A multi-charging pile cooperative working pressure monitoring method

Abstract

The application provides a multi-charging pile cooperative working pressure monitoring method, comprising: obtaining real-time transient power data from a local node of a power grid, drawing a transient power curve according to the real-time collected transient power, and determining a trigger cause of power demand surge by analyzing the change trend of the transient power curve; determining the degree of electromagnetic interference aggravation according to a climbing rate parameter, analyzing the electromagnetic wave characteristics of the inverter operation according to the degree of electromagnetic interference aggravation, adjusting the frequency range of the inverter control signal according to the electromagnetic wave characteristics, and determining the optimal configuration of interference suppression; if the charging pile cluster operates in a preset load balancing and resource utilization efficiency interval, the power scheduling priority and the load response rate are dynamically updated in combination with real-time operating conditions and power grid available capacity monitoring data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a multi-charging pile cooperative work pressure monitoring method. BACKGROUND

[0002] In the modern energy system, the coordinated operation of electric vehicle charging facilities and the power grid has become a key area for promoting energy transformation and green transportation development. The importance of this field is self-evident, as it directly relates to the stable operation of the power grid and the reliability of user charging experience. However, current solutions for the interaction between charging facilities and the power grid still have significant shortcomings, particularly in terms of response capability when facing large-scale charging demand. There is a general lack of precise regulation of instantaneous power changes and effective mechanisms for equipment protection, leading to frequent problems in the power grid and equipment under high load scenarios. Specifically, the core challenges in this field are concentrated on the impact on the power grid when a cluster of charging facilities switches from low power to high power in a short period of time. First, due to the simultaneous start-up of multiple charging devices, local power grid nodes will experience a surge in power demand, leading to transient imbalance. This imbalance often causes voltage fluctuations, threatening the stability of the power grid. Unstable voltage further interferes with the control rhythm of charging devices, causing timing deviations in device operation, affecting charging efficiency and safety. More seriously, this timing deviation exacerbates the problem of electromagnetic interference within the device, not only reducing the reliability of device operation, but also potentially posing a threat to the surrounding system. These challenges are interconnected and collectively constitute the core problem in the coordinated operation of the power grid and charging facilities.

[0003] Therefore, how to ensure the stability of the power grid and the safety of the equipment in the scenario of high load access of the charging facility cluster, by monitoring the power grid power change and the device operation state in real time, dynamically adjusting the device start-up sequence, power ramp-up speed and interference suppression measures, has become a key problem that needs to be solved in this research. SUMMARY

[0004] The present application provides a multi-charging pile cooperative work pressure monitoring method, mainly including:

[0005] Obtain real-time transient power data from local nodes of the power grid, draw a transient power curve based on the real-time collected transient power, and determine the trigger cause of the surge in power demand by analyzing the trend of the transient power curve;

[0006] Obtain real-time phase angle and frequency offset data of the voltage instability state, analyze the voltage deviation during voltage instability in the time domain, compare the voltage deviation with the preset voltage range, obtain the deviation distribution, and determine the voltage instability degree based on the dynamic response characteristics of the power grid obtained from the trigger cause analysis;

[0007] If the instability degree exceeds the preset range, control timing data is extracted from the charging pile operation feedback, the timing dislocation risk in the control timing data is identified by pattern matching of the control signal and the feedback signal, the time difference of each charging pile start is analyzed, and the timing dislocation distribution is obtained;

[0008] According to the timing dislocation distribution, the communication delay and the data packet loss rate are identified, the delay correction value of each charging pile is obtained through the time delay compensation algorithm, the timing scheme is generated according to the delay correction value, and the timing scheme is optimized based on the timing dislocation distribution, the communication delay and the data packet loss rate, and the start sequence of the charging pile is determined;

[0009] According to the power distribution demand of each charging pile in the charging pile start sequence, the limit condition of the device overload protection is obtained from the device operation log, and the power climbing rate parameter is obtained by gradually increasing the power distribution mode in combination with the power distribution demand.

[0010] According to the climbing rate parameter, the electromagnetic interference aggravation degree is determined, the electromagnetic wave characteristics during the operation of the inverter are analyzed according to the electromagnetic interference aggravation degree, the frequency range of the inverter control signal is adjusted according to the electromagnetic wave characteristics, and the optimal configuration of interference suppression is determined.

[0011] If the charging pile cluster operates in the preset load balancing and resource utilization efficiency interval, the power scheduling priority and the load response rate are dynamically updated in combination with the real-time operation condition and the power grid available capacity monitoring data.

[0012] Further, the real-time transient power data is obtained from the local nodes of the power grid, the transient power curve is drawn according to the real-time collected transient power, and the trigger cause of the power demand surge is determined by analyzing the change trend of the transient power curve, including:

[0013] The real-time transient power data is obtained from the local nodes of the power grid, the transient power data is arranged as a time sequence, a power and time data set is constructed, the power difference value between adjacent time points in the power and time data set is calculated to generate a power change rate, when the power change rate exceeds a preset threshold, the corresponding time point is marked as a power mutation point, and a change curve with time as the horizontal axis and power value as the vertical axis is drawn; for the power mutation point, the power data sequence of a preset period before and after the power mutation point is extracted, the mean and standard deviation of the power data sequence are calculated, and the trend of the change curve is determined to be a load mutation type or a device fault type according to the mean difference and standard deviation comparison.

[0014] Further, the real-time phase angle and frequency offset data of the voltage instability state are obtained, and the voltage deviation during the voltage instability state is analyzed in the time domain, the voltage deviation is compared with the preset voltage range, the deviation distribution is obtained, and the voltage instability degree is judged in combination with the power grid dynamic response characteristics obtained by analyzing the trigger cause, including:

[0015] The phase angle and frequency offset data of the voltage instability state are acquired, the fundamental component is extracted from the signal of the voltage instability state through Fourier transform, and the offset data set is constructed according to the fundamental component; for the abnormal time in the offset data set, the voltage instantaneous value sequence of the corresponding period is extracted, the difference between the voltage effective value and the rated voltage is calculated to generate the voltage deviation, the voltage deviation is compared with the preset range, the interval proportion of the deviation distribution is counted, the phase angle and frequency change rate in the offset data set are analyzed in combination with the power demand change type, and the voltage instability degree is determined.

[0016] Further, the offset data set is constructed according to the fundamental component, including:

[0017] The instantaneous phase angle of the fundamental component is calculated, the instantaneous frequency value is calculated through the phase difference and time interval of adjacent sampling points, the time when the instantaneous frequency value deviates from the rated frequency is recorded, and the offset data set containing time, phase angle offset and frequency offset is generated.

[0018] Further, the time sequence misplacement risk in the control timing data is identified by pattern matching of the control signal and the feedback signal, the starting time difference of each charging pile is analyzed, and the time sequence misplacement distribution is obtained, including:

[0019] The control instruction sending time and the feedback time are extracted from the charging pile operation feedback, the control timing data set containing time pairs is generated, the response time sequence of the control signal and the feedback signal in the control timing data set is calculated, the response time sequence and the preset sequence are matched through the dynamic time warping algorithm, the time sequence misplacement risk is identified, the starting time difference of the charging pile existing the time sequence misplacement risk is counted, and the time sequence misplacement distribution is generated.

[0020] Further, the communication delay and the data packet loss rate are identified according to the time sequence misplacement distribution, and the delay correction value of each charging pile is obtained through the time delay compensation algorithm, including:

[0021] According to the time sequence misplacement distribution, the average length of the control instruction of the charging pile whose starting time difference exceeds the preset threshold is counted to generate the communication delay, and the proportion of the control instruction without feedback is calculated to generate the data packet loss rate; for the communication delay, the historical delay data sequence of the charging pile is extracted, the predicted delay value is calculated through the Kalman filtering algorithm, and the delay correction value of the charging pile is generated.

[0022] Further, the power allocation demand of each charging pile is determined according to the charging pile starting sequence, the limitation condition of the equipment overload protection is acquired from the equipment operation log, and the power climbing rate parameter is obtained through the power allocation mode of step-by-step increment in combination with the power allocation demand, including:

[0023] According to the startup sequence of the charging piles, the rated power value and target charging power value of each charging pile are extracted to generate power allocation requirements and arrange them to form a power requirement list; the transformer load power and line power limit are extracted from the equipment operation log to generate the overload protection power upper limit, the total power that can be allocated in the power requirement list is calculated, and the power ramp rate parameter is generated.

[0024] Furthermore, determining the degree of electromagnetic interference aggravation according to the ramp rate parameter, analyzing the electromagnetic wave characteristics of the inverter during operation according to the degree of electromagnetic interference aggravation, adjusting the frequency range of the inverter control signal according to the electromagnetic wave characteristics, and determining the optimal configuration for interference suppression include:

[0025] According to the power increment value and time interval in the power ramp rate parameter, the power change rate per unit time is calculated, the corresponding relationship between the change rate and the electromagnetic interference intensity is queried, and the electromagnetic interference level is determined; the voltage and current change rates are extracted from the inverter operation data, converted into frequency domain signals, the main interference frequency bands are identified, the frequency range and switching dead time of the inverter control signal are adjusted, and an optimized configuration for interference suppression is generated.

[0026] Furthermore, if the charging pile cluster operates within a preset load balancing and resource utilization efficiency range, the power scheduling priority and load response rate are dynamically updated in combination with real-time operating conditions and grid available capacity monitoring data, including:

[0027] The real-time charging power and remaining charging time of the charging pile are obtained, the power utilization rate is calculated, and an operating condition data set is generated; the transformer capacity and voltage deviation are obtained from the power grid monitoring point, the available capacity of the power grid is calculated, and a capacity margin value is generated; according to the operating condition data set, the power utilization rate and the remaining charging time are arranged to generate a power scheduling priority, and the capacity margin value is adjusted to generate a load response rate.

[0028] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0029] The present invention discloses a method for monitoring the collaborative working pressure of multiple charging piles. By real-time monitoring of the transient power data of local nodes and analyzing the trend of power curve changes, the cause of the demand surge is determined, and the degree of voltage instability is judged in combination with parameters such as voltage phase angle and frequency offset. For the charging facility cluster, real-time power data is collected to predict load fluctuations, identify the risk of control timing misalignment, and optimize the charging pile startup sequence. According to the power distribution requirements and equipment constraints, the power ramp rate parameters are determined, the electromagnetic interference characteristics are analyzed, and the inverter control is adjusted. Within the load balancing interval, the power scheduling priority and load response rate are dynamically updated. The present invention realizes the refined management of the charging facility cluster, effectively balances the grid load, improves system stability and resource utilization efficiency, and provides reliable protection for large-scale charging facilities to access the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Fig. 1 This is a flow chart of a method for monitoring pressure of multiple charging piles working in collaboration according to the present invention.

[0031] Fig. 2 Schematic diagram of a method for monitoring pressure of multiple charging piles working in collaboration according to the present invention. DETAILED DESCRIPTION

[0032] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0033] like Figs. 1-2 In this embodiment, a method for monitoring pressure of multiple charging piles working in collaboration may specifically include:

[0034] S101. Acquire real-time transient power data from local nodes of the power grid, draw a transient power curve based on the real-time collected transient power, and determine the triggering cause of the power demand surge by analyzing the changing trend of the transient power curve.

[0035] Real-time transient power data is acquired from local nodes in the power grid. This power data is arranged in a time series to construct a two-dimensional power and time dataset. The power change rate is calculated based on the power difference between adjacent time points. When the power change rate exceeds a preset change rate threshold, the time point is marked as a power mutation point, and a transient power curve is plotted with time as the horizontal axis and power as the vertical axis. For each marked power mutation point, the power data series for a preset number of sampling periods before and after the mutation point are extracted from the two-dimensional power and time dataset. The mean and standard deviation of the power series before the mutation, as well as the mean of the power series after the mutation, are calculated. If the difference between the post-mutation mean and the pre-mutation mean is positive and exceeds a preset multiple of the pre-mutation standard deviation, the transient power curve is determined to indicate a surge in power demand caused by a sudden load change. If the difference is negative or does not exceed a preset multiple, the curve morphological features at the power mutation point are extracted from the transient power curve, the slope change of the rising edge of the curve is calculated by the power values ​​of the three adjacent sampling points, and the oscillation amplitude is calculated by the difference between the maximum and minimum power values. When the slope change shows an increasing trend and the oscillation amplitude exceeds a preset proportional threshold of the power mean before the mutation, it is determined that the change trend of the transient power curve is a surge in power demand caused by equipment failure.

[0036] In one possible implementation, the process of obtaining real-time transient power data from local nodes of the power grid involves deploying high-precision power collection devices at key locations such as transformer busbars, power cabinet outlet terminals, or important load access points. These devices monitor electrical parameters in real time through current transformers and voltage transformers, with a sampling frequency usually set to more than 1000 times per second, thereby capturing millisecond-level power changes. After the obtained transient power data is arranged in chronological order, a continuous power-time two-dimensional data set is formed, where each data point contains a timestamp and a corresponding power value.

[0037] Specifically, the power change rate is calculated by dividing the power difference between adjacent time points by the time interval. When a large motor in a factory is started, the power may jump from 100 kW to 500 kW in 0.1 seconds, and the power change rate reaches 4000 kW / s. The system marks such time points with sharp changes as power mutation points and displays them with special markers on the transient power curve. The transient power curve has time as the horizontal axis and power value as the vertical axis, intuitively showing the trend of power change over time, facilitating subsequent analysis and judgment.

[0038] It should be noted that the analysis of the power mutation point requires comparison of the data sequences before and after the mutation. The system extracts data of 10 sampling periods before and after the mutation point from the constructed power-time two-dimensional data set, and calculates the average power and standard deviation of the data before the mutation. The standard deviation reflects the fluctuation range of the power under normal operating conditions. When the difference between the average power after the mutation and the average power before the mutation exceeds 3 times the standard deviation, it indicates that the change has far exceeded the normal fluctuation range. This judgment method is based on statistical principles and can effectively distinguish between normal fluctuations and abnormal mutations.

[0039] In one embodiment, when the judgment result shows that the power increases but does not reach the determination standard of load mutation, the system further analyzes the curve shape characteristics. By extracting the power values of three consecutive sampling points, the slope change of the curve is calculated. The slope usually remains relatively stable when the load increases normally, while the power change caused by equipment failure often shows an increasing slope. At the same time, the oscillation amplitude is obtained by calculating the difference between the maximum and minimum power values. Internal equipment faults such as winding short circuit or bearing damage can cause periodic fluctuations in power, with an oscillation amplitude of more than 20% of the normal power. This oscillation feature is significantly different from the monotonic power rise caused by load mutation, thereby achieving accurate identification of different triggering causes.

[0040] S102, obtain real-time phase angle and frequency offset data of the voltage instability state, analyze the voltage deviation in the voltage instability state through time domain analysis, compare the voltage deviation with the preset voltage range, obtain the deviation distribution, and judge the voltage instability degree in combination with the power grid dynamic response characteristics obtained from the triggering cause analysis.

[0041] The real-time phase angle and frequency offset data of the voltage instability state are acquired, the fundamental component is extracted from the collected voltage signal by using fast Fourier transform, the instantaneous phase angle of the fundamental component is calculated by using arctangent function, the instantaneous frequency value is obtained by dividing the phase difference of adjacent sampling points by the sampling time interval, when the frequency value deviates from the rated frequency by more than a preset threshold, the phase angle offset, the frequency offset and the corresponding time stamp at this moment are recorded, and a three-dimensional offset data set containing time, phase angle offset and frequency offset is constructed. For each abnormal moment recorded in the offset data set, the voltage instantaneous value sequence within one cycle before and after the corresponding moment is extracted, the voltage effective value is obtained by calculating the root mean square value, the voltage deviation is obtained by calculating the difference between the voltage effective value and the rated voltage, the voltage deviation is compared with the preset upper and lower limits of the voltage, the number of deviations falling within the normal range, the mild over-limit and the serious over-limit is counted, and the proportion of each interval is calculated to form the voltage deviation distribution characteristic. The sum of the proportions of mild over-limit and serious over-limit is extracted from the voltage deviation distribution characteristic as the over-limit proportion, when the over-limit proportion exceeds the preset threshold, the phase angle offset sequence and the frequency offset sequence in the corresponding period in the offset data set are analyzed, the phase angle change rate and the frequency change rate are calculated, and if both change rates show a continuous increasing trend, it is determined that the dynamic response characteristic of the power grid is related to the triggering reason, and the voltage instability degree is comprehensively judged in combination with the over-limit proportion value and the change rate growth amplitude.

[0042] In a possible implementation, the real-time monitoring of the voltage instability state is realized by deploying a synchronous phasor measurement device at a key node of the power grid. Fast Fourier transform, as a classic signal processing method, can convert the time-domain voltage signal to the frequency domain, and extract the fundamental component therefrom. The fundamental component represents the main component of 50Hz in the voltage signal, and the phase angle thereof calculated by using the arctangent function can accurately reflect the position of the voltage waveform on the time axis. When the power grid is disturbed, the phase angle will be offset, for example, the phase angle may be offset by more than 5 degrees at the moment of starting a large motor.

[0043] Specifically, the calculation of the instantaneous frequency is based on the phase difference principle of adjacent sampling points. Assuming that the sampling interval is 0.1 millisecond, the phase of the current sampling point is 30 degrees, and the phase of the next sampling point is 30.18 degrees, then the instantaneous frequency is 50.05 Hz, which deviates from the rated frequency by 0.05 Hz. This small frequency offset is within the allowable range during normal load fluctuation, but when multiple high-power devices are simultaneously put into or cut off, the frequency offset may reach 0.2 Hz or even higher, at which time the system will record the complete information of the abnormal moment, including the time stamp, the phase angle offset and the frequency offset, and form a three-dimensional offset data set.

[0044] It should be noted that the calculation of voltage effective value adopts the root mean square method, because the alternating voltage is a sine waveform, and the instantaneous value fluctuates between positive and negative. Through the process of squaring, averaging and taking the square root of all instantaneous values in a complete cycle, the effective value reflecting the actual power of the voltage is obtained. When the calculated effective value is 218V and the rated voltage is 220V, the voltage deviation is-2V. The preset upper limit of voltage is usually 106% of the rated value, that is, 233.2V, and the lower limit is 94%, that is, 206.8V. The deviation within ±6% is within the normal range, 6%-10% is a slight overrun, and more than 10% is a serious overrun.

[0045] In an embodiment, the forming process of the voltage deviation distribution feature involves statistical analysis of a large amount of sampling data. Among the 1000 sampling points, 850 are within the normal range, 120 are slightly overrun, and 30 are seriously overrun, corresponding to proportions of 85%, 12% and 3% respectively. The overrun proportion is the sum of the proportions of slight overrun and serious overrun, which is 15%. When this proportion exceeds the preset threshold of 10%, it indicates that the power grid is in an unstable state. Further analysis of the change rate of the phase angle and the frequency can reveal the correlation between the dynamic response characteristics of the power grid and the triggering causes.

[0046] For example, voltage instability caused by load mutation usually shows that the change rate increases sharply at the initial stage and then gradually tends to be stable, while instability caused by equipment failure shows a continuous increasing trend. By comprehensively considering the numerical value of the overrun proportion and the growth rate of the change rate, the degree of voltage instability can be accurately judged, thereby providing a reliable basis for subsequent control measures.

[0047] Real-time power data of the charging facility cluster is obtained, the power state of each facility is collected by a sensor, a power change curve is obtained, if the power change curve shows a rapid switching from low power to high power, a preset threshold is used to judge the switching speed, an instantaneous impact load is determined, according to the instantaneous impact load, in combination with historical power grid operation data, a support vector machine algorithm is used to predict the load fluctuation amplitude.

[0048] Real-time power data of the charging facility cluster is acquired, instantaneous active power values are collected by power sensors deployed at output ends of each charging pile, the power data stream is formed by arranging the power values in time sequence, a fixed time length sliding window processing is performed on the power data stream, power difference values and time intervals between adjacent sampling points in each window are calculated, and a power change curve with time as the horizontal axis and power value as the vertical axis is drawn. For the power change curve, a curve segment in which the power value continuously rises from below a preset low power threshold to above a preset high power threshold is identified as a conversion segment, a switching speed is calculated by dividing the power difference value of the start and end points of the conversion segment by the time experienced, and if the switching speed exceeds a preset speed threshold, a difference between the power value at the end point of the conversion segment and an average power value in a period of time before the start point of the conversion segment is determined as an instantaneous impact load. According to the instantaneous impact load value and the time when the instantaneous impact load value occurs, a historical load sequence and a corresponding load fluctuation record in the same time interval are extracted from a historical power grid operation database, a feature vector is combined from the instantaneous impact load value, the hour of the time when the instantaneous impact load value occurs, and the average load value in the historical same period, and the feature vector is input into a support vector machine regression model using a radial basis kernel function to obtain a predicted load fluctuation amplitude.

[0049] In a possible implementation, real-time power monitoring of the charging facility cluster relies on a high-precision power sensor network. These sensors usually use the Hall effect principle and can measure the current and voltage flowing through the charging pile in real time, and then calculate the instantaneous active power. When an electric vehicle accesses the charging pile to start charging, the power will jump from near 0 to tens of kilowatts in an instant, and this sharp change needs to be accurately captured through high-frequency sampling. The formation process of the power data stream is to arrange the power values at each sampling time in chronological order to form a continuous data sequence.

[0050] Specifically, sliding window processing is a commonly used time series data analysis method. Assuming that the window length is set to 5 seconds and the window slides every 1 second, the first window contains data from 0 to 5 seconds, the second window contains data from 1 to 6 seconds, and so on. In each window, the power difference value between adjacent sampling points is calculated, such as 3 kW at a certain time and 7 kW at the next sampling time, and the power difference value is 4 kW. The data processed in this way can more clearly show the dynamic change characteristics of the power, and the power change curve drawn can intuitively reflect the power fluctuation in the charging process.

[0051] It should be noted that the identification of the conversion section is a key link for judging the instantaneous impact load. The preset low power threshold is usually set to 1.5 times the standby power of the charging pile, for example, if the standby power is 0.1 kW, the low power threshold is 0.15 kW; the high power threshold is set according to 80% of the rated power of the charging pile, for example, if the rated power of the fast charging pile is 60 kW, the high power threshold is 48 kW. When the power rises from 0.1 kW to 50 kW in 2 seconds, the switching speed reaches 25 kW / s, which is much higher than the normal load growth speed. The average power in the time period before the start of the conversion section represents the stable operation state, and the difference between the conversion section end power is the instantaneous impact load, which reflects the sudden load increment that the power grid needs to bear.

[0052] In an embodiment, the application of the support vector machine regression model fully considers the nonlinear characteristics of the charging load. The radial basis kernel function can map the low-dimensional feature space to the high-dimensional space, making the originally linearly inseparable data separable. The construction of the feature vector comprehensively considers multiple dimensions: the instantaneous impact load value reflects the current impact intensity, the hour of occurrence reflects the characteristics of the peak electricity consumption period, such as 18:00-22:00 in the evening is the peak charging period, and the historical average load value at the same period provides a reference. These three characteristics are interrelated and jointly determine the possible amplitude of the load fluctuation. After training on a large amount of historical data, the model can learn the fluctuation rules under different time periods and impact intensities, thereby accurately predicting the future load fluctuation amplitude and providing an important reference for power grid dispatching.

[0053] S103, if the instability degree exceeds the preset range, control timing data is extracted from the operation feedback of the charging pile, the timing misalignment risk in the control timing data is identified by pattern matching the control signal and the feedback signal, the start time difference of each charging pile is analyzed, and the timing misalignment distribution is obtained.

[0054] If the instability degree exceeds the preset range, the control instruction sending time and the execution completion feedback time are extracted from the operation feedback data of each charging pile controller, the sending time of each control instruction is paired with its corresponding execution feedback time to form a control timing data set containing time point pair information. For each charging pile in the control timing data set, the response time between the control signal and the feedback signal is calculated, a plurality of consecutive response times are combined to form a time length sequence, and the actual time length sequence is matched with the preset normal response time length sequence by dynamic time warping algorithm. When the matching distance exceeds the preset threshold, it is identified that the charging pile has a timing misalignment risk. For the charging pile identified as having a timing misalignment risk, the time difference between the time when the start instruction is received and the actual start charging time is calculated as the start time difference. The start time differences of all charging piles with misalignment risk are classified and counted according to the preset time interval to obtain the charging pile quantity distribution in each time interval, and the timing misalignment distribution is formed.

[0055] In one possible implementation, the operation feedback data of the charging pile controller records the complete process of each control interaction. When the charging management system sends a start charging instruction to a certain charging pile, the controller records the sending time, such as 14:30:15.235; when the charging pile completes the execution and returns the confirmation signal, the receiving time is recorded as 14:30:15.487. This time record accurate to the millisecond level is crucial for identifying system operation abnormalities. The construction process of the control timing data set is to systematically organize these scattered time information, and each pair of control-feedback time represents a complete control cycle.

[0056] Specifically, the calculation of the response length reflects the real-time response performance of the charging pile. Normally, the time length from sending a control instruction to receiving an execution feedback should be kept within the range of 200-300 milliseconds. However, when the internal communication module of the charging pile fails or the network is congested, the response length may be extended to 500 milliseconds or even longer. The response length of 10 consecutive control operations forms a sequence, such as [252, 248, 255, 501, 498, 503, 251, 249, 502, 505], which clearly shows that there is an abnormal delay segment in the middle.

[0057] It should be noted that the dynamic time warping algorithm is a classic algorithm specifically used for time series similarity measurement. This algorithm can handle the non-linear shift problem of time series on the time axis. The pre-set normal response length sequence is usually derived from the statistical average during the normal operation of the system, such as [250, 250, 250, 250, 250, 250, 250, 250, 250, 250]. The algorithm calculates the matching distance by finding the optimal alignment path between the two sequences. When there are consecutive abnormal values in the actual sequence, the matching distance will significantly increase, and when it exceeds the pre-set threshold, it can be determined that there is a risk of time sequence misalignment.

[0058] In an embodiment, the analysis of the start time difference reveals the coordination problem of the charging facility cluster. Ideally, the charging pile should respond immediately after receiving the start instruction, and the start time difference should be controlled within 1 second. But in actual operation, due to hardware aging, software bugs or power grid capacity limitations, the start time difference of some charging piles may reach 3-5 seconds. By classifying in preset time intervals, such as 0-1 second, 1-2 seconds, 2-3 seconds, and more than 3 seconds, statistics show that 60% of the charging piles have a start time difference in the 0-1 second interval, 25% in the 1-2 second interval, 10% in the 2-3 second interval, and 5% more than 3 seconds. This timing misalignment distribution intuitively reflects the overall operation state of the charging facility cluster. A high proportion of short time differences indicates that the system is running well as a whole, while the presence of long time difference charging piles points to equipment that needs to be focused on. By regularly analyzing the trends of timing misalignment distribution, early signs of system performance degradation can be detected in a timely manner, providing data support for preventive maintenance and avoiding the impact of individual equipment failure on the service quality of the entire charging station.

[0059] S104, identify communication delay and data packet loss rate according to timing misalignment distribution, obtain delay correction value of each charging pile through time delay compensation algorithm, generate timing scheme according to delay correction value, and optimize timing scheme based on timing misalignment distribution, communication delay and data packet loss rate to determine the start order of charging piles.

[0060] According to the timing misalignment distribution, identify the time interval in which the charging pile with a start time difference exceeding the preset threshold value is located, count the average length of time from sending to receiving feedback in the interval as the communication delay, and calculate the proportion of the number of control instructions without receiving feedback signals to the total number of sending to obtain the data packet loss rate. For the communication delay, extract the latest preset number of historical delay data sequences of each charging pile, use Kalman filtering algorithm to recursively calculate the predicted delay value of the next moment with the historical delay sequence as the observation value and the current communication state as the state variable, and output the predicted delay value. The difference between the predicted delay value and the actual measured delay value is determined as the delay correction value of each charging pile. According to the delay correction value, calculate the maximum value of the delay correction value of all charging piles, add a fixed safety time allowance to the maximum value as the start time interval of adjacent charging piles, sort the charging piles according to the delay correction value from small to large to form a priority sequence, and combine the start time interval and the priority sequence to form a timing scheme. Based on the timing scheme, when the data packet loss rate exceeds the preset loss rate threshold, increase the start time interval of the corresponding charging pile and adjacent charging piles, adjust the charging pile with a communication delay exceeding the preset delay threshold to the end of the priority sequence, and determine the start order of the charging piles according to the adjusted start time interval and the priority sequence.

[0061] In one possible implementation, the analysis of the timing skew distribution reveals communication bottlenecks in the charging facility cluster. If 20 charging piles in a charging station need to start simultaneously during the evening rush hour, and the start time difference of 8 of them all falls within the 3-5 second interval, it indicates that these charging piles have obvious response delays. Through analysis, it is found that these 8 charging piles are connected to the same communication gateway, which has a queuing phenomenon when processing concurrent requests. Statistics show that the average time taken from the charging management center sending a control command to the charging pile returning an execution confirmation is 800 milliseconds, far exceeding the normal level of 200 milliseconds, which is a specific manifestation of communication delay.

[0062] Specifically, the calculation of the data packet loss rate reflects the reliability of network transmission. Assuming that within an hour, the system sends 1000 control commands to the charging piles, but only receives 950 feedback confirmations, then the data packet loss rate is 5%. This loss may be due to network congestion, wireless signal interference, or device failure. A high loss rate means that some control commands need to be retransmitted, further exacerbating the communication delay problem.

[0063] It should be noted that the application of the Kalman filter algorithm in this scenario is of great significance. This algorithm is a recursive optimal estimation method, especially suitable for handling dynamic systems with noise. The algorithm takes the historical delay data of each charging pile as an observation value, such as the last 10 delay values [210, 205, 450, 460, 215, 208, 455, 465, 220, 215] milliseconds. The algorithm predicts that the delay at the next time may be 230 milliseconds by establishing state equations and observation equations, taking into account the dynamic changes of network state and measurement errors. When the actual measurement value is 250 milliseconds, the 20 millisecond difference is the delay correction value, which is used to compensate for the prediction bias.

[0064] In one embodiment, the generation process of the timing scheme fully considers the stability requirements of the system. Through calculation, it is known that the maximum delay correction value of all charging piles is 50 milliseconds, plus a safety time margin of 100 milliseconds, the start time interval of adjacent charging piles is determined to be 150 milliseconds. This means that the first charging pile needs to wait at least 150 milliseconds before starting the second one. At the same time, charging piles with smaller delay correction values are placed in the front to start first, such as charging piles with a correction value of only 5 milliseconds have the highest priority, while charging piles with a correction value of 50 milliseconds are placed at the end. When the system detects that the data packet loss rate of some charging piles exceeds the threshold of 10%, the dynamic adjustment strategy comes into effect. These high loss rate charging piles are identified as unstable nodes, their start time interval is increased from 150 milliseconds to 300 milliseconds, and they are adjusted to the end of the priority sequence. This adjustment ensures that stable and reliable charging piles are started first, avoiding the impact of communication problems of individual devices on the overall efficiency of the charging station.

[0065] S105, according to the charging pile start-up sequence, determine the power distribution requirement of each charging pile, obtain the limit condition of device overload protection from the device operation log, and combine the power distribution requirement to obtain the power ramp rate parameter through the step-by-step incremental power distribution method.

[0066] According to the charging pile start-up sequence, the rated power value of each charging pile and the target charging power value carried in the charging request are extracted in the scheduled order, and the smaller value of the two is determined as the power distribution requirement of the charging pile. The power requirement list containing the charging pile identifier, power distribution requirement and start-up sequence is arranged in order. The maximum allowable load power of the transformer, the maximum carrying current value of the line and the protection switch action power threshold are read from the device operation log. The line power limit value is calculated according to the product of the line maximum carrying current value and the line rated voltage. The minimum value of the transformer maximum allowable load power, the line power limit value and the protection switch action power threshold is taken as the power upper limit of overload protection. According to the power requirement list and the power upper limit of overload protection, the initial allocatable total power is obtained by multiplying the power upper limit by a preset safety factor. The total number of charging piles that have not started is counted from the power requirement list. The initial allocatable total power is divided by the total number of charging piles and then divided by a preset incremental number to obtain the power increment value of each increment. The time interval of each power increment is set. The power increment value and the time interval are taken as the power ramp rate parameter.

[0067] In a possible implementation, the determination of the charging pile start-up sequence lays the foundation for subsequent power distribution. When the charging station receives multiple charging requests, the system will process them one by one according to the determined start-up sequence.

[0068] For example, the rated power of a certain 60kW fast-charging pile is 60kW, but the electric vehicle of the user only supports 40kW charging, so the target charging power value carried in the charging request is 40kW. After the system compares the two values, 40kW is taken as the power distribution requirement of the charging pile. This small value strategy not only protects the vehicle battery, but also avoids power waste. The power requirement list is arranged in the start-up sequence, such as the first charging pile requiring 40kW, the second requiring 30kW, and the third requiring 50kW, forming a clear power distribution sequence.

[0069] Specifically, the device operation log records various operating parameters and historical fault information of the power equipment. The maximum allowable load power of the transformer is usually marked on the equipment nameplate, such as 1000 kW; the maximum carrying current value of the line is determined according to the cross-section and material of the conductor, such as a copper core cable of 400 A; and the protection switch action power threshold is the setting value of the circuit breaker. The calculation of the line power limit value is based on the basic formula of electric power. When the line rated voltage is 380 V and the maximum carrying current is 400 A, the power limit value is about 263 kW. The system compares 1000 kW, 263 kW and the 800 kW threshold of the protection switch, and finally determines 263 kW as the upper limit of the overload protection, which ensures the safety of the weakest link in the entire power supply link.

[0070] It should be noted that the design of the power ramp-up rate parameter embodies the idea of gradual loading. The preset safety factor is usually 0.8, which means that the actual available power is 80% of the power limit, i.e. about 210 kW. Assuming that there are 10 charging piles waiting to start and the preset incremental number is 5 times, then the power increment is 210 ÷ 10 ÷ 5 = 4.2 kW each time. This gradual incremental method avoids the impact of instantaneous high power on the power grid.

[0071] In one embodiment, the actual execution process of power ramp-up presents a step-like feature. When the first charging pile starts, it is only allocated 20% of its required power, such as 40 kW required, then only 8 kW is given initially; after 30 seconds, it is increased by 4.2 kW to reach 12.2 kW; after another 30 seconds, it is increased to 16.4 kW, and so on until the target power is reached. At the same time, the second charging pile starts its ramp-up process 30 seconds after the first charging pile starts. This combination strategy of staggered peak starting and gradual loading not only meets the charging demand of users, but also ensures the stable operation of the power grid. By accurately controlling the two key parameters of power increment and time interval, the system realizes the smooth input of charging load, effectively avoiding the voltage sag and equipment tripping problems that may be caused by the traditional direct starting method.

[0072] S106, determining the degree of electromagnetic interference aggravation according to the ramp-up rate parameter, analyzing the electromagnetic wave characteristics of the inverter operation according to the degree of electromagnetic interference aggravation, adjusting the frequency range of the inverter control signal according to the electromagnetic wave characteristics, and determining the optimal configuration of interference suppression.

[0073] According to the power increment value and the time interval in the climbing rate parameter, the power change rate per unit time is calculated, a pre-stored power change rate and electromagnetic interference intensity corresponding relationship table is queried, and a corresponding electromagnetic interference intensity value is obtained. If the intensity value exceeds the pre-set low interference threshold but does not exceed the high interference threshold, it is determined that the electromagnetic interference aggravation degree is at a medium level, and if it exceeds the high interference threshold, it is determined that it is at a high level. For the electromagnetic interference aggravation degree level, when it is at a medium level or a high level, the voltage change rate and the current change rate generated by the switching action are extracted from the inverter operation monitoring data, the time domain signal is converted into a frequency domain signal through Fourier transform, the amplitudes and phases of each frequency component in the frequency domain signal are analyzed, the frequency points with amplitudes exceeding the pre-set threshold are identified, and the electromagnetic wave feature data set is composed of these frequency points and their corresponding amplitudes and phases. The first few frequency points with the largest amplitudes are extracted from the electromagnetic wave feature data set as the main interference frequency band, the difference between each interference frequency point and the current control signal center frequency of the inverter is calculated, if the difference is less than the pre-set frequency interval threshold, the control signal center frequency is shifted by a pre-set step length away from the interference frequency band, and the switching dead time of the pulse width modulation is adjusted to reduce the harmonic content, and the adjusted control signal frequency range and dead time are combined to determine the optimal configuration for interference suppression.

[0074] In a possible implementation, there is a close physical correlation between the power change rate and the electromagnetic interference. When the charging power rapidly climbs in a short time, the power switching devices inside the inverter need to frequently perform switching actions, and each switching instant will produce steep voltage and current changes.

[0075] For example, when the power increment is 5kW and the time interval is 30 seconds, the power change rate is 167W / s. The system pre-stores a corresponding relationship table based on a large amount of measured data, and the table shows that when the power change rate is in the range of 100-200W / s, the electromagnetic interference intensity value is usually at a medium level. The establishment of this corresponding relationship takes into account the switching characteristics and circuit topological structure of different types of inverters.

[0076] Specifically, the classification of the electromagnetic interference level has important practical significance. The low interference threshold is usually set at 50% of the limit value specified in the electromagnetic compatibility standard, and the high interference threshold is set at 80%. When the interference intensity is at a medium level, it means that the system still has a certain safety margin, but preventive measures need to be taken; a high level indicates that the interference has reached the standard limit value and must be immediately suppressed. This grading management method enables the system to take suppression measures of corresponding intensity according to the actual interference degree, avoiding excessive intervention affecting the charging efficiency.

[0077] It's important to note that Fourier transforms play a key role in analyzing electromagnetic wave characteristics. The switching action of the inverter generates voltage and current waveforms at the output containing multiple frequency components. By collecting this time-domain waveform data, the Fourier transform decomposes it into sinusoidal components of varying frequencies.

[0078] For example, when the inverter switching frequency is 20kHz, in addition to the fundamental frequency, harmonic components are also generated at frequencies multipled to 40kHz and 60kHz. The greater the voltage change rate, the higher the amplitude of the higher harmonics. The threshold values ​​are typically set based on the limit requirements in electromagnetic compatibility standards, and frequency components exceeding the threshold are identified as potential interference sources.

[0079] In one embodiment, the adjustment of the control signal frequency embodies the principle of frequency avoidance. Assume that the electromagnetic wave signature dataset indicates strong interference at 19.5kHz, 20kHz, and 20.5kHz, while the inverter's current control signal center frequency is exactly 20kHz. System calculations reveal that the difference between these interference frequencies and the control frequency is less than the 1kHz frequency interval threshold, indicating a risk of frequency overlap. In this case, the control signal center frequency is shifted toward higher frequencies by 2kHz to 22kHz, away from the interference frequency band. Simultaneously, the pulse width modulation dead time is increased from 1 microsecond to 1.5 microseconds. This small time interval effectively reduces voltage spikes during switching and lowers high-frequency harmonic content. The optimized configuration combines the new frequency range of 21-23kHz with a dead time of 1.5 microseconds to form a complete interference suppression solution, ensuring the normal operation of the inverter while significantly reducing electromagnetic interference levels.

[0080] S107. If the charging pile cluster operates within a preset load balancing and resource utilization efficiency range, the power scheduling priority and load response rate are dynamically updated based on the real-time operating conditions and grid available capacity monitoring data.

[0081] If the ratio of the current total power of the charging pile cluster to the rated total power is in the preset load balancing interval, and the ratio of the allocated power to the allocatable power reaches the preset efficiency threshold, the real-time charging power and the remaining charging duration of each charging pile are obtained, the ratio of the real-time charging power of each charging pile to its rated power is calculated as the power utilization rate, and the charging pile identifier, the power utilization rate, and the remaining charging duration are combined to form an operating condition data set. The current total load is obtained by summing the real-time charging power of each charging pile in the operating condition data set, the transformer rated capacity, the current used capacity, and the bus voltage deviation value are obtained from the power grid monitoring point, the transformer rated capacity is subtracted from the current used capacity, and then the capacity loss corresponding to the voltage deviation is subtracted to obtain the available capacity of the power grid, and the difference between the available capacity and the current total load is calculated as the capacity margin value. Based on the capacity margin value and the operating condition data set, the charging piles with a power utilization rate lower than a preset utilization rate threshold are arranged in ascending order of utilization rate, and the charging piles with a power utilization rate higher than the threshold are arranged in ascending order of remaining charging duration, and the two sequences are combined to form an updated power scheduling priority, and the ratio of the capacity margin value to a preset capacity threshold is calculated as an adjustment coefficient multiplied by the current load response rate to obtain an updated load response rate.

[0082] In a possible implementation, the setting of the load balancing interval reflects the balance between the economy and safety of the operation of the charging station. When the charging station is equipped with 10 fast charging piles of 60kW, the total rated power is 600kW, and the current 6 charging piles are working, the total power reaches 360kW, and the load rate is 60%. The preset load balancing interval is usually 40%-80%, which not only ensures the full utilization of the equipment, but also reserves the emergency capacity. The resource utilization efficiency evaluates the system performance from another dimension. If the allocatable power is 500kW and the allocated power is 400kW, the efficiency reaches 80%, indicating that the system is in good operating condition.

[0083] Specifically, the calculation of the power utilization rate provides a basis for the fine management of each charging pile. The rated power of a certain charging pile is 60kW, but due to the reasons such as the vehicle battery approaching full charge or temperature protection, the real-time charging power is only 30kW, and the power utilization rate is 50%. In this case, the charging pile has a large power improvement space. The remaining charging duration is predicted by the battery state and the charging curve fed back by the vehicle, such as a vehicle that still needs to be charged for 45 minutes. The operating condition data set integrates these information to form the data basis of dynamic scheduling.

[0084] It should be noted that the calculation of the available capacity of the power grid involves the comprehensive consideration of multiple limiting factors. The rated capacity of the transformer represents the maximum capacity of the power supply device. For example, a 1000 kW transformer has theoretically 300 kW of remaining capacity if it is currently supplying 700 kW to other loads. However, the bus voltage deviation further limits the actual available capacity. When the voltage is 3% lower, the actual available capacity decreases by about 6% (18 kW) according to the relationship between power and voltage squared. Therefore, the available capacity of the power grid is 300-18 = 282 kW. The difference between this value and the current total charging load of 360 kW is -78 kW, indicating that the system is slightly overloaded.

[0085] In one embodiment, the dynamic updating strategy of power scheduling priority fully considers the urgency of different charging demands. Charging piles with power utilization rate below 70% are considered to have adjustment space. For example, if the utilization rates of three charging piles are 50%, 55%, and 65% respectively, they are arranged in this order. Charging piles with utilization rates higher than 70% are usually in the fast charging stage, and at this time, the remaining charging time becomes a key factor. Vehicles with 10 minutes of remaining time are more in need of guaranteeing charging continuity than vehicles with 40 minutes of remaining time, and therefore have higher priority. The adjustment of the load response rate reflects the adaptive ability of the system. When the capacity margin value is positive and large, such as a margin of 100 kW and a threshold of 50 kW, the ratio is 2, and the system can speed up the response speed; when the margin is close to zero or negative, the ratio is less than 1, and the system needs to slow down the response to avoid overload. The original response rate may be 2 charging requests per minute, multiplied by the adjustment coefficient 0.5 to become 1 per minute. This dynamic adjustment ensures the stable operation of the power grid while maximizing the satisfaction of user charging demands.

[0086] The above embodiments are only used to illustrate the technical solutions of the present application and are not limited. The present application has been described in detail with reference to the preferred embodiments. Those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for monitoring the coordinated working pressure of multiple charging piles, characterized in that: The method includes: acquiring real-time transient power data from local nodes of the power grid, drawing a transient power curve based on the real-time collected transient power, and determining the triggering cause of the power demand surge by analyzing the changing trend of the transient power curve; acquiring real-time phase angle and frequency offset data of the voltage instability state, and analyzing the voltage deviation in the voltage instability state through time domain, comparing the voltage deviation with a preset voltage range to obtain a deviation distribution, and judging the degree of voltage instability in combination with the dynamic response characteristics of the power grid obtained by the triggering cause analysis; if the degree of instability exceeds the preset range, extracting control timing data from the charging pile operation feedback, identifying the timing misalignment risk in the control timing data by pattern matching the control signal and the feedback signal, and analyzing the startup time difference of each charging pile to obtain the timing misalignment distribution; identifying the communication delay and data packet loss rate based on the timing misalignment distribution, and using the delay compensation algorithm Obtain the delay correction value of each charging pile, generate a timing plan based on the delay correction value, and optimize the timing plan based on the timing misalignment distribution, communication delay and data packet loss rate to determine the charging pile startup sequence; determine the power allocation requirement of each charging pile according to the charging pile startup sequence, obtain the equipment overload protection restriction conditions from the equipment operation log, and obtain the power ramp rate parameter through a gradually increasing power allocation method in combination with the power allocation requirement; determine the degree of electromagnetic interference aggravation according to the ramp rate parameter, analyze the electromagnetic wave characteristics of the inverter during operation according to the degree of electromagnetic interference aggravation, adjust the frequency range of the inverter control signal according to the electromagnetic wave characteristics, and determine the optimal configuration for interference suppression; if the charging pile cluster operates in the preset load balancing and resource utilization efficiency range, dynamically update the power scheduling priority and load response rate in combination with the real-time operating conditions and grid available capacity monitoring data.

2. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The method of obtaining real-time transient power data from a local node of the power grid, drawing a transient power curve based on the real-time collected transient power, and determining the triggering cause of the power demand surge by analyzing the change trend of the transient power curve includes: obtaining real-time transient power data from a local node of the power grid, arranging the transient power data into a time series, constructing a power and time data set, calculating the power difference between adjacent time points in the power and time data set to generate a power change rate, when the power change rate exceeds a preset threshold, marking the corresponding time point as a power mutation point, and drawing a change curve with time as the horizontal axis and power value as the vertical axis; for the power mutation point, extracting the power data sequence of a preset period before and after the power mutation point, calculating the mean and standard deviation of the power data sequence, and determining whether the trend of the change curve is a load mutation type or an equipment failure type based on the comparison of the mean difference and the standard deviation.

3. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The method obtains real-time phase angle and frequency offset data of the voltage instability state, analyzes the voltage deviation during the voltage instability state through time domain, compares the voltage deviation with a preset voltage range to obtain a deviation distribution, and judges the degree of voltage instability in combination with the dynamic response characteristics of the power grid obtained by the trigger cause analysis, including: obtaining the phase angle and frequency offset data of the voltage instability state, extracting the fundamental component from the signal of the voltage instability state through Fourier transform, and constructing an offset data set based on the fundamental component; for abnormal moments in the offset data set, extracting a sequence of instantaneous voltage values ​​of a corresponding period, calculating the difference between the effective voltage value and the rated voltage to generate a voltage deviation, comparing the voltage deviation with a preset range, statistically analyzing the interval proportion of the deviation distribution, analyzing the phase angle and frequency change rate in the offset data set in combination with the power demand change type, and determining the degree of voltage instability.

4. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 3, characterized in that: The method of constructing an offset data set based on the fundamental wave component includes: calculating the instantaneous phase angle of the fundamental wave component, calculating the instantaneous frequency value through the phase difference and time interval of adjacent sampling points, recording the moment when the instantaneous frequency value deviates from the rated frequency, and generating an offset data set including time, phase angle offset, and frequency offset.

5. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The method identifies the timing misalignment risk in the control timing data by performing pattern matching on the control signal and the feedback signal, calculates and analyzes the startup time difference of each charging pile, and obtains the timing misalignment distribution, including: extracting the control instruction sending time and the execution feedback time from the charging pile operation feedback, generating a control timing data set containing time pairs, calculating the response duration sequence of the control signal and the feedback signal in the control timing data set, matching the response duration sequence with a preset sequence through a dynamic time warping algorithm, identifying the timing misalignment risk, counting the startup time differences of the charging piles with the timing misalignment risk, and generating the timing misalignment distribution.

6. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The method of identifying communication delays and packet loss rates based on the timing misalignment distribution and obtaining a delay correction value for each charging pile through a delay compensation algorithm includes: based on the timing misalignment distribution, calculating the average duration of control instructions for charging piles whose startup time differences exceed a preset threshold to generate communication delays, and calculating the proportion of control instructions for which no feedback has been received to generate a packet loss rate; and extracting a historical delay data sequence of the charging pile for the communication delay, calculating a predicted delay value through a Kalman filter algorithm, and generating a delay correction value for the charging pile.

7. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The power allocation requirement of each charging pile is determined according to the charging pile startup sequence, the restriction conditions of the equipment overload protection are obtained from the equipment operation log, and the power climbing rate parameter is obtained by combining the power allocation requirement with a gradually increasing power allocation method, including: according to the charging pile startup sequence, the rated power value and the target charging power value of each charging pile are extracted, the power allocation requirement is generated, and the power demand list is arranged; the transformer load power and the line power limit are extracted from the equipment operation log to generate the overload protection power upper limit, the total power that can be allocated in the power demand list is calculated, and the power climbing rate parameter is generated.

8. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: The method of determining the degree of electromagnetic interference aggravation based on the ramp rate parameter, analyzing the electromagnetic wave characteristics of the inverter during operation based on the degree of electromagnetic interference aggravation, adjusting the frequency range of the inverter control signal based on the electromagnetic wave characteristics, and determining the optimal configuration for interference suppression includes: calculating the power change rate per unit time based on the power increment value and time interval in the power ramp rate parameter, querying the corresponding relationship between the change rate and the electromagnetic interference intensity, and determining the electromagnetic interference level; extracting the voltage and current change rates from the inverter operation data, converting them into frequency domain signals, identifying the main interference frequency bands, adjusting the frequency range and switching dead time of the inverter control signal, and generating the optimal configuration for interference suppression.

9. The method for monitoring the coordinated working pressure of multiple charging piles according to claim 1, characterized in that: If the charging pile cluster operates in a preset load balancing and resource utilization efficiency range, the power scheduling priority and load response rate are dynamically updated in combination with the real-time operating conditions and the grid available capacity monitoring data, including: obtaining the real-time charging power and remaining charging time of the charging pile, calculating the power utilization rate, and generating an operating condition data set; obtaining the transformer capacity and voltage deviation from the grid monitoring point, calculating the grid available capacity, and generating a capacity margin value; arranging the power utilization rate and the remaining charging time according to the operating condition data set, generating a power scheduling priority, calculating the ratio of the capacity margin value to the preset capacity threshold, and multiplying the ratio of the capacity margin value to the preset capacity threshold as an adjustment coefficient by the current load response rate to obtain an updated load response rate.

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