Storage battery capacity checking method of parallel intelligent direct-current power supply system

Abstract

The invention discloses a storage battery capacity checking method for a parallel intelligent direct-current power supply system, and relates to the technical field of storage battery capacity checking, which comprises the following steps of: respectively deploying electromagnetic sensing modules with nanosecond-level response capability at a master control end and a slave node end of a communication bus, acquiring voltage disturbance waveform signals of the communication bus in an operation process, and performing feature extraction operation on the transient high-frequency interference pulse detected each time, and constructing a multi-dimensional feature sequence of the high-energy transient electromagnetic interference. By deploying the high-response electromagnetic sensing module and combining an active suppression mechanism of interference feature extraction and risk index driving, time alignment modeling and self-adaptive regulation and control of electromagnetic interference and communication performance are realized, the communication stability, the battery state recognition accuracy and the capacity accounting continuity of the system in a strong interference environment are effectively improved, and the system reliability is improved. And the anti-interference capability and the operation safety of the parallel direct-current power supply system are obviously enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery capacity control, and in particular to a battery capacity control method for a parallel intelligent direct current power supply system. Background Art

[0002] "Battery capacity verification for a parallel intelligent DC power supply system" refers to the process of dynamically verifying and accurately evaluating the actual capacity of each battery group in an intelligent DC power supply system composed of multiple battery groups connected in parallel, using key parameters such as voltage, current, state of charge (SOC), and temperature during system operation, through a specific algorithm model. Since different battery groups in a parallel system may have different initial capacities, inconsistent aging rates, or fluctuating operating conditions, traditional methods find it difficult to accurately determine the true health status of each battery group. This method intelligently collects the working data of each channel during the parallel operation of the system, combines the battery equivalent model with the capacity attenuation law, and extracts characteristic indicators that reflect the battery's discharge capacity and available capacity, thereby realizing the calculation of the current effective capacity of a single or entire battery group, thereby providing an accurate basis for subsequent maintenance strategy optimization, balanced scheduling control, and capacity compensation, ensuring the reliability of system operation and the stability of power supply.

[0003] The existing technology has the following deficiencies: In a parallel intelligent DC power supply system, in order to achieve dynamic accounting of the capacity of multiple battery groups, the system needs to rely on the establishment of a stable real-time communication link between the main control unit and multiple battery management modules (BMS) or acquisition nodes to synchronously obtain the operating parameters such as voltage, current, temperature and state of charge of each battery group. However, in actual application environments, high-energy transient electromagnetic interference (such as lightning surges, power system switching, motor startup and electrostatic discharge) is often present. These interferences may propagate in the form of occasional pulses on the communication bus (such as CAN, LIN or RS485), thereby interfering with the communication process. Affected by this, the communication link may experience abnormal conditions such as message transmission delays, data frame loss or field misalignment, resulting in incomplete, incoherent or even incorrect parsing of the data obtained by the main control unit. Especially in the capacity calculation process, the continuity and accuracy of data synchronization are crucial. If communication anomalies are not identified and corrected in a timely manner, it is very easy to cause problems such as capacity assessment deviation, battery performance misjudgment, and power distribution imbalance. In severe cases, it will cause the system to mistakenly trigger the protection mechanism, mistakenly isolate healthy battery packs, or execute incorrect charging and discharging scheduling strategies, reducing the operating stability and safety of the power system.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a battery capacity verification method for a parallel intelligent DC power supply system. By deploying a high-response electromagnetic sensing module and combining interference feature extraction with an active suppression mechanism driven by a risk index, time-aligned modeling and adaptive regulation of electromagnetic interference and communication performance are achieved, effectively improving the system's communication stability, battery status recognition accuracy, and capacity calculation continuity in a strong interference environment, significantly enhancing the anti-interference capability and operational safety of the parallel DC power supply system, thereby solving the problems in the above-mentioned background technology.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solution: a battery capacity verification method for a parallel intelligent DC power supply system, comprising the following steps:

[0007] Electromagnetic sensing modules with nanosecond response capabilities are deployed at the master and slave nodes of the communication bus to collect voltage disturbance waveform signals during operation. Feature extraction is performed on each detected transient high-frequency interference pulse to construct a multi-dimensional feature sequence of high-energy transient electromagnetic interference.

[0008] Based on the constructed multi-dimensional feature sequence of high-energy transient electromagnetic interference, the key communication performance parameters of the communication bus are synchronously sampled within the corresponding time window according to a fixed-length time sliding window mechanism. A time-aligned mapping relationship between interference features and communication performance indicators is established to generate an original matching dataset of interference-performance impact.

[0009] A weighted model is constructed for each set of interference features and corresponding communication anomaly indicators in the original matching dataset. Multivariate risk weights are calculated using the interference intensity normalization factor and the communication parameter sensitivity coefficient. A time-varying response matrix is ​​constructed to reflect the relationship between interference morphology and communication performance changes.

[0010] The obtained time-varying response matrix is ​​input into the principal component analysis module to extract the principal component data reflecting the changing trend of the main interference characteristics. The extreme point of the interference peak amplitude and the time derivative of the communication performance attenuation are combined as input variables and introduced into the risk index generation model. A set of high-energy transient electromagnetic interference risk indices between 0 and 1 are generated using nonlinear function mapping.

[0011] When the generated high-energy transient electromagnetic interference risk index exceeds the preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with inverted polarity on the communication bus, and the high-frequency interference energy is offset at the physical layer by modulating the interference intervention phase.

[0012] Preferably, the steps of deploying electromagnetic sensing modules at the master end and the slave node end of the communication bus and collecting voltage disturbance waveform signals are as follows:

[0013] A high-bandwidth front-end signal conditioning circuit is introduced into the electromagnetic sensing module to perform multi-stage signal amplification and high-pass filtering on the collected communication bus disturbance signals.

[0014] Based on the conditioned disturbance waveform signal, the time domain difference algorithm and the first-order differential operator are used to analyze the signal change process, calculate the slope of the rising edge of the disturbance signal in real time, and extract the maximum amplitude and its corresponding duration in the local voltage waveform, thereby generating a transient pulse feature vector containing three dimensions: rising edge steepness, amplitude peak, and pulse duration.

[0015] During the continuous sampling period, each detected transient pulse feature vector is structured and stored in chronological order to form a multi-dimensional interference feature sequence that records the disturbance intensity, time interval and feature evolution trend.

[0016] Preferably, the steps of establishing a mapping relationship between interference and communication performance based on the constructed high-energy transient electromagnetic interference multi-dimensional feature sequence are as follows:

[0017] A fixed-length time sliding window mechanism is set to clearly define the time period and data sampling frequency covered by each time window. The generated multi-dimensional transient pulse feature vector is synchronously selected within each window to ensure that each interference pulse event is fully captured in at least one time window and that there is no data truncation or omission.

[0018] The communication performance collection task is started in each time window, and multiple communication stability parameters including the number of data frame retransmissions, communication round-trip delay, number of cyclic redundancy check failures, and data frame effective reception rate are recorded. Each performance parameter is then bound to the corresponding interference feature vector in the current time window one by one to form a synchronous sampling data structure with time consistency.

[0019] The binding structures of interference features and communication performance parameters formed in multiple continuous time windows are sorted and archived in chronological order to construct an original matching data set consisting of interference-performance mapping record units.

[0020] Preferably, the steps of performing weighted modeling on each set of interference features and corresponding communication anomaly indicators in the original matching data set and constructing a time-varying response matrix reflecting the relationship between interference form and communication performance change are as follows:

[0021] Based on the preset interference intensity normalization factor, each set of interference characteristic data is standardized, and the three indicators of interference rise steepness, peak amplitude and duration are converted into a dimensionless unified numerical vector to eliminate the differences in dimensional scales of different interference events;

[0022] A communication parameter sensitivity coefficient is assigned to each communication anomaly indicator, quantified based on its response to interference changes in historical samples. The normalized interference signature is then multiplied and added with the sensitivity coefficient matrix using a weighted linear combination to obtain a multivariate risk weight for the impact of a single interference event on each communication performance parameter within a time window.

[0023] The multivariate risk weights calculated in all time windows are embedded in the corresponding data positions according to their time sequence, and a time-varying response matrix consisting of time labels, normalized interference features, and communication anomaly indicator weights is constructed.

[0024] Preferably, the specific steps of inputting the obtained time-varying response matrix into the principal component analysis module and generating the high-energy transient electromagnetic interference risk index by using nonlinear function mapping are as follows:

[0025] The constructed time-varying response matrix is ​​input into the principal component analysis module, and covariance analysis and feature dimensionality reduction processing are performed on the multi-dimensional interference characteristics and communication performance indicators contained in the matrix to extract the principal component data set representing the interference change trend;

[0026] The extreme point index corresponding to the interference peak amplitude is located from the extracted principal component data, and the time derivative characteristics of the communication performance index are extracted under the time index to characterize the attenuation rate of communication performance with interference changes. The principal component sequence, extreme point index and communication performance derivative characteristics are combined into a multi-dimensional input vector with a unified structure.

[0027] The multi-dimensional input vector is input into the risk index generation model, and a continuous mapping operation is performed based on the set nonlinear function mapping mechanism to compress the multi-variable disturbance input into a continuous output value with an interval value between zero and one, thereby forming a quantifiable high-energy transient electromagnetic interference risk index that can be dynamically updated over time.

[0028] Preferably, the constructed time-varying response matrix is ​​input into the principal component analysis module, and covariance analysis and feature dimensionality reduction processing are performed on the multidimensional interference characteristics and communication performance indicators contained in the matrix to extract the principal component data set representing the interference change trend. The specific steps are as follows:

[0029] Performing data preprocessing operations on all interference characteristic parameters and communication performance indicators in the time-varying response matrix;

[0030] Based on the standardized data, the covariance matrix is ​​constructed to calculate the degree of coordinated changes between all feature dimensions, identify the direction of change with strong linear correlation in the data, and perform eigendecomposition on the covariance matrix in order of eigenvalue size to extract the principal component vector set of the disturbance trend;

[0031] The minimum number of principal components to be retained is determined according to the retention threshold set according to the cumulative contribution rate. The multidimensional feature data in the original time-varying response matrix is ​​projected into the selected principal component vector space to complete the mapping transformation from high-dimensional features to low-dimensional features and generate a principal component data set.

[0032] Preferably, when the generated high-energy transient electromagnetic interference risk index exceeds a preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with an inverted polarity on the communication bus, and the specific steps of offsetting the high-frequency interference energy at the physical layer by modulating the interference intervention phase are as follows:

[0033] Based on the generated high-energy transient electromagnetic interference risk index and the set high-energy transient electromagnetic interference risk index reference threshold, a suppression driving factor for controlling the jitter signal strength is constructed. The calculation expression is as follows:

[0034]

[0035] , where R is the high-energy transient electromagnetic interference risk index, which is used to reflect the transient electromagnetic disturbance intensity faced by the communication link at this moment, with a value range of 0-1, R ref is the reference threshold of high-energy transient electromagnetic interference risk index, ξ(t) is the suppression driving factor;

[0036] After the suppression driving factor ξ(t) is calculated, a set of pseudo-random modulation parameter vectors J(t) is dynamically generated based on the suppression driving factor, where J(t) = [A(t), f(t), φ(t)], where:

[0037]

[0038] , where A(t) is the real-time amplitude of the dither signal, i.e., the signal injection amplitude dynamically generated according to the suppression driving factor ξ(t), and A max is the maximum injection amplitude, α is the compression factor of the amplitude adjustment, tanh is the hyperbolic tangent function, f(t) is the instantaneous frequency of the dither signal, f0 is the reference frequency of the dither signal, β is the amplitude factor of the frequency perturbation, φ(t) is the initial phase of the signal, and π is the circumference of the circle.

[0039] According to the generated A(t), f(t), φ(t), the actual injection signal is constructed as follows:

[0040] s(t)=A(t)·sin(2πf(t)t+φ(t))·r(t)

[0041] , where s(t) is the final injected suppression signal, r(t) is the pseudo-random perturbation factor, 2π is the periodic conversion constant, and t is the time variable.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0043] This invention implements an adaptive intelligent regulation path from interference identification to risk response by deploying highly responsive electromagnetic sensing modules at both ends of the communication link, combining feature extraction, performance mapping, risk index generation, and dynamic physical layer control. Its greatest benefit lies in: for the first time, electromagnetic interference characteristics and communication performance indicators are time-aligned and modeled, and active signal injection driven by risk indexes is used to achieve real-time suppression and control of sudden interference. This significantly improves the system's communication reliability, battery status identification accuracy, and capacity calculation continuity in strong interference environments, thereby enhancing the anti-interference robustness and operational safety of the entire parallel power supply system. It has broad engineering promotion value and industrial application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 The present invention is a flow chart of a method for battery capacity verification in a parallel intelligent DC power supply system. DETAILED DESCRIPTION

[0046] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0047] The present invention provides Figure 1 The battery capacity verification method of the parallel intelligent DC power supply system shown includes the following steps:

[0048] Electromagnetic sensing modules with nanosecond response capabilities are deployed at the master and slave nodes of the communication bus to collect voltage disturbance waveform signals during operation. Feature extraction is performed on each detected transient high-frequency interference pulse to construct a multi-dimensional feature sequence of high-energy transient electromagnetic interference.

[0049] The steps to deploy electromagnetic sensing modules on the master and slave nodes of the communication bus and collect voltage disturbance waveform signals are as follows:

[0050] A high-bandwidth front-end signal conditioning circuit is introduced into the electromagnetic sensing module. The collected communication bus disturbance signal is subjected to multi-stage signal amplification and high-pass filtering in sequence to effectively eliminate low-frequency background noise and enhance the signal-to-noise ratio of nanosecond high-frequency pulse signals, thereby improving the response speed and recognition accuracy of subsequent signal feature extraction.

[0051] Based on the conditioned disturbance waveform signal, the time domain difference algorithm and the first-order differential operator are used to analyze the signal change process, calculate the slope of the rising edge of the disturbance signal in real time, and extract the maximum amplitude and its corresponding duration in the local voltage waveform, thereby generating a transient pulse feature vector containing three dimensions: rising edge steepness, amplitude peak, and pulse duration.

[0052] During the continuous sampling period, each detected transient pulse feature vector is structured and stored in chronological order to form a multi-dimensional interference feature sequence that records the disturbance intensity, time interval and feature evolution trend, which serves as the data input basis for subsequent risk modeling and communication link status response analysis.

[0053] Introducing a high-bandwidth front-end signal conditioning circuit in the electromagnetic sensing module involves placing dedicated hardware circuitry for preliminary signal processing at the very front end of the interference detection chain. This circuit features a high frequency response (typically in the hundreds of megahertz range or higher), enabling real-time reception and processing of transient high-frequency voltage disturbances on the communication bus. Its implementation typically involves two steps: first, a multi-stage low-noise amplifier (such as a high-speed operational amplifier) ​​amplifies the weak disturbance signal to a recognizable amplitude; second, a series high-pass filter (such as an RC or active filter structure) filters out background noise below a set threshold, such as power supply ripple and thermal noise, while retaining high-frequency interference components with extremely steep nanosecond rise times. This signal conditioning significantly improves the sensing module's detection sensitivity and response speed to high-frequency transient interference signals, ensuring that subsequent feature extraction algorithms can accurately identify key interference pulse parameters, such as rising edge slope, peak amplitude, and duration, even in high-noise environments. This provides high-quality raw data for interference risk modeling and communication link protection.

[0054] The time-domain difference (TDD) algorithm is a digital processing method based on the difference between consecutive sampling points, used to analyze the temporal trend of a signal. It reflects the signal's growth or decay over time by calculating the voltage difference between adjacent sampling points. It is a common method for simulating continuous derivatives in discrete signal processing. The first-order differential operator, on the other hand, is a signal analysis tool primarily used to detect the rate of change of a signal. It can be used to identify the signal's slope, edge characteristics, and breakpoints. The combined use of these two methods enables real-time monitoring of the dynamic changes in voltage disturbance signals collected on a communication bus. In particular, they can keenly capture the rising edge position and intensity of high-frequency pulse signals, helping to extract the temporal and energy characteristics of pulse interference. This is a key foundation for the identification and quantitative analysis of transient interference.

[0055] In practical implementation, the analog disturbance signal collected on the communication bus is first continuously sampled using a high-speed analog-to-digital converter (ADC) to obtain a sequence of discrete voltage data points with equal time intervals. The system then uses a time-domain difference algorithm to compare the voltage values ​​of two adjacent sampling points in real time, calculating the difference change to determine the amplitude of the signal change at each sampling moment. This sequence is then scanned using a first-order differential operator to mark the location where the voltage changes fastest, which typically corresponds to the rising edge of the interference signal. The system then further analyzes the voltage waveform around this area where the slope rises significantly, extracting the highest voltage value (peak value) in the interference waveform and the time period during which the peak value remains high, thereby completing the feature extraction of a single transient pulse, including the three dimensions of rise steepness, peak amplitude, and duration. This process is implemented by programming the corresponding logic in the digital signal processing module, supporting rapid response and stable identification of high-frequency interference.

[0056] Electromagnetic sensing modules with nanosecond response capabilities are deployed at both the master and slave nodes of the communication bus. These modules collect voltage disturbance waveform signals during bus operation in real time. Their core function is to achieve high-precision sensing and precise analysis of high-energy transient electromagnetic interference (TEMI) events, such as lightning surges, motor startup transients, and electrostatic discharges. These transient interference events are characterized by extremely short duration, high frequency, and concentrated energy density. They often cause data transmission interruptions, frame misalignment, or redundancy check failures before the communication system recognizes them in time, seriously threatening the system's communication stability and the accuracy of battery capacity calculations. By deploying sensing modules with nanosecond response characteristics at both ends of the bus, these high-frequency interference pulses can be quickly captured and fully recorded in sub-millisecond timeframes, preventing interference information from being filtered out or obscured by other signals during propagation. The system also performs feature extraction on each captured interference pulse signal, extracting key characteristic parameters such as the rising edge slope, peak voltage, duration, and repetition frequency, thereby constructing a multi-dimensional signature sequence of TEMI. This sequence not only provides quantitative data support for subsequent communication anomaly diagnosis, risk identification, and mitigation control, but also lays the foundation for building electromagnetic interference models and improving the system's anti-interference capabilities. This step is the perception entry point for the entire interference identification and response mechanism, directly determining the system's response speed and accuracy to electromagnetic security threats. It is a key link in ensuring the stable operation of the power system and the reliability of capacity accounting.

[0057] Based on the constructed multi-dimensional feature sequence of high-energy transient electromagnetic interference, the key communication performance parameters of the communication bus are synchronously sampled within the corresponding time window according to a fixed-length time sliding window mechanism. A time-aligned mapping relationship between interference features and communication performance indicators is established to generate an original matching dataset of interference-performance impact.

[0058] The steps for establishing the mapping relationship between interference and communication performance based on the constructed high-energy transient electromagnetic interference multi-dimensional feature sequence are as follows:

[0059] A fixed-length time sliding window mechanism is set to clearly define the time period and data sampling frequency covered by each time window. The generated multi-dimensional transient pulse feature vector is synchronously selected within each window to ensure that each interference pulse event is fully captured in at least one time window and that there is no data truncation or omission.

[0060] The communication performance collection task is started in each time window, and multiple communication stability parameters including the number of data frame retransmissions, communication round-trip delay, number of cyclic redundancy check failures, and data frame effective reception rate are recorded. Each performance parameter is then bound to the corresponding interference feature vector in the current time window one by one to form a synchronous sampling data structure with time consistency.

[0061] The binding structures of interference features and communication performance parameters formed in multiple continuous time windows are organized and archived in chronological order to construct an original matching data set consisting of interference-performance mapping record units, providing a structured input data source with quantitative basis for subsequent risk modeling, communication link status prediction and formulation of system response control strategies.

[0062] Binding each communication performance parameter to the corresponding interference feature vector in the current time window, forming a time-consistent, synchronously sampled data structure, requires operations based on a unified time base and structured data format. First, during system initialization, a unified high-precision timestamp mechanism is established, ensuring that the interference feature extraction module and the communication performance sampling module record data on the same timeline, using the sliding window start time as an identifier. Subsequently, during each active time window, the interference feature extraction module caches all transient interference feature vectors captured within that time period into a temporary data table. The system then simultaneously initiates the communication performance sampling program to collect key communication parameters within that window in real time. The system then time-matches the interference feature data with the communication performance data based on the timestamps, encapsulating them into a complete data structure in the format of "window start time → interference feature set → communication performance parameter set." This structure is stored in a database or cache as a time series, forming a synchronously sampled data record with temporal consistency, content integrity, and traceability, providing a precise and reliable foundation for subsequent data modeling and analysis.

[0063] Based on the constructed multidimensional signature sequence of high-energy transient electromagnetic interference (HETEMI), a fixed-length sliding window mechanism is used to synchronously sample key communication performance parameters of the communication bus within the corresponding time window. A time-aligned mapping relationship is then established between the interference signatures and communication performance indicators. This method aims to reveal the dynamic correlation between interference events and communication performance, providing a data foundation and causal logic support for subsequent system risk assessment, anomaly prediction, and intelligent control strategies. HETEMI is characterized by uncertainty, suddenness, and temporal compactness. Its impact on communication systems often evolves in a phased and nonlinear manner. Therefore, without establishing a synchronous correlation between interference events and communication performance anomalies in the temporal dimension, effective impact modeling and response mechanism design are difficult. This step introduces a sliding window mechanism to unify interference signatures and communication performance parameters into the same time frame. Each time window serves as an independent observation unit, fully encapsulating the electromagnetic interference pattern and communication link stability status within the current period, thereby achieving data structuring, synchronization, and comparability. The interference-performance impact raw matching dataset, composed of records from multiple continuous windows, can not only reflect the immediate impact of a single interference, but also capture the cumulative effects and pattern change trends of multiple interferences. It lays a quantitative and traceable data foundation for the subsequent construction of interference risk assessment models, the formulation of communication anomaly fault tolerance strategies and dynamic capacity assessment and compensation mechanisms, and is a key intermediate link in achieving the transition from "perceiving interference" to "understanding the impact" and then to "intelligent response."

[0064] A weighted model is constructed for each set of interference features and corresponding communication anomaly indicators in the original matching dataset. Multivariate risk weights are calculated using the interference intensity normalization factor and the communication parameter sensitivity coefficient. A time-varying response matrix is ​​constructed to reflect the relationship between interference morphology and communication performance changes.

[0065] The steps for weighted modeling of each set of interference features and corresponding communication anomaly indicators in the original matching data set and constructing a time-varying response matrix that reflects the relationship between interference morphology and communication performance changes are as follows:

[0066] Based on the preset interference intensity normalization factor, each set of interference feature data is standardized, converting the three indicators of interference rise steepness, peak amplitude and duration into a dimensionless unified numerical vector. This eliminates the differences in the dimensional scale of different interference events and ensures that the interference features of each dimension have a consistent comparison basis when the model is processed.

[0067] A communication parameter sensitivity coefficient is assigned to each communication anomaly indicator, quantified based on its response to interference changes in historical samples. The normalized interference signature is then multiplied and added with the sensitivity coefficient matrix using a weighted linear combination to obtain a multivariate risk weight for the impact of a single interference event on each communication performance parameter within a time window.

[0068] The multivariate risk weights calculated in all time windows are embedded in the corresponding data positions according to their chronological order, and a time-varying response matrix consisting of time labels, normalized interference characteristics, and communication anomaly indicator weights is constructed to express the changing trend of electromagnetic interference on communication performance at different times, providing structured modeling input for subsequent risk clustering analysis, dynamic interference intensity assessment, and response strategy formulation.

[0069] The setting of interference intensity normalization factors is typically based on a statistical analysis of historical interference data in the system and an understanding of the physical significance of key indicators. The goal is to convert interference characteristics of varying dimensions and magnitudes into a unified numerical scale, facilitating subsequent modeling. Specifically, representative interference characteristic parameters, such as rising edge steepness, peak amplitude, and pulse duration, are first selected for standardization. Then, based on a large amount of sample data, the statistical distribution characteristics of each parameter (such as maximum, minimum, mean, and standard deviation) are determined. Finally, based on empirical thresholds for the system's sensitivity to interference in actual applications, a normalization factor is set for each characteristic. For example, the normalization factor for peak amplitude can be set to the system's maximum allowable voltage disturbance, while the normalization factor for duration can be set to the critical value of a single communication frame. During the actual normalization process, the characteristic value of each interference event is divided by the corresponding normalization factor, mapping it to a dimensionless range between 0 and 1. This normalization process maintains the comparability of the relative strengths of interference characteristics while effectively preventing a single characteristic from dominating the modeling results due to its excessively large value, thereby achieving a balanced representation of multidimensional interference information.

[0070] The core purpose of assigning a communication parameter sensitivity coefficient to each communication anomaly indicator is to measure its response to changes in electromagnetic interference. This can be achieved through statistical analysis and fitting modeling based on historical operating data. First, data samples of key communication performance indicators are collected before and after different interference events, including data frame retransmission counts, communication latency, checksum failure counts, and frame loss rate. Interference characteristic parameters for the corresponding time periods are also recorded. Correlation analysis methods (such as the Pearson correlation coefficient and mutual information analysis) are then used to assess the strength of the response relationship between each communication anomaly indicator and various interference characteristics (such as peak value and duration), quantifying its sensitivity to interference. Next, using a regression model or normalized weight fitting, the response sensitivity value of each communication anomaly indicator is converted into a corresponding sensitivity coefficient, typically set between 0 and 1, with higher values ​​indicating a more significant interference response. The resulting set of sensitivity coefficients serves as weighting factors in the modeling process, guiding the calculation of multivariate risk weights and enabling the model to more realistically reflect the dynamic impact of interference on the communication system.

[0071] A weighted modeling approach is used to combine each set of interference features and corresponding communication anomaly indicators in the raw matching dataset. Multivariate risk weights are calculated using the interference intensity normalization factor and the communication parameter sensitivity coefficient. Furthermore, a time-varying response matrix is ​​constructed to reflect the relationship between interference patterns and communication performance changes. This approach quantifies and dynamically models the impact of electromagnetic interference on communication link performance, providing a scientific basis for subsequent risk assessment, interference trend identification, and system control strategies. In actual operation, the impact of different types of high-energy transient electromagnetic interference on communication systems is complex and diverse. For example, some interference may primarily cause data frame loss, while others are more likely to cause latency fluctuations or verification failures. Therefore, relying solely on raw sampled data makes it difficult to accurately determine the severity and impact of interference. By normalizing the interference features, the scale bias caused by different physical dimensions is eliminated, allowing each interference feature to be modeled under a unified standard. Furthermore, a communication parameter sensitivity coefficient is introduced to reflect the sensitivity of different communication performance indicators to interference changes, thereby enabling differentiated weight calculation. The calculation of multivariate risk weights not only reflects the immediate impact of a single interference event but also constructs a response matrix through time series, demonstrating the dynamic evolution of the communication system under different interference patterns. This matrix, serving as the input for time series modeling and pattern recognition, effectively supports the system's early warning of potential communication failures, providing critical support for interference source location, communication scheduling strategy optimization, and capacity assessment and correction. It is the key link in transforming interference perception results into actionable engineering decisions.

[0072] The obtained time-varying response matrix is ​​input into the principal component analysis module to extract the principal component data reflecting the changing trend of the main interference characteristics. The extreme point of the interference peak amplitude and the time derivative of the communication performance attenuation are combined as input variables and introduced into the risk index generation model. A set of high-energy transient electromagnetic interference risk indices between 0 and 1 are generated using nonlinear function mapping.

[0073] The specific steps of inputting the obtained time-varying response matrix into the principal component analysis module and using nonlinear function mapping to generate the high-energy transient electromagnetic interference risk index are as follows:

[0074] The constructed time-varying response matrix is ​​input into the principal component analysis module. Covariance analysis and feature dimensionality reduction are performed on the multi-dimensional interference characteristics and communication performance indicators contained in the matrix. The principal component data set representing the interference change trend is extracted. The original data dimension is compressed while retaining the main change information characteristics, which improves the computational convergence speed and model stability of subsequent risk index modeling.

[0075] Principal component analysis is used to reduce the dimensionality of the time-varying response matrix and extract the key principal component data that best represents the interference trend and communication performance changes. This reduces the computational complexity while retaining the core feature information, thereby improving the efficiency and accuracy of risk modeling.

[0076] The extreme point index corresponding to the interference peak amplitude is located from the extracted principal component data, and the time derivative characteristics of the communication performance index are extracted under the time index to characterize the attenuation rate of communication performance with changes in interference. The principal component sequence, extreme point index, and communication performance derivative characteristics are combined into a multidimensional input vector with a unified structure, which serves as the input data basis for the risk index model.

[0077] The interference peak extreme points and the communication performance attenuation rate in the corresponding time period are extracted from the principal component data, and the intensity characteristics of the interference changes are organically linked with the communication performance degradation behavior, thus constructing a multi-dimensional input vector with clear structure and strong expression ability for the risk index generation model.

[0078] The multi-dimensional input vector is input into the risk index generation model, and a continuous mapping operation is performed based on the set nonlinear function mapping mechanism. The Sigmoid function or piecewise linear function is used to compress the multi-variable disturbance input into a continuous output value with an interval value between zero and one, forming a quantifiable high-energy transient electromagnetic interference risk index that can be dynamically updated over time, providing a digital judgment basis for the real-time interference response mechanism and fault-tolerant control strategy triggering of the communication link.

[0079] The constructed multi-dimensional input vector is mapped to a risk index between 0 and 1 to achieve a quantitative assessment of the degree to which the current communication link is affected by electromagnetic interference, providing a clear and operational numerical basis for the system's real-time response strategy.

[0080] To locate the extreme point index corresponding to the peak interference amplitude from the extracted principal component data, the local maximum position must first be identified in the constructed principal component time series. Typically, a sliding window comparison method or an extreme value detection algorithm is used to mark the points in the change curve where the value suddenly rises and then quickly falls, identifying these as the extreme point index representing the most concentrated interference intensity. Subsequently, using the time index of this extreme point as the basis for positioning, the communication performance indicators at this time point and several sampling points before and after it are synchronously extracted from the original response data. A differential calculation method is used to solve the time derivative characteristics of the performance parameters, reflecting their degradation rate or fluctuation trend during periods of significantly increased interference intensity. The core purpose of this process is to precisely align the interference peak with the communication degradation behavior on the time axis, achieving dynamic causal modeling between interference intensity changes and system responses. This provides key feature input for building a time-sensitive risk assessment model, enabling the model to more accurately predict the real-time stability changes of the communication link under high-intensity interference impacts.

[0081] The nonlinear function mapping mechanism uses a function model with nonlinear mathematical properties to comprehensively convert multiple input variables (such as interference intensity and communication degradation rate) into a target output value. Its key is to simulate the complex nonlinear relationships between the input variables and compress or map the results into a specified numerical range. Its purpose is to process multidimensional and multi-scale feature input vectors through a unified function model to generate a continuous and interpretable risk index output, typically limited to between 0 and 1, to facilitate the system's dynamic response strategy based on the risk level. In specific implementations, functions such as the Sigmoid function, hyperbolic tangent function, or piecewise linear penalty function are often used. The weighted sum of the input variables is then fed into the function as the independent variable. The output value smoothly transitions within a preset range as the input variables change. For example, when interference intensity and communication performance degradation indicators continue to increase, the Sigmoid function output value gradually approaches 1, indicating a high-risk state; when the input changes are small, the output value approaches 0, indicating a stable system. Through this nonlinear mapping mechanism, "mutational" responses can be effectively avoided, and the system's fault tolerance and control smoothness to disturbances can be improved. It is the core means to achieve dynamic quantification of high-energy transient electromagnetic interference risk index.

[0082] The constructed time-varying response matrix is ​​input into the principal component analysis module. Covariance analysis and feature dimensionality reduction are performed on the multi-dimensional interference characteristics and communication performance indicators contained in the matrix to extract the principal component data set representing the interference change trend. The specific steps are as follows:

[0083] Perform data preprocessing on all interference characteristic parameters and communication performance indicators in the time-varying response matrix. Use the standardization method of zeroing the mean and normalizing the variance to normalize the values ​​of each parameter, eliminating the differences in physical units and numerical ranges of different parameters, and ensuring that the subsequent covariance calculation has a unified numerical scale.

[0084] Based on the standardized data, the covariance matrix is ​​constructed to calculate the degree of coordinated changes between all feature dimensions, identify the direction of change with strong linear correlation in the data, and perform eigendecomposition on the covariance matrix in order of eigenvalue size to extract the principal component vector set of the disturbance trend;

[0085] The minimum number of principal components to be retained is determined based on the retention threshold set according to the cumulative contribution rate. The multidimensional feature data in the original time-varying response matrix is ​​projected into the selected principal component vector space to complete the mapping transformation from high-dimensional features to low-dimensional features, and generate a principal component data set, providing concise and effective core input data for subsequent risk index modeling and interference trend identification.

[0086] The resulting time-varying response matrix is ​​input into the principal component analysis module, which extracts principal component data reflecting the changing trends of the main interference characteristics. This data, combined with the extreme points of the interference peak amplitude and the time derivative of communication performance degradation, is introduced as input variables into the risk index generation model. Finally, a set of high-energy transient electromagnetic interference risk indices ranging from 0 to 1 is generated using nonlinear function mapping. This step is crucial for achieving a dynamic and quantitative assessment of the system's current electromagnetic interference risk level and, based on this, providing real-time and reliable risk perception results, providing scientific support for the system's fault-tolerant control, protection strategy triggering, and capacity assessment accuracy correction. This step is the decision-making output link in the entire interference impact modeling chain, undertaking the critical transition from "data modeling" to "risk expression."

[0087] Specifically, the principal component analysis module first performs dimensionality reduction on the multidimensional interference features and communication performance indicators in the time-varying response matrix, compressing numerous redundant or highly correlated data dimensions into a small number of representative principal components, extracting a low-dimensional feature set that reveals the core changing trends in the system response. Compared to the raw data, principal components not only remove noise and redundancy, but also facilitate the convergence and stability of the modeling algorithm, reducing the computational burden and improving the model's sensitivity to key changing signals. Furthermore, the extreme points in the principal component curve are further identified as the critical moments when the interference peak occurs. Together with the corresponding time derivative of the communication performance decay at that moment, i.e., the speed of change of the performance indicator, these together form the dynamic characteristic input of the system's response intensity and rate of change to sudden interference.

[0088] Subsequently, by constructing a multidimensional feature input vector, the principal component data, extreme point locations, and performance derivative information are introduced as variables into the risk index generation model. Based on a predefined nonlinear mapping mechanism (such as a sigmoid function or piecewise linear function), this model comprehensively calculates and compresses different variables into a numerical range between 0 and 1, forming a continuous, real-time updateable risk index. Values ​​closer to 1 indicate that the system is experiencing high-intensity, high-rate interference, severely threatening the stability of the communication link; values ​​closer to 0 indicate that the system is stable and the impact of interference is negligible. This risk index not only possesses quantitative capabilities but also exhibits good response smoothness and trend identifiability, making it suitable for driving real-time protection logic, fault-tolerant algorithm triggering mechanisms, and communication scheduling adjustment strategies. Overall, this step achieves an intelligent mapping from complex signal modeling to risk digital outputs, serving as the core supporting mechanism for adaptive perception and dynamic control of the electromagnetic environment.

[0089] When the generated high-energy transient electromagnetic interference risk index exceeds the preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with reverse polarity on the communication bus. By modulating the interference intervention phase, the high-frequency interference energy is offset at the physical layer, achieving real-time response and local suppression of transient electromagnetic disturbances, maintaining the stability of the communication link and ensuring the accuracy of battery capacity calculation;

[0090] When the generated high-energy transient electromagnetic interference risk index exceeds the preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with reverse polarity on the communication bus. By modulating the interference intervention phase, the specific steps to offset the high-frequency interference energy at the physical layer are as follows:

[0091] Based on the generated high-energy transient electromagnetic interference risk index and the set high-energy transient electromagnetic interference risk index reference threshold, a suppression driving factor for controlling the jitter signal strength is constructed. The calculation expression is as follows:

[0092]

[0093] , where R is the high-energy transient electromagnetic interference risk index, which is used to reflect the transient electromagnetic disturbance intensity faced by the communication link at this moment, with a value range of 0-1, R ref is the reference threshold of the high-energy transient electromagnetic interference risk index, is the reference tolerance threshold of the high-energy transient electromagnetic interference risk index, indicating the risk intensity at which the communication system can maintain basically stable operation, ξ(t) is the suppression driving factor, which is used to control the modulation parameters such as amplitude, frequency, and phase of the subsequent injection signal. Its value range is [0, +∞), and it is the input reference variable in the signal modulation stage. The larger its value is, the more urgent the system needs to suppress high-intensity interference. It is the dynamic quantitative trigger basis for the communication link protection strategy.

[0094] The whole step constructs a nonlinear enhancement mechanism: when R approaches R ref When R>>R ref When ξ(t) increases rapidly, the suppression strategy responds more drastically, achieving an adaptive transition from "normal tolerance" to "emergency suppression." This structure ensures the system's real-time response and control sensitivity to electromagnetic interference, and is the response foundation of the entire dynamic protection system.

[0095] The above steps perform nonlinear enhancement processing on the high-energy transient electromagnetic interference risk index currently perceived by the system and the set reference threshold to generate a continuous, adjustable suppression driving factor. This factor is used to quantify the impact of the current interference intensity on the communication link and provides a numerical basis for the dynamic generation of the amplitude, frequency, and phase parameters of the subsequent anti-interference signal. By amplifying the response in high-risk areas, this factor ensures that the system can quickly make physical layer suppression decisions when interference intensifies, thereby realizing an intelligent, strength-adaptive communication protection mechanism.

[0096] After the suppression driving factor ξ(t) is calculated, a set of pseudo-random modulation parameter vectors J(t) is dynamically generated based on the suppression driving factor, where J(t) = [A(t), f(t), φ(t)], where:

[0097]

[0098] , where A(t) is the real-time amplitude of the dither signal, i.e., the signal injection amplitude dynamically generated according to the suppression driving factor ξ(t), and A max is the maximum injection amplitude, the upper limit of the physical voltage injection allowed by the system design, which is used to limit the maximum output of A(t) to prevent the modulated signal from overloading or damaging the communication bus. α is the compression factor of the amplitude adjustment, which controls The growth rate of ξ(t) adjusts the response sensitivity of ξ(t) to A(t); the smaller the value, the more sensitive the amplitude growth; the larger the value, the slower the growth, which is suitable for tolerating larger disturbances. tang is the hyperbolic tangent function, f(t) is the instantaneous frequency of the jitter signal, and the current injection signal frequency after perturbation modulation based on the base frequency f0 is used to guide the injection signal and the interference signal to produce nonlinear overlap in the frequency domain, forming an interference cancellation effect; it also prevents external interference from locking a single frequency point for a long time. f0 is the reference frequency of the jitter signal, β is the amplitude factor of the frequency perturbation, and controls the maximum adjustable range of the risk input ξ(t) to the frequency perturbation. φ(t) is the initial phase of the signal, and the phase opposite to the interference signal is dynamically constructed so that the two signals form a cancellation effect at the physical layer. When ξ(t) = 1, φ(t) = 0, achieving maximum anti-phase overlap; when ξ(t) → 0, φ(t) → π, that is, turning off intervention, π is pi;

[0099] Tanh is the hyperbolic tangent function, a common nonlinear activation function with an output range between -1 and 1. It changes nearly linearly when the input value is small, and gradually stabilizes as the input value increases. Its role in this step is to smoothly compress the calculated suppression driving factor, making the corresponding signal amplitude adjustment process softer and more continuous, avoiding sudden changes in the signal amplitude when the interference risk changes drastically, thereby ensuring the stability and security of the injected signal at the physical layer. Through the nonlinear saturation characteristics of tanh, an amplitude control strategy of "strong response to strong interference, weak response to weak interference" can be implemented, enhancing the system's sensitivity to high-risk interference, while suppressing excessive intervention in low-risk states, and improving the robustness and practicality of the overall anti-interference mechanism.

[0100] When a high-energy transient electromagnetic interference risk index ξ(t) = 1 is detected, indicating an extremely high interference risk, the initial phase φ(t) is automatically adjusted to 0 radians. At this point, the injected signal and the interfering signal completely overlap on the time axis but have opposite polarity, creating the strongest anti-phase interference effect, which can maximize the offset of the interference energy. When φ(t) = 0, indicating an extremely low interference risk, the system gradually adjusts the initial phase to π radians (i.e., 180 degrees), keeping the injected signal and the interfering signal in the same direction, thereby automatically disabling the intervention and avoiding unnecessary impact on the communication link. Here, π serves as the limiting value for phase adjustment, playing a key role in dynamically controlling the range and mode of action of the injected interference signal and is the mathematical core of risk-aware driven phase response.

[0101] According to the generated A(t), f(t), φ(t), the actual injection signal is constructed as follows:

[0102] s(t)=A(t)·sin(2πf(t)t+φ(t))·r(t)

[0103] Where, s(t) is the final injected suppression signal, r(t) is the pseudo-random perturbation factor, a pseudo-random sequence with a value range of {-1, +1}, which is used to modulate the signal polarity. Its function is to introduce the randomness of high-frequency perturbations, enhance the uncertainty of the injected signal, make the interference source difficult to predict or synchronize, and enhance the anti-interference ability. 2π is the period conversion constant, which represents the radian representation of a complete sine cycle (360°). The product of frequency and time is converted into an angular unit, so that the sine function produces a periodic fluctuation. t is the time variable.

[0104] Based on the calculated suppression drive factor, the aforementioned steps dynamically generate the amplitude, frequency, and phase parameters of the jitter signal. They then construct an anti-interference signal with anti-phase characteristics and inject it into the communication bus in real time, effectively canceling out high-frequency transient interference energy at the physical layer. By dynamically modulating the signal characteristics, the injected signal is ensured to be sufficiently strong to counter interference while avoiding excessive disruption to normal communications, achieving proactive protection and stable control of the communication link.

[0105] When the generated high-energy transient electromagnetic interference risk index exceeds the system's preset reference threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random dither signal with inverted polarity onto the communication bus. By modulating the interference interference phase, the system cancels out the high-frequency interference energy at the physical layer. This creates a dynamic, real-time active electromagnetic suppression mechanism to mitigate the impact of transient strong interference on the communication link, ensuring stable system operation and data integrity during battery capacity calculation. Unlike traditional passive shielding or hardware filtering methods, this step utilizes a sensing-driven signal injection approach. An algorithmic model determines the severity of the interference and triggers real-time control actions, achieving a closed-loop control model of "interference response, response correction." The pseudo-random dither signal uses a dynamic modulation mechanism, with its amplitude, frequency, and phase adjusted nonlinearly based on the real-time interference risk index. This ensures that the signal's suppression capability always matches the interference intensity, avoiding both insufficient suppression and excessive interference to normal communications. Specifically, the introduction of inverted polarity and phase modulation strategies creates phase interference and energy cancellation with the interference signal in both the frequency and time domains, fundamentally weakening the transient impact waveform of the high-frequency interference. This is particularly important for industrial bus systems such as CAN and RS485, which are extremely sensitive to physical layer interference. Furthermore, the introduction of pseudo-random characteristics increases the unpredictability of the interference suppression signal, making it difficult for interference sources to adapt to or circumvent it, thereby improving the long-term effectiveness and safety of the anti-interference system. Overall, this step deeply integrates interference perception, risk identification, and physical layer regulation to construct an intelligent electromagnetic interference control strategy with "predictive-triggerable-adaptive" capabilities. This strategy is a key step in improving the stability, communication reliability, and capacity calculation accuracy of parallel intelligent DC power supply systems, and is particularly suitable for distributed energy, power communication, and energy storage scheduling systems in complex, high-interference environments.

[0106] The battery capacity verification method for a parallel intelligent DC power supply system proposed in this invention achieves full closed-loop control of the real-time perception, quantitative assessment, and active suppression of high-energy transient electromagnetic interference, effectively addressing the impact of interference-induced communication anomalies on battery capacity calculation accuracy and system operational stability. Compared to existing passive protection methods that rely solely on hardware anti-interference and communication protocol fault-tolerance mechanisms, this solution implements an adaptive intelligent regulation path from interference identification to risk response by deploying high-response electromagnetic sensing modules at both ends of the communication link, combining feature extraction, performance mapping, risk index generation, and dynamic physical layer control. Its greatest benefit lies in the first time-aligned modeling of electromagnetic interference characteristics and communication performance indicators, and the real-time suppression control of sudden interference through active signal injection driven by risk indexes. This significantly improves the system's communication reliability, battery status identification accuracy, and capacity calculation continuity in strong interference environments, thereby enhancing the anti-interference robustness and operational safety of the entire parallel power supply system. The method has broad engineering promotion value and industrial application prospects.

[0107] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0108] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0109] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0110] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0115] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0116] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A battery capacity verification method for a parallel intelligent DC power supply system, characterized in that: The following steps are involved: Electromagnetic sensing modules with nanosecond response capabilities are deployed at the master and slave nodes of the communication bus to collect voltage disturbance waveform signals during operation. Feature extraction is performed on each detected transient high-frequency interference pulse to construct a multi-dimensional feature sequence of high-energy transient electromagnetic interference. Based on the constructed multi-dimensional feature sequence of high-energy transient electromagnetic interference, the key communication performance parameters of the communication bus are synchronously sampled within the corresponding time window according to a fixed-length time sliding window mechanism. A time-aligned mapping relationship between interference features and communication performance indicators is established to generate an original matching dataset of interference-performance impact. A weighted model is constructed for each set of interference features and corresponding communication anomaly indicators in the original matching dataset. Multivariate risk weights are calculated using the interference intensity normalization factor and the communication parameter sensitivity coefficient. A time-varying response matrix is ​​constructed to reflect the relationship between interference morphology and communication performance changes. The obtained time-varying response matrix is ​​input into the principal component analysis module to extract the principal component data reflecting the changing trend of the main interference characteristics. The extreme point of the interference peak amplitude and the time derivative of the communication performance attenuation are combined as input variables and introduced into the risk index generation model. A set of high-energy transient electromagnetic interference risk indices between 0 and 1 are generated using nonlinear function mapping. When the generated high-energy transient electromagnetic interference risk index exceeds the preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with inverted polarity on the communication bus, and the high-frequency interference energy is offset at the physical layer by modulating the interference intervention phase.

2. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 1, characterized in that: The steps to deploy electromagnetic sensing modules on the master and slave nodes of the communication bus and collect voltage disturbance waveform signals are as follows: A high-bandwidth front-end signal conditioning circuit is introduced into the electromagnetic sensing module to perform multi-stage signal amplification and high-pass filtering on the collected communication bus disturbance signals. Based on the conditioned disturbance waveform signal, the time domain difference algorithm and the first-order differential operator are used to analyze the signal change process, calculate the slope of the rising edge of the disturbance signal in real time, and extract the maximum amplitude and its corresponding duration in the local voltage waveform, thereby generating a transient pulse feature vector containing three dimensions: rising edge steepness, amplitude peak, and pulse duration. During the continuous sampling period, each detected transient pulse feature vector is structured and stored in chronological order to form a multi-dimensional interference feature sequence that records the disturbance intensity, time interval and feature evolution trend.

3. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 1, characterized in that: The steps for establishing the mapping relationship between interference and communication performance based on the constructed high-energy transient electromagnetic interference multi-dimensional feature sequence are as follows: A fixed-length time sliding window mechanism is set to clearly define the time period and data sampling frequency covered by each time window. The generated multi-dimensional transient pulse feature vector is synchronously selected within each window to ensure that each interference pulse event is fully captured in at least one time window and that there is no data truncation or omission. The communication performance collection task is started in each time window, and multiple communication stability parameters including the number of data frame retransmissions, communication round-trip delay, number of cyclic redundancy check failures, and data frame effective reception rate are recorded. Each performance parameter is then bound to the corresponding interference feature vector in the current time window one by one to form a synchronous sampling data structure with time consistency. The binding structures of interference features and communication performance parameters formed in multiple continuous time windows are sorted and archived in chronological order to construct an original matching data set consisting of interference-performance mapping record units.

4. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 1, characterized in that: The steps for weighted modeling of each set of interference features and corresponding communication anomaly indicators in the original matching data set and constructing a time-varying response matrix that reflects the relationship between interference morphology and communication performance changes are as follows: Based on the preset interference intensity normalization factor, each set of interference characteristic data is standardized, and the three indicators of interference rise steepness, peak amplitude and duration are converted into a dimensionless unified numerical vector to eliminate the differences in dimensional scales of different interference events; A communication parameter sensitivity coefficient is assigned to each communication anomaly indicator, quantified based on its response to interference changes in historical samples. The normalized interference signature is then multiplied and added with the sensitivity coefficient matrix using a weighted linear combination to obtain a multivariate risk weight for the impact of a single interference event on each communication performance parameter within a time window. The multivariate risk weights calculated in all time windows are embedded in the corresponding data positions according to their time sequence, and a time-varying response matrix consisting of time labels, normalized interference features, and communication anomaly indicator weights is constructed.

5. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 1, characterized in that: The specific steps of inputting the obtained time-varying response matrix into the principal component analysis module and using nonlinear function mapping to generate the high-energy transient electromagnetic interference risk index are as follows: The constructed time-varying response matrix is ​​input into the principal component analysis module, and covariance analysis and feature dimensionality reduction processing are performed on the multi-dimensional interference characteristics and communication performance indicators contained in the matrix to extract the principal component data set representing the interference change trend; The extreme point index corresponding to the interference peak amplitude is located from the extracted principal component data, and the time derivative characteristics of the communication performance index are extracted under the time index to characterize the attenuation rate of communication performance with interference changes. The principal component sequence, extreme point index and communication performance derivative characteristics are combined into a multi-dimensional input vector with a unified structure. The multi-dimensional input vector is input into the risk index generation model, and a continuous mapping operation is performed based on the set nonlinear function mapping mechanism to compress the multi-variable disturbance input into a continuous output value with an interval value between zero and one, thereby forming a quantifiable high-energy transient electromagnetic interference risk index that can be dynamically updated over time.

6. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 5, characterized in that: The constructed time-varying response matrix is ​​input into the principal component analysis module. Covariance analysis and feature dimensionality reduction are performed on the multi-dimensional interference characteristics and communication performance indicators contained in the matrix to extract the principal component data set representing the interference change trend. The specific steps are as follows: Performing data preprocessing operations on all interference characteristic parameters and communication performance indicators in the time-varying response matrix; Based on the standardized data, the covariance matrix is ​​constructed to calculate the degree of coordinated changes between all feature dimensions, identify the direction of change with strong linear correlation in the data, and perform eigendecomposition on the covariance matrix in order of eigenvalue size to extract the principal component vector set of the disturbance trend; The minimum number of principal components to be retained is determined according to the retention threshold set according to the cumulative contribution rate. The multidimensional feature data in the original time-varying response matrix is ​​projected into the selected principal component vector space to complete the mapping transformation from high-dimensional features to low-dimensional features and generate a principal component data set.

7. The battery capacity verification method for a parallel intelligent DC power supply system according to claim 1, characterized in that: When the generated high-energy transient electromagnetic interference risk index exceeds the preset threshold, the main control unit is immediately controlled to inject a small-amplitude pseudo-random jitter signal with reverse polarity on the communication bus. By modulating the interference intervention phase, the specific steps to offset the high-frequency interference energy at the physical layer are as follows: Based on the generated high-energy transient electromagnetic interference risk index and the set high-energy transient electromagnetic interference risk index reference threshold, a suppression driving factor for controlling the jitter signal strength is constructed. The calculation expression is as follows: Where R is the high-energy transient electromagnetic interference risk index, which is used to reflect the intensity of transient electromagnetic disturbance faced by the communication link at this moment, with a value range of 0-1. ref is the reference threshold of high-energy transient electromagnetic interference risk index, ξ(t) is the suppression driving factor; After the suppression driving factor ξ(t) is calculated, a set of pseudo-random modulation parameter vectors J(t) is dynamically generated based on the suppression driving factor, where J(t) = [A(t), f(t), φ(t)], where: Where A(t) is the real-time amplitude of the jitter signal, that is, the signal injection amplitude dynamically generated according to the suppression driving factor ξ(t), A max is the maximum injection amplitude, α is the compression factor of the amplitude adjustment, tanh is the hyperbolic tangent function, f(t) is the instantaneous frequency of the dither signal, f0 is the reference frequency of the dither signal, β is the amplitude factor of the frequency perturbation, φ(t) is the initial phase of the signal, and π is the circumference of the circle. According to the generated A(t), f(t), φ(t), the actual injection signal is constructed as follows: s(t)=A(t)·sin(2πf(t)t+φ(t))·r(t) Where s(t) is the final injected suppression signal, r(t) is the pseudo-random perturbation factor, 2π is the periodic conversion constant, and t is the time variable.

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