A soft service end cooperative grounding grid stray current stripping measurement system

Abstract

The present application relates to the technical field of electrical measurement and fault location, and relates to a grounding net stray current stripping measurement system cooperating with a soft service end. The present application aims at the problem that the signal is distorted due to the saturation of the front-end collection hardware under strong electromagnetic interference. The system continuously collects multi-channel responses to construct a baseline current situation matrix, analyzes the time-space gradient flow characteristics to extract the common-mode space phase angle; according to this, an analog impedance weight frame is generated to drive the front-end program-controlled impedance modulation array to actively physically discharge, preventing the saturation distortion of the analog-to-digital converter; at the same time, the out-of-bound signal is monitored to generate a hardware time-stamp instruction, and the service end extracts the time sequence safety data segment according to the truncated invalid interval; finally, the covariance matrix is calculated to perform orthogonal transformation, and the residual noise is hedged by subtracting the high-dimensional background stray tensor projection. The present application constructs two-level defense of physics and digital, and realizes the high-fidelity extraction of the grounding net body characteristic current.

Description

Technical Field

[0001] This invention relates to the field of electrical measurement and fault location technology, and to a grounding grid stray current stripping measurement system in collaboration with a soft-connect server. Background Technology

[0002] In power systems, communication base stations, and large industrial facilities, the safety and integrity of the grounding grid are fundamental to ensuring the normal operation of equipment and the safety of personnel. Accurate measurement of the electrical characteristics of the grounding grid, such as grounding resistance and leakage current, is a key means of assessing its health status and diagnosing potential defects such as corrosion or breakpoints. However, in actual operating environments, the grounding grid, as a vast underground conductor network, is highly susceptible to coupling with various electromagnetic interferences from the surrounding environment, forming complex stray currents. These stray currents originate from diverse sources, including but not limited to power frequencies and harmonics from nearby high-voltage transmission lines, return current from electrified railways, electromagnetic leakage from large frequency converters, and transient events such as lightning induction. Their amplitudes are often far greater than the weak characteristic current signals that characterize the grounding grid's own state, thus posing a significant challenge to measurement.

[0003] Currently, various technical solutions exist in the industry to address interference issues in grounding grid measurements. One type is the offline measurement method, which temporarily disconnects the grounding grid from the power system, injects a test current of known frequency and amplitude into the grid using a high-power signal generator, and then measures its voltage response at different locations to calculate the grounding impedance. Another type is the online measurement method, which does not require system shutdown. It directly collects the current signal during operation by deploying sensors on the grounding grid and attempts to separate useful information from the mixed signal. In online methods, some solutions employ hardware filtering techniques, i.e., setting fixed low-pass, high-pass, or band-stop filters at the acquisition front end to filter out interference of specific frequencies, such as power frequency interference. More advanced solutions rely entirely on digital signal processing, uploading the acquired raw digital signal containing strong interference to a backend server, and using algorithms such as Fast Fourier Transform, Wavelet Transform, or Adaptive Filtering for digital-level interference cancellation and signal extraction.

[0004] The aforementioned traditional methods have certain limitations in specific application scenarios. While offline injection methods yield relatively clean measurement results, they require interrupting the normal operation of the power system, are highly complex to implement, and lack real-time continuous monitoring capabilities, failing to reflect dynamic changes in the grounding grid status in a timely manner. Online solutions using fixed hardware filters have fixed filtering characteristics, offering limited suppression of complex stray currents whose frequency and amplitude dynamically change over time. Furthermore, the hardware filter itself may introduce phase distortion, affecting the accuracy of subsequent analysis. Online solutions relying entirely on digital signal processing face a critical physical bottleneck: when the amplitude of the stray current at the front end far exceeds the range of the analog-to-digital converter (ADC), the acquired signal undergoes clipping saturation. Once the signal saturates, the weak characteristic current information contained within it is permanently lost, and any subsequent digital algorithm cannot recover the original valid information from the distorted data. This is particularly prominent in situations with strong transient impacts or high-amplitude steady-state interference.

[0005] Based on the above problems, the present invention aims to solve the problem that in the case of strong stray current interference, traditional online measurement methods suffer from signal distortion due to the saturation of the front-end acquisition hardware, which makes it impossible to accurately separate the characteristic current of the grounding grid. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a grounding grid stray current stripping measurement system with the cooperation of the soft-connect server is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a grounding grid stray current stripping measurement system with Softcom server collaboration, comprising: a matrix construction module, which controls a multi-state flexible acquisition terminal to continuously acquire electromagnetic response analog quantities to generate multi-channel hybrid data frames, and reassembles the multi-channel hybrid data frames according to the time dimension to construct a baseline current situation matrix.

[0008] The common-mode spatial phase angle analysis module calculates the spatiotemporal gradient manifold features of the baseline current situation matrix, extracts the far-field wave propagation component from the spatiotemporal gradient manifold features, and analyzes the common-mode spatial phase angle.

[0009] The impedance weighted frame generation module calculates a specific reverse interference coefficient based on the common-mode spatial phase angle, generates a simulated impedance weighted frame based on the specific reverse interference coefficient, and sends it to the multi-mode flexible acquisition terminal.

[0010] The pre-stripping current signal generation module uses a multi-state flexible acquisition terminal to analyze the analog impedance weight frame, adjust the programmable impedance modulation array, and call the programmable impedance modulation array to discharge stray currents to the electromagnetic response analog quantity, thereby generating an analog pre-stripping current signal.

[0011] The hardware timestamp instruction generation module monitors the level input status of the analog pre-stripping current signal. When the level input status triggers the preset defense boundary, it activates an abnormal interrupt and generates a hardware timestamp instruction that records the overflow trigger time.

[0012] The secure data segment extraction module extracts hardware timestamp instructions and converts them into absolute time anchors. It then discards the failed calculation intervals along the absolute time anchors and retains stable data combinations that have not undergone distortion before the absolute time anchors as time-series secure data segments.

[0013] The current sequence output module calculates the joint covariance matrix of the time-series safe data segment, performs an orthogonal transformation to extract the principal basis vector set, and combines the feature vectors in the principal basis vector set that have an energy ratio greater than a preset threshold into a high-dimensional background stray tensor. The time-series safe data segment is projected and subtracted from the high-dimensional background stray tensor to filter noise, and the characteristic current sequence of the grounding grid body is output.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention constitutes the first level of physical defense through server-side collaborative analysis and front-end hardware programmable impedance modulation. The server can separate the common-mode spatial phase angle characterizing external traveling wave interference by analyzing the spatiotemporal gradient manifold characteristics of the baseline current situation matrix uploaded by multiple acquisition terminals. Based on this phase angle, the server can calculate the reverse physical intervention parameters for destructive interference and generate an analog impedance weight frame to be sent to the front end. The front-end terminal adjusts its programmable impedance modulation array according to this weight frame to form a low-impedance discharge path for the dominant stray current component, bypassing most of its energy before the signal enters the analog-to-digital converter. This mechanism enables the acquisition system to dynamically adapt to the changing electromagnetic environment and actively suppress large-amplitude stray currents at the physical level, thereby avoiding saturation distortion caused by the large dynamic range of the input signal in the analog-to-digital converter, ensuring that subsequent weak feature signals can be digitized without loss, and providing high-fidelity raw data for subsequent accurate digital separation.

[0015] (2) This invention establishes a second-level data protection defense against extreme transient disturbances by setting up a hardware overflow monitoring and timestamp reporting mechanism at the acquisition terminal. The hardware limiter circuit deployed at the terminal can detect extreme burst pulses that penetrate the first-level physical defense at nanosecond speeds and immediately trigger the highest-priority hardware interrupt. This interrupt can capture the precise moment of the overflow event and report it to the server as a hardware timestamp instruction. After receiving the timestamp, the server can use it as an absolute time anchor point, forcibly truncate its normal digital calculation process, and accurately remove all erroneous and invalid data frames caused by hardware saturation after the time anchor point. This mechanism can prevent distorted data caused by unpredictable strong pulses such as lightning strikes from polluting the entire dataset, ensuring the purity of the data segments used for final analysis, and improving the robustness of the entire measurement method and the reliability of the results in harsh electromagnetic environments.

[0016] (3) This invention performs digital decomposition based on orthogonal basis subspace projection on the time-secure data segment obtained after two-level defense at the server end, realizing deep stripping of residual noise. This method can construct a mathematical covariance matrix for the multi-channel time-secure data segment and solve its orthogonal principal basis vectors through the Jacobian rotation algorithm. The system can lock the vector with macroscopic extreme energy attributes and fit it as a spurious data background tensor characterizing the residual high-frequency parasitic response in the channel. By subtracting the original full data vector from this spurious background tensor through orthogonal projection, the energy components related to spurious modes can be stripped from the data. Since this operation is performed on a high signal-to-noise ratio data segment that has been physically preprocessed and time-cleaned, the calculation of the covariance matrix and the identification of principal components are more accurate, avoiding the misleading effect of strong interference on the decomposition algorithm. This mechanism realizes the effective separation of residual noise that is highly coupled with the target signal in the frequency or time domain, and finally obtains the body characteristic current sequence that can completely characterize the truth of the near-field lossless leakage of the grounding grid. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system module structure connection of the present invention.

[0019] Figure 2 This is a diagram illustrating the effect of the first level of physical defense in this invention.

[0020] Figure 3 This is a diagram illustrating the effect of the second level of digital defense in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 This invention provides a grounding grid stray current stripping measurement system with Softcom server collaboration, including: a matrix construction module, a common-mode spatial phase angle analysis module, an impedance weight frame generation module, a pre-stripping current signal generation module, a hardware timestamp instruction generation module, a security data segment extraction module, and a current sequence output module.

[0023] The matrix construction module is connected to the common-mode space phase angle analysis module, the common-mode space phase angle analysis module is connected to the impedance weight frame generation module, the impedance weight frame generation module is connected to the pre-stripped current signal generation module, the pre-stripped current signal generation module is connected to the hardware timestamp instruction generation module, the hardware timestamp instruction generation module is connected to the secure data segment extraction module, and the secure data segment extraction module is connected to the current sequence output module.

[0024] The matrix construction module controls the multi-state flexible acquisition terminal to continuously acquire electromagnetic response analog quantities to generate multi-channel mixed data frames, and reassembles the multi-channel mixed data frames according to the time dimension to construct a baseline current situation matrix.

[0025] In a specific embodiment of the present invention, the control of the multi-state flexible acquisition terminal to continuously acquire electromagnetic response analog quantities to generate multi-channel hybrid data frames, and the reconstruction of the multi-channel hybrid data frames according to the time dimension to construct a baseline current situation matrix, including: introducing global satellite pulse signals into the system as a time base alignment to lock the fixed-length acquisition frequency of the analog-to-digital converter.

[0026] The driver microprocessor chip continuously extracts analog electromagnetic response quantities at a fixed acquisition frequency and attaches corresponding device identifiers and time tags.

[0027] Collect sequences carrying device identifiers, eliminate duplicates and redundancies, and synchronously align the row and vertical coordinates at a fixed-length acquisition frequency to assemble a grid and generate a baseline current situation matrix.

[0028] Specifically, multiple multi-mode flexible acquisition terminals are first deployed at different locations on the grounding grid under test. To ensure the effectiveness of subsequent spatial gradient calculations, the physical locations of the multiple multi-mode flexible acquisition terminals should not be on the same straight line. Each multi-mode flexible acquisition terminal integrates a zero-flux current sensor, which is coupled to the conductor shunt node of the grounding grid via a clamp structure, to sense in real time the original electromagnetic response analog quantity flowing through the node, including the grounding grid's own response and external stray interference. Subsequently, each terminal utilizes its built-in unified satellite timing module, which receives timing signals broadcast by systems such as BeiDou or GPS, to generate a highly stable pulse-per-second signal. This pulse-per-second signal is used as the system's master clock reference, strictly locking the operating timing of the analog-to-digital converter, and performing high-speed discretization sampling of the original electromagnetic response analog quantity at equal intervals. This ensures that the sampling times of all terminals are precisely aligned on the time axis, thereby constructing a time-aligned multi-channel hybrid data frame. The multi-channel hybrid data frame is essentially a digital sequence carrying a precise start timestamp and a unique terminal identifier. Finally, each multi-mode flexible acquisition terminal establishes a TCP / IP connection with the remote server through its built-in wireless communication module, such as a 5G or Narrow-Band-Internet-of-Things (NB-IoT) module, and uploads the encapsulated multi-channel hybrid data frames. After receiving the data frames from all terminals, the server's data aggregation logic unit performs final alignment and verification based on the timestamp information in the frame header, and aggregates the data streams of each channel into a two-dimensional matrix. The row index of this matrix corresponds to different acquisition terminals, and the column index corresponds to the synchronous sampling time point. This constructs a complete initial computational data structure that characterizes the grounding grid current distribution state within a specific time window, namely the baseline current situation matrix.

[0029] The multi-mode flexible acquisition terminal is an embedded device integrating data sensing, processing, and communication functions. Its core hardware includes, but is not limited to, a clamp-on zero-flux current sensor, a high-precision signal conditioning circuit, a high-speed analog-to-digital converter (ADC) with configurable sampling frequency, a microcontroller (MCU) or FPGA with a built-in real-time clock and support for external PPS signal synchronization, and a wireless communication module supporting long-distance data transmission. A unified satellite timing reference aims to provide a globally unified time scale for distributed acquisition systems, with synchronization accuracy typically better than 100ns, which is a prerequisite for ensuring the accuracy of subsequent spatiotemporal feature analysis. Assuming the application scenario is high-frequency transient interference monitoring in substations, to capture rapidly changing waveform details, the equally spaced high-speed discretized sampling rate can be set from 1MSPS to 10MSPS. A multi-channel hybrid data frame is a structured data packet. Its payload is a sequence of quantized current values ​​output by the ADC, and the frame header contains metadata such as the terminal identification ID, the UTC timestamp of the sampling start time, and the data sequence length. Baseline current situation matrix. It is a dimension A number matrix, in which The total number of multi-mode flexible acquisition terminals can be set from 4 to 16, depending on the size of the grounding grid and the requirements for diagnostic accuracy. This parameter represents the number of sampling points contained in a single data frame. The setting of this parameter requires a trade-off between communication bandwidth and the waveform length of a single analysis. Typical values ​​can be set to 2048 or 4096.

[0030] For example, suppose three multi-mode flexible data acquisition terminals, numbered T1, T2, and T3, are deployed non-collinearly along different branches of the grounding network of a substation. The system is set to a sampling rate of 2 MSPS and a data frame length of 1024 sampling points. At 14:30:00.000000 UTC, the satellite timing modules of all terminals simultaneously receive the synchronization pulse and trigger the ADC to start sampling. The analog current detected by the zero flux sensor of terminal T1 is converted by the ADC to obtain a series of digital values, for example, the current values ​​of the first three sampling points are 1.5A, 1.7A, and 1.6A. Terminal T1 packages these 1024 sampling values ​​together with metadata with terminal ID T1 and a start timestamp of 14:30:00.000000 into a multi-channel hybrid data frame and sends it to the server via the 5G network. Meanwhile, terminals T2 and T3 also perform the same operation. Assume that the first three current values ​​collected by T2 are -0.8A, -0.9A, and -0.7A, and the first three current values ​​collected by T3 are 3.2A, 3.1A, and 3.3A. After receiving these three data frames, the server verifies that their timestamps are consistent, then parses and aggregates them to construct a 3-row, 1024-column baseline current situation matrix. The first row of this matrix contains 1024 current data points from terminal T1, and the second and third rows contain data from terminals T2 and T3, respectively. For example, this baseline current situation matrix... The first 3 columns of data are This matrix provides the basic input for subsequent spatiotemporal gradient analysis.

[0031] The common-mode spatial phase angle analysis module calculates the spatiotemporal gradient manifold features of the baseline current situation matrix, extracts the far-field wave propagation component from the spatiotemporal gradient manifold features, and analyzes the common-mode spatial phase angle.

[0032] In a specific embodiment of the present invention, the calculation of the spatiotemporal gradient manifold features of the baseline current situation matrix, the extraction of the far-field wave propagation component in the spatiotemporal gradient manifold features, and the analysis of the common-mode spatial phase angle include: decomposing the baseline current situation matrix, deriving the evolution of the single-point forward channel along the time base node, and obtaining the set of backward partial derivatives in the time dimension.

[0033] Extract the calibration coordinates of the multi-state flexible acquisition terminal, derive the distance parameters of the distance between adjacent devices, and obtain the set of partial derivatives of the translation of the bridging spatial dimension.

[0034] The set of backward partial derivatives in the time dimension and the set of translational partial derivatives across the spatial dimension are combined to generate a scalar map describing the divergence field of signal flow, which serves as a spatiotemporal manifold partial derivative operator.

[0035] Specifically, the execution entity is the spatial manifold analysis engine within the remote server, which receives and processes the generated baseline current situation matrix. First, to analyze the spatiotemporal dynamics inherent in the baseline current situation matrix, the engine calculates its gradients in both time and space dimensions. For the time dimension, the engine uses the backward difference method to calculate the discrete partial derivatives of each row of the baseline current situation matrix (i.e., the time-series data of each acquisition channel), obtaining the time-series evolution partial derivatives characterizing the instantaneous rate of change of the current. Specifically, for any element in the matrix, its time partial derivative is obtained by subtracting the element value from the previous time step in the same row and then dividing by the sampling time interval. In parallel, for the spatial dimension, the engine also uses the difference method to calculate the discrete partial derivatives of each column of the baseline current situation matrix (i.e., the cross-channel data at each synchronous sampling time), obtaining the cross-channel spatial translation partial derivatives characterizing the gradient of the current in the physical space distribution. This calculation involves the physical spacing between sensors in adjacent channels. Subsequently, the engine combines the time-series evolution partial derivatives and the cross-channel spatial translation partial derivatives to construct a gradient vector field describing the change in the current field, and calculates the divergence of this gradient field. This divergence value is defined as the calculation result of the spatiotemporal manifold partial derivative operator in this scheme. Physically, this calculation result characterizes the intensity of the current field at that spatiotemporal point's source or sink. Finally, the engine iterates through the obtained spatiotemporal manifold partial derivative operator calculation result matrix, comparing the absolute value of each element with a preset minimum gradient threshold. If the calculation result at a certain point approaches zero, i.e., is less than the threshold, it is determined that the current fluctuation corresponding to that point is dominated by an alternating traveling wave propagating in the far field, because an ideal plane traveling wave has no energy loss along its propagation path, and the corresponding gradient field divergence is zero. The engine gathers all data points identified as such far-field wave propagation components and performs a Fourier transform on these data points in the spatial dimension to extract the phase information corresponding to their dominant spatial frequency components. This phase information is the common-mode spatial phase angle that needs to be separated.

[0036] When calculating the spatiotemporal gradient, the time series evolution partial derivative... and cross-channel spatial translation partial derivative The specific calculations are as follows:

[0037]

[0038]

[0039] Combining the two partial derivatives mentioned above, the calculation result of the spatiotemporal manifold partial derivative operator in this embodiment is achieved by calculating the divergence of the gradient field, and its discrete calculation form is as follows:

[0040]

[0041] in, The first in the baseline current situation matrix The first channel in the The current value at each sampling time. It is the sampling time interval. It is the first The and the first The physical straight-line distance between the multi-mode flexible acquisition terminals. That is to be in The results of the spatiotemporal manifold partial derivative operator calculation for the location.

[0042] Perform a dimensional check on the above formula. The unit is A / s. The unit is A / m. First item The unit is (A / m) / m, which is A / m². (Second item) The unit is (A / s) / s, or A / s². Since the dimensions of the two are inconsistent, directly adding them is physically inaccurate. To solve this problem, a normalization coefficient is introduced to unify the spatial and temporal variables into the dimensionless domain, or wave velocity is introduced. Dimensional unification is performed. In this embodiment, it is assumed that the propagation speed of external electromagnetic waves in the grounding grid is... Then space and time can be... Correlation. Therefore, multiply the spatial partial derivative terms by This unifies the dimensions of the two quantities to A / s². To ensure that the operator result approaches zero when there is no energy loss along the propagation path of the ideal plane traveling wave, the two partial derivative terms must be subtracted. The corrected formula is:

[0043]

[0044] Among them, the time-series evolution partial derivative characterizes the rate of change of current intensity with time at a single acquisition node. The cross-channel spatial translation partial derivative characterizes the rate of change of current intensity along the spatial path formed by different acquisition nodes at the same moment. The spatiotemporal manifold partial derivative operator is a scalar field defined in this scheme, the value of which at each point is used to quantify the divergence of the current field at that spatiotemporal location, essentially a measure of the second-order differential characteristics of the spatiotemporal distribution of the current field. A preset minimum gradient threshold is used. This is a criterion used to distinguish between far-field waves and near-field sources. It is based on the statistical average of the spatiotemporal manifold partial derivative operator calculation results generated by the induced current in the grounding network from known external high-voltage lines, communication base stations, and other far-field interference sources under numerous actual operating conditions. It is typically set to 1.5 to 2.0 times this statistical average to ensure robustness of the discrimination. For example, in a typical ultra-high-voltage substation environment, this threshold can be set to a value less than 0.5% of the maximum signal dynamic range in the corresponding dimension, based on historical monitoring data. The far-field wave propagation component refers to the subset of data in the baseline current situation matrix whose absolute values ​​of the corresponding spatiotemporal manifold partial derivative operator calculation results are less than the preset minimum gradient threshold. Common-mode spatial phase angle. It is obtained by performing spatial Fourier analysis on the far-field wave propagation components. It represents the unified spatial phase evolution law exhibited by the external stray traveling wave as a whole when it passes through the entire sensor array.

[0045] For example, the server obtained a 3x1024 baseline current situation matrix. Assuming terminals T1, T2, and T3 are deployed at equal intervals, the spacing... The sampling time interval is 10m. The value is 0.5µs. Assume the propagation speed of external electromagnetic waves in the grounding grid conductor is... Approximately Now we calculate the spatiotemporal manifold partial derivative operator results for the third sampling time of the second channel, i.e., point (2,3). First, calculate the required first-order partial derivatives.

[0046]

[0047]

[0048] Here, it is assumed that a virtual channel 0 exists, or a central difference is used; for simplicity, a forward difference is used. The calculation is as follows: and .

[0049]

[0050] For ease of explanation, it is assumed here that... If it is 2.0A, then .

[0051] The second-order partial derivative components are: the time-order second-order partial derivative term: .

[0052] Spatial second-order partial derivative terms: .

[0053] but The value is approximately the difference between the two terms, and the calculated result is far from zero. Suppose at another point (k', n'), the result obtained through calculation... The value is much smaller than the preset minimum gradient threshold. Then this point is identified as the far-field wave propagation component. Assume that at time n', the current values ​​of the three channels are... All of these are identified as far-field wave propagation components. This spatial vector... Inputting data into a one-dimensional Fast Fourier Transform (FFT) algorithm yields a spectrum in complex form. Assuming the peak of the spectrum occurs at the first non-zero frequency point, its corresponding complex value is... Then the common-mode space phase angle at this moment. That is, the argument of the complex number, calculated as This value will be used for the next step of reverse physics intervention.

[0054] The impedance weighted frame generation module calculates a specific reverse interference coefficient based on the common-mode spatial phase angle, generates a simulated impedance weighted frame based on the specific reverse interference coefficient, and sends it to the multi-mode flexible acquisition terminal.

[0055] In a specific embodiment of the present invention, a specific reverse interference coefficient is calculated based on the common-mode spatial phase angle, and an analog impedance weight frame is generated based on the specific reverse interference coefficient and sent to the multi-state flexible acquisition terminal. This includes: calling a preset phase cancellation analysis engine to calculate the target bypass needs to bear an equal amount of unloading target resistance parameter based on the specific reverse interference coefficient.

[0056] The internal full permutation structure map is searched to find the optimal diversion control contact drive gating state matrix that approximates the equal unloading target resistance parameter.

[0057] The optimal shunt control contact drive gating state matrix is ​​packaged, cyclically verified, encoded, and tamper-proof authentication identifiers are generated and encapsulated into an analog impedance weighted frame.

[0058] Specifically, once the parsing process is complete, the physical intervention mapping module within the server immediately starts, receiving the extracted common-mode spatial phase angle as input. This module first calls the built-in spatial phase cancellation algorithm library, which stores response models of grounding grid conductor materials to electromagnetic waves of different frequencies. The module takes the common-mode spatial phase angle and the stray wave dominance frequency derived from the time characteristics of the far-field wave propagation components as input. Based on this, the algorithm library queries or calculates the reverse interference and amplitude attenuation mapping coefficients required to achieve the expected destructive interference. Specifically, the reverse interference coefficient determines the phase-opposite response required to cancel the external traveling wave, while the amplitude attenuation mapping coefficient calculates the current to be discharged through the bypass based on the estimated amplitude of the external traveling wave, thus converting it into a target impedance value. The physical mechanism is as follows: based on the stray wave dominance frequency, the equivalent complex impedance of the programmable impedance modulation array exhibiting pure parallel resonance characteristics is calculated and matched, thereby forming the lowest impedance discharge path to ground at that specific frequency. Next, based on the calculated reverse interference and amplitude attenuation mapping coefficients, the server calculates the required parallel impedance network state for each multi-state flexible acquisition terminal. The server possesses a digital twin model of the programmable impedance modulation array within each terminal, which describes the equivalent complex impedance formed by different combinations of parallel switches. The server solves this model using an optimization algorithm to find the set of switch states that best matches the target impedance value, thereby generating a binary parallel impedance network cascaded gating state matrix. Each row of this matrix corresponds to a terminal, and each bit in the row represents the on / off state of a parallel branch switch within that terminal. Finally, to ensure the integrity and security of control commands, the server encapsulates the parallel impedance network cascaded gating state matrix. During encapsulation, the module first performs a cyclic redundancy check (CRC) algorithm on the parallel impedance network cascaded gating state matrix to generate a phase check code, and then places this check code along with the matrix into a predefined data frame structure to form the final analog impedance weight frame. This frame carries direct control over the front-end physical hardware array, establishes a wireless communication link, and sends callbacks to the designated polymorphic flexible acquisition terminals with high priority.

[0059] Parallel impedance network cascaded gating state matrix The generation of this is the core calculation step. If the target complex impedance is... The terminal contains Several independently controllable parallel branches, each with an impedance of... The corresponding switch state is The server then determines the state vector by retrieving the internal permutation structure graph, traversing all switch combinations, and solving the following optimization problem. :

[0060] The constraints are

[0061] Among them, constraints are added. This is to prevent all bypass switches from being activated during the full permutation traversal of the algorithm. All values ​​are zero, leading to the mathematical undefined disaster of "division by zero"; when the bypass network must be completely disconnected, the system treats its equivalent impedance as infinity at the logic level.

[0062] The generated parallel impedance network cascaded gating state matrix For one The matrix, where Total number of terminals. Phase check code. The generation can be achieved using the standard CRC-32 algorithm:

[0063]

[0064] For the whole The matrix data is used to calculate a 32-bit checksum.

[0065] The spatial phase cancellation algorithm library is a dataset or dynamic link library stored in a server-side database. Its content, based on electromagnetic field simulation and experimental calibration, establishes a nonlinear mapping relationship between stray wave frequency, phase, amplitude, and the required discharge impedance. The reverse interference and amplitude attenuation mapping coefficients are a set of parameters, including a target phase shift. and a target attenuation factor Ideally, The goal of the setup is to ensure that the locally generated intervention response is exactly out of phase with the incident stray wave. Radius. The gating state matrix of a cascaded parallel impedance network is a binary matrix. Indicates the first The terminal's first The impedance branch switch is closed. This indicates a disconnection. The phase check code is an error detection code used to verify that no bit errors occurred during the transmission of the sent analog impedance weight frame, ensuring that the hardware executes correct instructions. An analog impedance weight frame is a data packet conforming to a specific protocol format. Its header contains the target terminal address, and its data payload consists of the cascaded gating state matrix of the parallel impedance network and the phase check code.

[0066] For example, the server obtains a common-mode spatial phase angle that identifies the far-field interference wave. Its value is 1.047 rad. Assume that analysis shows the amplitude of the interference wave is approximately 3.0 A. The physical intervention mapping module within the server receives this information. First, the spatial phase cancellation algorithm library determines that, in order to maximize the efficiency of bypassing stray current discharge, the algorithm library does not adopt the ideal but unrealizable absolute anti-phase addition. Instead of intervention, it calculates the passive complex impedance that can form the optimal shunt match with the equivalent input impedance of the grounding grid at that frequency. Specifically, it effectively bypasses a current of 3.0A and satisfies the absolute physical constraint that the real part resistance of the passive network is greater than or equal to zero, that is, the impedance angle must be within the range of 0°. to Between these values, the algorithm library calculates the required equivalent target complex impedance amplitude. Combining this with the extracted stray phase angle, the optimal discharge target complex impedance calculated by the system is... Next, assuming we need to configure terminal T3, its internal parallel impedance network has 8 controllable branches. The server's digital twin model, by consulting its pre-stored impedance-switch state mapping table, finds that when switches 2, 5, and 8 are closed, and the rest are open, the resulting equivalent impedance is closest to... Therefore, the strobe state behavior generated for terminal T3 Assume that state rows are also generated for T1 and T2, which together form a 3x8 parallel impedance network cascaded gating state matrix. Subsequently, the server processed the entire... The binary data stream of the matrix undergoes CRC-32 calculation to obtain a 32-bit phase check code, for example, a hexadecimal value of... Finally, the server sets the target address to the broadcast address of T1, T2, and T3, and sets the parallel impedance network cascaded gating state matrix. and phase check code It is encapsulated into a simulated impedance weighted frame and sent to the three terminals via the 5G network, ready to execute the next step of physical isolation.

[0067] See Figure 2 The pre-stripping current signal generation module analyzes the analog impedance weight frame of the multi-state flexible acquisition terminal, adjusts the programmable impedance modulation array, calls the programmable impedance modulation array to discharge stray currents to the electromagnetic response analog quantity, and generates an analog pre-stripping current signal.

[0068] In a specific embodiment of the present invention, the multi-state flexible acquisition terminal parses the analog impedance weight frame, adjusts the programmable impedance modulation array, calls the programmable impedance modulation array to discharge stray currents to the electromagnetic response analog quantity, and generates an analog pre-stripping current signal, including: unpacking and verifying the analog impedance weight frame permission identifier, releasing the terminal's internal underlying bus lock control switch command issuance action authorization.

[0069] After the analog impedance weight frame is loaded and parsed, a driving signal is generated and sent to the solid-state gate circuit inside the array to turn on or off the field-effect switching transistors, thereby modifying the capacitive and resistive distribution combination in the programmable impedance modulation array to generate a bypass grounding point.

[0070] The electromagnetic response analog quantity that drives the large influx of electromagnetic input rushes into the bypass grounding point to release the peak amplitude energy of the corresponding frequency band, discarding the waste source and retaining the purified analog pre-stripping current signal.

[0071] Specifically, the execution entity is the hardware control logic inside the multi-state flexible acquisition terminal deployed at the grounding grid node. Upon receiving an analog impedance weight frame from the server, the terminal's microcontroller first decodes the data frame, extracting the embedded phase check code and the parallel impedance network cascaded gating state matrix. The microcontroller then performs the same cyclic redundancy check algorithm as the server on the payload portion of the received parallel impedance network cascaded gating state matrix, generating a locally calculated check code. This locally calculated check code undergoes a strict bit-to-bit comparison with the phase check code carried in the analog impedance weight frame. Only when both are completely identical is the instruction confirmed as legitimate and unaltered during transmission, thus activating the subsequent hardware driver. Essentially, the check code acts as the underlying hardware security unlocking key, activating the programmable impedance modulation array inside the multi-state flexible acquisition terminal. After activation, the microcontroller, according to the row vector data specified in the parallel impedance network cascaded gating state matrix, outputs the corresponding drive level to the gate of each field-effect transistor in the programmable impedance modulation array through its general-purpose input / output pins. Specifically, if a position in the matrix is ​​1, the corresponding pin outputs a high level, turning on the field-effect switch; if it is 0, it outputs a low level, turning it off. This switching changes the combination of impedance branches connected in parallel to the main signal path, thereby dynamically adjusting the bypass impedance value of the front-end acquisition node to ground. Finally, the adjusted programmable impedance modulation array forms a low-impedance discharge path for the currently dominant stray large current component. When the original electromagnetic response analog quantity flows through this node, most of the dominant stray large current component it contains is bypassed to ground because its frequency and phase characteristics match the resonant characteristics of the bypass network, while the useful signal component related to the grounding grid's response is retained due to mismatch. After this real-time intervention at the physical level, the signal output from the modulation array is the analog pre-stripping current signal after completing the first level of defense intervention.

[0072] The programmable impedance modulation array (PIMMA) is a hardware circuit network consisting of multiple parallel branches. Each branch contains an impedance element, such as a precision resistor, capacitor, or inductor, connected in series with a solid-state switch controlled by a microcontroller. Field-effect transistors (FETs), typically chosen for their extremely high switching speed and low on-resistance, are used to control the on / off state of the impedance branches. The gate drive level is the voltage signal applied to the gate of the FET by the microcontroller, usually a standard logic level, such as 0V for logic low and 3.3V or 5V for logic high. The bypass impedance is the equivalent complex impedance formed by connecting the impedance elements of the conducting parallel branches in parallel; its value directly determines the discharge capability for signals of a specific frequency. The analog pre-stripped current signal is an analog quantity, a residual signal after the original electromagnetic response analog quantity has been physically filtered and attenuated by the PIMMA. Its DC bias and AC dynamic range are significantly reduced compared to the original signal, thus avoiding saturation distortion in the subsequent analog-to-digital converter.

[0073] For example, the multi-state flexible acquisition terminal deployed at location T3 receives an analog impedance weighted frame containing its gating state row vector [0,1,0,0,1,0,0,1] and phase check code 0xAF10E889. The microcontroller at terminal T3 first performs a CRC-32 calculation on the received state row vector [0,1,0,0,1,0,0,1], obtaining a result of 0xAF10E889, which perfectly matches the phase check code carried in the frame. The hardware security lock is unlocked, and the programmable impedance modulation array is activated. Subsequently, the microcontroller configures the output levels of its eight general-purpose input / output (GPIO) pins according to this row vector: setting the pins connected to the gates of the 2nd, 5th, and 8th switches to a high level of 3.3V, and setting the remaining pins to a low level of 0V. This enables the parallel branches of the 2nd, 5th, and 8th pins in the programmable impedance modulation array to conduct, forming a specific bypass impedance. At this point, assume the total current of the original electromagnetic response analog quantity flowing in from the zero flux sensor at a certain moment is 3.2A, which includes a 3.1A stray current component consistent with the server prediction and a 0.1A grounding grid leakage current signal. Since the bypass impedance of the programmable impedance modulation array is precisely set to discharge this 3.1A stray current, most of this stray component, for example 3.0A, is successfully discharged to ground through the bypass branch. Therefore, the current continuing to flow to the next stage circuit is the original total current minus the discharged current, i.e. This 0.2A analog signal, whose amplitude has been significantly reduced, is the analog pre-stripping current signal after the first layer of defense intervention. It will be sent to the analog-to-digital converter for further processing.

[0074] See Figure 3The hardware timestamp instruction generation module monitors the input level of the analog pre-stripping current signal. When the input level triggers the preset defense boundary, it activates an abnormal interrupt and generates a hardware timestamp instruction that records the overflow trigger time.

[0075] In a specific embodiment of the present invention, the level input state of the simulated pre-stripping current signal is monitored, and an abnormal interrupt is activated when the level input state triggers the preset defense boundary, generating a hardware timestamp instruction to record the overflow trigger time, including: introducing the simulated pre-stripping current signal into a threshold trigger probe with a nanosecond-level response time, and detecting whether the dynamic wave pattern violates the design upper limit critical saturation level reversal boundary.

[0076] The system captures the pin state abrupt change caused by the transition of the pin across the critical saturation level boundary of the design upper limit, generating an emergency fault pulse and triggering the highest priority overflow hardware interrupt operation mechanism.

[0077] Record the nanosecond-level high-precision oscillator stop counter count value and package it into a hardware timestamp instruction with a unique time series location address segment.

[0078] Specifically, after the analog pre-stripped current signal is output, the monitoring and triggering logic is executed uninterruptedly within the multi-state flexible acquisition terminal. First, the analog pre-stripped current signal is guided into the front-end hardware limiter loop of the analog-to-digital converter (ADC), which runs parallel to the ADC's normal analog input channel. The terminal's underlying control chip continuously monitors the voltage level of the core component within this loop, the limiter comparator port. Under normal operating conditions, the amplitude of the analog pre-stripped current signal is within the ADC's safe range, and the limiter comparator port maintains a stable logic low level. However, when there is an extreme sudden disturbance transient that cannot be completely discharged by the front-end programmable impedance modulation array, such as a strong pulse generated by near-field lightning strike induction or high-voltage switch operation, this transient overshoot causes the amplitude of the analog pre-stripped current signal to momentarily exceed the ADC's safe input voltage limit. At this time, the limiter comparator port will generate an overflow high-level signal with a nanosecond-level response time. This overflow high-level signal is directly hard-connected to a dedicated interrupt pin of the underlying control chip, such as an FPGA or MCU, and immediately triggers the chip's built-in highest-priority overflow hardware interrupt. Once the interrupt service routine is activated, its primary task is to immediately capture the precise moment when the highest-priority overflow hardware interrupt was triggered. This is achieved by reading the current count value of an internal high-precision counter synchronized with satellite time. Finally, this count value is encapsulated into a standard-format data instruction, namely a hardware timestamp instruction characterizing the time when the non-stationary distorted spurious waveform breaks through the physical defenses. This hardware timestamp instruction is then reliably reported back to the server via the uplink wireless communication network.

[0079] The hardware limiter circuit of an analog-to-digital converter (ADC) is a protection circuit located at the analog input front end. Its core function is to prevent the input voltage from exceeding the full-scale range of the ADC, which could lead to permanent damage or saturation distortion. It is typically composed of components such as a high-speed operational amplifier and a Schottky diode. The limiting comparator is the core decision-making element in the hardware limiter circuit. Its positive and negative input terminals are connected to the measured signal and preset upper and lower limit reference voltages, respectively. For example, for an ADC with an input range of 0V to 2.5V, the upper and lower limit reference voltages can be set to 0.05V and 2.45V, respectively, to allow for normal signal fluctuations. The overflow high-level signal is a logic high-level signal generated when the input signal voltage exceeds the reference voltage range, causing a sharp flip in the output state of the limiting comparator. Its response time is typically on the order of nanoseconds to ensure the capture of extremely fast transient events. The highest-priority overflow hardware interrupt is an asynchronous interrupt request preset in a microcontroller or FPGA with the highest response priority. Once triggered, it immediately suspends all other currently executing tasks, forcing the CPU to execute the service routine bound to the interrupt, ensuring minimal latency in event response. The hardware timestamp instruction is a data structure containing precise time information, typically represented by a 64-bit long integer value with nanosecond-level precision. It records the absolute moment the highest-priority overflow hardware interrupt occurred, and this moment is consistent with a unified satellite timekeeping reference, ensuring system-wide time uniformity.

[0080] For example, in terminal T3, the simulated pre-stripping current signal obtained after the first layer of physical defense has a nominal amplitude of approximately 0.2A, corresponding to a voltage of 0.8V at the ADC input. This signal is sent to the hardware limiter circuit before the ADC. Assuming the safe input voltage range of the ADC is 0V to 2.5V, the corresponding upper and lower limit reference voltages of the limiting comparator are set to 2.45V and 0.05V, respectively. At a certain moment, an extreme pulse caused by lightning strikes penetrates the front-end physical defense, superimposing itself on the simulated pre-stripping current signal, causing its instantaneous voltage to spike to 3.0V. Since 3.0V exceeds the upper limit reference voltage of 2.45V, the output port of the limiting comparator immediately flips from logic low (0V) to logic high (3.3V), generating a nanosecond-wide overflow high-level signal. This high-level signal triggers the highest-priority overflow hardware interrupt on the microcontroller. The interrupt service routine is executed immediately, and the value of the internal high-precision counter synchronized with satellite time is read. This value corresponds to 14:30:00.000512345 UTC. The microcontroller encapsulates this time information into a 64-bit integer hardware timestamp instruction. Subsequently, this instruction is reported to the server via the 5G module, explicitly informing the server that the hardware acquisition link of terminal T3 encountered a saturation overflow event at 14:30:00.000512345.

[0081] The secure data segment extraction module extracts hardware timestamp instructions and converts them into absolute time anchors. It then discards the failed calculation intervals along the absolute time anchors and retains stable data combinations that have not undergone distortion before the absolute time anchors as time-series secure data segments.

[0082] In a specific embodiment of the present invention, the hardware timestamp instruction is extracted and converted into an absolute time anchor point. The failure calculation interval along the absolute time anchor point is abandoned, and the stable data combination that has not been distorted before the absolute time anchor point is retained as a time-safe data segment. This includes: retrieving the overflow failure frame source point corresponding to the collision failure cut-in of the sliding window action associated with the absolute time anchor point in the running background process stream.

[0083] The disordered stacking fragments that are derived from the source point of the overflow failure frame that is cut off from the collision failure are determined as the failure calculation interval due to the loss of physical waveform caused by the electronic saturation truncation variation.

[0084] The available, stable response data segment is intercepted and locked as a time-safe data segment because the source point of the overflow failure frame that was extracted by the reverse extraction of the collision failure has not yet experienced clipping distortion.

[0085] Specifically, running continuously on the server side, its core function is to dynamically intervene in its own digital computing process using events reported by the front-end hardware, thereby ensuring the effectiveness of data processing. First, the server's main communication control bus module continuously monitors the uplink data streams from all polymorphic flexible acquisition terminals. Once a hardware timestamp instruction is captured in this data stream, the precise time information carried by the instruction is immediately extracted and forcibly converted into an absolute time anchor point on the system's global timing data calculation axis by the system timing management unit. This conversion process ensures that the occurrence of front-end physical hardware events can be accurately mapped to the time axis of the continuous data stream being processed by the server. Next, the server's digital cleaning process, namely the previously executed continuous Jacobian integral sliding accumulation process for extracting the common-mode spatial phase angle, is directly interfered with by this absolute time anchor point. Specifically, along the time series scale where this absolute time anchor point is located, the server forcibly terminates the ongoing integration and accumulation operations for the digital quantization data of the analog pre-stripped current signal. This is to prevent distorted data caused by ADC hardware saturation from contaminating subsequent calculation results. Finally, the server performs data culling and retention operations. It marks and removes erroneous failure frames containing singular data waveforms acquired after the absolute time anchor point, corresponding to the period of hardware saturation distortion on the backplane. Simultaneously, the system fully retains stable channel data packets acquired within the effective dynamic range before the highest-priority overflow hardware interrupt action. These retained, undistorted data segments are defined as time-safe data segments, and they will be sent to subsequent refined analysis.

[0086] The system's global timing data calculation axis is a virtual time coordinate system constructed by the server to uniformly process data from different sources and timestamps. It uses UTC time as a reference and has sufficient resolution to accommodate nanosecond-level timestamp information. An absolute time anchor point is a precise time point marked on this axis, serving as a reference or boundary for subsequent data processing operations. The continuous Jacobian integral sliding accumulation process is an algorithm continuously executed by the server for spatiotemporal gradient analysis in the absence of hardware interrupts. It tracks system dynamics by sliding across a time window and continuously accumulating calculation results. Analog pre-stripped current signal digital quantization data refers to the digital sequence formed after the output analog signal is converted by an ADC; it is the direct object of digital calculations performed by the server. Error failure frames refer to one or more data frames generated by an oversaturated ADC after the time point indicated by the hardware timestamp instruction. Their values ​​no longer accurately reflect the physical quantities of the input signal, typically exhibiting a clipped or flattened waveform. The timing-safe data segment is a high-fidelity multi-channel data block that has been identified and confirmed by the server to be unaffected by hardware saturation distortion. It is a reliable basis for extracting the characteristic current sequence of the final grounding grid.

[0087] For example, the server's main communication control bus receives a hardware timestamp instruction reported from terminal T3, with a timestamp of 14:30:00.000512345. The system timing management unit immediately marks this timestamp as an absolute time anchor on the server's global computation timeline. At this time, the server is performing a spatial manifold operation on the continuous data stream received from T3. This operation is performed over a sliding time window, for example, a window covering data from 000512000 to 000512500. Since the absolute time anchor 000512345 falls within this computation window, the server's control logic immediately forcibly terminates the Jacobian integration process for the current window. Subsequently, the server's data management module begins performing a culling operation. It marks all data frames with timestamps greater than or equal to 14:30:00.000512345, such as data from the sampling point corresponding to time 000512345 to 000512999, as erroneous and invalid frames and removes them from the main data stream. However, all data with timestamps earlier than 14:30:00.000512345, i.e., all data from the start of the data stream to the corresponding time 000512344, is completely preserved. This preserved data set, containing all synchronization data collected from multiple channels before the arrival of the lightning pulse, constitutes a time-safe data segment and is ready to be submitted for the final digital orthogonal decomposition operation.

[0088] The current sequence output module calculates the joint covariance matrix of the time-series safe data segment, performs an orthogonal transformation to extract the principal basis vector set, and combines the feature vectors in the principal basis vector set that have an energy ratio greater than a preset threshold into a high-dimensional background stray tensor. The time-series safe data segment is projected and subtracted from the high-dimensional background stray tensor to filter noise, and the characteristic current sequence of the grounding grid body is output.

[0089] In a specific embodiment of the present invention, the joint covariance matrix of the time-series safe data segment is calculated, and an orthogonal transformation is performed to extract the principal basis vector set. The feature vectors with an energy ratio greater than a preset threshold in the principal basis vector set are combined into a high-dimensional background spurious tensor. The projection of the time-series safe data segment and the high-dimensional background spurious tensor is subtracted to filter noise, and the characteristic current sequence of the grounding grid body is output. This includes: extracting the sampling time window of each iteration of the time-series safe data segment to obtain the current period's full dynamic spectrum sampling vector set.

[0090] The full dynamic spectrum sampling vector set is calculated and mapped to a high-dimensional background stray tensor structure framework to generate co-frequency associated interference trajectories.

[0091] The full dynamic spectrum sampling vector set is directly deducted to remove the co-frequency associated interference trajectory, and the interference components in the orthogonal projection are removed to extract the effective target signal and present the characteristic current sequence of the output grounding grid body.

[0092] Specifically, this process is executed within the core digital signal processing module on the server side. It utilizes the generated time-safe data segments to perform high-dimensional data analysis to accurately extract the characteristic current sequence of the grounding grid. First, the module performs continuous iterative operations on the multi-channel observation sequences associated with the time-safe data segments to construct their mathematical covariance matrix. This covariance matrix reflects the linear correlation between different acquisition channels and is the basis for identifying the internal structure of the data. In each iteration, the module uses the Jacobi-Rotation-Algorithm to perform eigenvalue decomposition on the covariance matrix, thereby obtaining the orthogonal principal basis vector space parameters. The Jacobi-Rotation-Algorithm gradually reduces the off-diagonal elements to zero through a series of orthogonal transformations, ultimately obtaining a matrix where the diagonal elements are eigenvalues ​​and the column vectors are eigenvectors. These eigenvectors constitute the so-called orthogonal principal basis vectors. Subsequently, the module analyzes the obtained orthogonal principal basis vector space parameters. Since external common-mode spurious currents have been significantly suppressed through physical defenses and timestamp truncation, the orthogonal principal basis vectors of the remaining extreme energy properties typically correspond to the high-frequency parasitic response noise still entangled with the useful signal. The module locks these common sub-intervals of spurious residuals with macroscopic extreme energy properties, which are typically represented by eigenvectors corresponding to the largest eigenvalues ​​in the covariance matrix. Next, the module performs linear combination or nonlinear fitting on these locked eigenvectors to fit the ensemble of the remaining high-frequency parasitic response noise in the current channel into a high-dimensional spurious data background tensor. This tensor accurately describes the noise patterns that still exist in the time-safe data segment but are independent of the grounding network itself. Finally, the module performs the core signal stripping operation: subtracting the current full data vector of the time-safe data segment from the high-dimensional spurious data background tensor through orthogonal projection to offset the signal. Specifically, the module calculates the projection of the full data vector onto the subspace spanned by the high-dimensional stray data background tensor. Then, it subtracts this projection component from the full data vector to remove interference components from the orthogonal projection and extract the effective target signal. Through this orthogonal projection, energy components spatially orthogonal to the high-dimensional stray data background tensor are fully preserved, while components related to stray modes are substantially stripped away. The final output is a complete characteristic current sequence of the grounding grid body, fully representing the true nature of near-field non-destructive leakage in the system. This sequence is almost entirely free of external stray interference and can be directly used for accurate diagnosis and evaluation of the grounding grid.

[0093] When processing time-safe data segments (dimension is) When calculating the covariance matrix, first calculate its covariance matrix. :

[0094]

[0095] in, It is zero-mean data. This is the length of the secure data segment. Covariance matrix. The dimension is .

[0096] Then, the covariance matrix is ​​solved using the Jacobi rotation algorithm. eigenvalues and the corresponding feature vector .

[0097] Locking in the common subinterval of stray residuals of macroscopic extreme energy properties involves selecting several eigenvectors corresponding to the largest eigenvalue (e.g., accounting for more than 95% of the total energy V) to form the basis of the stray subspace. ,in The number of feature vectors selected.

[0098] The current full data vector in the time-series safe data segment, for example, at a certain moment. column vector With high-dimensional stray data background tensor By performing projection subtraction and offsetting, the characteristic current sequence of the grounding grid body is obtained. :

[0099]

[0100] This formula represents the original data vector Subtract it from the stray subspace The projection onto the surface is used to obtain the ontological feature components orthogonal to the stray subspace.

[0101] The multi-channel observation sequence is a time slice of the time-safe data segment, i.e., a vector composed of the current values ​​of all acquired channels at a specific moment. The mathematical covariance matrix is ​​a square matrix whose diagonal elements represent the variance of the current in each channel, and whose off-diagonal elements represent the covariance between any two channel currents; its value reflects the strength of the linear correlation of the signal. The Jacobian rotation algorithm is a classic numerical calculation method used to perform eigenvalue decomposition on a real symmetric matrix, iteratively reducing the off-diagonal elements to zero through planar rotation, ultimately obtaining the eigenvalues ​​and eigenvectors of the matrix. The orthogonal principal basis vector space parameters are a set of mutually orthogonal vectors that constitute the main directions of data variation and are ordered according to their contribution to the total variance of the data. The spurious residual common subinterval of macroscopic extreme energy attributes refers to the subspace spanned by eigenvectors corresponding to larger eigenvalues ​​in the eigenvalue decomposition results. For example, eigenvectors with a cumulative contribution rate exceeding 90% often capture the most significant, usually common-mode, interference components in the data. High-dimensional spurious data background tensor. It is a matrix whose column vectors are orthogonal principal basis vectors representing residual stray noise patterns after filtering and combination. Orthogonal projection subtraction hedging is a linear algebraic operation that effectively removes components related to a subspace from the original vector while retaining orthogonal components by subtracting the projection of a vector onto its subspace from the original vector. The grounding grid's characteristic current sequence is the final output multi-channel current time series, which theoretically contains only the current response caused by the physical characteristics of the grounding grid itself, excluding all external electromagnetic interference components.

[0102] For example, the server obtained a length of This is a time-safe data segment with 3 sampling points and 3 channels. The data has now had the saturation distortion caused by the lightning strike removed. The server processes this segment... The data was used to calculate a 3x3 mathematical covariance matrix. ,For example: .

[0103] Next, the Jacobi rotation algorithm is used to... Perform eigenvalue decomposition to obtain eigenvalues. and their corresponding orthogonal eigenvectors .

[0104] For example, suppose , , .

[0105] Analysis revealed that, It consumes over 70% of the main energy (hypothetical example value). Therefore, server-side locking... As the dominant vector of the common subinterval of stray residuals with macroscopic extreme energy properties, and as the background tensor of high-dimensional stray data. Now, take the observation vector at a certain moment in the time-safe data segment. .Will Projected to superior: .

[0106] Finally, by performing projection subtraction and offsetting, the characteristic current sequence of the grounding grid body is obtained. :

[0107]

[0108] This final vector This refers to the characteristic current sequence of the grounding grid body after residual stray currents have been stripped away at an instant. It represents the true signal of local leakage or defects in the grounding grid and can be used for subsequent accurate diagnosis.

[0109] In a specific embodiment of the present invention, after outputting the characteristic current sequence of the grounding grid body, the method includes: reading the configuration knowledge base to obtain the dynamic safety threshold range of leakage current amplitude under normal operating conditions.

[0110] The characteristic current sequence of the grounding grid body obtained by analysis is imported into the synchronous sliding monitoring comparison model to investigate whether the current vernier pole spike exceeds the dynamic safety threshold range.

[0111] After detecting the behavior of the vernier peak penetrating the lower limit of the boundary, the environmental mapping of that time period is extracted, the channel physical source address is bound, and an automatic alarm maintenance assignment document is printed.

[0112] Specifically, after executing the aforementioned output grounding grid characteristic current sequence, the server further executes a diagnostic and automated maintenance dispatch process for the grounding grid's health status. First, the server reads the configuration knowledge base, which stores expert models built upon extensive historical normal operation data and expert experience, from which it obtains the dynamic safety threshold range for leakage current amplitude under normal operating conditions. This threshold range is not a fixed static threshold, but rather a safe operating boundary curve dynamically and adaptively adjusted based on environmental parameters such as the current grid load and soil temperature and humidity. Next, the server imports the grounding grid characteristic current sequence obtained from the previous analysis into the sliding monitoring engine, performing synchronous sliding monitoring comparison over time. During this process, the engine calculates local extrema within the sliding window in real time to investigate whether the current vernier spike exceeds the dynamic safety threshold range. When the leakage current characteristics abruptly change due to local fractures, poor welding, or severe corrosion of the grounding grid, abnormal pulses or drops will inevitably appear in the corresponding pure characteristic sequence. When the system detects that the vernier spike has crossed the lower boundary limit, it confirms that a substantial physical defect has occurred. Subsequently, the system immediately extracts the physical source address of the channel mapped to the time when the anomaly occurred. This involves associating the channel ID containing the abnormal data with the real 3D geographic coordinates of the corresponding multi-mode flexible acquisition terminal, the substation area, and equipment ledger information. Finally, the server-side scheduling work order management module integrates the above-mentioned key information such as the time of the anomaly, the exact physical source address, and the predicted fault type, automatically triggering and dispatching work orders to print automatic alarm maintenance assignment documents, accurately distributing the troubleshooting task to front-line maintenance personnel.

[0113] During the monitoring and comparison process, for the first Each channel, the lower boundary of the dynamic security threshold range. It can be calculated using the following formula:

[0114]

[0115] in, and These are the mean and standard deviation of the steady-state leakage current of the channel under the specified health scenario amplitude law, read from the configuration knowledge base; This is the fault tolerance tracking coefficient, used to adjust the sensitivity of the alarm and prevent false alarms; This refers to the environmental compensation item mapped from the environment at that time. It is a comprehensive set of environmental parameters covering temperature, humidity, soil electrical conductivity, etc.

[0116] Let the output characteristic current sequence of the grounding grid body be in the th... The first channel The value at each sampling time is The calculation time width is The current vernier spike within the sliding window :

[0117]

[0118] Determine whether the lower limit is crossed by comparing the discriminant function:

[0119]

[0120] like This will trigger subsequent address binding and order dispatching actions.

[0121] The configuration knowledge base is a relational database system containing substation topology models, historical benchmark data, material aging curves, and waveform characteristics of various typical faults. The dynamic safety threshold range for leakage current amplitude under normal operating conditions is the upper and lower safety threshold range that allows the normal leakage current of the grounding grid to fluctuate within a certain range. Its "dynamic" nature is reflected in its real-time updates with the seasons and grid operating conditions, and its "fault tolerance" is reflected in the elimination of random spikes and false alarms caused by residual thermal noise from front-end sensors. Current vernier spikes refer to the most representative extreme value abrupt change signal after stripping stray currents within a specified time window, usually corresponding to local nonlinear distortion or discharge of the grounding grid structural resistance. Penetration boundary lower limit behavior refers to the amplitude of abnormal current pulses exceeding the safety tolerance downwards, which usually indicates that the cross-sectional area of ​​the underground conductor has sharply decreased due to electrochemical corrosion or that there is a serious breakpoint causing the branch to lose its current conductivity. The time-period environment mapping binding channel physical source address is the real-time environmental state corresponding to the alarm time and the geographical latitude and longitude coordinates or construction grid number of the specific polymorphic flexible acquisition terminal associated with the abnormal channel within the substation. The automatic alarm maintenance assignment document is a standardized maintenance work order automatically generated and issued by the system. It includes the fault response level, the exact coordinates of the fault location, the expected defect type (such as main network breakage), and corresponding safe excavation treatment suggestions.

[0122] For example, after orthogonal decomposition and offsetting, the server obtains the instantaneous value of the characteristic current sequence of the grounding grid body of the second channel at a certain moment. The server first reads the parameters for the area where monitoring node No. 2 of the substation is located from the configuration knowledge base, assuming the current temperature and humidity environment... The steady-state mean calculated using the environmental model is as follows. Standard deviation Fault-tolerant tracking coefficient Set at 5, Environmental Compensation Item The lower boundary of the dynamic safety threshold range is obtained by calculation using the formula. The server performs sliding monitoring and extracts the current vernier spike of the second channel within this time window. That is The comparison revealed that... It is significantly smaller than and penetrates the lower boundary. The system determined that a probe peak had penetrated the lower boundary limit. The system immediately extracted the data time period information of channel 2 (e.g., 14:30:00) and mapped it to the physical source address of the channel based on the environment at that time. It was found that the channel was bound to the physical source address of "110kV main transformer neutral point grounding down conductor - grid coordinates X:154, Y:087". Finally, the system automatically triggered a diagnostic process in the background, combining the above time, location, and diagnostic conclusion of "highly suspected severe corrosion and breakage of the grounding down conductor, with obstructed current discharge." An automatic alarm maintenance assignment document containing a field navigation QR code was printed and sent directly to the network printer in the duty room and the handheld terminals of frontline maintenance personnel, achieving a complete business closed loop from environmental stray removal to precise defect location.

[0123] In a specific embodiment of the present invention, before calculating the spatiotemporal gradient manifold features of the baseline current situation matrix, the method includes: extracting the baseline current situation matrix and adding bias weights based on the three-dimensional coordinate occupancy distribution environment of different probe deployment nodes.

[0124] High-density measurement analysis was used to screen and identify outlier distortion pulses that randomly emerge in the non-overlapping system due to instantaneous short circuits in the line, resulting in continuous fluctuations.

[0125] Outlier distorted pulses are blocked and removed, and the spatial nearest interpolation algorithm is activated to compensate for backfilling, generating a smoothed and denoised transmission source.

[0126] Specifically, after constructing the baseline current situation matrix and before performing spatial manifold analysis, to prevent unsteady, extremely contaminated data from causing divergent damage to subsequent precise gradient calculations, the system adds a first-stage pre-processing spatial metric and smoothing cleansing. First, the server extracts the baseline current situation matrix and adds bias weights based on the three-dimensional coordinate occupancy distribution of different probe deployment nodes to each channel sequence of the matrix. These weights are generated based on the measured soil resistivity and the spatial distribution characteristics such as the physical depth and latitude / longitude of each multi-mode flexible acquisition terminal, used to compensate for inconsistent signal attenuation caused by differences in geological media at different locations. Subsequently, the server scans the weighted situation matrix using high-density metric analysis. This analysis method assesses the neighborhood clustering degree of each sample value by constructing a local reachability density model of data points in both time and space dimensions. This filters out outlier distorted pulses that emerge due to random physical events such as transient short circuits in surrounding low-voltage lines or transient flashovers on insulator surfaces, which are highly non-overlapping with surrounding data points and disrupt the continuous fluctuation pattern of the system. These spikes typically appear as isolated, extremely high-amplitude pulses; failure to remove them would directly cause partial derivative calculations to fail. Next, the system performs a blocking and removal operation on the identified coordinate points, setting these distorted spike data to null or invalid values. Finally, the system initiates a spatial nearest interpolation algorithm, using the valid sample values ​​from several normal terminals closest to the anomaly point at the same time, and performs compensation and backfilling according to an inverse distance weight. After this repair process, the dataset, originally containing transient contamination, is reconstructed into a smoothed and denoised transmission source. This source, as a clean and continuous situational matrix, is then formally transmitted as its actual computational input.

[0127] During the high-density metric analysis and spatial interpolation process, the generated baseline current situation matrix... Let the first The three-dimensional coordinates of the probe corresponding to each channel are: First, calculate and apply the environment-supported bias weights. :

[0128]

[0129] in, , This refers to the soil resistivity parameter associated with this coordinate point.

[0130] Next, calculate the local density metric. The local density metric is defined as the value of a point relative to its spatial neighborhood. The reciprocal of the mean Euclidean distance of all data points within the range, if a certain point satisfy:

[0131] and

[0132] The data at that point is then determined to be an outlier distortion pulse and is removed (denoted as NaN). For the preset density threshold, The threshold value for the jump amplitude, For its spatial neighborhood, It represents the median of the valid sampled values ​​within the neighborhood.

[0133] In a specific embodiment of the present invention, the jump amplitude threshold The settings are based on the maximum spatial dispersion of background leakage current between adjacent detection nodes under normal operating conditions and the historical background noise tolerance. To effectively avoid misjudgments caused by fluctuations in conventional power grid load, It is typically set to 5 to 10 times the median of the effective sampled values ​​in the local spatial neighborhood, or directly set to 10A to 15A in engineering absolute terms, for example, which can effectively capture anomalous jumps up to 45.0A in the embodiment.

[0134] Preset density threshold The setting is based on the minimum permissible cohesion of a normal multi-channel data cluster in the spatiotemporal characteristic space. Since the local density metric is defined as the reciprocal of the average spatial distance, when a channel experiences severe distortion, its distance to neighboring nodes increases dramatically, causing the density value to rapidly approach zero. Under the premise of standard ampere-level dimensional statistics for the input current, The typical engineering experience value range is usually set to 0.05 to 0.15.

[0135] After the excision, the spatial nearest interpolation algorithm is activated to backfill:

[0136]

[0137] in, In order to be with the first The set of valid neighbor nodes that are spatially closest to each other and whose data has not been truncated. For nodes With nodes The three-dimensional Euclidean distance between them. Generated after backfilling. That is, the smoothed and denoised transmission source, which is substituted to replace the original one. Perform partial derivative calculations.

[0138] Among them, the three-dimensional coordinate occupancy distribution environment-enhanced bias weight is a spatial calibration coefficient used to eliminate the uneven signal coupling efficiency caused by different sensor burial depths and differences in the dielectric constant of the surrounding soil, making the data of each channel spatially comparable on the same reference plane. High-density measurement analysis is a density-based anomaly detection algorithm (such as a variant of the Local Anomaly Factor (LOF) algorithm). It does not rely on the global statistical distribution of the data, but accurately locates isolated anomalies by comparing the density differences between data points and their local spatial and temporal neighbors. Outlier distortion pulses refer to non-stationary impact noise that is extremely narrow in width, huge in amplitude, and cannot find a synchronous corresponding energy in adjacent spatial channels. This type of noise is usually caused by accidental physical arcing or poor contact, which is different from the far-field common-mode interference wave we want to extract. The spatial nearest interpolation algorithm is a missing value estimation method built on the first law of geography (i.e., the closer things are in space, the greater their correlation). It reconstructs the cut-off waveform by assigning higher weights to effective probes that are closer in distance. The smoothed and denoised propagation source refers to the high-quality two-dimensional data matrix formed after weighting, elimination, and interpolation reconstruction. It is a prerequisite guarantee to ensure that the calculation of the spatiotemporal manifold partial derivative operator does not result in mathematical divergence and singular errors.

[0139] For example, the server receives a baseline current situation matrix containing terminals T1, T2, and T3. Because T2 is installed in a relatively deep location near a waterlogged area, its three-dimensional coordinate Z-axis value is relatively large, and the soil resistivity is low. Therefore, the system assigns an environmental bias weight to T2. The first matrix is ​​1.05, while T1 and T3 are 1.0. The matrix is ​​first obtained by weighting. At the 500th sampling time, the high-density metric analysis engine detected a sudden jump in the current value of channel T2 from approximately -0.8A to 45.0A, while the adjacent channels T1 and T3 at the same time showed values ​​of 1.5A and 3.2A respectively, without significant fluctuations. This 45.0A data point had an extremely low density metric value in its spatiotemporal neighborhood and deviated significantly from the median. The system immediately identified it as an outlier distortion pulse caused by wind contact with a low-voltage line. The system first blocked and removed the 45.0A data point from channel T2 at that time, making it a missing value. Then, it invoked the spatial nearest interpolation algorithm to calculate the actual three-dimensional distances from T1 and T3 to T2, assuming T1 is 8 meters from T2 and T3 is 12 meters from T2. The system weighted the distances inversely, with weights of 1 / 8 and 1 / 12 respectively. By using a weighted average of 1.5A for T1 and 3.2A for T3 to calculate T2, and backfilling interpolation, the reasonable smoothed value for T2 at that moment is 2.18A. Through traversal and similar interpolation operations, the data that originally contained outliers was completely cleaned, generating a smooth and coherent smoothed and denoised propagation source, ensuring the accuracy and stability of subsequent spatiotemporal partial derivative calculations.

[0140] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A grounding grid stray current stripping measurement system in collaboration with a Softcom server, characterized in that, include: The matrix construction module controls the multi-state flexible acquisition terminal to continuously acquire electromagnetic response analog quantities to generate multi-channel mixed data frames, and reassembles the multi-channel mixed data frames according to the time dimension to construct a baseline current situation matrix; The common-mode spatial phase angle analysis module calculates the spatiotemporal gradient manifold features of the baseline current situation matrix, extracts the far-field wave propagation components from the spatiotemporal gradient manifold features, and analyzes the common-mode spatial phase angle. The impedance weighted frame generation module calculates a specific reverse interference coefficient based on the common-mode spatial phase angle, generates a simulated impedance weighted frame based on the specific reverse interference coefficient, and sends it to the multi-mode flexible acquisition terminal. The pre-stripping current signal generation module uses a multi-state flexible acquisition terminal to analyze the analog impedance weight frame, adjust the programmable impedance modulation array, and call the programmable impedance modulation array to discharge stray currents to the electromagnetic response analog quantity, generating an analog pre-stripping current signal. The hardware timestamp instruction generation module monitors the level input state of the analog pre-stripping current signal, activates an abnormal interrupt when the level input state triggers the preset defense boundary, and generates a hardware timestamp instruction that records the overflow trigger time. The secure data segment extraction module extracts hardware timestamp instructions and converts them into absolute time anchors. It then discards the failed calculation intervals along the absolute time anchors and retains stable data combinations that have not undergone distortion before the absolute time anchors as time-series secure data segments. The current sequence output module calculates the joint covariance matrix of the time-series safe data segment, performs an orthogonal transformation to extract the principal basis vector set, and combines the feature vectors in the principal basis vector set that have an energy ratio greater than a preset threshold into a high-dimensional background stray tensor. The time-series safe data segment is projected and subtracted from the high-dimensional background stray tensor to filter noise, and the characteristic current sequence of the grounding grid body is output.

2. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server as described in claim 1, characterized in that: The control multi-state flexible acquisition terminal continuously acquires electromagnetic response analog quantities to generate multi-channel hybrid data frames, and reassembles the multi-channel hybrid data frames according to the time dimension to construct a baseline current situation matrix, including: Introduce global satellite pulse signals into the system as a fixed-length acquisition frequency for time-base aligned and locked analog-to-digital converters; The driver microprocessor chip continuously extracts analog electromagnetic response quantities at a fixed-length acquisition frequency and attaches corresponding device identifiers and time-fixed tags; Collect sequences carrying device identifiers, eliminate duplicates and redundancies, and synchronously align the row and vertical coordinates at a fixed-length acquisition frequency to assemble a grid and generate a baseline current situation matrix.

3. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The calculation of the spatiotemporal gradient manifold features of the baseline current situation matrix, extraction of the far-field wave propagation component from the spatiotemporal gradient manifold features, and analysis of the common-mode spatial phase angle include: Decompose the baseline current situation matrix and derive it along the time base node to deduce the evolution of the single-point forward channel and obtain the set of backward partial derivatives in the time dimension; Extract the installation and calibration coordinates of the multi-state flexible acquisition terminal, derive the distance parameters of the distance-diameter change between adjacent devices, and obtain the set of partial derivatives of the translation of the bridging spatial dimension. The set of backward partial derivatives in the time dimension and the set of translational partial derivatives across the spatial dimension are combined to generate a scalar map describing the divergence field of signal flow, which serves as a spatiotemporal manifold partial derivative operator.

4. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The process of calculating a specific reverse interference coefficient based on the common-mode spatial phase angle, generating an analog impedance weighted frame based on the specific reverse interference coefficient, and sending it to the multi-mode flexible acquisition terminal includes: The preset phase cancellation analysis engine is invoked to calculate the target bypass resistance parameter that needs to bear an equal amount of unloading target resistance based on a specific reverse interference coefficient; Search the internal full permutation structure map to find the optimal diversion control contact drive gating state matrix that approximates the equal unloading target resistance parameter; The optimal shunt control contact drive gating state matrix is ​​packaged, cyclically verified, encoded, and tamper-proof authentication identifiers are generated and encapsulated into an analog impedance weighted frame.

5. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The multi-state flexible acquisition terminal analyzes the analog impedance weight frame to adjust the programmable impedance modulation array, calls the programmable impedance modulation array to discharge stray currents to the analog electromagnetic response, and generates an analog pre-stripping current signal, including: Unpack and verify the analog impedance weight frame permission identifier to release the terminal's internal low-level bus lock control switch command and authorize the action. After loading and parsing the analog impedance weight frame, a driving signal is generated and sent to the solid-state gate circuit inside the array to turn on or block the field-effect switching transistor, thereby modifying the capacitive and resistive distribution combination in the programmable impedance modulation array to generate a bypass grounding point. The electromagnetic response analog quantity that drives the large influx of electromagnetic input rushes into the bypass grounding point to release the peak amplitude energy of the corresponding frequency band, discarding the waste source and retaining the purified analog pre-stripping current signal.

6. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The monitoring of the simulated pre-stripping current signal's level input state activates an abnormal interrupt when the level input state triggers a preset defense boundary, generating a hardware timestamp instruction to record the overflow trigger time, including: A simulated pre-stripping current signal is introduced into a threshold trigger probe with a nanosecond response time to detect whether the dynamic wave pattern violates the design upper limit critical saturation level reversal boundary. The system captures the pin state change caused by the flipping action that crosses the design upper limit critical saturation level boundary, generating an emergency fault pulse and triggering the highest priority overflow hardware interrupt operation mechanism. Record the nanosecond-level high-precision oscillator stop counter count value and package it into a hardware timestamp instruction with a unique time series location address segment.

7. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The extracted hardware timestamp command is converted into an absolute time anchor point. The failed calculation interval along the absolute time anchor point is discarded, and the stable data combination before the absolute time anchor point that has not undergone distortion is retained as a time-safe data segment, including: Search for the source point of the overflow failure frame corresponding to the collision failure cut-in of the absolute time anchor point associated with the digital parsing sliding window action in the running background process stream; The disordered stacking fragments derived from the source point of the overflow failure frame that is cut off from the collision failure are determined as the failure calculation interval due to the loss of physical waveform caused by the electronic saturation truncation variation. The available, stable response data segment is intercepted and locked as a time-safe data segment because the source point of the overflow failure frame that was extracted by the reverse extraction of the collision failure has not yet experienced clipping distortion.

8. The grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: The joint covariance matrix of the calculated time-series security data segment is subjected to orthogonal transformation to extract the principal basis vector set. Feature vectors with an energy percentage greater than a preset threshold within the principal basis vector set are concatenated into a high-dimensional background stray tensor. Noise is filtered by subtracting the projection of the time-series security data segment from the high-dimensional background stray tensor, and the characteristic current sequence of the grounding grid body is output, including: Extract the time-series safe data segment and obtain the full dynamic spectrum sampling vector set of the current period in each iteration sampling window; The full dynamic spectrum sampling vector set is calculated and mapped to a high-dimensional background stray tensor structure framework to generate co-frequency associated interference trajectories. The full dynamic spectrum sampling vector set is directly deducted to remove the co-frequency associated interference trajectory, and the interference components in the orthogonal projection are removed to extract the effective target signal and present the characteristic current sequence of the output grounding grid body.

9. A grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 8, characterized in that: Following the characteristic current sequence of the output grounding grid body, it includes: Read the configuration knowledge base to obtain the dynamic safety threshold range of leakage current amplitude under normal operating conditions; The analytically obtained characteristic current sequence of the grounding grid body is imported into the synchronous sliding monitoring comparison model to investigate whether the current vernier pole spike exceeds the dynamic safety threshold range. After detecting the behavior of the vernier peak penetrating the lower limit of the boundary, the environmental mapping of that time period is extracted, the channel physical source address is bound, and an automatic alarm maintenance assignment document is printed.

10. A grounding grid stray current stripping measurement system in collaboration with a soft-connect server according to claim 1, characterized in that: Before calculating the spatiotemporal gradient manifold characteristics of the baseline current situation matrix, the following is included: The baseline current situation matrix is ​​extracted and the three-dimensional coordinate occupancy distribution environment of different probe deployment nodes is added to enhance the bias weight; High-density measurement analysis was used to screen and identify outlier distortion pulses that randomly emerge in the non-overlapping system due to instantaneous short circuits in the line, resulting in continuous fluctuations in the system's morphology. Outlier distorted pulses are blocked and removed, and the spatial nearest interpolation algorithm is activated to compensate for backfilling, generating a smoothed and denoised transmission source.

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