedback coefficient for each ant is calculated. Based on the fitness value, the probability that each candidate solution is selected as the guiding benchmark for this round of local search is determined. Each ant performs a local search based on the guiding benchmark selected according to the probability in the current iteration, updates its own position in the solution space according to the step size update rule, and obtains new candidate filter feedback coefficients. After completing a single iteration, a global pheromone update is performed, synchronously updating the current global optimal feedback coefficient; Determine whether the preset convergence condition is met. If it is met, terminate the iteration and output the global optimal feedback coefficient. If it is not met, return to the fitness calculation step and continue the iteration.

6. The method according to claim 5, characterized in that, The expression for step size update in the ant colony optimization algorithm is: in, For the adaptive correction feedback coefficient during ant search process The step size update amount; This is the optimal solution for the current iteration; This is the optimal solution from the previous iteration; This is the step size adjustment factor; This is a random factor used to maintain the diversity of the solution space.

7. The method according to claim 1, characterized in that, The filter parameter update formula is as follows: in, The response strength of the filter after parameter update; This is the initial response of the filter; The amount of feedback coefficient update obtained by optimizing the ant colony.

8. An adaptive electromagnetic interference suppression device for inspection equipment, characterized in that, include: The interference signal modeling unit is used to model the electromagnetic interference signal in the power facility area where the inspection equipment is located, and to acquire the frequency band distribution and intensity information of the electromagnetic interference signal in real time. The objective function construction unit is used to construct an optimized objective function based on the frequency band distribution and intensity information of the electromagnetic interference signal, with the goal of minimizing the filter's response intensity in the interference signal frequency band and controlling the effective signal loss. The ant colony adaptive optimization unit is used to adaptively optimize the feedback coefficients of the filter using the ant colony optimization algorithm to determine the globally optimal feedback coefficients. The ant colony optimization algorithm calculates the fitness of each individual ant using the optimization objective function, and performs local search and global pheromone update based on the fitness, iterating until convergence to obtain the globally optimal feedback coefficients. The filter execution output unit is used to apply the globally optimal feedback coefficient to the filter, and use the updated filter to perform real-time dynamic suppression of the electromagnetic interference signal, and output the effective signal of the inspection equipment after interference suppression.

9. An adaptive electromagnetic interference suppression device for inspection equipment, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the adaptive suppression method for electromagnetic interference of inspection equipment as described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the adaptive suppression method for electromagnetic interference of inspection equipment as described in any one of claims 1-7.

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