A method for monitoring the bottom noise of charging pile load radiation disturbance

Abstract

The application provides a charging pile load radiation disturbance noise monitoring method, comprising: acquiring real-time current data and magnetic core internal magnetic flux density values in the operation of the charging pile, determining an initial current threshold of the saturation degree of the magnetic core, and calculating a deviation value of the magnetic flux density from a preset saturation threshold to obtain a saturation deviation value; according to the saturation deviation value, the magnetic field distribution intensity of the magnetic core under the current load is identified by using a finite element analysis method, and the degree of weakening of radiation suppression is judged to obtain a radiation weakening multiple; according to the radiation weakening multiple, the real-time waveform data of the noise signal generated by the internal circuit of the charging pile and received by the magnetic core detection is processed, and the trigger point of the electromagnetic unstable cycle is determined; the magnetic core configuration value is adjusted according to the offset amplitude prediction value to restore the radiation intensity control, the real-time calibration noise fluctuation level is identified, and the attenuation effect of the electromagnetic unstable cycle is monitored in the calibration process to obtain the optimized compatibility performance value.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for monitoring the background noise of radiated interference from charging pile loads. Background Technology

[0002] In the era of rapid popularization of electric vehicles, charging infrastructure has become a core pillar for ensuring energy transition and carbon emission reduction. Charging piles, as key nodes, directly impact grid stability and user experience. Even minor electromagnetic interference can be amplified into system-level failures, threatening the safe operation of the entire ecosystem. This makes research on the electromagnetic compatibility of charging piles particularly urgent. Traditional charging pile designs often rely on static magnetic core configurations to suppress radiated interference, but these methods ignore the interactive effects under dynamic loads, leading to a disconnect between the magnetic core's operating state and noise performance. Especially in high-power charging scenarios, existing solutions often fail to capture the chain reaction of internal magnetic core changes on external radiation, causing the accuracy of noise floor monitoring to fluctuate repeatedly in practical applications. This is because when the charging power suddenly increases from 60kW to 120kW, the millisecond-level drastic change in the magnetic flux density of the magnetic core generates high-frequency harmonics that directly radiate through the shielding layer. Static monitoring systems cannot track this transient process in real time, resulting in a 5%-15% deviation in monitoring data during different charging cycles, making reliable real-time intervention impossible. There is a close interaction between the magnetic core saturation level and the noise level of the communication signal; this interaction stems from the change in magnetic flux density of the charging pile's magnetic core under load fluctuations. Specifically, when a charging pile handles a sudden peak current, the magnetic flux density of the core increases sharply, and the radiated emission intensity exceeds the threshold. Once the magnetic flux density of the charging pile's core approaches the saturation limit, the permeability drops sharply, significantly weakening the core's suppression of high-frequency radiation. This weakened suppression capability amplifies the fluctuation amplitude of the noise floor signal, creating a cyclically escalating electromagnetic instability. For example, when three 350kW supercharging piles in a highway service area are operating simultaneously, the chain reaction triggered by core saturation causes communication anomalies in all six charging piles at the entire charging station. Since the increase in radiated emission intensity directly interferes with the signal acquisition accuracy of the noise floor monitoring circuit, causing a sudden increase in the noise floor signal amplitude, it further interferes with the stable transmission of the communication module, leading to vehicle charging interruptions or data loss, becoming a common pain point in on-site maintenance. Summary of the Invention

[0003] This invention provides a method for monitoring the background noise of radiated interference from a charging pile load, mainly including:

[0004] The system acquires real-time current data and magnetic flux density data inside the charging pile during operation, determines the initial threshold for core saturation, and calculates the deviation between the magnetic flux density data and the preset saturation threshold to obtain saturation deviation data. Based on the saturation deviation data, it analyzes the magnetic field distribution intensity of the core under the current load, compares the quantified value of radiation suppression capability with the preset suppression threshold, and determines the radiation suppression reduction factor. Based on the radiation suppression reduction factor, it processes the background noise signal waveform data generated by the internal circuit of the charging pile to determine the trigger point of the electromagnetic instability cycle. Based on the trigger point of the electromagnetic instability cycle, it monitors the data transmission flow between the charging pile and the electric vehicle to obtain interference duration data. Based on the interference duration data, it analyzes the load fluctuation pattern during the charging process, identifies the operating point offset trajectory, calculates the matching degree between the offset amplitude and the load current change in the operating point offset trajectory, and obtains offset amplitude prediction data. Based on the offset amplitude prediction data, it adjusts the core configuration parameters, monitors the decay state of the electromagnetic instability cycle, and obtains compatibility performance data. Based on the compatibility performance data, it tracks the background noise signal fluctuation trend and updates the load radiation interference background noise monitoring benchmark data.

[0005] Furthermore, the process of acquiring real-time current data and magnetic flux density data inside the charging pile during operation, determining an initial threshold for the saturation degree of the magnetic core, and calculating the deviation between the magnetic flux density data and the preset saturation threshold to obtain saturation deviation data includes:

[0006] Instantaneous operating current data of the main circuit of the charging pile is obtained through a current transformer, and magnetic flux density data on the cross-section of the magnetic core is measured through a sensor. Based on the characteristic curve of the magnetic core material, the saturation critical value of the magnetic flux density data is determined. When the instantaneous operating current data reaches the current value corresponding to the saturation critical value, the current value is set as the initial threshold for the degree of magnetic core saturation. The maximum value is extracted from the magnetic flux density data, and the difference between it and the saturation critical value is calculated to obtain the saturation deviation data.

[0007] Furthermore, the step of analyzing the magnetic field distribution intensity of the magnetic core under the current load based on the saturation deviation data, comparing the quantified value of radiation suppression capability with the preset suppression threshold, and determining the radiation suppression reduction factor includes:

[0008] A three-dimensional mesh model of the magnetic core is constructed based on the saturation deviation data. The saturation deviation data is converted into a permeability correction coefficient, and the corrected permeability values ​​are assigned to the mesh nodes. The magnetic field distribution equation is solved iteratively to obtain magnetic flux density distribution data and magnetic field intensity distribution map. The surface normal magnetic flux component is extracted from the magnetic flux density distribution data, the spatial radiation field intensity is calculated, and it is compared with the reference radiation field intensity to obtain the radiation suppression coefficient. The radiation suppression coefficient is compared with a preset suppression threshold, the difference ratio is calculated, and the radiation suppression reduction factor is determined.

[0009] Furthermore, the step of processing the background noise signal waveform data generated by the internal circuit of the charging pile based on the radiation suppression attenuation factor to determine the trigger point of the electromagnetic instability cycle includes:

[0010] The sampling frequency is determined based on the radiation suppression reduction factor to obtain the background noise signal waveform data generated by the charging pile switching power supply; the background noise signal waveform data is transformed in the frequency domain to extract the amplitude of each frequency component; the difference between the current cycle amplitude and the previous cycle amplitude is calculated and recorded as the background noise fluctuation amplitude; the background noise fluctuation amplitude is used to form a time series to identify the time period that continuously exceeds the preset threshold, and the starting time is determined as the trigger point of the electromagnetic instability cycle.

[0011] Furthermore, determining the trigger point of the electromagnetic instability cycle includes:

[0012] The signal receiving sensitivity is adjusted according to the radiation suppression reduction factor, and the noise floor signal voltage data when the switch is turned on is collected; the noise floor signal voltage data is compared with the reference voltage value to obtain voltage deviation data; when the voltage deviation data exceeds the limit, the duration is recorded; at the same time, the frequency offset of the noise floor signal waveform data is measured; if the voltage deviation data and the frequency offset simultaneously meet the preset conditions and the duration reaches the threshold, the current moment is determined to be the trigger point of the electromagnetic instability cycle.

[0013] Furthermore, the step of monitoring the data transmission flow between the charging pile and the electric vehicle and obtaining interference duration data based on the trigger point of the electromagnetic instability cycle includes:

[0014] Based on the trigger point of the electromagnetic instability cycle, the carrier signal power and background noise power are extracted from the communication link, and the signal-to-noise ratio (SNR) data is calculated. The SNR data is compared with a preset interference threshold, and the interference start time is recorded. The SNR data is continuously monitored until it recovers to above the threshold, and the recovery time is recorded. The difference between the recovery time and the start time is calculated to obtain the interference duration data.

[0015] Furthermore, the step of analyzing the load fluctuation pattern during the charging process based on the interference duration data, identifying the operating point offset trajectory, calculating the matching degree between the offset amplitude and the load current change in the operating point offset trajectory, and obtaining offset amplitude prediction data includes:

[0016] Based on the interference duration data, a data acquisition time window is set, and instantaneous data of the charging pile output current are continuously acquired. The current change rate sequence is calculated, and the periodic change characteristics are extracted as the load fluctuation law. The current value in the load fluctuation law is mapped to the magnetic core characteristic curve to determine the working point coordinates, and each working point is connected to form an offset trajectory. The displacement of the working point offset trajectory is extracted as the offset amplitude, and the correlation coefficient between the offset amplitude and the load current change is calculated as the matching degree. Based on the matching degree, a functional relationship between the offset amplitude and the load current is established, and the expected current value is substituted to obtain the offset amplitude prediction data.

[0017] Furthermore, the identification of the working point offset trajectory includes:

[0018] A sampling time window is set based on the interference duration data, instantaneous data of the charging pile output current is collected, the current change is calculated, and a current change data chain is formed; the actual current value is restored based on the current change data chain, and the working point coordinates are calculated; the working point coordinates are connected to form a movement path, and the path segment length and offset angle are calculated; if the path segment length or offset angle exceeds the preset range, it is marked as the working point offset trajectory.

[0019] Furthermore, the step of adjusting the core configuration parameters based on the predicted offset amplitude data, monitoring the decay state of the electromagnetic instability cycle, and obtaining compatibility performance data includes:

[0020] Calculate the permeability compensation value based on the predicted offset amplitude data, and adjust the core configuration parameters; based on the deviation between the noise floor signal amplitude and the reference value, output a control quantity to adjust the signal amplification factor, update the frequency response parameters, and calibrate the noise floor fluctuation level; continuously collect the electromagnetic interference signal amplitude and calculate the amplitude ratio of adjacent sampling points; when the continuous ratio is less than a preset threshold, determine the electromagnetic instability cycle attenuation, and count the attenuation duration; based on the attenuation duration and the reduction ratio of the noise floor amplitude, obtain the compatibility performance data through comprehensive scoring.

[0021] Furthermore, the step of tracking the fluctuation trend of the noise floor signal and updating the load radiated interference noise floor monitoring benchmark data based on the compatibility performance data includes:

[0022] The load power range is divided according to the compatibility performance data, and the radiated disturbance intensity value of each range is recorded to form a corresponding relationship table. The radiation intensity value of the current load is extracted from the corresponding relationship table, the amplitude sequence of the noise floor signal is continuously collected, the change slope is calculated, and the fluctuation trend data is formed. The monitoring benchmark weight is adjusted according to the fluctuation trend data, and the weighted calculation is performed with the benchmark value of the previous period and the current noise floor mean value to obtain the updated load radiated disturbance noise floor monitoring benchmark data.

[0023] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0024] This invention discloses a method for monitoring the noise floor of radiated interference from charging pile loads, focusing on the correlation between magnetic core saturation, weakened radiation suppression, and electromagnetic instability cycles in operational scenarios. By real-time acquisition of charging pile current and magnetic core flux density data, the saturation threshold deviation is determined, and finite element analysis is used to evaluate the magnetic field distribution and radiation suppression capability, quantifying the radiation reduction factor. The noise floor signal is then processed to extract fluctuation amplitude and accurately locate the electromagnetic instability trigger point. Simultaneously, based on the trigger point, communication interference and load fluctuations are monitored, the operating point offset trajectory is predicted, and the magnetic core configuration and noise floor calibration are dynamically adjusted to optimize compatibility performance. Finally, a numerical relationship table between load changes and radiated interference is established, providing a benchmark for subsequent monitoring. This invention significantly improves the electromagnetic compatibility of charging piles under complex loads through adaptive control and real-time calibration, reducing interference risks and ensuring a stable and efficient charging process. Attached Figure Description

[0025] Figure 1 This is a flowchart of a method for monitoring the background noise of radiated disturbances from a charging pile load according to the present invention.

[0026] Figure 2 This is a schematic diagram of a method for monitoring the background noise of radiated disturbances from a charging pile load according to the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] like Figure 1-2 This embodiment of a method for monitoring the background noise of radiated interference from a charging pile load may specifically include:

[0029] S101. Obtain real-time current data and magnetic flux density value inside the magnetic core during the operation of the charging pile, determine the initial current threshold for the saturation degree of the magnetic core, and calculate the deviation value between the magnetic flux density and the preset saturation threshold to obtain the saturation deviation value.

[0030] The instantaneous value of the operating current is obtained through the current transformer in the main circuit of the charging pile. Simultaneously, a Hall sensor measures the magnetic flux density on the cross-section of the magnetic core. The critical value for magnetic flux density saturation is determined based on the inflection point of the BH curve of the magnetic core material. When the instantaneous value of the operating current reaches the current value corresponding to the saturation critical value, this current value is set as the initial current threshold for the degree of magnetic core saturation. The maximum value of the magnetic flux density measured by the Hall sensor is extracted, and the difference between this maximum value and the critical value for magnetic flux density saturation is calculated. When the difference is less than zero, the magnetic core is determined to be operating in the linear region, and the saturation deviation value is recorded as zero. When the difference is greater than or equal to zero, this difference is used as the saturation deviation value.

[0031] Specifically, in one implementation, the current transformer in the main circuit of the charging pile adopts a through-hole structure, with the charging cable passing directly through the center hole of the transformer core. When the charging current passes through, an induced voltage is generated in the secondary winding, which is converted into a 0-5V analog signal for acquisition by a signal conditioning circuit. A Hall sensor is installed in the air gap of the magnetic core, using the Hall effect to convert the magnetic field strength into a voltage signal, with a measurement range covering the magnetic flux density range of 0 to 1.8 Tesla.

[0032] Specifically, the BH curve of the magnetic core material describes the nonlinear relationship between magnetic flux density B and magnetic field strength H. When the magnetic field strength increases to a certain value, the rate of increase in magnetic flux density slows down significantly; this inflection point is the saturation onset point. By consulting the BH curve chart in the magnetic core material datasheet, the magnetic field strength corresponding to a saturation magnetic flux density of 1.5 Tesla is determined. Then, the current value required to generate this magnetic field strength is calculated according to Ampere's law and set as the initial current threshold. The saturation deviation value is calculated using a real-time comparison method. When the magnetic flux density measured by the Hall sensor exceeds the saturation critical value, the deviation value directly reflects the depth to which the magnetic core has entered the saturation region.

[0033] S102. Based on the saturation deviation value, the finite element analysis method is used to identify the magnetic field distribution intensity of the magnetic core under the current load, and to determine the degree of radiation suppression reduction, thereby obtaining the radiation reduction factor.

[0034] A three-dimensional finite element mesh model of the magnetic core is constructed based on the saturation deviation value. The saturation deviation value is converted into a permeability correction coefficient, and the corrected permeability value is assigned to the mesh nodes. The magnetic flux density distribution data and magnetic field intensity distribution map of each node are obtained by iteratively solving the magnetic field distribution equation. The normal magnetic flux component of the magnetic core surface is extracted from the magnetic flux density distribution data, and the radiation field intensity generated by the leakage magnetic flux in space is calculated. This radiation field intensity is compared with the reference radiation field intensity of the linear working area of ​​the magnetic core to obtain the radiation suppression coefficient under the current state. The radiation suppression coefficient is compared with a preset standard suppression threshold. If the radiation suppression coefficient is lower than a preset proportion of the standard suppression threshold, the radiation suppression capability of the magnetic core is determined to be weakened. The percentage of the difference between the standard suppression threshold and the radiation suppression coefficient relative to the standard suppression threshold is calculated to obtain the degree of radiation suppression weakening. Based on the degree of radiation suppression weakening, the corresponding magnetic field attenuation rate is obtained by querying the magnetic core material characteristic curve. The reciprocal of the magnetic field attenuation rate is determined as the radiation reduction factor.

[0035] Specifically, in one implementation, the saturation deviation value serves as a quantitative indicator of core performance degradation, directly reflecting the degree to which the core deviates from its ideal operating state. When a charging station handles high-power charging tasks, the magnetic flux density inside the core approaches or exceeds the saturation critical value, causing a sharp drop in permeability and significantly weakening the core's ability to suppress high-frequency electromagnetic interference. By constructing a three-dimensional finite element mesh model, the complex magnetic field distribution within the core can be accurately simulated.

[0036] Specifically, the finite element mesh is discretized using tetrahedral elements to represent the core geometry. Mesh refinement is applied in regions with drastically changing magnetic fields, such as the air gap and winding interfaces, to ensure computational accuracy. Saturation deviation values ​​are embedded into each mesh node as permeability correction coefficients, which decay exponentially, with the coefficients approaching 0.1 in the saturated region and remaining at 1.0 in the unsaturated region. The magnetic field distribution equation is calculated using an iterative solver, gradually approximating the convergent solution through the Newton-Raphson iterative method. Each iteration updates the nodal magnetic vector potential values ​​until the residual between two adjacent iterations is less than a preset convergence threshold. After solving, magnetic flux density distribution data is derived from the nodal magnetic vector potential values, forming a three-dimensional magnetic field intensity distribution map that clearly shows the direction and density variations of the magnetic field lines within the core.

[0037] It should be noted that leakage flux refers to the magnetic flux that fails to be completely confined within the magnetic core and escapes into the surrounding space. The process of extracting the normal magnetic flux component from the magnetic core surface in the magnetic flux density distribution data involves coordinate transformation, projecting the magnetic flux density vector in the Cartesian coordinate system onto the surface normal direction, and then integrating to calculate the total magnetic flux penetrating the magnetic core surface.

[0038] In one possible implementation, the intensity of the radiated field generated by the leakage flux in space is calculated using a near-field to far-field conversion formula. Within the operating frequency range of the charging pile, the time-varying magnetic field generated by the leakage flux excites electromagnetic waves to radiate outward, and the radiation intensity is proportional to the rate of change of the leakage flux. The reference radiated field intensity in the linear operating region of the magnetic core is obtained through experimental calibration. The radiated emission level in the frequency band from 30MHz to 1GHz is measured under conditions where the magnetic core is far from saturated, and the peak value is taken as the reference value.

[0039] For example, when a 120kW DC charging pile is running at full load, the working magnetic flux density of the transformer core reaches 1.45 Tesla, approaching the saturation point of 1.5 Tesla for silicon steel sheets. At this time, the radiation suppression coefficient drops from 0.95 during normal operation to 0.62, indicating a severe degradation in the electromagnetic shielding effectiveness of the core. The calculation of the degree of radiation suppression reduction uses the relative deviation method, dividing the difference between the standard suppression threshold and the measured radiation suppression coefficient by the standard suppression threshold to obtain the percentage reduction. The standard suppression threshold is determined according to the electromagnetic compatibility limits for charging piles specified in the national standard GB / T18487, ensuring that the charging pile meets electromagnetic compatibility requirements under various operating conditions. When the reduction exceeds a preset proportion, the core operating state adjustment mechanism is triggered. The core material characteristic curve includes the permeability variation law, hysteresis loop characteristics, and frequency response characteristics of the material under different magnetic field strengths. The query process is performed between discrete characteristic data points using an interpolation algorithm. Based on the current degree of radiation suppression reduction, the corresponding operating point is located on the characteristic curve, and the magnetic field attenuation rate at that point is read. The magnetic field attenuation rate characterizes the ability of a magnetic core material to attenuate an alternating magnetic field, and its reciprocal directly reflects the degree of amplification of radiated energy.

[0040] For example, when the magnetic flux density of the ferrite core material increases from 0.3 Tesla to 0.45 Tesla at a working frequency of 100 kHz, the magnetic field attenuation rate decreases from 0.85 to 0.42, and the corresponding radiation attenuation factor increases from 1.18 to 2.38.

[0041] S103. Based on the radiation attenuation factor, process the real-time waveform data of the background noise signal generated by the internal circuit of the charging pile and received by the magnetic core detection, and determine the trigger point of the electromagnetic instability cycle.

[0042] The real-time sampling frequency of the magnetic core induction coil is determined by multiplying the radiation attenuation factor by a preset sampling rate benchmark. This sampling frequency is used to acquire the original waveform of the noise floor signal generated by the charging pile's switching power supply during turn-on and turn-off. A Hanning window function is applied to the original waveform to eliminate spectral leakage and obtain a noise floor signal sequence. A Fast Fourier Transform is performed on the noise floor signal sequence to obtain frequency domain spectral data. The amplitude of each frequency component is extracted from the frequency domain spectral data. The difference between the amplitude of the current sampling period and the amplitude of the previous sampling period is calculated. When the difference exceeds a preset change threshold, the difference is recorded as the noise floor fluctuation amplitude value and its corresponding time stamp is stored. Based on the time sequence composed of the noise floor fluctuation amplitude value and its time stamp, time periods in which the fluctuation amplitude value continuously exceeds a preset threshold are identified. If the fluctuation amplitude value exceeds a preset multiple of the threshold value within a preset number of consecutive sampling periods, the start time of this time period is determined as the trigger point of the electromagnetic instability cycle.

[0043] Specifically, in one implementation, the radiation attenuation factor reflects the degree of degradation in electromagnetic shielding performance caused by core saturation. A higher factor indicates more severe radiation leakage and a more complex interference component in the noise floor signal. According to the Nyquist sampling theorem, the sampling frequency needs to be more than twice the highest frequency of the signal to accurately reconstruct the waveform. As the radiation attenuation factor increases, high-frequency interference components increase, requiring a corresponding increase in the sampling rate to capture these rapidly changing noise characteristics.

[0044] Specifically, the preset sampling rate benchmark is usually set to 20 times the charging pile switching frequency. When the radiation reduction factor is 2, the actual sampling frequency is adjusted to twice the benchmark value, that is, 40 times the switching frequency. The magnetic core induction coil is wound around the magnetic core and picks up the induced voltage generated by the leakage magnetic field through the principle of electromagnetic induction. This voltage signal is the original waveform of the noise floor signal.

[0045] It should be noted that the Hanning window function is a cosine window, characterized by a moderate main lobe width and rapid side lobe attenuation. When windowing the original waveform, the amplitude of each sampling point is multiplied by the corresponding window function coefficient, allowing the signal to smoothly transition to zero at the time domain boundary, reducing leakage effects during spectral analysis and improving frequency resolution. The Fast Fourier Transform converts the time-domain noise floor signal sequence to the frequency domain, obtaining the complex representation of each frequency component, and taking its magnitude is the amplitude at that frequency. The amplitude difference between adjacent sampling periods reflects the dynamic changes in the noise floor; when the charging power changes abruptly or the core operating point shifts, the difference will increase significantly.

[0046] For example, at the moment of startup of a 350kW supercharger, the amplitude of the noise floor signal at 50kHz jumps from 0.2V to 0.8V, a difference of 0.6V, far exceeding the preset change threshold of 0.1V. Similar large fluctuations occur for five consecutive sampling cycles, and the fluctuation amplitude exceeds three times the threshold value. At this point, the system determines that the electromagnetic environment has entered an unstable state, marks the moment when the anomaly first occurs as the trigger point, and initiates subsequent suppression measures.

[0047] The signal receiving sensitivity of the magnetic core induction coil is adjusted based on the radiation attenuation factor. The voltage value of the background noise signal generated when the internal switching tube of the charging pile is turned on is collected. The voltage value of the background noise signal is compared with the normal operating voltage reference value to obtain the voltage deviation. When the voltage deviation exceeds the set upper limit, the duration is accumulated. At the same time, the frequency of the background noise signal is monitored to see if it deviates from the standard operating frequency range. If the voltage deviation continues to exceed the limit and the frequency deviation occurs at the same time and reaches the preset duration threshold, it is determined to be an electromagnetic instability cycle trigger point. The voltage deviation and frequency deviation at the trigger point are stored as trigger conditions for later use.

[0048] Multiplying the radiation attenuation factor by a preset sensitivity adjustment coefficient yields the target gain value of the preamplifier for the magnetic core induction coil, which is then set. At the instant the switching transistor inside the charging pile transitions from the off state to the on state, the voltage value of the background noise signal at the output of the induction coil is collected. This background noise signal voltage value is subtracted from a pre-calibrated normal operating voltage reference value to obtain the voltage deviation. The voltage deviation is compared with a preset upper limit threshold. If it exceeds the threshold, a duration timer is started to record the start time of the over-limit. Simultaneously, the time interval between two adjacent peaks of the background noise signal is measured to calculate the real-time frequency. This real-time frequency is compared with preset upper and lower limits of the standard operating frequency. If it exceeds the range, the frequency deviation is calculated. The cumulative time of the voltage deviation exceeding the limit and the existence status of the frequency deviation are monitored. If both conditions are met simultaneously and the cumulative time reaches a preset duration threshold, the current moment is determined to be an electromagnetic instability cycle trigger point. The voltage deviation value and frequency deviation value at this moment are stored as trigger condition parameters in a buffer for later use.

[0049] Specifically, in one implementation, the radiation attenuation factor directly affects the signal pickup capability of the magnetic core induction coil. As the magnetic core saturation deepens, the radiation attenuation factor increases, and the leakage magnetic field strengthens. In this case, the gain of the preamplifier needs to be reduced to avoid signal saturation. The sensitivity adjustment coefficient is set empirically between 0.5 and 2; in this embodiment, it is 0.5. Multiplying this value by the radiation attenuation factor yields the actual gain adjustment value.

[0050] Specifically, the internal switching transistor of the charging pile uses an insulated-gate bipolar transistor (IGBT). During its turn-on, a rapid rate of change of current, di / dt, is generated, inducing a voltage spike on the parasitic inductance. The electromagnetic noise generated by this transient process is coupled to the induction coil through the magnetic core, forming a background noise signal. The normal operating voltage reference value is obtained by averaging the background noise signal over 100 cycles during stable operation of the charging pile, serving as a reference standard for judging anomalies.

[0051] It should be noted that voltage deviation reflects an abnormality in the intensity of the noise floor signal, while frequency deviation reflects a change in the noise spectrum. The operating frequency of the switching transistor is typically in the range of 20kHz to 100kHz. When the magnetic core saturates, nonlinear effects generate harmonic components, causing a shift in the dominant frequency of the noise floor signal. By measuring the time interval between adjacent peaks, the instantaneous frequency can be calculated. The upper and lower limits of the standard operating frequency are set at ±5% of the rated frequency.

[0052] Preferably, the dual-judgment condition setting improves the accuracy of trigger point identification. A simple voltage over-limit may be caused by occasional interference, while a frequency deviation indicates a fundamental change in the working state of the magnetic core. Only when both conditions are met simultaneously and persist for a certain period of time can it be confirmed that the electromagnetic environment has entered an unstable state. The preset duration threshold is usually set to 10 switching cycles, which can both filter out transient interference and respond promptly to real anomalies.

[0053] For example, during power switching at a 150kW charging station, the noise floor signal voltage jumped from 0.5V to 1.2V, with a deviation of 0.7V, exceeding the upper limit threshold of 0.3V. Simultaneously, the measured frequency shifted from 50kHz to 53kHz, a deviation of 3kHz. After these two abnormal states persisted for 200 microseconds, a trigger point was determined to be established, and 0.7V and 3kHz were stored as trigger condition parameters.

[0054] S104. Based on the trigger point of the electromagnetic instability cycle, monitor the data transmission flow between the charging pile and the electric vehicle, evaluate the signal-to-noise ratio, and if the noise ratio is higher than the preset interference threshold, obtain the interference duration and get the interference duration value.

[0055] Based on the trigger point of the electromagnetic instability cycle, the carrier signal power and background noise power are extracted from the communication link between the charging pile and the electric vehicle. The carrier signal power is divided by the background noise power to obtain the signal-to-noise ratio (SNR). Simultaneously, the data packet transmission log recorded by the communication module is read. The SNR is compared with a preset interference threshold. If the SNR is lower than the preset interference threshold, communication interference is determined to exist. The start time of the interference is recorded. The change in the SNR is continuously monitored until it recovers to above the preset interference threshold, and the recovery time is recorded. The time difference between the recovery time and the start time is calculated and converted to milliseconds to obtain the interference duration. A lower SNR indicates a stronger interference intensity and a longer interference duration.

[0056] Specifically, in one implementation, communication between the charging pile and the electric vehicle uses power line carrier communication or control guide line communication, following the communication protocol specified in GB / T27930 standard. The trigger point of the electromagnetic instability cycle indicates an abnormal operating state of the magnetic core. At this time, the leaked electromagnetic field couples onto the communication line, forming common-mode interference and differential-mode interference, affecting data transmission quality.

[0057] Specifically, the carrier signal power is measured by the received signal strength indicator of the communication module, reflecting the energy level of the useful signal. Background noise power is calculated by collecting interference signals on the line during communication idle time slots, including broadband noise from the charging pile's switching power supply, grid harmonics, and magnetic core saturation. The signal-to-noise ratio is expressed in decibels and should normally be above 20 dB.

[0058] It should be noted that the preset interference threshold is set based on the bit error rate requirements of the communication protocol. When the signal-to-noise ratio (SNR) is below 15 dB, the bit error rate rises sharply, the number of data packet retransmissions increases, and communication efficiency decreases. At this point, communication interference is determined to exist, and interference events are recorded. The start and end times of the interference are precisely recorded using a timestamp mechanism, with a time resolution down to the millisecond level.

[0059] Preferably, the interference duration is statistically analyzed using a sliding window method, with the signal-to-noise ratio (SNR) measurement updated every 100 milliseconds. When the SNR is below a threshold for three consecutive measurement cycles, the interference is considered persistent. This method can filter out the influence of transient noise spikes and improve the accuracy of interference detection.

[0060] For example, during a 240kW high-power charging process, when the magnetic core enters a deep saturation state, the signal-to-noise ratio (SNR) drops from the normal 25dB to 12dB, below the interference threshold of 15dB. The interference started at 13:25:36.128, and continuous monitoring showed that the SNR recovered above the threshold at 13:25:38.456, calculating the interference duration to be 2328 milliseconds. The measured data indicates that the lower the SNR, i.e., the greater the interference intensity, the longer it takes for the magnetic core to recover to normal operating conditions, verifying the positive correlation between interference intensity and duration.

[0061] S105. Analyze the load fluctuation pattern during the charging process based on the interference duration, identify the potential trajectory of the operating point offset, and evaluate the matching degree between the offset amplitude and the dynamic correlation of the load current change in the potential trajectory of the operating point offset to obtain the predicted value of the offset amplitude.

[0062] The data acquisition time window length for the charging process is set according to the interference duration. Within this window, the instantaneous value of the charging pile output current is continuously acquired at a preset sampling frequency. The ratio of the current difference between adjacent sampling points to the time interval is calculated to obtain the current change rate sequence. The current change rate sequence is processed by moving average, and the periodic change characteristics are extracted as the load fluctuation law. The current value at each moment in the load fluctuation law is mapped onto the magnetic core working characteristic curve. The corresponding magnetic flux density and magnetic field strength coordinate points are determined as working points based on the current value. The working points are connected in chronological order to form a movement path. When the working point deviates from the preset linear region, the deviation distance and deviation angle are measured to obtain the potential trajectory of the working point deviation. The displacement between the working point and the reference working point at each moment is extracted from the potential trajectory of the working point deviation as the deviation amplitude. The load current change at the corresponding moment is obtained, and the Pearson correlation coefficient between the deviation amplitude and the load current change is calculated. This correlation coefficient is used as the matching degree of dynamic association. Based on the matching degree and the current load current change trend, a linear regression is used to establish a functional relationship between the deviation amplitude and the load current. The expected current value at future moments is substituted into the functional relationship to calculate the predicted value of the deviation amplitude.

[0063] Specifically, in one implementation, the disturbance duration reflects the duration of the charging pile in an electromagnetically unstable state, which is closely related to the severity of load fluctuations. Load fluctuations during charging mainly originate from the dynamic adjustment of electric vehicle battery management, the switching of power modules, and disturbances on the power grid side. By analyzing these fluctuation patterns, the offset trend of the magnetic core operating point can be predicted, providing a basis for subsequent compensation control.

[0064] Specifically, the data acquisition time window is set to 10 times the duration of interference to ensure the capture of complete fluctuation cycles. The sampling frequency is set to 100 times the switching frequency to meet the requirements of Shannon's sampling theorem. The charging pile output current is measured using a high-precision Hall current sensor, covering a range of 0 to 500 amperes with a resolution of 0.1 amperes. The time interval between adjacent sampling points is fixed at 10 microseconds, and the instantaneous rate of change is obtained by dividing the current difference by the time interval. The moving average processing uses a Hamming window with a window width of 50 sampling points, sliding 5 points at a time. This overlapping processing method can smooth noise while preserving fluctuation characteristics. The processed data is analyzed using Fast Fourier Transform to identify the main periodic components, and these periodic characteristics constitute the core content of the load fluctuation law.

[0065] It should be noted that the core operating characteristic curve describes the nonlinear relationship between magnetic flux density B and magnetic field strength H. This curve is divided into three regions: the linear region, the knee region, and the saturation region. In the linear region, the permeability is essentially constant, and the core's inductance is stable. The knee region is the transition area from linearity to saturation, where the permeability begins to decrease. In the saturation region, the permeability decreases sharply, and the core loses its energy storage capacity. Each current value corresponds to a specific magnetic field strength, and the operating point on the BH curve can be determined using the ampere-turn relationship. When the charging power changes, the operating point moves along the curve, forming a dynamic trajectory.

[0066] Preferably, the identification of operating point offset employs a vector analysis method. The point corresponding to the rated operating current is taken as the reference operating point, and the line connecting the real-time operating point and the reference point is the offset vector. The offset distance is calculated using the Euclidean distance formula, and the offset angle is obtained using the arctangent function. When the operating point moves out of the linear region boundary, the time of deviation, the location of deviation, and the degree of deviation are recorded; this information constitutes the potential trajectory of the operating point offset.

[0067] In one possible implementation, the Pearson correlation coefficient is used to quantify the linear correlation between offset amplitude and load current change. The coefficient ranges from -1 to 1, with a value closer to 1 indicating a stronger correlation. The calculation involves first calculating the mean of both the offset amplitude sequence and the current change sequence, then calculating the covariance of the two sequences, and finally dividing by the product of their standard deviations. A correlation coefficient greater than 0.7 indicates a strong correlation, allowing for the establishment of a reliable prediction model. Furthermore, the linear regression model is built using the least squares method. Offset amplitudes and corresponding load current values ​​from historical operating data are collected to form a training sample set. The regression coefficients are solved by minimizing the sum of squared errors between the predicted and actual values. The regression equation is in the form that the offset amplitude equals the slope multiplied by the current change plus the intercept. The slope reflects the change in offset amplitude caused by a unit change in current, while the intercept represents the baseline offset at zero current.

[0068] For example, in a test at a 350kW supercharging station, when the charging power suddenly increased from 100kW to 300kW, the load current jumped from 200A to 600A within 2 seconds. Analysis using the above method identified a shift in the operating point from the edge of the linear region towards the saturation region, with a shift of 0.35 Tesla. The Pearson correlation coefficient was calculated to be 0.89, indicating a strong correlation. The established regression model predicted a shift of 0.42 Tesla at the next moment, while the measured value was 0.41 Tesla, resulting in a prediction error of only 2.4%.

[0069] Understandably, the predicted offset amplitude provides a quantitative basis for real-time control of the magnetic core's operating state. By predicting the offset trend in advance, compensation parameters can be proactively adjusted to prevent the magnetic core from entering deep saturation, thus maintaining the stability and electromagnetic compatibility of the charging process.

[0070] The sampling time window for the charging load current is set based on the interference duration value. Within this time window, the instantaneous value of the charging pile output current is continuously collected. The difference between the current values ​​at adjacent time points is calculated to obtain the current change. The current change is arranged in chronological order to form a current change data chain, and the current working point of the charging pile is determined. The coordinates of the working point at consecutive time moments are connected in chronological order to form the working point movement path. The distance length and offset angle of each segment in the working point movement path are measured. When the offset distance exceeds the normal working range or the offset angle deviates from the preset direction, the potential trajectory of the working point offset is marked.

[0071] The sampling time window length is determined by multiplying the interference duration by a preset factor. Instantaneous current values ​​at the charging pile output are continuously acquired within this window according to a fixed sampling period. The current change is obtained by subtracting the previous current value from the current value at the current moment. These current changes and their corresponding moments are arranged chronologically to form a current change data chain. Based on this current change data chain, the actual current value at each moment is reconstructed by accumulating the current changes at each moment. Each current value is substituted into a preset magnetic core magnetization characteristic curve equation to calculate the corresponding magnetic flux density and magnetic field strength values ​​as the working point coordinates. The working point coordinates are connected chronologically to form the working point movement path. The Euclidean distance between adjacent working points in the working point movement path is calculated as the path segment length. Using the line connecting the center point of the linear working area of ​​the magnetic core to the current working point as a reference, the angle between the path segment direction and the reference line is calculated as the offset angle. If the path segment length exceeds a preset distance threshold or the offset angle exceeds a preset angle range, the path segment is marked as a potential trajectory for working point offset.

[0072] Specifically, in one implementation, the duration of interference is directly related to the stability of the charging process; a longer interference duration means more severe fluctuations in the magnetic core's operating state. The preset multiplier is typically set to 5 to 10 times to ensure the sampling window covers the complete interference cycle and its recovery process. The fixed sampling period is determined based on the charging pile's control frequency, with a typical value of 100 microseconds, corresponding to a sampling rate of 10 kHz.

[0073] Specifically, the construction of the current change data chain involves differential operations and timing management. Each data node contains three elements: a timestamp, the amount of current change, and the accumulated current value. When reconstructing the actual current value through accumulation operations, an initial current reference needs to be set, typically the steady-state current value before the disturbance occurred. This data structure facilitates the tracking of the current state and trend at any given time.

[0074] It should be noted that the magnetization characteristic curve equation of the magnetic core is expressed in piecewise function form. In the linear region, the magnetic flux density B and the magnetic field strength H are linearly related, where B equals the permeability μ multiplied by H. Near saturation, the relationship becomes nonlinear, requiring polynomial fitting. Substituting the current value I into the equation H equals N × I / le, where N is the number of coil turns and le is the effective magnetic path length, the corresponding operating point coordinates can be obtained.

[0075] Preferably, the formation of the working point movement path reflects the dynamic magnetization process of the magnetic core. The Euclidean distance between adjacent working points is calculated using a two-dimensional coordinate system, with the horizontal axis representing magnetic field strength and the vertical axis representing magnetic flux density. The distance threshold is set to one-tenth of the width of the linear region to ensure timely identification of abnormal offsets.

[0076] For example, the offset angle is calculated using a polar coordinate system with the center point of the linear working area of ​​the magnetic core as the origin. The line connecting the origin to the current working point is used as the reference direction, and the angle between the path segment and the reference line is obtained using the vector dot product formula. The preset angle range is typically ±30 degrees; exceeding this range indicates that the working point is rapidly moving towards the saturation region. Furthermore, the potential trajectory is marked using binary flags: normal path segments are marked as 0, and abnormal path segments are marked as 1. When multiple path segments marked as 1 appear consecutively, a complete offset trajectory is formed, which records the complete process of the magnetic core transitioning from a normal working state to an abnormal state.

[0077] S106. Adjust the core configuration value according to the predicted offset amplitude value to restore radiation intensity control, identify the real-time calibration noise fluctuation level, and monitor the attenuation effect of electromagnetic instability cycle during the calibration process to obtain the optimized compatibility performance value.

[0078] The permeability compensation value is obtained by multiplying the predicted offset amplitude value by a preset adjustment coefficient. This compensation value is then added to the current core operating parameters to obtain the adjusted core configuration value. Configuration adjustment is achieved by adjusting the core air gap width or changing the number of coil turns, thus restoring radiation intensity control. A proportional-integral-derivative (PID) control method is employed, using the deviation between the real-time amplitude of the noise floor signal and a preset reference value as input. The output control quantity is used to adjust the signal amplification factor. Simultaneously, the frequency response parameters are updated based on historical noise floor data and current measurements using a recursive least squares method to calibrate the noise floor fluctuation level in real time. During the calibration process, the amplitude of the electromagnetic interference signal is continuously acquired, and the amplitude ratio of adjacent sampling points is calculated. When multiple consecutive ratios are less than a preset attenuation threshold, the electromagnetic instability cycle is determined to have entered an attenuation state, and the duration of the attenuation process is recorded. The system recovery speed is evaluated based on the attenuation process duration, and the reduction ratio of the noise floor amplitude before and after calibration is calculated. The recovery speed and reduction ratio are comprehensively scored to obtain the optimized compatibility performance value.

[0079] Specifically, in one implementation, the predicted offset amplitude directly reflects the degree to which the magnetic core deviates from its ideal operating point. A larger value indicates that the magnetic core is closer to saturation and requires stronger compensation measures. The preset adjustment coefficient is determined based on the characteristics of the magnetic core material; for ferrite materials, the adjustment coefficient is between 0.8 and 1.5, while for silicon steel sheets, it is between 0.5 and 1.2. The permeability compensation value is calculated using a lookup table interpolation method to ensure that the compensation accuracy is within 5% of the permeability change.

[0080] Specifically, the physical adjustment of the magnetic core configuration is achieved primarily through two methods. The first is adjusting the air gap width of the magnetic core. A stepper motor drives a precision lead screw to adjust the relative position of the two halves of the core. For every 0.1 mm increase in the air gap, the equivalent magnetic reluctance increases by approximately 15%, thereby reducing the equivalent permeability of the core and moving the operating point away from the saturation region. The second method is changing the number of coil turns. A relay switches between different coil taps, achieving a step-wise adjustment of the number of turns. For every 10 turns added, the magnetic field strength decreases proportionally, thus adjusting the operating point. These two methods can be used individually or in combination for more precise adjustment.

[0081] It should be noted that the application of the proportional-integral-derivative (PID) control method in noise floor calibration involves the synergistic effect of three control components. The proportional component immediately generates a control action based on the current deviation, and its gain coefficient Kp determines the response speed. The integral component accumulates historical deviations and eliminates steady-state errors, and its integral time Ti determines how quickly the error is eliminated. The derivative component predicts the trend of deviation changes and adjusts in advance, and its derivative time Td affects the system stability. The outputs of the three components are superimposed to form the total control quantity, which is used to adjust the gain of the signal amplification circuit to stabilize the noise floor signal amplitude within the target range.

[0082] Preferably, the core of the recursive least squares method lies in continuously updating the system parameter estimates using new measurement data. Initially, prior estimates of the frequency response parameters and the covariance matrix are set. When a new noise floor measurement value arrives, the prediction error is calculated, which is the difference between the measured value and the predicted value based on the current parameters. A gain vector is calculated based on the prediction error and the covariance matrix; this gain determines the degree of influence of the new data on parameter updates. The parameter update formula adds the old parameter value to the product of the gain vector and the prediction error to obtain the new parameter estimate. Simultaneously, the covariance matrix is ​​updated to prepare for the next recursion. Through this recursive method, changes in the noise floor characteristics can be tracked in real time, and calibration parameters can be adaptively adjusted. Furthermore, the attenuation process of the electromagnetic interference signal amplitude exhibits an exponential law. In the initial stage of calibration, the interference amplitude is large and fluctuates violently. As the control action accumulates, the amplitude gradually decreases and tends to stabilize. The amplitude ratio of adjacent sampling points reflects the attenuation rate. When this ratio is less than 0.95 for 10 consecutive sampling periods, it indicates that the system has entered a stable attenuation state. The determination of the preset attenuation threshold takes into account the power level and electromagnetic environment requirements of the charging pile, and the threshold setting for high-power charging piles is more stringent.

[0083] In one possible implementation, the duration of the decay process is statistically measured from the moment the decay state is confirmed until the amplitude drops to 10% of its initial value. This time reflects the dynamic response characteristics of the system; a shorter time indicates better control performance. The recovery rate is calculated by dividing the time by the percentage of recovery per second.

[0084] For example, the percentage reduction in noise floor amplitude is calculated using a root mean square (RMS) comparison method. The RMS values ​​of the noise floor signal are calculated for 100 sampling points before and after calibration, and the difference is divided by the value before calibration to obtain the percentage reduction. In one actual measurement, the RMS value before calibration was 2.3 volts, and after calibration it dropped to 0.8 volts, a reduction of 65%.

[0085] Understandably, the comprehensive score uses a weighted summation method, with the recovery speed weighted at 0.4 and the reduction ratio weighted at 0.6, reflecting the emphasis on steady-state performance. The compatibility performance values ​​are normalized to a range of 0 to 100, with higher values ​​indicating better electromagnetic compatibility performance.

[0086] S107. Based on the optimized compatibility performance values, track the fluctuation trend of the noise floor signal in real time, obtain the updated load radiation interference noise floor monitoring benchmark value, and use it as the data collection basis for the next charging pile load radiation interference noise floor cycle monitoring.

[0087] Based on the optimized compatibility performance values, a performance level classification standard is determined. The charging load power is divided into multiple intervals, and the measured value of radiated interference intensity for each load segment under the current compatibility performance level is recorded. A correspondence between load power values ​​and radiated intensity values ​​is established, forming a numerical relationship table and storing it. The radiated intensity value corresponding to the current load in the numerical relationship table is extracted as a reference. The real-time amplitude sequence of the noise floor signal is continuously collected, and the ratio of the difference in amplitude between adjacent sampling times to the time interval is calculated to obtain the slope of change. The slope sequence is arranged to form fluctuation trend data. The direction of noise floor change is determined based on the fluctuation trend data. If the trend is upward, the monitoring benchmark weight is increased; if the trend is downward, the weight is decreased. The monitoring benchmark value of the previous period is multiplied by the weight coefficient and added to the current measured average noise floor value to obtain the updated load radiated interference noise floor monitoring benchmark value. This benchmark value is stored as the data collection basis for the next charging pile load radiated interference noise floor cycle monitoring.

[0088] Specifically, in one implementation, the optimized compatibility performance value provides a quantitative evaluation of the system's electromagnetic environment status. Based on this value, performance is divided into three levels: excellent, good, and acceptable, corresponding to numerical ranges of 90-100, 70-90, and 50-70, respectively. The charging load power is divided using a logarithmic interval method, starting from 10kW, followed by power segments of 20kW, 40kW, 80kW, 160kW, and 320kW, covering the entire range from slow charging to super-fast charging.

[0089] Specifically, establishing the numerical relationship table requires multiple measurements for each power range. Within a specific power range, after the charging pile has been running stably for 10 minutes, the radiated interference intensity in the 150kHz to 30MHz frequency band is measured using a spectrum analyzer, and the peak value is taken as the representative value for that power range. Power values ​​are used as the x-axis, and radiated intensity values ​​as the y-axis to form a discrete set of data points. Intermediate values ​​are then filled in using linear interpolation to create a complete numerical relationship table.

[0090] It should be noted that the real-time amplitude acquisition frequency of the noise floor signal is set to 100 times per second, forming dense time series data. The slope of the change is calculated using the least squares method, performing linear fitting on 10 consecutive sampling points. The slope of the fitted line is the rate of change for that period. When 5 consecutive slope values ​​are positive, it is determined to be an upward trend; when they are all negative, it is determined to be a downward trend; alternating positive and negative values ​​indicate an oscillation state.

[0091] Preferably, the adjustment of the weighting coefficient follows an adaptive principle. During an upward trend, the weighting coefficient is set to 1.2, indicating that the system is in a deteriorating state and monitoring sensitivity needs to be increased; during a downward trend, the weighting coefficient is set to 0.8, indicating that the system is improving; and during oscillations, it remains unchanged at 1.0. The monitoring baseline value of the previous period is stored in non-volatile memory to ensure that data is not lost after a power outage.

[0092] For example, during the morning rush hour, a charging station experiences a sudden increase in load power from 50kW to 200kW due to multiple vehicles charging simultaneously. The detected floor noise amplitude rises from 0.5V to 1.8V, with the slope sequence showing a clear upward trend. According to the weighting adjustment rules, the previous period's baseline value of 1.0V is multiplied by 1.2 to obtain 1.2V, which is then weighted and averaged with the current average value of 1.8V in a 3:7 ratio to obtain a new baseline value of 1.62V. This value is stored and used as the reference standard for the next monitoring period, achieving dynamic adaptive updating of the monitoring baseline.

[0093] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring the background noise of radiated interference from a charging pile load, characterized in that, include: The system acquires real-time current data and magnetic flux density data inside the magnetic core during charging pile operation, determines the initial threshold for magnetic core saturation, calculates the deviation between the magnetic flux density data and the preset saturation threshold, and obtains saturation deviation data. Based on the saturation deviation data, the system analyzes the magnetic field distribution intensity of the magnetic core under the current load, compares the quantified value of radiation suppression capability with the preset suppression threshold, and determines the radiation suppression reduction factor. Based on the radiation suppression attenuation factor, the process involves processing the background noise signal waveform data generated by the internal circuitry of the charging pile to determine the trigger point of the electromagnetic instability cycle. This includes: determining the sampling frequency based on the radiation suppression attenuation factor to acquire the background noise signal waveform data generated by the charging pile's switching power supply; performing frequency domain transformation on the background noise signal waveform data to extract the amplitude of each frequency component; calculating the difference between the current cycle amplitude and the previous cycle amplitude, and recording the difference as the background noise fluctuation amplitude; constructing a time series based on the background noise fluctuation amplitude, identifying time periods that continuously exceed a preset threshold, and determining the starting time as the trigger point of the electromagnetic instability cycle; and further determining the trigger point of the electromagnetic instability cycle based on the electromagnetic instability... The system uses a fixed-cycle trigger point to monitor the data transmission flow between the charging pile and the electric vehicle, and obtains interference duration data. Based on the interference duration data, it analyzes the load fluctuation pattern during the charging process, identifies the operating point offset trajectory, calculates the matching degree between the offset amplitude and the load current change in the operating point offset trajectory, and obtains offset amplitude prediction data. This includes: setting a data acquisition time window based on the interference duration data, continuously acquiring instantaneous data of the charging pile output current, calculating the current change rate sequence, and extracting periodic change characteristics as the load fluctuation pattern; mapping the current value in the load fluctuation pattern to the magnetic core characteristic curve to determine the operating... Point coordinates are used to connect each working point to form an offset trajectory; the displacement of the offset trajectory is extracted as the offset amplitude, and the correlation coefficient between the offset amplitude and the load current change is calculated as the matching degree; a functional relationship between the offset amplitude and the load current is established based on the matching degree, and the expected current value is substituted to obtain the offset amplitude prediction data; the permeability compensation value is calculated based on the offset amplitude prediction data, and the core configuration parameters are adjusted; based on the deviation between the noise floor signal amplitude and the reference value, the control quantity is output to adjust the signal amplification factor, update the frequency response parameters, and calibrate the noise floor fluctuation level; the amplitude of the electromagnetic interference signal is continuously collected, and the amplitude ratio of adjacent sampling points is calculated; when continuous If the ratio is less than a preset threshold, electromagnetic instability and cyclic decay are determined, and the decay duration is statistically analyzed. Based on the decay duration and the reduction ratio of the noise floor amplitude, a comprehensive score is obtained to acquire compatibility performance data. The load power range is divided according to the compatibility performance data, and the radiated disturbance intensity value of each range is recorded to form a corresponding relationship table. The radiated intensity value of the current load is extracted from the corresponding relationship table, and the noise floor signal amplitude sequence is continuously collected. The change slope is calculated to form fluctuation trend data. The monitoring benchmark weight is adjusted according to the fluctuation trend data, and weighted with the previous cycle benchmark value and the current noise floor mean value to obtain updated load radiated disturbance noise floor monitoring benchmark data.

2. The method for monitoring the background noise of radiated interference from charging pile loads according to claim 1, characterized in that, The process of acquiring real-time current data and magnetic flux density data inside the core of the charging pile during operation, determining an initial threshold for core saturation, and calculating the deviation between the magnetic flux density data and the preset saturation threshold to obtain saturation deviation data includes: acquiring instantaneous operating current data of the charging pile's main circuit through a current transformer, and simultaneously measuring magnetic flux density data on the cross-section of the core through a sensor; determining the saturation critical value of the magnetic flux density data based on the core material characteristic curve; setting the current value as the initial threshold for core saturation when the instantaneous operating current data reaches the current value corresponding to the saturation critical value; extracting the maximum value from the magnetic flux density data and calculating the difference between it and the saturation critical value to obtain the saturation deviation data.

3. The method for monitoring the background noise of radiated interference from charging pile loads according to claim 1, characterized in that, The step of analyzing the magnetic field distribution intensity of the magnetic core under the current load based on the saturation deviation data, comparing the quantified value of radiation suppression capability with the preset suppression threshold, and determining the radiation suppression reduction factor includes: constructing a three-dimensional mesh model of the magnetic core based on the saturation deviation data, converting the saturation deviation data into a permeability correction coefficient, assigning corrected permeability values ​​to the mesh nodes, iteratively solving the magnetic field distribution equation to obtain magnetic flux density distribution data and magnetic field intensity distribution map; extracting the surface normal magnetic flux component from the magnetic flux density distribution data, calculating the spatial radiation field intensity, comparing it with the reference radiation field intensity to obtain the radiation suppression coefficient; comparing the radiation suppression coefficient with the preset suppression threshold, calculating the difference ratio, and determining the radiation suppression reduction factor.

4. The method for monitoring the background noise of radiated interference from a charging pile load according to claim 1, characterized in that, The process of determining the trigger point of the electromagnetic instability cycle includes: adjusting the signal receiving sensitivity according to the radiation suppression attenuation factor, and collecting the noise floor signal voltage data when the switch is turned on; comparing the noise floor signal voltage data with a reference voltage value to obtain voltage deviation data; recording the duration when the voltage deviation data exceeds the limit; simultaneously measuring the frequency offset of the noise floor signal waveform data; and determining the current moment as the trigger point of the electromagnetic instability cycle if the voltage deviation data and the frequency offset simultaneously meet preset conditions and the duration reaches a threshold.

5. The method for monitoring the background noise of radiated interference from a charging pile load according to claim 1, characterized in that, The step of monitoring the data transmission flow between the charging pile and the electric vehicle and obtaining interference duration data based on the trigger point of the electromagnetic instability cycle includes: extracting carrier signal power and background noise power from the communication link based on the trigger point of the electromagnetic instability cycle, and calculating signal-to-noise ratio data; comparing the signal-to-noise ratio data with a preset interference threshold and recording the interference start time; continuously monitoring the signal-to-noise ratio data until it recovers to above the threshold and recording the recovery time; and calculating the difference between the recovery time and the start time to obtain interference duration data.

6. The method for monitoring the background noise of radiated interference from a charging pile load according to claim 1, characterized in that, The identification of the working point offset trajectory includes: setting a sampling time window based on the interference duration data, collecting instantaneous data of the charging pile output current, calculating the current change, and forming a current change data chain; restoring the actual current value based on the current change data chain, and calculating the working point coordinates; connecting the working point coordinates to form a movement path, and calculating the path segment length and offset angle; if the path segment length or offset angle exceeds a preset range, it is marked as a working point offset trajectory.

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